From 445a14ca948d2c71e86564d2f957faf60c7090b7 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 20 Nov 2022 01:58:14 +0530 Subject: [PATCH 001/656] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f6ceaa89..871f6927 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ This book can be consumed in 3 ways: - A nice web interface - [Link](https://animator.github.io/learn-python/) ![Learn Python 3 Website](images/web.png) -- A Downloadable PDF - [Link](https://github.com/animator/learn-python/blob/main/pdf/learn-python-v2022.10.pdf) +- A Downloadable PDF - [Link](https://github.com/animator/learn-python/raw/main/pdf/learn-python-v2022.10.pdf) ![Learn Python 3 PDF](images/pdf.png) - Directly on GitHub - [Link](https://github.com/animator/learn-python) From 9452b3c858be2fee1dc404d2b4726c46e737073b Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Wed, 15 Feb 2023 09:40:14 +0530 Subject: [PATCH 002/656] Minor fixes --- .gitignore | 2 ++ README.md | 24 ++++++++++++------------ 2 files changed, 14 insertions(+), 12 deletions(-) diff --git a/.gitignore b/.gitignore index c2bb62ee..40363d06 100644 --- a/.gitignore +++ b/.gitignore @@ -11,3 +11,5 @@ book.pdf cover.pdf README-pdf.md style.theme +images/banner.png +images/favicon.ico diff --git a/README.md b/README.md index 871f6927..dd23c17b 100644 --- a/README.md +++ b/README.md @@ -211,7 +211,7 @@ A **software** is a collection of programs where each program provides a sequenc These instructions have to be provided in **machine language** or **low level language** (0s and 1s) that is difficult to read or write for a human being. -This led to the invention of **high-level programming languages** in which programs can be easily written and managed. The human-readable programs written using high-level languages are converted into computer-readable machine code or bytecode using **compilers** or **interpreters**. +This led to the invention of **high-level programming languages** in which programs can be easily written and managed. The human-readable programs written using high-level languages are converted into computer-readable machine code or byte-code using **compilers** or **interpreters**. There are many high-level programming languages that are currently in wide use. @@ -287,7 +287,7 @@ Python programmers also have at their disposal the vast ecosystem of more than 2 **6. Web Application Development** -Some of the most popular web development frameworks (django, flask, etc.) are written in Python. This coupled with the availablity of packages to connect to any database makes Python a great choice for web application development. +Some of the most popular web development frameworks (django, flask, etc.) are written in Python. This coupled with the availability of packages to connect to any database makes Python a great choice for web application development. ## Installing Python in Windows @@ -2300,7 +2300,7 @@ Evaluate the expression **Solution** -Parantesized expression `(...)` has the highest precedence so `+` is evaluated first +Parenthesized expression `(...)` has the highest precedence so `+` is evaluated first `15 - (2 + 4)` = `15 - 6` = `9` @@ -2725,7 +2725,7 @@ Exception handling is the process of properly handling an exception which can po When an error occurs, the program throws an exception. -The runtime system attempts to find an **exception handler**, a block of code that can handle a particular type of error. Once located, the suitable exception handler **catches the exeception** and executes the code block which can attempt to recover from the error. In case the error is unrecoverable, the handler provides a way to gently exit the program. +The runtime system attempts to find an **exception handler**, a block of code that can handle a particular type of error. Once located, the suitable exception handler **catches the exception** and executes the code block which can attempt to recover from the error. In case the error is unrecoverable, the handler provides a way to gently exit the program. The `try` statement in Python specifies the exception handlers and/or cleanup code for a code block. @@ -2734,7 +2734,7 @@ The various parts of a try statement are: - `try` block: The block of statements within which an exception might be thrown. - `except` clause(s): One or more exception handlers. Each `except` clause handles a particular type of exception. In case an exception of a particular type occurs in the `try` block, the corresponding `except` clause code block is executed. - `else` clause: An optional `else` clause can also be included after the last `except` block. In case no exception is raised, none of the `except` blocks are executed. In this case, the `else` code block is executed. -- `finally` clause: An optional `finally` clause can be added at the end of the try statement which includes a block of statements that are executed regardless of whether or not any error occured inside the try block. This block is usually setup for code cleanup and closing all open file objects. +- `finally` clause: An optional `finally` clause can be added at the end of the try statement which includes a block of statements that are executed regardless of whether or not any error occurred inside the try block. This block is usually setup for code cleanup and closing all open file objects. Here's the general form of these statements: @@ -3997,7 +3997,7 @@ It has to be noted that the method counts non-overlapping occurrences, so it doe 2 ``` -In the above example, `ala` is counted twice as the first occurence is in `valh"ala"` and the next occurance is in `"ala"la`. Although `ala` can be located again in `al"ala"`, it overlaps with the occurance `"ala"la`, hence it is not counted. +In the above example, `ala` is counted twice as the first occurrence is in `valh"ala"` and the next occurrence is in `"ala"la`. Although `ala` can be located again in `al"ala"`, it overlaps with the occurrence `"ala"la`, hence it is not counted. ### find() @@ -4639,7 +4639,7 @@ If you do not wish to modify the existing list and create a new list with items Python lists have a built-in `sort()` method which sorts the items in-place using `<` comparisons between items. -The method also accepts 2 keyworded arguments: +The method also accepts 2 key-worded arguments: - `key` is used to specify a function which is called on each list element prior to making the comparisons. - `reverse` is a boolean which specifies whether the list is to be sorted in descending order. @@ -5481,7 +5481,7 @@ In case of a tuple, the modified tuple is actually a completely new tuple with c The original tuple is not modified as it is immutable. But, as `t` is no longer pointing to the original tuple, it is freed from memory. -Thus, it is recommended that instead of `+=`, `append()` and `extend()` methods should be employed to add new items programatically as it will raise an error in case the code is trying to modify a tuple. +Thus, it is recommended that instead of `+=`, `append()` and `extend()` methods should be employed to add new items programmatically as it will raise an error in case the code is trying to modify a tuple. ``` python >>> l = ["Hi", "Ed", "Punk"] @@ -5669,7 +5669,7 @@ This method accepts the following: {'yr': 15, 18: True, 'name': 'Ed'} ``` -**3. Keyworded arguments** +**3. Key-worded arguments** ``` python >>> d = {"yr": 20, 18: True} @@ -7557,9 +7557,9 @@ print(d) Once a function is defined in the Python interpreter, it can be called any number of times. But, these function definitions are lost upon exiting the interpreter. -To solve this problem we can create a python script with the function definitions at the beginning of teh file, followed by the rest of the code which includes statements invoking the defined functions. +To solve this problem we can create a python script with the function definitions at the beginning of the file, followed by the rest of the code which includes statements invoking the defined functions. -But, this process is tedious and not managable as what makes user-defined functions powerful is that the programmer can - **Write once, and use many times**. +But, this process is tedious and not manageable as what makes user-defined functions powerful is that the programmer can - **Write once, and use many times**. Instead of repeating the function definition again and again for each new program, one can put all the function definitions in a file from which the required function can be imported and invoked either in script mode or interactive mode. @@ -7746,6 +7746,6 @@ restaurant/ A package is simply the directory containing sub-packages and modules, but when this package or a collection of packages are made available for others to use (eg. via PyPI) it is known as a **library**. -For example, `restaurant` can be called a library if it provides reusable codes to manage a restaurant and is built using multiple packages which handle the various aspects of a restaurant like human resource management, inventory management, order fulfilment and billing, etc. +For example, `restaurant` can be called a library if it provides reusable codes to manage a restaurant and is built using multiple packages which handle the various aspects of a restaurant like human resource management, inventory management, order fulfillment and billing, etc. One should note that the above definition is not strict and often the terms package and library are used interchangeably. From f3392f3ef312b867981e176ded4fe1b6e6802c5d Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Wed, 29 Mar 2023 06:24:22 +0530 Subject: [PATCH 003/656] Minor fixes --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index dd23c17b..efde22bd 100644 --- a/README.md +++ b/README.md @@ -658,7 +658,7 @@ The backslash (`\`) character can be used in a string literal to escape characte | `\"` | Double quote (`"`) | | `\a` | ASCII Bell (BEL) | | `\b` | ASCII Backspace (BS) | -| `\f` | ASCII Formfeed (FF) | +| `\f` | ASCII Form-feed (FF) | | `\n` | ASCII Linefeed (LF) | | `\r` | ASCII Carriage Return (CR) | | `\t` | ASCII Horizontal Tab (TAB) | @@ -3539,7 +3539,7 @@ The backslash (`\`) character can be used in a string to escape characters that | `\"` | Double quote (`"`) | | `\a` | ASCII Bell (BEL) | | `\b` | ASCII Backspace (BS) | -| `\f` | ASCII Formfeed (FF) | +| `\f` | ASCII Form-feed (FF) | | `\n` | ASCII Linefeed (LF) | | `\r` | ASCII Carriage Return (CR) | | `\t` | ASCII Horizontal Tab (TAB) | @@ -6181,7 +6181,7 @@ Python has built-in functions to handle various aspects of datatypes such as che `type()` and `isinstance()` builtin functions are used for checking the data type of objects. -Check out the **Type Checking** section in the chapter **Variable, Objects & Data Types** to learn more about it in detail. +Check out [Type Checking](#type-checking) section in the chapter [Variable, Objects & Data Types](#variables-objects--data-types) to learn more about it in detail. ### Built-in Type Functions @@ -6202,7 +6202,7 @@ The following functions are often used to assign a default value or create an em | `frozenset()` | `frozenset()` | | `dict()` | `{}` | -The **Type Casting** section of the chapter **Variable, Objects & Data Types** covers these functions in detail in case any argument is passed. +The [Type Casting](#type-casting) section of the chapter [Variable, Objects & Data Types](#variables-objects--data-types) covers these functions in detail in case any argument is passed. ## I/O Functions From 52048d3d5ae02deb3b26b31d5326c50d60691f45 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Wed, 29 Mar 2023 06:46:18 +0530 Subject: [PATCH 004/656] Update dictionary |= operator --- README.md | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/README.md b/README.md index efde22bd..d357fb3f 100644 --- a/README.md +++ b/README.md @@ -5811,6 +5811,38 @@ In case of common keys between the two operands (dictionaries), the values of th In the above example, both `d` and `n` shared a common key `yr`. The value corresponding to `yr` in `n` gets priority. +The `|=` (union) augmented assignment operator can be used to update the dictionary with keys and values from another dictionary or an iterable of (key, value) pairs. + +``` python +>>> d = {"book": "Python", "year": 1990} +>>> dnew = {"author": "Guido"} +>>> d |= dnew +>>> d +{'book': 'Python', 'year': 1990, 'author': 'Guido'} +``` + +If both the dictionaries share a common key, then the value corresponding to the key in the right operand is the updated value. + +``` python +>>> d = {"book": "Python", "year": 1990} +>>> dnew = {"author": "Guido", "year": 2000} +>>> d |= dnew +>>> d +{'book': 'Python', 'year': 2000, 'author': 'Guido'} +``` + +In the above example, both `d` and `dnew` shared a common key `year`. The value (`1990`) corresponding to `year` in `d` is updated with the new value. + +The `|=` operator also works in case the right operand is an iterable instead of a dictionary as shown in the example below. + +``` python +>>> d = {"book": "Python", "year": 1990} +>>> inew = [("author", "Guido"), ("year", 2000)] +>>> d |= inew +>>> d +{'book': 'Python', 'year': 2000, 'author': 'Guido'} +``` + ## Traversing a Dictionary Compared to position based indexing of `list` or `tuple`, dictionaries are indexed based on the key. From 2752111ddfeed3f6556a7405c6ede0827296cb48 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Wed, 29 Mar 2023 06:52:13 +0530 Subject: [PATCH 005/656] Builtin functions list & sorted on dict --- README.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/README.md b/README.md index d357fb3f..b38ebc76 100644 --- a/README.md +++ b/README.md @@ -5968,6 +5968,26 @@ In case of string keys, they return the first and last occurring strings alphabe 3 ``` +### list() + +To get the list of all keys in a dictionary, use the `list()` built-in function. + +``` python +>>> d = {"book": "Python", "year": 1990, "author": "Guido"} +>>> list(d) +['book', 'year', 'author'] +``` + +### sorted() + +To get a sorted list of keys, you can use the `sorted()` built-in function. + +``` python +>>> d = {"book": "Python", "year": 1990, "author": "Guido"} +>>> sorted(d) +['author', 'book', 'year'] +``` + ## Creating a Copy of a Dictionary A new copy of a dictionary can be made using `copy()` method: From d3dc992ccc8bdd4263fb232836ce07a03b7eaccc Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Wed, 29 Mar 2023 07:07:00 +0530 Subject: [PATCH 006/656] Add copy module --- README.md | 64 ++++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 63 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index b38ebc76..69718105 100644 --- a/README.md +++ b/README.md @@ -6375,11 +6375,12 @@ Converts the integer Unicode code point into the corresponding Unicode string. Apart from built-in functions, the **Python Standard Library** also contains a wide range of built-in modules which are a group of functions organized based on functionality. -Some of the commonly used modules are mentioned below: +Some commonly used modules are: - `math` - Mathematical functions - `random` - Generate pseudo-random numbers - `statistics` - Statistical functions +- `copy` - Create shallow and deep copy of objects **Accessing Modules** @@ -6611,6 +6612,67 @@ If there are multiple modes with the same count, the first occurrence in the seq 'a' ``` +## copy Module + +### Limitation of Shallow Copy + +`copy()` method does not recurse to create copies of the child objects, so if the child objects are mutable (example nested list) any modification in the child object will get reflected in the both the parent objects. + +``` python +>>> old_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] + +# Copying old list into a new list +>>> new_list = old_list.copy() + +# Checking if both lists are pointing to the same object +>>> id(new_list)==id(old_list) +False + +# Checking if items of both lists are pointing to the same objects +>>> [id(new_list[idx])==id(old_list[idx]) for idx in range(len(old_list))] +[True, True, True] + +# Modify new list +>>> new_list[1][1] = 0 +>>> new_list +[[1, 2, 3], [4, 0, 6], [7, 8, 9]] +>>> old_list +[[1, 2, 3], [4, 0, 6], [7, 8, 9]] +``` + +As we can see in the output, `new_list[1][1]` was modified which is reflected in both `new_list` and `old_list`. + +The `copy` module provides the `deepcopy()` function which is helpful in mitigating this issue. + +### Deep Copy - deepcopy(x[, memo]) + +Deep copy overcomes the shortcomings of `copy()` and recursively creates copies of the child objects found in the original list. This leads to the creation of an independent copy of the original. + +``` python +>>> import copy +>>> old_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] + +# Copying old list into a new list +>>> new_list = copy.deepcopy(old_list) + +# Checking if both lists are pointing to the same object +>>> id(new_list)==id(old_list) +False + +# Checking if items of both lists are pointing to the same objects +>>> [id(new_list[idx])==id(old_list[idx]) for idx in range(len(old_list))] +[False, False, False] + +# Modify new list +>>> new_list[1][1] = 0 +>>> new_list +[[1, 2, 3], [4, 0, 6], [7, 8, 9]] +>>> old_list +[[1, 2, 3], [4, 5, 6], [7, 8, 9]] +``` + +Note the change in the `id()` equality of the children for `[True, True, True]` to `[False, False, False]`. As we can see in the output, `new_list[1][1]` was modified which gets reflected only in the `new_list`. + # File Handling ## File Handling in Python - Introduction & Overview From f5144e2ad9570dfe354c5521cdab0898ba41a427 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 11 May 2024 10:28:27 +0530 Subject: [PATCH 007/656] Create hello.py --- hello.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 hello.py diff --git a/hello.py b/hello.py new file mode 100644 index 00000000..7df869a1 --- /dev/null +++ b/hello.py @@ -0,0 +1 @@ +print("Hello, World!") From 7adeacd494e9dcdea4fe8104e44df0ee14d5cc06 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 11 May 2024 18:23:17 +0530 Subject: [PATCH 008/656] update readme --- .gitignore | 1 + README.md | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 40363d06..912ae74d 100644 --- a/.gitignore +++ b/.gitignore @@ -13,3 +13,4 @@ README-pdf.md style.theme images/banner.png images/favicon.ico +cover.png diff --git a/README.md b/README.md index 69718105..4e226272 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,5 @@ +[![Discord Server Invite](https://img.shields.io/badge/DISCORD-JOIN%20SERVER-green?style=for-the-badge&logo=discord&logoColor=white)](https://bit.ly/heyfoss) + ![Learn Python 3 Logo](images/learn-python.png) Learn Python 3 @@ -5,8 +7,6 @@ Learn Python 3 by **Ankit Mahato** [[About](https://animator.github.io)] *Version 2022.10* -[![Discord Server Invite](https://img.shields.io/badge/DISCORD-JOIN%20SERVER-brightgreen?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/2s49SCNfyJ) - # How to read this book? This book can be consumed in 3 ways: From 5aed43fad7a54a8302f70bbcaa9ee1431726d085 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 11 May 2024 18:27:34 +0530 Subject: [PATCH 009/656] Update README.md --- README.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 4e226272..e00bbc40 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,10 @@ -[![Discord Server Invite](https://img.shields.io/badge/DISCORD-JOIN%20SERVER-green?style=for-the-badge&logo=discord&logoColor=white)](https://bit.ly/heyfoss) +[![Discord Server Invite](https://img.shields.io/badge/DISCORD-JOIN%20SERVER-5663F7?style=for-the-badge&logo=discord&logoColor=white)](https://bit.ly/heyfoss) + +This project is an participating in GSSoC 2024. + +![gssoc-logo](https://github.com/foss42/awesome-generative-ai-apis/assets/1382619/670b651a-15d7-4869-a4d1-6613df09fa37) + +Contributors should go through the [Contributing Guide](https://github.com/animator/learn-python/blob/main/CONTRIBUTING.md) to learn how you can contribute to the project. ![Learn Python 3 Logo](images/learn-python.png) From a78b3c64accd9698099aa722c16956c04ecf2c17 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 11 May 2024 19:08:11 +0530 Subject: [PATCH 010/656] contrib --- contrib/advanced-python/index.md | 3 +++ contrib/api-development/index.md | 3 +++ contrib/database/index.md | 3 +++ contrib/ds-algorithms/index.md | 3 +++ contrib/machine-learning/index.md | 3 +++ contrib/mini-projects/index.md | 3 +++ contrib/numpy/index.md | 3 +++ contrib/pandas/index.md | 3 +++ contrib/plotting-visualization/index.md | 3 +++ contrib/question-bank/index.md | 3 +++ contrib/scipy/index.md | 3 +++ contrib/web-scrapping/index.md | 3 +++ 12 files changed, 36 insertions(+) create mode 100644 contrib/advanced-python/index.md create mode 100644 contrib/api-development/index.md create mode 100644 contrib/database/index.md create mode 100644 contrib/ds-algorithms/index.md create mode 100644 contrib/machine-learning/index.md create mode 100644 contrib/mini-projects/index.md create mode 100644 contrib/numpy/index.md create mode 100644 contrib/pandas/index.md create mode 100644 contrib/plotting-visualization/index.md create mode 100644 contrib/question-bank/index.md create mode 100644 contrib/scipy/index.md create mode 100644 contrib/web-scrapping/index.md diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md new file mode 100644 index 00000000..82596a2f --- /dev/null +++ b/contrib/advanced-python/index.md @@ -0,0 +1,3 @@ +# List of sections + +- [Section title](filename.md) diff --git a/contrib/api-development/index.md b/contrib/api-development/index.md new file mode 100644 index 00000000..82596a2f --- /dev/null +++ b/contrib/api-development/index.md @@ -0,0 +1,3 @@ +# List of sections + +- [Section title](filename.md) diff --git a/contrib/database/index.md b/contrib/database/index.md new file mode 100644 index 00000000..82596a2f --- /dev/null +++ b/contrib/database/index.md @@ -0,0 +1,3 @@ +# List of sections + +- [Section title](filename.md) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md new file mode 100644 index 00000000..82596a2f --- /dev/null +++ b/contrib/ds-algorithms/index.md @@ -0,0 +1,3 @@ +# List of sections + +- [Section title](filename.md) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md new file mode 100644 index 00000000..82596a2f --- /dev/null +++ b/contrib/machine-learning/index.md @@ -0,0 +1,3 @@ +# List of sections + +- [Section title](filename.md) diff --git a/contrib/mini-projects/index.md b/contrib/mini-projects/index.md new file mode 100644 index 00000000..82596a2f --- /dev/null +++ b/contrib/mini-projects/index.md @@ -0,0 +1,3 @@ +# List of sections + +- [Section title](filename.md) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md new file mode 100644 index 00000000..82596a2f --- /dev/null +++ b/contrib/numpy/index.md @@ -0,0 +1,3 @@ +# List of sections + +- [Section title](filename.md) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md new file mode 100644 index 00000000..82596a2f --- /dev/null +++ b/contrib/pandas/index.md @@ -0,0 +1,3 @@ +# List of sections + +- [Section title](filename.md) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md new file mode 100644 index 00000000..82596a2f --- /dev/null +++ b/contrib/plotting-visualization/index.md @@ -0,0 +1,3 @@ +# List of sections + +- [Section title](filename.md) diff --git a/contrib/question-bank/index.md b/contrib/question-bank/index.md new file mode 100644 index 00000000..82596a2f --- /dev/null +++ b/contrib/question-bank/index.md @@ -0,0 +1,3 @@ +# List of sections + +- [Section title](filename.md) diff --git a/contrib/scipy/index.md b/contrib/scipy/index.md new file mode 100644 index 00000000..82596a2f --- /dev/null +++ b/contrib/scipy/index.md @@ -0,0 +1,3 @@ +# List of sections + +- [Section title](filename.md) diff --git a/contrib/web-scrapping/index.md b/contrib/web-scrapping/index.md new file mode 100644 index 00000000..82596a2f --- /dev/null +++ b/contrib/web-scrapping/index.md @@ -0,0 +1,3 @@ +# List of sections + +- [Section title](filename.md) From 4e00f6d01ad2e1a94f4ff0922536034cf6fc10cb Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 11 May 2024 19:43:27 +0530 Subject: [PATCH 011/656] Update CONTRIBUTING.md --- CONTRIBUTING.md | 54 +++++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 46 insertions(+), 8 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index d307781b..48329a36 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,14 +1,52 @@ # Contributing Guidelines -Contributions welcome! +We value your participation in this open source project. This page will give you a quick overview of how to get involved. -**Before spending lots of time on something, ask for feedback on your idea first!** +## Important Note -Please search issues and pull requests before adding something new to avoid duplicating efforts and conversations. +- Do not raise issues or send PRs for changing issue template, adding header-footer, badges or any buttons. +- Do not raise issues or send PRs for for any website changes. -This project welcomes following types of contributions: +## How can I contribute? -- **Ideas**: participate in an issue thread or start your own to have your voice heard. -- **Writing**: contribute your expertise in an area by helping expand the included content. -- **Copy editing**: fix typos, clarify language, and generally improve the quality of the content. -- **Formatting**: help keep content easy to read with consistent formatting. +You can contribute to this project by adding content on a new topic or improving existing content. + +The list of topics for which we are looking for content are provided below along with the location where the content has to be added: + +- Advanced Python - [Link](https://github.com/animator/learn-python/tree/main/contrib/advanced-python) +- Pandas - [Link](https://github.com/animator/learn-python/tree/main/contrib/pandas) +- NumPy - [Link](https://github.com/animator/learn-python/tree/main/contrib/numpy) +- SciPy - [Link](https://github.com/animator/learn-python/tree/main/contrib/scipy) +- Data Science & Machine Learning - [Link](https://github.com/animator/learn-python/tree/main/contrib/machine-learning) +- Plotting & Visualization - [Link](https://github.com/animator/learn-python/tree/main/contrib/plotting-visualization) +- Interacting with Databases - [Link](https://github.com/animator/learn-python/tree/main/contrib/database) +- Web Scrapping - [Link](https://github.com/animator/learn-python/tree/main/contrib/web-scrapping) +- API Development - [Link](https://github.com/animator/learn-python/tree/main/contrib/api-development) +- Data Structures & Algorithms - [Link](https://github.com/animator/learn-python/tree/main/contrib/ds-algorithms) +- Python Mini Projects - [Link](https://github.com/animator/learn-python/tree/main/contrib/mini-projects) +- Python Question Bank - [Link](https://github.com/animator/learn-python/tree/main/contrib/question-bank) + +You can check out some content ideas below. + +## Process + +**Step 1**: Raise a **new issue** that you want to "Add content". We will assign the issue to you and label it. +**Step 2**: Star and fork THIS repository. +**Step 3**: Now in your fork, go the correct topic folder as provided in the links above. +**Step 4**: Edit `index.md` file to add the title of the content and the corresponding file name where the content will be available in this folder. +**Step 5**: Add the content in the file. +**Step 6**: Raise a PR with your changes. Accept the pledge that the content is original and not stolen from any other source. +**Step 7**: Wait for review and PR merge. + +### Some Content Ideas + +- **NumPy**: Introduction, Arrays, Indexing and Slicing, Operations on Arrays, Concatenating Arrays, Reshaping Arrays, Splitting Arrays, Statistical Operations on Arrays, Loading Arrays from Files, Saving NumPy Arrays in Files, etc. +- **Pandas**: Introduction, Importing and Exporting Data, DataFrames, Pandas Series Vs NumPy ndarray, Descriptive Statistics, Data Aggregations, Sorting a DataFrame, Group by Functions, Altering the Index, Other DataFrame Operations, Handling Missing Values, Import and Export of Data between Pandas and MySQL, Pandas plotting, etc. +- **Plotting & Visualization**: Matplotlib, Seaborn, Customisation of Plots (Marker, colour, Line width and Line Style), Line chart, Bar Chart, Histogram, Scatter Chart, Plotting Quartiles and Box plot, Pie Chart, etc. +- **Data Science & Machine Learning**: Scikit-learn, TensorFlow, PyTorch, regression, classification, clustering, ensemble model, deep learning, etc. +- **API Development**: Building APIs using FastAPI, CRUD API Development, etc. +- **Advanced Python**: OOP In Python, Generators, List Comprehensions, Lambda functions, In-depth Function Arguments, Regular Expressions, Exception Handling, Partial functions, Code Introspection, Closures, Decorators, Map, Filter, Reduce, etc. + +### Any doubt? + +In case you are new to the open source ecosystem, we would be more than happy to guide you through the entire process. Just join our [Discord server](https://bit.ly/heyfoss) and drop a message in relevant channel. From 880bf7ae38969178f92bb7ed44c6d2027e076702 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 11 May 2024 19:44:29 +0530 Subject: [PATCH 012/656] Update CONTRIBUTING.md --- CONTRIBUTING.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 48329a36..47ed0e87 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -38,7 +38,7 @@ You can check out some content ideas below. **Step 6**: Raise a PR with your changes. Accept the pledge that the content is original and not stolen from any other source. **Step 7**: Wait for review and PR merge. -### Some Content Ideas +## Some Content Ideas - **NumPy**: Introduction, Arrays, Indexing and Slicing, Operations on Arrays, Concatenating Arrays, Reshaping Arrays, Splitting Arrays, Statistical Operations on Arrays, Loading Arrays from Files, Saving NumPy Arrays in Files, etc. - **Pandas**: Introduction, Importing and Exporting Data, DataFrames, Pandas Series Vs NumPy ndarray, Descriptive Statistics, Data Aggregations, Sorting a DataFrame, Group by Functions, Altering the Index, Other DataFrame Operations, Handling Missing Values, Import and Export of Data between Pandas and MySQL, Pandas plotting, etc. @@ -47,6 +47,6 @@ You can check out some content ideas below. - **API Development**: Building APIs using FastAPI, CRUD API Development, etc. - **Advanced Python**: OOP In Python, Generators, List Comprehensions, Lambda functions, In-depth Function Arguments, Regular Expressions, Exception Handling, Partial functions, Code Introspection, Closures, Decorators, Map, Filter, Reduce, etc. -### Any doubt? +## Any doubt? In case you are new to the open source ecosystem, we would be more than happy to guide you through the entire process. Just join our [Discord server](https://bit.ly/heyfoss) and drop a message in relevant channel. From e393e7dd86b27de068d6e943f7095e93e39fc6da Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 11 May 2024 19:45:04 +0530 Subject: [PATCH 013/656] Update CONTRIBUTING.md --- CONTRIBUTING.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 47ed0e87..b49beefc 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -32,9 +32,9 @@ You can check out some content ideas below. **Step 1**: Raise a **new issue** that you want to "Add content". We will assign the issue to you and label it. **Step 2**: Star and fork THIS repository. -**Step 3**: Now in your fork, go the correct topic folder as provided in the links above. -**Step 4**: Edit `index.md` file to add the title of the content and the corresponding file name where the content will be available in this folder. -**Step 5**: Add the content in the file. +**Step 3**: Now in your fork, go the correct topic folder as provided in the links above. +**Step 4**: Edit `index.md` file to add the title of the content and the corresponding file name where the content will be available in this folder. +**Step 5**: Add the content in the file. **Step 6**: Raise a PR with your changes. Accept the pledge that the content is original and not stolen from any other source. **Step 7**: Wait for review and PR merge. From e343bbdeef411b28d1de13681f99bbc270bdd0a6 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 11 May 2024 20:09:36 +0530 Subject: [PATCH 014/656] Create pull_request_template.md --- .github/pull_request_template.md | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) create mode 100644 .github/pull_request_template.md diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 00000000..a807cf4c --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,24 @@ +## PR Description + +_Add your description_ + +## Related Issues + +- Closes # + +### Checklist + +- [ ] I have gone through the [contributing guide](https://github.com/animator/learn-python/blob/main/CONTRIBUTING.md) +- [ ] I have updated my branch and synced it with project `main` branch before making this PR + +## Undertaking + +I declare that: + +1. The content I am submitting is original and has not been plagiarized. +2. No portion of the work has been copied from any other source without proper attribution. +3. The work has been checked for plagiarism, and I assure its authenticity. + +I understand that any violation of this undertaking may have legal consequences that I will bear and could result in the withdrawal of any recognition associated with the work. + +- [ ] I Agree From 7e23e7338aa453adace980ff39b27bb82f6c92c2 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 11 May 2024 20:11:37 +0530 Subject: [PATCH 015/656] Update CONTRIBUTING.md --- CONTRIBUTING.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index b49beefc..c3956ae0 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -34,7 +34,7 @@ You can check out some content ideas below. **Step 2**: Star and fork THIS repository. **Step 3**: Now in your fork, go the correct topic folder as provided in the links above. **Step 4**: Edit `index.md` file to add the title of the content and the corresponding file name where the content will be available in this folder. -**Step 5**: Add the content in the file. +**Step 5**: Add the content in markdown format in the file (extension `.md`). **Step 6**: Raise a PR with your changes. Accept the pledge that the content is original and not stolen from any other source. **Step 7**: Wait for review and PR merge. From f6e8a801c31987f893116e50729ef6d0b310118a Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 11 May 2024 20:14:06 +0530 Subject: [PATCH 016/656] Update CONTRIBUTING.md --- CONTRIBUTING.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index c3956ae0..825eb5f4 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -9,7 +9,7 @@ We value your participation in this open source project. This page will give you ## How can I contribute? -You can contribute to this project by adding content on a new topic or improving existing content. +You can contribute to this project by adding content on a new topic or improving existing content (in the `README.md` file). The list of topics for which we are looking for content are provided below along with the location where the content has to be added: From 43350844c1cf73d577b9ca09c5d9b64a599372ad Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 11 May 2024 20:32:32 +0530 Subject: [PATCH 017/656] Update CONTRIBUTING.md --- CONTRIBUTING.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 825eb5f4..7b4200d4 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -45,6 +45,7 @@ You can check out some content ideas below. - **Plotting & Visualization**: Matplotlib, Seaborn, Customisation of Plots (Marker, colour, Line width and Line Style), Line chart, Bar Chart, Histogram, Scatter Chart, Plotting Quartiles and Box plot, Pie Chart, etc. - **Data Science & Machine Learning**: Scikit-learn, TensorFlow, PyTorch, regression, classification, clustering, ensemble model, deep learning, etc. - **API Development**: Building APIs using FastAPI, CRUD API Development, etc. +- **Web Scrapping**: beautifulsoup, requests, etc. - **Advanced Python**: OOP In Python, Generators, List Comprehensions, Lambda functions, In-depth Function Arguments, Regular Expressions, Exception Handling, Partial functions, Code Introspection, Closures, Decorators, Map, Filter, Reduce, etc. ## Any doubt? From 2e2515bfc93190175a8b106e993898bac2c13abe Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 11 May 2024 20:46:05 +0530 Subject: [PATCH 018/656] Update CONTRIBUTING.md --- CONTRIBUTING.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 7b4200d4..4a366da8 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -5,7 +5,8 @@ We value your participation in this open source project. This page will give you ## Important Note - Do not raise issues or send PRs for changing issue template, adding header-footer, badges or any buttons. -- Do not raise issues or send PRs for for any website changes. +- Do not raise issues or send PRs for for any website changes. +- Do not choose an entire topic or a whole bunch of sections. Just choose a single small section for which you will be contributing when you raise a new issue. ## How can I contribute? @@ -31,6 +32,7 @@ You can check out some content ideas below. ## Process **Step 1**: Raise a **new issue** that you want to "Add content". We will assign the issue to you and label it. +**Do not choose an entire topic or a whole bunch of sections. Just choose a single small section for which you will be contributing when you raise a new issue.** **Step 2**: Star and fork THIS repository. **Step 3**: Now in your fork, go the correct topic folder as provided in the links above. **Step 4**: Edit `index.md` file to add the title of the content and the corresponding file name where the content will be available in this folder. From a7c884396760645cd80e90dfc4188f740197ed76 Mon Sep 17 00:00:00 2001 From: Sujan Midatani Date: Sat, 11 May 2024 23:02:34 +0530 Subject: [PATCH 019/656] added API methods to the documentation --- contrib/api-development/api-methods.md | 27 ++++++++++++++++++++++++++ contrib/api-development/index.md | 2 +- 2 files changed, 28 insertions(+), 1 deletion(-) create mode 100644 contrib/api-development/api-methods.md diff --git a/contrib/api-development/api-methods.md b/contrib/api-development/api-methods.md new file mode 100644 index 00000000..8c669195 --- /dev/null +++ b/contrib/api-development/api-methods.md @@ -0,0 +1,27 @@ +## API Methods + +- **GET**: + - The GET method is used to retrieve data from a specified resource. + - It is a safe and idempotent method, meaning that it should not have any side effects and can be called multiple times without changing the result. + - Use the GET method when you want to retrieve information from the server. + +- **POST**: + - The POST method is used to submit data to be processed to a specified resource. + - It is not idempotent, meaning that calling the same POST request multiple times may result in different outcomes. + - Use the POST method when you want to create a new resource on the server. + +- **PUT**: + - The PUT method is used to update a specified resource with new data. + - It is idempotent, meaning that calling the same PUT request multiple times should have the same effect as calling it once. + - Use the PUT method when you want to update an existing resource on the server. + +- **PATCH**: + - The PATCH method is used to apply partial modifications to a resource. + - It is not idempotent, meaning that calling the same PATCH request multiple times may result in different outcomes. + - Use the PATCH method when you want to update a resource with partial data. + +- **DELETE**: + - The DELETE method is used to delete a specified resource. + - It is idempotent, meaning that calling the same DELETE request multiple times should have the same effect as calling it once. + - Use the DELETE method when you want to remove a resource from the server. + diff --git a/contrib/api-development/index.md b/contrib/api-development/index.md index 82596a2f..75c702ac 100644 --- a/contrib/api-development/index.md +++ b/contrib/api-development/index.md @@ -1,3 +1,3 @@ # List of sections -- [Section title](filename.md) +- [API Methods](api-methods.md) From d4c8ab4d49caa7549ef0ce77c665e72bb768b94f Mon Sep 17 00:00:00 2001 From: Sujan Midatani Date: Sat, 11 May 2024 23:24:31 +0530 Subject: [PATCH 020/656] added additional API methods to the documentation --- contrib/api-development/api-methods.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/contrib/api-development/api-methods.md b/contrib/api-development/api-methods.md index 8c669195..61e10d77 100644 --- a/contrib/api-development/api-methods.md +++ b/contrib/api-development/api-methods.md @@ -25,3 +25,23 @@ - It is idempotent, meaning that calling the same DELETE request multiple times should have the same effect as calling it once. - Use the DELETE method when you want to remove a resource from the server. +- **OPTIONS**: + - The OPTIONS method is used to describe the communication options for the target resource. + - It is idempotent, meaning that calling the same OPTIONS request multiple times should have the same effect as calling it once. + - Use the OPTIONS method when you want to retrieve the communication options for a resource. + +- **HEAD**: + - The HEAD method is similar to the GET method, but it only retrieves the headers of the response without the body. + - It is idempotent, meaning that calling the same HEAD request multiple times should have the same effect as calling it once. + - Use the HEAD method when you want to check the headers of a resource without retrieving the body. + +- **TRACE**: + - The TRACE method is used to test the connectivity between the client and the server. + - It is idempotent, meaning that calling the same TRACE request multiple times should have the same effect as calling it once. + - Use the TRACE method when you want to test the connectivity between the client and the server. + +- **CONNECT**: + - The CONNECT method is used to establish a tunnel to the server using a proxy. + - It is not idempotent, meaning that calling the same CONNECT request multiple times may result in different outcomes. + - Use the CONNECT method when you want to establish a tunnel to the server using a proxy. + From 7ad18bd08e99f448c7671e0bc8c295928e5c62cd Mon Sep 17 00:00:00 2001 From: Sujan Midatani Date: Sat, 11 May 2024 23:59:31 +0530 Subject: [PATCH 021/656] added tabular explaination of API methods to the documentation --- contrib/api-development/api-methods.md | 34 ++++++++++++-------------- 1 file changed, 15 insertions(+), 19 deletions(-) diff --git a/contrib/api-development/api-methods.md b/contrib/api-development/api-methods.md index 61e10d77..8de91846 100644 --- a/contrib/api-development/api-methods.md +++ b/contrib/api-development/api-methods.md @@ -1,47 +1,43 @@ -## API Methods +# API Methods + +| Method | Summary | CRUD | Accepts Request Body | Idempotent | Safe | +|---------|----------------------------------------------------------|--------|-----------------------|------------|------| +| GET | To fetch a single resource or group of resources | Read | No | Yes | Yes | +| PUT | To update an entire resource in one go | Update | Yes | Yes | No | +| POST | To create a new resource | Create | Yes | No | No | +| PATCH | To partially update a resource | Update | Yes | No | No | +| DELETE | To delete a resource | Delete | No | Yes | No | +| OPTIONS | To get information on permitted operations | Read | No | Yes | Yes | +| HEAD | To get metadata of the endpoint | Read | No | Yes | Yes | +| TRACE | For diagnosing purposes | Read | No | Yes | Yes | +| CONNECT | To make the two-way connection between the client and the resource | - | No | No | No | + +## Method Details: - **GET**: - The GET method is used to retrieve data from a specified resource. - - It is a safe and idempotent method, meaning that it should not have any side effects and can be called multiple times without changing the result. - - Use the GET method when you want to retrieve information from the server. - **POST**: - The POST method is used to submit data to be processed to a specified resource. - - It is not idempotent, meaning that calling the same POST request multiple times may result in different outcomes. - - Use the POST method when you want to create a new resource on the server. - **PUT**: - The PUT method is used to update a specified resource with new data. - - It is idempotent, meaning that calling the same PUT request multiple times should have the same effect as calling it once. - - Use the PUT method when you want to update an existing resource on the server. - **PATCH**: - The PATCH method is used to apply partial modifications to a resource. - - It is not idempotent, meaning that calling the same PATCH request multiple times may result in different outcomes. - - Use the PATCH method when you want to update a resource with partial data. - **DELETE**: - The DELETE method is used to delete a specified resource. - - It is idempotent, meaning that calling the same DELETE request multiple times should have the same effect as calling it once. - - Use the DELETE method when you want to remove a resource from the server. - **OPTIONS**: - The OPTIONS method is used to describe the communication options for the target resource. - - It is idempotent, meaning that calling the same OPTIONS request multiple times should have the same effect as calling it once. - - Use the OPTIONS method when you want to retrieve the communication options for a resource. - **HEAD**: - The HEAD method is similar to the GET method, but it only retrieves the headers of the response without the body. - - It is idempotent, meaning that calling the same HEAD request multiple times should have the same effect as calling it once. - - Use the HEAD method when you want to check the headers of a resource without retrieving the body. - **TRACE**: - The TRACE method is used to test the connectivity between the client and the server. - - It is idempotent, meaning that calling the same TRACE request multiple times should have the same effect as calling it once. - - Use the TRACE method when you want to test the connectivity between the client and the server. - **CONNECT**: - The CONNECT method is used to establish a tunnel to the server using a proxy. - - It is not idempotent, meaning that calling the same CONNECT request multiple times may result in different outcomes. - - Use the CONNECT method when you want to establish a tunnel to the server using a proxy. From 2a3d288a72edb7b6a87ed32da0b7c1256eff8ea4 Mon Sep 17 00:00:00 2001 From: Sujan Midatani Date: Sun, 12 May 2024 00:10:48 +0530 Subject: [PATCH 022/656] added extra details to the table for explaination of API methods to the documentation --- contrib/api-development/api-methods.md | 41 +++++++++++++------------- 1 file changed, 20 insertions(+), 21 deletions(-) diff --git a/contrib/api-development/api-methods.md b/contrib/api-development/api-methods.md index 8de91846..de3228a1 100644 --- a/contrib/api-development/api-methods.md +++ b/contrib/api-development/api-methods.md @@ -1,43 +1,42 @@ # API Methods -| Method | Summary | CRUD | Accepts Request Body | Idempotent | Safe | -|---------|----------------------------------------------------------|--------|-----------------------|------------|------| -| GET | To fetch a single resource or group of resources | Read | No | Yes | Yes | -| PUT | To update an entire resource in one go | Update | Yes | Yes | No | -| POST | To create a new resource | Create | Yes | No | No | -| PATCH | To partially update a resource | Update | Yes | No | No | -| DELETE | To delete a resource | Delete | No | Yes | No | -| OPTIONS | To get information on permitted operations | Read | No | Yes | Yes | -| HEAD | To get metadata of the endpoint | Read | No | Yes | Yes | -| TRACE | For diagnosing purposes | Read | No | Yes | Yes | -| CONNECT | To make the two-way connection between the client and the resource | - | No | No | No | +| Method | Summary | CRUD | Accepts Request Body | Idempotent | Safe | Response Body | +|---------|----------------------------------------------------------|--------|-----------------------|------------|------|---------------| +| GET | To fetch a single resource or group of resources | Read | No | Yes | Yes | Yes | +| PUT | To update an entire resource in one go | Update | Yes | Yes | No | Yes | +| POST | To create a new resource | Create | Yes | No | No | Yes | +| PATCH | To partially update a resource | Update | Yes | No | No | Yes | +| DELETE | To delete a resource | Delete | No | Yes | No | No | +| OPTIONS | To get information on permitted operations | Read | No | Yes | Yes | Yes | +| HEAD | To get metadata of the endpoint | Read | No | Yes | Yes | No | +| TRACE | For diagnosing purposes | Read | No | Yes | Yes | No | +| CONNECT | To make the two-way connection between the client and the resource | - | No | No | No | No | ## Method Details: - **GET**: - - The GET method is used to retrieve data from a specified resource. + - The GET method is used to retrieve data from a specified resource. It does not typically alter the state of the resource and is safe and idempotent. It can accept query parameters in the URL to filter or sort the results. A response body is returned with the requested data. - **POST**: - - The POST method is used to submit data to be processed to a specified resource. + - The POST method is used to submit data to be processed to a specified resource. It is commonly used for creating new resources, and it may or may not require a request body containing the data to be created. It is not idempotent nor safe. A response body is returned with the newly created resource's details. - **PUT**: - - The PUT method is used to update a specified resource with new data. + - The PUT method is used to update a specified resource with new data. It typically requires a request body containing the complete representation of the resource to be updated. It is idempotent and not safe. A response body is returned with the updated resource's details. - **PATCH**: - - The PATCH method is used to apply partial modifications to a resource. + - The PATCH method is used to apply partial modifications to a resource. It typically requires a request body containing the specific changes to be made. It is not idempotent nor safe. A response body is returned with the updated resource's details. - **DELETE**: - - The DELETE method is used to delete a specified resource. + - The DELETE method is used to delete a specified resource. It does not typically require a request body, as the resource to be deleted is identified in the request URI. It is idempotent but not safe. No response body is returned. - **OPTIONS**: - - The OPTIONS method is used to describe the communication options for the target resource. + - The OPTIONS method is used to describe the communication options for the target resource. It does not typically require a request body and is safe and idempotent. A response body is returned with information about the supported HTTP methods and other metadata. - **HEAD**: - - The HEAD method is similar to the GET method, but it only retrieves the headers of the response without the body. + - The HEAD method is similar to the GET method, but it only retrieves the headers of the response without the body. It does not typically require a request body and is safe and idempotent. - **TRACE**: - - The TRACE method is used to test the connectivity between the client and the server. + - The TRACE method is used to test the connectivity between the client and the server. It does not typically require a request body and is safe and idempotent. - **CONNECT**: - - The CONNECT method is used to establish a tunnel to the server using a proxy. - + - The CONNECT method is used to establish a tunnel to the server using a proxy. It does not typically require a request body and is neither safe nor idempotent. From 237a00cdbfaab63481a5390fb92a2040ccc95fdd Mon Sep 17 00:00:00 2001 From: Sujan Midatani Date: Sun, 12 May 2024 00:19:57 +0530 Subject: [PATCH 023/656] added DEFINITIONS FOR ATTRIBUTES of API methods to the documentation --- contrib/api-development/api-methods.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/contrib/api-development/api-methods.md b/contrib/api-development/api-methods.md index de3228a1..ea419799 100644 --- a/contrib/api-development/api-methods.md +++ b/contrib/api-development/api-methods.md @@ -40,3 +40,17 @@ - **CONNECT**: - The CONNECT method is used to establish a tunnel to the server using a proxy. It does not typically require a request body and is neither safe nor idempotent. + +### Definitions: + +- **CRUD**: + - CRUD stands for Create, Read, Update, and Delete, representing the four basic functions of persistent storage. These operations are commonly used in database and RESTful API designs. + +- **Accepts Request Body**: + - Indicates whether the HTTP method typically accepts a request body containing data to be processed or modified. If yes, the method may require the client to include data in the request body. + +- **Idempotent**: + - An idempotent operation means that making the same request multiple times will produce the same result as making it once. In the context of HTTP methods, an idempotent method does not change the server state after multiple identical requests. + +- **Safe**: + - A safe operation does not modify the state of the server or its resources. It only retrieves data without causing any side effects. Safe methods are typically used for read-only operations. \ No newline at end of file From c7f165fa424bf1df4779566ae71abc4ac8889678 Mon Sep 17 00:00:00 2001 From: Drishan Gupta <66329991+drishangupta@users.noreply.github.com> Date: Sun, 12 May 2024 01:30:53 +0530 Subject: [PATCH 024/656] Create decorator-kwargs-args.md WIP --- .../advanced-python/decorator-kwargs-args.md | 93 +++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100644 contrib/advanced-python/decorator-kwargs-args.md diff --git a/contrib/advanced-python/decorator-kwargs-args.md b/contrib/advanced-python/decorator-kwargs-args.md new file mode 100644 index 00000000..9a9ab933 --- /dev/null +++ b/contrib/advanced-python/decorator-kwargs-args.md @@ -0,0 +1,93 @@ +# Advanced Python +## Functions as First class objects +Functions in Python are so called first class objects, which means they can be treated as variables, viz. functions can be used as arguments or they can be returned using the return keyword. + +**Example** + +```python +def func1(): + def func2(): + print("Printing from the inner function, func2") + return func2 + +``` +Assigning func1 to function_call object +```python +function_call=func1() +``` +Calling the function +```python +>> function_call() +``` +**Output** +``` +Printing from the inner function, func2 +``` +Here we have seen the use of function as a first class object, func2 was returned as the result of the execution of the outer function, func1. + +## *args +\* is an iterating operator used to unpack datatypes such as lists, tuples etc. +**For example** +```python +tuple1=(1,2,4,5,6,7) +print(tuple1) +print(*tuple1) +``` +In the above we have defined a tuple called tuple1 with the items (1,2,4,5,6,7). +First we print normally and the output for that is: +``` +(1, 2, 4, 5, 6, 7) + +``` +Then we print with the \* operator, where we will get the output as: +``` +1 2 4 5 6 7 +``` + +Here the \* operator has unpacked the tuple, tuple1. + +Now that you have understood why \* is used, we can take a look at *args. *args is used in functions so that positional arguments are stored in the variable args. *args is just a naming convention, *anything can be used +*args makes python functions flexible to handle dynamic arguments. +```python +def test1(*args): + print(args) + print(f"The number of elements in args = {len(args)}") +a=list(range(0,10)) +test1(*a) +``` +In the above snippet, we are sending a list of numbers to the test function which returns the following output: +``` +(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) +The number of elements in args = 10 +``` +If in the test1 we do not use \* in the argument + +```python +def test1(*args): + print(args) + print(f"The number of elements in args = {len(args)}") +a=list(range(0,10)) +test1(a) +``` +we get the following result. This is a tuple containing a list. +``` +([0, 1, 2, 3, 4, 5, 6, 7, 8, 9],) +The number of elements in args = 1 +``` +## **kwargs +**kwargs stands for keyword arguments. This is used for key and value pairs and similar to *args, this makes functions flexible enough to handle dynamic key value pairs in arguments. +```python +def test2(**kwargs): + print(kwargs) + print(f"The number of elements in kwargs = {len(kwargs)}") +test2(a=1,b=2,c=3,d=4,e=5) +``` +The above snippet uses some key-value pairs and out test2 function gives the following output: +``` +{'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5} +The number of elements in kwargs = 5 +``` +A dictionary with keys and values is obtained. + +## Decorators (@decorators) +Now that we understand what first class object, *args, **kwargs is, we can move to decorators. From bd0adc17fcc30dd619b59df1ea7c042b65a47801 Mon Sep 17 00:00:00 2001 From: Drishan Gupta <66329991+drishangupta@users.noreply.github.com> Date: Sun, 12 May 2024 03:23:39 +0530 Subject: [PATCH 025/656] Update decorator-kwargs-args.md --- .../advanced-python/decorator-kwargs-args.md | 55 ++++++++++++++++++- 1 file changed, 54 insertions(+), 1 deletion(-) diff --git a/contrib/advanced-python/decorator-kwargs-args.md b/contrib/advanced-python/decorator-kwargs-args.md index 9a9ab933..91f1eb44 100644 --- a/contrib/advanced-python/decorator-kwargs-args.md +++ b/contrib/advanced-python/decorator-kwargs-args.md @@ -90,4 +90,57 @@ The number of elements in kwargs = 5 A dictionary with keys and values is obtained. ## Decorators (@decorators) -Now that we understand what first class object, *args, **kwargs is, we can move to decorators. +Now that we understand what first class object, *args, **kwargs is, we can move to decorators. Decorators are used to perform a task that needs to be performed for existing functions. If some task has to be performed for each function, we can write a function which will perform the task without us having to make changes in each function. + +**Sample Code:** +``` +import time +def multiplication(a,b): + start=time.time() + c=a*b + total=time.time()-start + print("Time taken for execution of multiplication",total) + return c + +def addition(a,b): + start=time.time() + c=a+b + total=time.time()-start + print("Time taken for execution of addition ",total) + return c + +multiplication(4,5) +addition(4,5) +``` + +In the above code, we had to calculate time and print the execution time seperately for each function leading to repeatation of code. This is where decorators come in handy. +The same functionality can be achieved with the help of a decorator. + +**Here's how:** +``` +import time +def time_find(function): + def wrapper(*args, **kwargs): + starttime=time.time() + function(*args, **kwargs) + total=time.time()-starttime + print(f"Time Taken by {function.__name__} to run is ",total) + return wrapper + +@time_find #to use a decorator, simply use @ above a function. +def multiply(a, b): + print(a*b) + +@time_find +def addition(a,b): + print(a+b) + +multiply(4,5) +addition(4,5) +``` + +The above method eleminates redundant code and makes the code cleaner. You may have observed that we have used *args and **kwargs in the wrapper function. This is so that this decorator function is flexible for all types of functions and their parameters and this way it can find out the execution time of any function with as many parameters as needed, we just need to use our decorator @time_find. + + + + From dae21c7f6368bf35849be297e3bd45b2cd5a80eb Mon Sep 17 00:00:00 2001 From: Adarsh Amit Date: Sun, 12 May 2024 03:24:09 +0530 Subject: [PATCH 026/656] Added matplotlib_installation.md file and updated the index.md --- contrib/plotting-visualization/index.md | 2 +- .../matplotlib_installation.md | 25 +++++++++++++++++++ 2 files changed, 26 insertions(+), 1 deletion(-) create mode 100644 contrib/plotting-visualization/matplotlib_installation.md diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 82596a2f..67bee6dc 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -1,3 +1,3 @@ # List of sections -- [Section title](filename.md) +- [Installing Matplotlib](matplotlib_installation.md) diff --git a/contrib/plotting-visualization/matplotlib_installation.md b/contrib/plotting-visualization/matplotlib_installation.md new file mode 100644 index 00000000..e5c21ea2 --- /dev/null +++ b/contrib/plotting-visualization/matplotlib_installation.md @@ -0,0 +1,25 @@ +# Matplotlib Installation + +Matplotlib is a widely used Python library for creating static, animated, and interactive visualizations. It can be installed using `pip`, Python's package manager. + +## Prerequisites + +Before installing Matplotlib, ensure you have Python installed on your system. You can download and install Python from the [official Python website](https://www.python.org/). + +## Installation Steps + +1. **Install Matplotlib**: Open your terminal or command prompt and run the following command to install Matplotlib using `pip`: + +```bash +pip install matplotlib +``` + +2. **Verify Installation**: After installation, you can verify if Matplotlib is installed correctly by importing it in a Python environment: + +```python +import matplotlib + +print(matplotlib.__version__) + + +``` From 55f8871a9e3a21dace285a7ea12dcc786b2e00c9 Mon Sep 17 00:00:00 2001 From: Drishan Gupta <66329991+drishangupta@users.noreply.github.com> Date: Sun, 12 May 2024 03:24:56 +0530 Subject: [PATCH 027/656] Update index.md --- contrib/advanced-python/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 82596a2f..dac98905 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -1,3 +1,3 @@ # List of sections -- [Section title](filename.md) +- [Decorators/\*args/**kwargs](decorator-kwargs-args) From 4c979af5410b8bff9e9d14e1f79580c0abf42023 Mon Sep 17 00:00:00 2001 From: Drishan Gupta <66329991+drishangupta@users.noreply.github.com> Date: Sun, 12 May 2024 03:26:49 +0530 Subject: [PATCH 028/656] Updated the index --- contrib/advanced-python/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index dac98905..5ea5081c 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -1,3 +1,3 @@ # List of sections -- [Decorators/\*args/**kwargs](decorator-kwargs-args) +- [Decorators/\*args/**kwargs](decorator-kwargs-args.md) From f7caaca6803f43f863184ff17ea1e385d2a2c459 Mon Sep 17 00:00:00 2001 From: petergriffin29 <116427452+evilsaint000@users.noreply.github.com> Date: Sun, 12 May 2024 12:30:35 +0530 Subject: [PATCH 029/656] Update README.md First python release was 0.9.0 --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e00bbc40..e653892c 100644 --- a/README.md +++ b/README.md @@ -225,7 +225,7 @@ Some of the popular languages are Java, C, C++, C#, Go, Swift, JavaScript, PHP, ## Introduction to Python -Guido van Rossum started the development of Python in December 1989. He released the first version (0.9.9) of Python for general public on February 20, 1991. +Guido van Rossum started the development of Python in December 1989. He released the first version (0.9.0) of Python for general public on February 20, 1991. The language evolved over the next few decades and so did its definition, the current version of which is stated below: From a324545579de03abf78245aea4e24c2baf3d0440 Mon Sep 17 00:00:00 2001 From: Drishan Gupta <66329991+drishangupta@users.noreply.github.com> Date: Sun, 12 May 2024 13:50:31 +0530 Subject: [PATCH 030/656] Update decorator-kwargs-args.md --- contrib/advanced-python/decorator-kwargs-args.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/contrib/advanced-python/decorator-kwargs-args.md b/contrib/advanced-python/decorator-kwargs-args.md index 91f1eb44..402dcc6f 100644 --- a/contrib/advanced-python/decorator-kwargs-args.md +++ b/contrib/advanced-python/decorator-kwargs-args.md @@ -93,7 +93,7 @@ A dictionary with keys and values is obtained. Now that we understand what first class object, *args, **kwargs is, we can move to decorators. Decorators are used to perform a task that needs to be performed for existing functions. If some task has to be performed for each function, we can write a function which will perform the task without us having to make changes in each function. **Sample Code:** -``` +```python import time def multiplication(a,b): start=time.time() @@ -117,7 +117,7 @@ In the above code, we had to calculate time and print the execution time seperat The same functionality can be achieved with the help of a decorator. **Here's how:** -``` +```python import time def time_find(function): def wrapper(*args, **kwargs): From 8f5cebaea0680155455094f3a963c39c846fa27f Mon Sep 17 00:00:00 2001 From: Adarsh Amit Date: Sun, 12 May 2024 19:28:04 +0530 Subject: [PATCH 031/656] added the output of the verify installation --- contrib/plotting-visualization/matplotlib_installation.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/contrib/plotting-visualization/matplotlib_installation.md b/contrib/plotting-visualization/matplotlib_installation.md index e5c21ea2..263e99bd 100644 --- a/contrib/plotting-visualization/matplotlib_installation.md +++ b/contrib/plotting-visualization/matplotlib_installation.md @@ -23,3 +23,9 @@ print(matplotlib.__version__) ``` + +Output: + +``` +3.4.3 +``` From 5c3e1c2ed86b57838d16151e4d9c305eae565084 Mon Sep 17 00:00:00 2001 From: Ananyagra24 <155280507+Ananyagra24@users.noreply.github.com> Date: Sun, 12 May 2024 21:49:55 +0530 Subject: [PATCH 032/656] Create History --- contrib/History | 1 + 1 file changed, 1 insertion(+) create mode 100644 contrib/History diff --git a/contrib/History b/contrib/History new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/contrib/History @@ -0,0 +1 @@ + From 079cbf43d9584d3869ec467e1d3b2f9f8c3e8b98 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 13 May 2024 00:21:04 +0530 Subject: [PATCH 033/656] Create CODE_OF_CONDUCT.md --- CODE_OF_CONDUCT.md | 127 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 127 insertions(+) create mode 100644 CODE_OF_CONDUCT.md diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..41dd9ebe --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,127 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, religion, or sexual identity +and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or +permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. + +Community Impact Guidelines were inspired by [Mozilla's code of conduct +enforcement ladder](https://github.com/mozilla/diversity). + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see the FAQ at +https://www.contributor-covenant.org/faq. Translations are available at +https://www.contributor-covenant.org/translations. From bf7fd3c40f8ef94f33afc8eb04ee91d364c1f68a Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 13 May 2024 00:21:54 +0530 Subject: [PATCH 034/656] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e00bbc40..25903675 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ [![Discord Server Invite](https://img.shields.io/badge/DISCORD-JOIN%20SERVER-5663F7?style=for-the-badge&logo=discord&logoColor=white)](https://bit.ly/heyfoss) -This project is an participating in GSSoC 2024. +This project is participating in GSSoC 2024. ![gssoc-logo](https://github.com/foss42/awesome-generative-ai-apis/assets/1382619/670b651a-15d7-4869-a4d1-6613df09fa37) From b8e682d2fd427f8c76bde3bc30207cad327aec5d Mon Sep 17 00:00:00 2001 From: Ananyagra24 <155280507+Ananyagra24@users.noreply.github.com> Date: Mon, 13 May 2024 08:40:22 +0530 Subject: [PATCH 035/656] Delete contrib/History --- contrib/History | 1 - 1 file changed, 1 deletion(-) delete mode 100644 contrib/History diff --git a/contrib/History b/contrib/History deleted file mode 100644 index 8b137891..00000000 --- a/contrib/History +++ /dev/null @@ -1 +0,0 @@ - From bb1cb39110356e18b542917759dcddde10540544 Mon Sep 17 00:00:00 2001 From: Yatharth Date: Mon, 13 May 2024 11:58:31 +0530 Subject: [PATCH 036/656] added about bubble selection quick and merge sort --- contrib/ds-algorithms/index.md | 1 + contrib/ds-algorithms/sorting-algorithms.md | 329 ++++++++++++++++++++ 2 files changed, 330 insertions(+) create mode 100644 contrib/ds-algorithms/sorting-algorithms.md diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 82596a2f..810313c8 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -1,3 +1,4 @@ # List of sections - [Section title](filename.md) +- [Sorting Algorithms](soritng-algorithms.md) diff --git a/contrib/ds-algorithms/sorting-algorithms.md b/contrib/ds-algorithms/sorting-algorithms.md new file mode 100644 index 00000000..6423b4b3 --- /dev/null +++ b/contrib/ds-algorithms/sorting-algorithms.md @@ -0,0 +1,329 @@ +# Sorting Algorithms + +In computer science, a sorting algorithm takes a collection of items and arranges them in a specific order. This order is usually determined by comparing the items using a defined rule. + +## Real Life Example of Sorting +- Sorting a deck of cards +- Sorting names in alphabetical order +- Sorting a list of items, etc. + +# Some common sorting techniques + +# 1. Bubble Sort + +Bubble sort is a basic sorting technique that iteratively steps through a list, comparing neighboring elements. If elements are out of order, it swaps them. While easy to understand, bubble sort becomes inefficient for large datasets due to its slow execution time. + +**Algorithm Overview:** +- **Pass by Pass:** During each pass, the algorithm iterates through the list. +- **Comparing Neighbors:** In each iteration, it compares adjacent elements in the list. +- **Swapping for Order:** If the elements are in the wrong order (typically, the first being larger than the second), it swaps their positions. +- **Bubbling Up the Largest:** This swapping process effectively pushes the largest element encountered in a pass towards the end of the list, like a bubble rising in water. +- **Repeating Until Sorted:** The algorithm continues making passes through the list until no more swaps are needed. This indicates the entire list is sorted. + + +## Bubble Sort Code in Python + +```python +def bubble_sort(arr): + n = len(arr) + for i in range(n): + for j in range(0, n-i-1): + if arr[j] > arr[j+1]: + arr[j], arr[j+1] = arr[j+1], arr[j] + +arr = [5, 3, 8, 1, 2] +bubble_sort(arr) +print("Sorted array:", arr) # Output: [1, 2, 3, 5, 8] +``` +## Example with Visualization + +Let's sort the list `[5, 3, 8, 1, 2]` using bubble sort. + +1. **Pass 1:** + - Comparing neighbors: `[3, 5, 1, 2, 8]` + - Swapping: `[3, 5, 1, 2, 8]` → `[3, 1, 5, 2, 8]` → `[3, 1, 2, 5, 8]` + - Result: `[3, 1, 2, 5, 8]` + +2. **Pass 2:** + - Comparing neighbors: `[1, 3, 2, 5, 8]` + - Swapping: `[1, 3, 2, 5, 8]` → `[1, 2, 3, 5, 8]` + - Result: `[1, 2, 3, 5, 8]` + +3. **Pass 3:** + - Comparing neighbors: `[1, 2, 3, 5, 8]` + - No swapping needed, the list is already sorted. + +## Complexity Analysis + +- **Worst Case:** `O(n^2)` comparisons and swaps. This happens when the list is in reverse order, and we need to make maximum swaps. +- **Best Case:** `O(n)` comparisons. This occurs when the list is already sorted, but we still need O(n^2) swaps because of the nested loops. +- **Average Case:** `O(n^2)` comparisons and swaps. This is the expected number of comparisons and swaps over all possible input sequences. + +
+
+
+ +# 2. Selection Sort + +Selection sort is a simple sorting algorithm that divides the input list into two parts: a sorted sublist and an unsorted sublist. The algorithm repeatedly finds the smallest (or largest, depending on sorting order) element from the unsorted sublist and moves it to the sorted sublist. It's not efficient for large datasets but performs better than bubble sort due to fewer swaps. + +**Algorithm Overview:** +- **Initial State:** The entire list is considered unsorted initially. +- **Selecting the Minimum:** The algorithm repeatedly selects the smallest element from the unsorted sublist and moves it to the sorted sublist. +- **Expanding the Sorted Sublist:** As elements are moved to the sorted sublist, it expands until all elements are sorted. +- **Repeating Until Sorted:** The process continues until the entire list is sorted. + +## Example with Visualization + +Let's sort the list `[5, 3, 8, 1, 2]` using selection sort. + +1. **Pass 1:** + - Initial list: `[5, 3, 8, 1, 2]` + - Find the minimum: `1` + - Swap with the first element: `[1, 3, 8, 5, 2]` + +2. **Pass 2:** + - Initial list: `[1, 3, 8, 5, 2]` + - Find the minimum: `2` + - Swap with the second element: `[1, 2, 8, 5, 3]` + +3. **Pass 3:** + - Initial list: `[1, 2, 8, 5, 3]` + - Find the minimum: `3` + - Swap with the third element: `[1, 2, 3, 5, 8]` + +4. **Pass 4:** + - Initial list: `[1, 2, 3, 5, 8]` + - Find the minimum: `5` + - No swapping needed, the list is already sorted. + +## Selection Sort Code in Python + +```python +def selection_sort(arr): + n = len(arr) + for i in range(n): + min_index = i + for j in range(i+1, n): + if arr[j] < arr[min_index]: + min_index = j + arr[i], arr[min_index] = arr[min_index], arr[i] + +arr = [5, 3, 8, 1, 2] +selection_sort(arr) +print("Sorted array:", arr) # Output: [1, 2, 3, 5, 8] +``` + +## Complexity Analysis +- **Worst Case**: `O(n^2)` comparisons and O(n) swaps. This occurs when the list is in reverse order, and we need to make maximum comparisons and swaps. +- **Best Case**: `O(n^2)` comparisons and O(n) swaps. This happens when the list is in sorted order, but the algorithm still needs to iterate through all elements for comparisons. +- **Average Case**: `O(n^2)` comparisons and O(n) swaps. This is the expected number of comparisons and swaps over all possible input sequences. +
+
+
+ +# 3. Quick Sort +Quick sort is a popular divide-and-conquer sorting algorithm known for its efficiency on average. It works by selecting a 'pivot' element from the array and partitioning the other elements into two sub-arrays according to whether they are less than or greater than the pivot. The sub-arrays are then recursively sorted. + +**Algorithm Overview:** +- **Pivot Selection:** Choose a pivot element from the array. Common strategies include selecting the first, last, middle, or a randomly chosen element. +- **Partitioning:** Rearrange the array so that all elements less than the pivot are on its left, and all elements greater than the pivot are on its right. This step ensures that the pivot element is placed in its correct sorted position. +- **Recursion:** Apply the above steps recursively to the sub-arrays formed by partitioning until the base case is reached. The base case is usually when the size of the sub-array becomes 0 or 1, indicating it is already sorted. +- **Base Case:** If the sub-array size becomes 0 or 1, it is already sorted. + +## Example with Visualization + +Let's sort the list `[5, 3, 8, 1, 2]` using quick sort. + +1. **Initial Array:** `[5, 3, 8, 1, 2]` + +2. **Choose Pivot:** Let's choose the last element, `2`, as the pivot. + +3. **Partitioning:** + - We'll partition the array around the pivot `2`. All elements less than `2` will be placed to its left, and all elements greater than `2` will be placed to its right. + + - After partitioning, the array becomes `[1, 2, 5, 3, 8]`. The pivot element, `2`, is now in its correct sorted position. + +4. **Recursion:** + - Now, we recursively sort the sub-arrays `[1]` and `[5, 3, 8]`. + - For the sub-array `[5, 3, 8]`, we choose `8` as the pivot and partition it. + - After partitioning, the sub-array becomes `[3, 5, 8]`. The pivot element, `8`, is now in its correct sorted position. + + +5. **Concatenation:** + - Concatenating the sorted sub-arrays `[1]`, `[2]`, `[3, 5, 8]`, we get the final sorted array `[1, 2, 3, 5, 8]`. + +## Quick Sort Code in Python (Iterative) +```python +def partition(arr, low, high): + pivot = arr[high] + i = low - 1 + for j in range(low, high): + if arr[j] < pivot: + i += 1 + arr[i], arr[j] = arr[j], arr[i] + arr[i + 1], arr[high] = arr[high], arr[i + 1] + return i + 1 + +def quick_sort_iterative(arr): + stack = [(0, len(arr) - 1)] + while stack: + low, high = stack.pop() + if low < high: + pi = partition(arr, low, high) + stack.append((low, pi - 1)) + stack.append((pi + 1, high)) + +# Example usage: +arr = [38, 27, 43, 3, 9, 82, 10] +quick_sort_iterative(arr) +print("Sorted array:", arr) # Output: [3, 9, 10, 27, 38, 43, 82] + +``` +## Quick Sort Code in Python (Recursive) + +```python +def quick_sort(arr): + if len(arr) <= 1: + return arr + else: + pivot = arr[-1] + left = [x for x in arr[:-1] if x < pivot] + right = [x for x in arr[:-1] if x >= pivot] + return quick_sort(left) + [pivot] + quick_sort(right) + +arr = [5, 3, 8, 1, 2] +sorted_arr = quick_sort(arr) +print("Sorted array:", sorted_arr) # Output: [1, 2, 3, 5, 8] +``` +## Complexity Analysis + +- **Worst Case**: The worst-case time complexity of quick sort is `O(n^2)`. This occurs when the pivot selection consistently results in unbalanced partitioning, such as choosing the smallest or largest element as the pivot. +-**Best Case**: The best-case time complexity is `O(n log n)`. This happens when the pivot selection leads to well-balanced partitioning, halving the array size in each recursive call. +- **Average Case**: The average-case time complexity is `O(n log n)`. This is the expected time complexity when the pivot selection results in reasonably balanced partitioning across recursive calls. +- **Space Complexity**: Quick sort has an `O(log n)` space complexity for the recursion stack, as it recursively sorts sub-arrays. +
+
+
+ +# 4. Merge Sort + +Merge sort is a divide-and-conquer algorithm that recursively divides the input list into smaller sublists until each sublist contains only one element. Then, it repeatedly merges adjacent sublists while maintaining the sorted order until there is only one sublist remaining, which represents the sorted list. + +**Algorithm Overview:** +- **Divide:** Split the input list into smaller sublists recursively until each sublist contains only one element. +- **Merge:** Repeatedly merge adjacent sublists while maintaining the sorted order until there is only one sublist remaining, which represents the sorted list. + +## Example with Visualization + +Let's sort the list `[38, 27, 43, 3, 9, 82, 10]` using merge sort. + +1. **Initial Division:** + - Divide the list into sublists: `[38, 27, 43, 3, 9, 82, 10]` + - Visually it looks like + `[38], [27], [43], [3], [9], [82], [10]` + +2. **Merge Passes:** + - Merge adjacent sublists while maintaining sorted order: + - Pass 1: `[27, 38]`, `[3, 43]`, `[9, 82]`, `[10]` + - Pass 2: `[3, 27, 38, 43]`, `[9, 10, 82]` + - Pass 3: `[3, 9, 10, 27, 38, 43, 82]` + + +3. **Final Sorted List:** + - `[3, 9, 10, 27, 38, 43, 82]` + +## Merge Sort Code in Python (Iterative) + +```python +def merge_sort_iterative(arr): + n = len(arr) + curr_size = 1 + while curr_size < n: + left = 0 + while left < n - 1: + mid = min(left + curr_size - 1, n - 1) + right = min(left + 2 * curr_size - 1, n - 1) + merge(arr, left, mid, right) + left += 2 * curr_size + curr_size *= 2 + +def merge(arr, left, mid, right): + n1 = mid - left + 1 + n2 = right - mid + L = [0] * n1 + R = [0] * n2 + for i in range(n1): + L[i] = arr[left + i] + for j in range(n2): + R[j] = arr[mid + 1 + j] + i = j = 0 + k = left + while i < n1 and j < n2: + if L[i] <= R[j]: + arr[k] = L[i] + i += 1 + else: + arr[k] = R[j] + j += 1 + k += 1 + while i < n1: + arr[k] = L[i] + i += 1 + k += 1 + while j < n2: + arr[k] = R[j] + j += 1 + k += 1 + +arr = [38, 27, 43, 3, 9, 82, 10] +merge_sort_iterative(arr) +print("Sorted array:", arr) # Output: [3, 9, 10, 27, 38, 43, 82] +``` +## Merge Sort Code in Python (Recursive) +```python +def merge_sort(arr): + if len(arr) > 1: + mid = len(arr) // 2 + left_half = arr[:mid] + right_half = arr[mid:] + + merge_sort(left_half) + merge_sort(right_half) + + i = j = k = 0 + + # Merge the two sorted halves + while i < len(left_half) and j < len(right_half): + if left_half[i] < right_half[j]: + arr[k] = left_half[i] + i += 1 + else: + arr[k] = right_half[j] + j += 1 + k += 1 + + # Check if any elements are remaining in the left half + while i < len(left_half): + arr[k] = left_half[i] + i += 1 + k += 1 + + # Check if any elements are remaining in the right half + while j < len(right_half): + arr[k] = right_half[j] + j += 1 + k += 1 + +# Example usage: +arr = [38, 27, 43, 3, 9, 82, 10] +merge_sort(arr) +print("Sorted array:", arr) # Output: [3, 9, 10, 27, 38, 43, 82] +``` +## Complexity Analysis +- **Time Complexity**: `O(n log n)` for all cases. Merge sort always divides the list into halves until each sublist contains only one element, and then merges them back together, resulting in O(n log n) time complexity. +- **Space Complexity**: `O(n)` auxiliary space. In the iterative version, merge sort uses additional space for creating temporary sublists during merging operations. + +
+
+
From 0855a5ec6ea38dce5b9796097ed2b20e28b8b602 Mon Sep 17 00:00:00 2001 From: Yatharth Date: Mon, 13 May 2024 12:03:36 +0530 Subject: [PATCH 037/656] linked sorting-algorithms --- contrib/ds-algorithms/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 810313c8..5b521551 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -1,4 +1,4 @@ # List of sections - [Section title](filename.md) -- [Sorting Algorithms](soritng-algorithms.md) +- [Sorting Algorithms](sorting-algorithms.md) From ea62a209dce341eddb9c53a3b8865bd29f7c3843 Mon Sep 17 00:00:00 2001 From: Yatharth Date: Mon, 13 May 2024 12:25:42 +0530 Subject: [PATCH 038/656] added project Convert_JSON_to_CSV --- .../Convert_JSON_to_CSV/README.md | 20 +++++++++++++++++++ .../Convert_JSON_to_CSV/convert.py | 15 ++++++++++++++ .../Convert_JSON_to_CSV/input.json | 12 +++++++++++ .../Convert_JSON_to_CSV/output.csv | 3 +++ 4 files changed, 50 insertions(+) create mode 100644 contrib/mini-projects/Convert_JSON_to_CSV/README.md create mode 100644 contrib/mini-projects/Convert_JSON_to_CSV/convert.py create mode 100644 contrib/mini-projects/Convert_JSON_to_CSV/input.json create mode 100644 contrib/mini-projects/Convert_JSON_to_CSV/output.csv diff --git a/contrib/mini-projects/Convert_JSON_to_CSV/README.md b/contrib/mini-projects/Convert_JSON_to_CSV/README.md new file mode 100644 index 00000000..a14aeb66 --- /dev/null +++ b/contrib/mini-projects/Convert_JSON_to_CSV/README.md @@ -0,0 +1,20 @@ +# Convert a JSON File into a CSV + +This script takes a JSON file as input and generates a CSV file as output. + +## Prerequisites modules +- `json`: This module is required for handling JSON files. +- Run `pip install json` to install the required external module. + +## How to Run the Script +1. Make sure you have the necessary prerequisites installed. +2. Download or clone the script `converter.py`. +3. Open a terminal or command prompt. +4. Navigate to the directory where `converter.py` is located. +5. Run the script by executing the command `py converter.py`. + +## Additional Information +- The script reads data from the input JSON file and converts it into CSV format. +- It handles nested JSON structures and converts them into flattened CSV rows. +- The output CSV file is created in the same directory as the input JSON file. +- You can customize the behavior of the script by modifying the source code according to your requirements. diff --git a/contrib/mini-projects/Convert_JSON_to_CSV/convert.py b/contrib/mini-projects/Convert_JSON_to_CSV/convert.py new file mode 100644 index 00000000..0e526494 --- /dev/null +++ b/contrib/mini-projects/Convert_JSON_to_CSV/convert.py @@ -0,0 +1,15 @@ +import json + +if __name__ == '__main__': + try: + with open('input.json', 'r') as f: + data = json.loads(f.read()) + + output = ','.join([*data[0]]) + for obj in data: + output += f'\n{obj["Name"]},{obj["age"]},{obj["birthyear"]}' + + with open('output.csv', 'w') as f: + f.write(output) + except Exception as ex: + print(f'Error: {str(ex)}') \ No newline at end of file diff --git a/contrib/mini-projects/Convert_JSON_to_CSV/input.json b/contrib/mini-projects/Convert_JSON_to_CSV/input.json new file mode 100644 index 00000000..f9430062 --- /dev/null +++ b/contrib/mini-projects/Convert_JSON_to_CSV/input.json @@ -0,0 +1,12 @@ +[ + { + "Name": "Yatharth", + "age": 21, + "birthyear": "2003" + }, + { + "Name": "Sangita", + "age": 53, + "birthyear": "1971" + } + ] \ No newline at end of file diff --git a/contrib/mini-projects/Convert_JSON_to_CSV/output.csv b/contrib/mini-projects/Convert_JSON_to_CSV/output.csv new file mode 100644 index 00000000..d841013e --- /dev/null +++ b/contrib/mini-projects/Convert_JSON_to_CSV/output.csv @@ -0,0 +1,3 @@ +Name,age,birthyear +Yatharth,21,2003 +Sangita,53,1971 \ No newline at end of file From ba5b874e2b90bad100370020a8629d6280e48584 Mon Sep 17 00:00:00 2001 From: Yatharth31 <118633431+Yatharth31@users.noreply.github.com> Date: Mon, 13 May 2024 12:31:10 +0530 Subject: [PATCH 039/656] Delete contrib/mini-projects/Convert_JSON_to_CSV directory --- .../Convert_JSON_to_CSV/README.md | 20 ------------------- .../Convert_JSON_to_CSV/convert.py | 15 -------------- .../Convert_JSON_to_CSV/input.json | 12 ----------- .../Convert_JSON_to_CSV/output.csv | 3 --- 4 files changed, 50 deletions(-) delete mode 100644 contrib/mini-projects/Convert_JSON_to_CSV/README.md delete mode 100644 contrib/mini-projects/Convert_JSON_to_CSV/convert.py delete mode 100644 contrib/mini-projects/Convert_JSON_to_CSV/input.json delete mode 100644 contrib/mini-projects/Convert_JSON_to_CSV/output.csv diff --git a/contrib/mini-projects/Convert_JSON_to_CSV/README.md b/contrib/mini-projects/Convert_JSON_to_CSV/README.md deleted file mode 100644 index a14aeb66..00000000 --- a/contrib/mini-projects/Convert_JSON_to_CSV/README.md +++ /dev/null @@ -1,20 +0,0 @@ -# Convert a JSON File into a CSV - -This script takes a JSON file as input and generates a CSV file as output. - -## Prerequisites modules -- `json`: This module is required for handling JSON files. -- Run `pip install json` to install the required external module. - -## How to Run the Script -1. Make sure you have the necessary prerequisites installed. -2. Download or clone the script `converter.py`. -3. Open a terminal or command prompt. -4. Navigate to the directory where `converter.py` is located. -5. Run the script by executing the command `py converter.py`. - -## Additional Information -- The script reads data from the input JSON file and converts it into CSV format. -- It handles nested JSON structures and converts them into flattened CSV rows. -- The output CSV file is created in the same directory as the input JSON file. -- You can customize the behavior of the script by modifying the source code according to your requirements. diff --git a/contrib/mini-projects/Convert_JSON_to_CSV/convert.py b/contrib/mini-projects/Convert_JSON_to_CSV/convert.py deleted file mode 100644 index 0e526494..00000000 --- a/contrib/mini-projects/Convert_JSON_to_CSV/convert.py +++ /dev/null @@ -1,15 +0,0 @@ -import json - -if __name__ == '__main__': - try: - with open('input.json', 'r') as f: - data = json.loads(f.read()) - - output = ','.join([*data[0]]) - for obj in data: - output += f'\n{obj["Name"]},{obj["age"]},{obj["birthyear"]}' - - with open('output.csv', 'w') as f: - f.write(output) - except Exception as ex: - print(f'Error: {str(ex)}') \ No newline at end of file diff --git a/contrib/mini-projects/Convert_JSON_to_CSV/input.json b/contrib/mini-projects/Convert_JSON_to_CSV/input.json deleted file mode 100644 index f9430062..00000000 --- a/contrib/mini-projects/Convert_JSON_to_CSV/input.json +++ /dev/null @@ -1,12 +0,0 @@ -[ - { - "Name": "Yatharth", - "age": 21, - "birthyear": "2003" - }, - { - "Name": "Sangita", - "age": 53, - "birthyear": "1971" - } - ] \ No newline at end of file diff --git a/contrib/mini-projects/Convert_JSON_to_CSV/output.csv b/contrib/mini-projects/Convert_JSON_to_CSV/output.csv deleted file mode 100644 index d841013e..00000000 --- a/contrib/mini-projects/Convert_JSON_to_CSV/output.csv +++ /dev/null @@ -1,3 +0,0 @@ -Name,age,birthyear -Yatharth,21,2003 -Sangita,53,1971 \ No newline at end of file From e58671c6a26a99ac03ced86ac7b70dc8b431e32e Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Mon, 13 May 2024 14:18:50 +0530 Subject: [PATCH 040/656] Create Pandas_Series_Vs_NumPy_ndarray.md Created Pandas_Series_Vs_NumPy_ndarray.md file Added intro about Pandas series and NumPy ndarray Added Numpy ndarray section (Intro, key points, Used at) Added Pandas Series section (Intro, key points, Used at) --- .../pandas/Pandas_Series_Vs_NumPy_ndarray.md | 53 +++++++++++++++++++ 1 file changed, 53 insertions(+) create mode 100644 contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md diff --git a/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md b/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md new file mode 100644 index 00000000..d60a058b --- /dev/null +++ b/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md @@ -0,0 +1,53 @@ +# Pandas Series Vs NumPy ndarray + +NumPy ndarray and Pandas Series are two fundamental data structures in Python for handling and manipulating data. While they share some similarities, they also have distinct characteristics that make them suitable for different tasks. +## NumPy ndarray (n-dimensional array) + +NumPy is short form for Numerical Python, provides a powerful array object called `ndarray`, which is the backbone of many scientific and mathematical Python libraries. + +Here are key points about NumPy `ndarray`: + +- **Homogeneous Data**: All elements in a NumPy array are of the same data type, which allows for efficient storage and computation. +- **Efficient Computation**: NumPy arrays are designed for numerical operations and are highly efficient. They support vectorized operations, allowing you to perform operations on entire arrays rather than individual elements. +- **Multi-dimensional**: NumPy arrays can be multi-dimensional, making them suitable for representing complex numerical data structures like matrices and tensors. + +Example of creating a NumPy array: + +```python +import numpy as np + +narr = np.array(['A', 'B', 'C', 'D', 'E']) +print(narr) +``` +### Use NumPy ndarray: + +- When you need to perform mathematical operations on numerical data. +- When you’re working with multi-dimensional data. +- When computational efficiency is important. + +## Pandas Series + +Pandas, built on top of NumPy, introduces the `Series` data structure, which is designed for handling labeled one-dimensional data efficiently. + +Here are the key points about Pandas `Series`: + +- **Labeled Data**: Pandas Series associates a label (or index) with each element of the array, making it easier to work with heterogeneous or labeled data. + +- **Flexible Data Types**: Unlike NumPy arrays, Pandas Series can hold data of different types (integers, floats, strings, etc.) within the same object. + +- **Data Alignment**: One of the powerful features of Pandas Series is its ability to automatically align data based on label. This makes handling and manipulating data much more intuitive and less error-prone. + +Example of creating a Pandas Series: + +```python +import pandas as pd + +series = pd.Series([1, 3, 5, 7, 6, 8]) +print(series) +``` + +### Use Pandas Series: + +- When you need to manipulate and analyze labeled data. +- When you’re dealing with heterogeneous data or missing values. +- When you need more high-level, flexible data manipulation functions. From a269499c99f46be3d2ac087ff58c1e43888aa09a Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Mon, 13 May 2024 14:20:18 +0530 Subject: [PATCH 041/656] Update Pandas_Series_Vs_NumPy_ndarray.md Extended Introduction --- contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md b/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md index d60a058b..e71282be 100644 --- a/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md +++ b/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md @@ -1,6 +1,9 @@ # Pandas Series Vs NumPy ndarray NumPy ndarray and Pandas Series are two fundamental data structures in Python for handling and manipulating data. While they share some similarities, they also have distinct characteristics that make them suitable for different tasks. + +While both NumPy ndarray and Pandas Series are essential tools for data manipulation in Python, Choosing between them depends on the nature of your data and the specific tasks you need to perform. + ## NumPy ndarray (n-dimensional array) NumPy is short form for Numerical Python, provides a powerful array object called `ndarray`, which is the backbone of many scientific and mathematical Python libraries. From f61ceca303eeac22d84684a856a00fcffe18d926 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Mon, 13 May 2024 14:23:23 +0530 Subject: [PATCH 042/656] Update index.md Added Section title and Link to it --- contrib/pandas/index.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 82596a2f..cff81366 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -1,3 +1,5 @@ # List of sections +- Pandas Series Vs NumPy ndarray - [Section title](filename.md) +- [Pandas Series Vs NumPy ndarray)(Pandas_Series_Vs_NumPy_ndarray.md) From 33bae7b53f64715fccf0b2b2aa3aae481285eaf1 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Mon, 13 May 2024 14:24:24 +0530 Subject: [PATCH 043/656] Update index.md --- contrib/pandas/index.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index cff81366..c21ad574 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -1,5 +1,6 @@ # List of sections - Pandas Series Vs NumPy ndarray -- [Section title](filename.md) -- [Pandas Series Vs NumPy ndarray)(Pandas_Series_Vs_NumPy_ndarray.md) + + +- [Pandas Series Vs NumPy ndarray](Pandas_Series_Vs_NumPy_ndarray.md) From f87701802f2a48d476342096f64eb12e953cfdd7 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Mon, 13 May 2024 15:33:52 +0530 Subject: [PATCH 044/656] Update Pandas_Series_Vs_NumPy_ndarray.md Added Output for Code Snippets --- .../pandas/Pandas_Series_Vs_NumPy_ndarray.md | 40 +++++++++++++------ 1 file changed, 28 insertions(+), 12 deletions(-) diff --git a/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md b/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md index e71282be..d4931368 100644 --- a/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md +++ b/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md @@ -2,17 +2,17 @@ NumPy ndarray and Pandas Series are two fundamental data structures in Python for handling and manipulating data. While they share some similarities, they also have distinct characteristics that make them suitable for different tasks. -While both NumPy ndarray and Pandas Series are essential tools for data manipulation in Python, Choosing between them depends on the nature of your data and the specific tasks you need to perform. +Both NumPy ndarray and Pandas Series are essential tools for data manipulation in Python, Choosing between them depends on the nature of your data and the specific tasks you need to perform. -## NumPy ndarray (n-dimensional array) +## NumPy ndarray -NumPy is short form for Numerical Python, provides a powerful array object called `ndarray`, which is the backbone of many scientific and mathematical Python libraries. +NumPy is short form for Numerical Python, provides a powerful array object called `ndarray`, which is the backbone of many scientific and mathematical Python libraries. ndarray is also called n-dimensional array. Indexing in ndarray is integer based indexing. -Here are key points about NumPy `ndarray`: +Features of NumPy `ndarray`: - **Homogeneous Data**: All elements in a NumPy array are of the same data type, which allows for efficient storage and computation. -- **Efficient Computation**: NumPy arrays are designed for numerical operations and are highly efficient. They support vectorized operations, allowing you to perform operations on entire arrays rather than individual elements. -- **Multi-dimensional**: NumPy arrays can be multi-dimensional, making them suitable for representing complex numerical data structures like matrices and tensors. +- **Efficient Computation and Performance**: NumPy arrays are designed for numerical operations and are highly efficient. They support vectorized operations, allowing you to perform operations on entire arrays rather than individual elements. +- **Multi-dimensional**: NumPy arrays can be multi-dimensional, making them suitable for representing complex numerical data structures like matrices and n-dimensional arrays. Example of creating a NumPy array: @@ -22,21 +22,26 @@ import numpy as np narr = np.array(['A', 'B', 'C', 'D', 'E']) print(narr) ``` -### Use NumPy ndarray: +Output: +```python +['A' 'B' 'C' 'D' 'E'] +``` +### Usage of NumPy ndarray: - When you need to perform mathematical operations on numerical data. - When you’re working with multi-dimensional data. - When computational efficiency is important. +- When you need to store data of same data type. ` ## Pandas Series -Pandas, built on top of NumPy, introduces the `Series` data structure, which is designed for handling labeled one-dimensional data efficiently. +Pandas is a Python library used for data manipulation and analysis, introduces the `Series` data structure, which is designed for handling labeled one-dimensional data efficiently. -Here are the key points about Pandas `Series`: +Features of Pandas `Series`: - **Labeled Data**: Pandas Series associates a label (or index) with each element of the array, making it easier to work with heterogeneous or labeled data. -- **Flexible Data Types**: Unlike NumPy arrays, Pandas Series can hold data of different types (integers, floats, strings, etc.) within the same object. +- **Heterogeneous Data**: Unlike NumPy arrays, Pandas Series can hold data of different types (integers, floats, strings, etc.) within the same object. - **Data Alignment**: One of the powerful features of Pandas Series is its ability to automatically align data based on label. This makes handling and manipulating data much more intuitive and less error-prone. @@ -45,12 +50,23 @@ Example of creating a Pandas Series: ```python import pandas as pd -series = pd.Series([1, 3, 5, 7, 6, 8]) +series = pd.Series([1,'B', 5, 7, 6, 8], index = ['a','b','c','d','e','f']) print(series) ``` +Output: +```python +a 1 +b B +c 5 +d 7 +e 6 +f 8 +dtype: object +``` -### Use Pandas Series: +### Usage of Pandas Series: - When you need to manipulate and analyze labeled data. - When you’re dealing with heterogeneous data or missing values. - When you need more high-level, flexible data manipulation functions. +- When you are dealing with One-dimensional data. From 7cd0ec140e3a036a02b65a366d8336c02a8c016d Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Mon, 13 May 2024 15:58:31 +0530 Subject: [PATCH 045/656] Update Pandas_Series_Vs_NumPy_ndarray.md --- contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md b/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md index d4931368..e739766b 100644 --- a/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md +++ b/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md @@ -6,7 +6,7 @@ Both NumPy ndarray and Pandas Series are essential tools for data manipulation i ## NumPy ndarray -NumPy is short form for Numerical Python, provides a powerful array object called `ndarray`, which is the backbone of many scientific and mathematical Python libraries. ndarray is also called n-dimensional array. Indexing in ndarray is integer based indexing. +NumPy is short form for Numerical Python, provides a powerful array object called `ndarray`, It is very important for many scientific and mathematical Python libraries. ndarray is also called n-dimensional array. Indexing in ndarray is integer based indexing (like arr[0], arr[3], etc.). Features of NumPy `ndarray`: @@ -31,11 +31,11 @@ Output: - When you need to perform mathematical operations on numerical data. - When you’re working with multi-dimensional data. - When computational efficiency is important. -- When you need to store data of same data type. ` +- When you need to store data of same data type. ## Pandas Series -Pandas is a Python library used for data manipulation and analysis, introduces the `Series` data structure, which is designed for handling labeled one-dimensional data efficiently. +Pandas is a Python library used for data manipulation and analysis, introduces the `Series` data structure, which is designed for handling labeled one-dimensional data efficiently. Indexing in Pandas Series is Label-based. It effectively handles heterogeneous data. Features of Pandas `Series`: @@ -43,7 +43,7 @@ Features of Pandas `Series`: - **Heterogeneous Data**: Unlike NumPy arrays, Pandas Series can hold data of different types (integers, floats, strings, etc.) within the same object. -- **Data Alignment**: One of the powerful features of Pandas Series is its ability to automatically align data based on label. This makes handling and manipulating data much more intuitive and less error-prone. +- **Data Alignment**: One of the powerful features of Pandas Series is its ability to automatically align data based on label. Example of creating a Pandas Series: From d6184b240ea244d8804f450c91bbcca79fa521fe Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Mon, 13 May 2024 16:04:53 +0530 Subject: [PATCH 046/656] Update index.md --- contrib/pandas/index.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index c21ad574..8c7fa42f 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -1,6 +1,3 @@ # List of sections -- Pandas Series Vs NumPy ndarray - - - [Pandas Series Vs NumPy ndarray](Pandas_Series_Vs_NumPy_ndarray.md) From 13bc6d8f3b5af50d9cb6676939e466d7c5e6a1e1 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Mon, 13 May 2024 17:07:48 +0530 Subject: [PATCH 047/656] Rename Pandas_Series_Vs_NumPy_ndarray.md to pandas_series_Vs_numpy_ndarray.md Renamed file name into lowercase --- ...ries_Vs_NumPy_ndarray.md => pandas_series_Vs_numpy_ndarray.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/pandas/{Pandas_Series_Vs_NumPy_ndarray.md => pandas_series_Vs_numpy_ndarray.md} (100%) diff --git a/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md b/contrib/pandas/pandas_series_Vs_numpy_ndarray.md similarity index 100% rename from contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md rename to contrib/pandas/pandas_series_Vs_numpy_ndarray.md From 45f494f9c017ecadbae17e979d16e52fe46d8f07 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Mon, 13 May 2024 17:08:49 +0530 Subject: [PATCH 048/656] Update index.md Linked properly --- contrib/pandas/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 8c7fa42f..5c0f2b43 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -1,3 +1,3 @@ # List of sections -- [Pandas Series Vs NumPy ndarray](Pandas_Series_Vs_NumPy_ndarray.md) +- [Pandas Series Vs NumPy ndarray](pandas_series_vs_numpy_ndarray.md) From 950a3136a0ef4b083b06d36bbef9453186c2add0 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Mon, 13 May 2024 17:10:35 +0530 Subject: [PATCH 049/656] Rename pandas_series_Vs_numpy_ndarray.md to pandas_series_vs_numpy_ndarray.md renamed file --- ...ries_Vs_numpy_ndarray.md => pandas_series_vs_numpy_ndarray.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/pandas/{pandas_series_Vs_numpy_ndarray.md => pandas_series_vs_numpy_ndarray.md} (100%) diff --git a/contrib/pandas/pandas_series_Vs_numpy_ndarray.md b/contrib/pandas/pandas_series_vs_numpy_ndarray.md similarity index 100% rename from contrib/pandas/pandas_series_Vs_numpy_ndarray.md rename to contrib/pandas/pandas_series_vs_numpy_ndarray.md From 83809066303a7f962f2063d1613bad26f9776c1d Mon Sep 17 00:00:00 2001 From: Ananyagra24 <155280507+Ananyagra24@users.noreply.github.com> Date: Mon, 13 May 2024 21:10:18 +0530 Subject: [PATCH 050/656] Create dice_roller.md --- contrib/mini-projects/dice_roller.md | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) create mode 100644 contrib/mini-projects/dice_roller.md diff --git a/contrib/mini-projects/dice_roller.md b/contrib/mini-projects/dice_roller.md new file mode 100644 index 00000000..c0839343 --- /dev/null +++ b/contrib/mini-projects/dice_roller.md @@ -0,0 +1,19 @@ +``` +import random +def dice(): + dice_no = random.choice([1,2,3,4,5,6]) + return "You got " + str(dice_no) +def roll_dice(): + print("Hey Guys, you will now roll a single dice using Python!") + while True: + start=input("Type \'k\' to roll the dice: ").lower() + if start != 'k': + print("Invalid input. Please try again.") + continue + print(dice()) + roll_again = input("Do you want to reroll? (Yes/No): ").lower() + if roll_again != 'yes': + break + print("Thanks for rolling the dice.") +roll_dice() +``` From f8a67cf65dc3d70cf0a81a0cebd55770f0ffc728 Mon Sep 17 00:00:00 2001 From: Ananyagra24 <155280507+Ananyagra24@users.noreply.github.com> Date: Mon, 13 May 2024 21:11:01 +0530 Subject: [PATCH 051/656] Update index.md --- contrib/mini-projects/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/mini-projects/index.md b/contrib/mini-projects/index.md index 82596a2f..f60b286b 100644 --- a/contrib/mini-projects/index.md +++ b/contrib/mini-projects/index.md @@ -1,3 +1,3 @@ # List of sections -- [Section title](filename.md) +- [Dice Roller](dice_roller.md) From 378dbddc67a91ecebc6ccfb1fb38a8d45feaa0e2 Mon Sep 17 00:00:00 2001 From: Drishan Gupta <66329991+drishangupta@users.noreply.github.com> Date: Tue, 14 May 2024 08:44:20 +0530 Subject: [PATCH 052/656] Removed Formatting errors --- .../advanced-python/decorator-kwargs-args.md | 40 +++++++++---------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/contrib/advanced-python/decorator-kwargs-args.md b/contrib/advanced-python/decorator-kwargs-args.md index 402dcc6f..63a41b36 100644 --- a/contrib/advanced-python/decorator-kwargs-args.md +++ b/contrib/advanced-python/decorator-kwargs-args.md @@ -6,9 +6,9 @@ Functions in Python are so called first class objects, which means they can be t ```python def func1(): - def func2(): - print("Printing from the inner function, func2") - return func2 + def func2(): + print("Printing from the inner function, func2") + return func2 ``` Assigning func1 to function_call object @@ -17,7 +17,7 @@ function_call=func1() ``` Calling the function ```python ->> function_call() +>>> function_call() ``` **Output** ``` @@ -50,8 +50,8 @@ Now that you have understood why \* is used, we can take a look at *args. *args *args makes python functions flexible to handle dynamic arguments. ```python def test1(*args): - print(args) - print(f"The number of elements in args = {len(args)}") + print(args) + print(f"The number of elements in args = {len(args)}") a=list(range(0,10)) test1(*a) ``` @@ -64,8 +64,8 @@ If in the test1 we do not use \* in the argument ```python def test1(*args): - print(args) - print(f"The number of elements in args = {len(args)}") + print(args) + print(f"The number of elements in args = {len(args)}") a=list(range(0,10)) test1(a) ``` @@ -78,8 +78,8 @@ The number of elements in args = 1 **kwargs stands for keyword arguments. This is used for key and value pairs and similar to *args, this makes functions flexible enough to handle dynamic key value pairs in arguments. ```python def test2(**kwargs): - print(kwargs) - print(f"The number of elements in kwargs = {len(kwargs)}") + print(kwargs) + print(f"The number of elements in kwargs = {len(kwargs)}") test2(a=1,b=2,c=3,d=4,e=5) ``` The above snippet uses some key-value pairs and out test2 function gives the following output: @@ -96,18 +96,18 @@ Now that we understand what first class object, *args, **kwargs is, we can move ```python import time def multiplication(a,b): - start=time.time() - c=a*b - total=time.time()-start - print("Time taken for execution of multiplication",total) - return c + start=time.time() + c=a*b + total=time.time()-start + print("Time taken for execution of multiplication",total) + return c def addition(a,b): - start=time.time() - c=a+b - total=time.time()-start - print("Time taken for execution of addition ",total) - return c + start=time.time() + c=a+b + total=time.time()-start + print("Time taken for execution of addition ",total) + return c multiplication(4,5) addition(4,5) From fbbef5e5726765ed0bfff9131972f1f6d80374dc Mon Sep 17 00:00:00 2001 From: AritraCh2005 <153842880+AritraCh2005@users.noreply.github.com> Date: Tue, 14 May 2024 19:13:22 +0530 Subject: [PATCH 053/656] Update index.md --- contrib/numpy/index.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 82596a2f..db6f0eb5 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -1,3 +1,12 @@ # List of sections -- [Section title](filename.md) +* [Basic Mathematics](#BasicMaths) + * [What is a Matrix?](#Matrix-Intro) + * [Scalars and Vectors](#Scalars-Vectors) + * [Arrays in Python](#Arrays) + * [What is a Tensor?](#Tensors) + * [Addition and Subtraction in Matrices](#Arithmetic) + * [Transpose of Matrices & Vectors](#Transpose) + * [Dot Product](#DotProduct) + * [Dot Product of Matrices](#DotMatrices) + From c1726d428a368dd1a9f72f896ef1699159ec976e Mon Sep 17 00:00:00 2001 From: AritraCh2005 <153842880+AritraCh2005@users.noreply.github.com> Date: Tue, 14 May 2024 19:14:52 +0530 Subject: [PATCH 054/656] Create basic_math.md Added basic mathematics useful while working with numpy --- contrib/numpy/basic_math.md | 371 ++++++++++++++++++++++++++++++++++++ 1 file changed, 371 insertions(+) create mode 100644 contrib/numpy/basic_math.md diff --git a/contrib/numpy/basic_math.md b/contrib/numpy/basic_math.md new file mode 100644 index 00000000..d19f2f88 --- /dev/null +++ b/contrib/numpy/basic_math.md @@ -0,0 +1,371 @@ +# Basic Mathematics +## What is a Matrix? +A matrix is a collection of numbers ordered in rows and columns. Here is one. + + + + + + + + + + + + + + + + + + +
123
456
789
+ + + +A matrix is generally written within square brackets[]. The dimensions of a matrix is represented by (Number of rows x Number of columns).The dimensions of the above matrix is 3x3. + +Matrices are the main characters in mathematical operations like addition, subtraction etc, especially those used in Pandas and NumPy. They can contain only numbers, symbols or expressions. + +In order to refer to a particular element in the matrix we denote it by : +Aij + +where i represents the ith row and j represents the jth column of the matrix. + +## Scalars and Vectors +### Scalars +There exists specific cases of matrices which are also widely used. + +A matrix with only one row and column i.e. containing only one element is commonly referred to as a scalar. + +The numbers ```[12] ; [-5] ; [0] ; [3.14]``` all represent scalars. Scalars have 0 dimensions. + +### Vectors +Vectors are objects with 1 dimension. They sit somewhere between scalars and matrices. They can also be referred to as one dimensional matrices. + +```[1 3 5]``` represents a vector with dimension 1x3. + +A vector is the simplest linear algebraic object.A matrix can be refered to as a collection of vectors + +Vectors are broadly classified into 2 types: +- Row Vectors: Is of the form 1 x n where n refers to the number of columns the vector has. +- Column Vectors: Is of the form m x 1 where m refers to the number of rows the vector has. + +m or n are also called as the length of the column and row vector respectively. + +## Arrays in Python + +To understand arrays, first let us start by declaring scalars, vectors and matrices in Python. + +First we need to import numpy. We do so by importing it as 'np' as it provides better readability, namespace clarity and also aligns with the community guidelines. + +```python +import numpy as np +``` +Next up, we declare a scalar s as, +``` +s = 5 +``` + + + +Now we declare a vector, +```python +v = np.array([5,-2,4]) +``` +On printing v we get the following output, +```python +array([5,-2,4]) +``` +By default, a vector is declared as a **'row vector'**. + +Finally, we declare matrices, +```python +m=np.array([[5,12,6],[-3,0,14]]) +``` +On printing m we get, +```python +array([[5,12,6], + [-3,0,14]]) +``` +> The type() function is used to return the data type of a given variable. + +* The type(s) will return **'int'**. + +* The type(v) will return **'numpy.ndarray'** which represents a **n-dimensional array**, since it is a 1 dimensional array. + + * The type(m) will also return **'numpy.ndarray'** since it is a 2-dimensional array. + +These are some ways in which arrays are useful in python. + +> The shape() function is used to return the shape of a given variable. + +* m.shape() returns (2,3) since we are dealing with a (2,3) matrix. + +* v.shape() returns(3,) indicates it has only one dimensional or that it stores 3 elements in order. + +* However, 'int' objects do not have shape and therefore s.shape() gives an error. + +## What is a Tensor? +A Tensor can be thought of as a collection of matrices. It has dimensions k x m x n. + +**NOTE:** Scalars, vectors and matrices are also tensors of rank 0,1,2 respectively. + +Tensors can be stored in ndarrays. + +Let's create a tensor with 2 matrices, +```python +m1=np.array([[5,12,6],[-3,0,14]]) +m2=np.array([[2,1,8],[-6,2,0]]) +t=np.array([m1,m2]) +``` +Upon printing t we get, +```python +array([[[5,12,6], + [-3,0,14]], + + [[2,1,8], + [-6,2,0]]]) +``` +If we check it's shape, we see that is is a **(2,2,3)** object. + +If we want to manually create a tensor we write, +```python +t=np.array([[[5,12,6], [-3,0,14]],[[2,1,8], [-6,2,0]]]) +``` + ## Addition and Subtraction in Matrices + + ### Addition + For 2 matrices to be added to one another they must have **same dimensions**. + + If we have 2 matrices say, + +```python +A=np.array([[5,12,6],[-3,0,14]]) +B=np.array([[2,1,8],[-6,2,0]]) +C= A+B + ``` +The element at position Aij gets added to the element at position Bij. It's that simple! +The above input will give the resultant C as: +```python +array([[7,13,14], + [-9,2,14]]) +``` +### Subtraction + +As we know, subtraction is a type of addition, the same rules apply here. + + If we have 2 matrices say, + +```python +A=np.array([[5,12,6],[-3,0,14]]) +B=np.array([[2,1,8],[-6,2,0]]) +C= A-B + ``` + +The element at position Bij gets subtracted from the element at position Aij. +The above input will give the resultant C as: +```python +array([[3,11,-2], + [3,-2,14]]) +``` +Similarly the same operations can be done with **floating point numbers** as well. + +In a similar fashion, we can add or subtract vectors as well with the condition that they must be of the **same length**. +```python +A=np.array([1,2,3,4,5]) +B=np.array([6,7,8,9,10]) +C= A+B + ``` +The result is a vector of length 5 with C as, +```python +array([7,9,11,13,15]) +``` + ### Addition of scalars with vectors & matrices + + Scalars show unique behaviour when added to matrices or vectors. + + To demonstrate their behaviour, let's use an example, + Let's declare a matrix, + +```python +A=np.array([[5,12,6],[-3,0,14]]) +A+1 +``` +We see that if we perform the above function, i.e. add scalar [1] to the matrix A we get the output, +```python +array([[6,13,7],[-2,1,15]]) +``` +We see that the scalar is added to the matrix elementwise, i.e. each element gets incremented by 1. + +**The same applies to vectors as well.** + +Mathematically, it is not allowed as the shape of scalars are different from vectors or matrices but while programming in Python it works. + +## Transpose of Matrices & Vectors +### Transposing Vectors + +If X is the vector, then the transpose of the vector is represented as XT. It changes a vector of dimension n x 1 into a vector of dimension 1 x n, i.e. a row vector to a column vector and vice versa. + +> * The values are not changing or transforming ; only their position is. +> * Transposing the same vector (object) twice yields the initial vector (object). + +```python +x=np.array([1,2,3)) +``` +Transposing this in python using ```x.T``` will give +```python +array([1,2,3)) +``` +which is the same vector as the one taken as input. + +> 1-Dimensional arrays don't really get transposed (in the memory of the computer) + +To transpose a vector, we need to reshape it first. +```python +x_new= x.reshape(1,3) +x_new.T +``` +will now result in the vector getting transposed, +```python +array([[1], + [2], + [3]]) +``` + +### Transposing Matrices + +If M is a matrix, then the transpose of the matrix M is represented as MT. When transposed, a m x n matrix becomes a n x m matrix. + +The element Mij of the initial matrix becomes the Nji where N is the transposed matrix of M. + +Let's understand this further with the help of of an example, +```python +A = np.array([[1,5,-6],[8,-2,0]]) +``` +The output for the above code snippet will be, +```python +array([[1,5,-6], + [8,-2,0]]) +``` +> **array.T** returns the transpose of an array (matrix). + +```python +A.T +``` +will give the output as, +```python +array([[1,8], + [5,-2], + [-6,0]]) +``` + +Hope the following examples have cleared your concept on transposing. + +## Dot Product + +> **np.dot()** returns the dot product of two objects +> Dot product is represented by ( * ), for example, x(dot)y = x * y +> +### Scalar * Scalar +Let's start with scalar multiplication first. + +``` [6] * [5] = [30] + [10] * [-2] = [-20] +``` +It is the same multiplication that we are familiar with since learnt as kids. + Therefore, ```np.dot([6]*[5])``` returns ```30```. + +### Vector * Vector +To multiply vectors with one another, they must be of **same length**. + +Now let's understand this with an example, +```python +x = np.array([2,8,-4]) +y = np.array([1,-7,3]) +``` + +The dot product returns the elementwise product of the vector i.e. +x * y = ( x1 * y1 ) + ( x2 * y2 ) + ( x3 * y3 ) in the above example. + +Therefore, ```np.dot(x,y)``` gives ```[-66]``` as the input. + +We observe that **dot product of 2 vectors returns a scalar**. + +### Scalar * Vector + +When we multiply a scalar with a vector, we observe that each element of the vector gets multiplied to the scalar individually. + +A scalar k when multiplied to a vector v([x1,x2,x3]) gives the product = [(k * x1) + (k * x2) + (k * x3)] + +An example would bring further clarity, +```python +y = np.array([1,-7,3]) +y*5 +``` +will give the following output +```python +array[(5,-35,15)] +``` + +We observe that **dot product of 2 vectors returns a scalar**. + +We observe that **dot product of a vector and a scalar returns a vector**. + +## Dot Product of Matrices + +### Scalar * Matrix + Dot product of a scalar with a matrix works similar to dot product of a vector with a scalar. +Now, we come to a very important concept which will be very useful to us while working in Python. + +Each element of the vector gets multiplied to the scalar individually. + +```python +A = np.array([[1,5,-6],[8,-2,0]]) +B = 3 * A +``` +will give the resultant B as +```python +array([[3,15,-18], + [24,-6,0]]) +``` +Thus each element gets multiplied by 3. +> NOTE: The dot product of a scalar and a matrix gives a matrix of the same shape as the input matrix. + +### Matrix * Matrix + A matrix can be multipied to a matrix. However it has certain compatibility measures, + * We can only multiply an m x n matrix with an n x k matrix + * Basically the 2nd dimension of the first matrix has to match the 1st dimension of the 2nd matrix. +> The output of a m x n matrix with a n x k matrix gives a **m x k** matrix. + +**Whenever we have a dot product of 2 matrices, we multiply row vectors within one matrix to the column vector of 2nd matrix.** + +For example, let's use multiply a row vector to a column vector to understand it further. + +``` + ([[1] + ([2 8 4]) * [2] = [(2*1) + (8*2) + (4*3)] = [30] + [3]]) +``` +Now, let's multiply a 2 x 3 matrix with a 3 x 2 matrix. +``` + ([[A1,A2,A3], * ([[B1,B2] ([[(A1 * B1 + A2 * B3 + A3 * B5) , (A1 * B2 + A2 * B4 + A3 * B6)] + [A4,A5,A6]]) [B3,B4], = [ (A4 * B1 + A5 * B3 + A6 * B5) , (A4 * B2 + A5 * B4 + A6 * B6)]]) + [B5,B6]]) +``` +Thus we obtain a 2 x 2 matrix. + +We use the np.dot() method to directly obtain the dot product of the 2 matrices. + +Now let's do an example using python just to solidify our knowledge. + +```python +A=np.array([[5,12,6],[-3,0,14]]) +B=np.array([[2,-1],[8,0],[3,0]]) +np.dot(A,B) +``` +The output we obtain is, +```python +array[[124,-5], + [36, 3]]) +``` From ea32f2bd8ce5c16960c66e72b6e886d9b260f51d Mon Sep 17 00:00:00 2001 From: Hema Sree Date: Wed, 15 May 2024 17:49:44 +0530 Subject: [PATCH 055/656] Added intro to numpy --- contrib/numpy/Introduction.md | 36 +++++++++++++++++++++++++++++++++++ contrib/numpy/index.md | 2 +- 2 files changed, 37 insertions(+), 1 deletion(-) create mode 100644 contrib/numpy/Introduction.md diff --git a/contrib/numpy/Introduction.md b/contrib/numpy/Introduction.md new file mode 100644 index 00000000..59a19381 --- /dev/null +++ b/contrib/numpy/Introduction.md @@ -0,0 +1,36 @@ +# Introduction + +## What is Numpy? + +NumPy is a general-purpose array-processing package. It provides a high-performance multidimensional array object and tools for working with these arrays. It is the fundamental package for scientific computing with Python. It is open-source software. + +## Features of NumPy + +NumPy has various features including these important ones: + +1. A powerful N-dimensional array object +2. Sophisticated (broadcasting) functions +3. Tools for integrating C/C++ and Fortran code +4. Useful linear algebra, Fourier transform, and random number capabilities + +## Install Numpy + +Before installing Numpy, ensure you have Python installed on your system. You can download and install Python from the [official Python website](https://www.python.org/). + +Numpy can be installed via the following pip command: + +```bash +pip install numpy + +``` + +# How to import NumPy + +To access NumPy and its functions import it in your Python code like this: + +```bash +import numpy as np + +``` + +We shorten the imported name to np for better readability of code using NumPy. This is a widely adopted convention that makes your code more readable for everyone working on it. We recommend to always use import numpy as np. diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 82596a2f..c6bf23c9 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -1,3 +1,3 @@ # List of sections -- [Section title](filename.md) +- [Introduction](Introduction.md) From 59e79c7627db41589dc554e1440d0ee5aacf3c9c Mon Sep 17 00:00:00 2001 From: Hema Sree Date: Wed, 15 May 2024 17:57:30 +0530 Subject: [PATCH 056/656] Added numpy intro --- contrib/numpy/Introduction.md | 32 +++++++++++++------------------- 1 file changed, 13 insertions(+), 19 deletions(-) diff --git a/contrib/numpy/Introduction.md b/contrib/numpy/Introduction.md index 59a19381..c437c481 100644 --- a/contrib/numpy/Introduction.md +++ b/contrib/numpy/Introduction.md @@ -1,36 +1,30 @@ # Introduction -## What is Numpy? +## What is NumPy? -NumPy is a general-purpose array-processing package. It provides a high-performance multidimensional array object and tools for working with these arrays. It is the fundamental package for scientific computing with Python. It is open-source software. +NumPy is a powerful array-processing library in Python, essential for scientific computing. It provides efficient data structures and tools for working with multidimensional arrays. -## Features of NumPy +## Key Features -NumPy has various features including these important ones: +1. **Efficient Arrays:** NumPy offers high-performance N-dimensional array objects for swift data manipulation. +2. **Broadcasting:** Advanced broadcasting enables seamless element-wise operations on arrays of varying shapes. +3. **Interoperability:** NumPy seamlessly integrates with C, C++, and Fortran, enhancing performance and versatility. +4. **Mathematical Tools:** Comprehensive support for linear algebra, Fourier transforms, and random number generation. -1. A powerful N-dimensional array object -2. Sophisticated (broadcasting) functions -3. Tools for integrating C/C++ and Fortran code -4. Useful linear algebra, Fourier transform, and random number capabilities +## Installation -## Install Numpy - -Before installing Numpy, ensure you have Python installed on your system. You can download and install Python from the [official Python website](https://www.python.org/). - -Numpy can be installed via the following pip command: +Ensure Python is installed in your system. If not you can install it from here([official Python website](https://www.python.org/)),then install NumPy via: ```bash pip install numpy - ``` -# How to import NumPy +# Importing NumPy -To access NumPy and its functions import it in your Python code like this: +To access NumPy functions, import it with the alias `np`: -```bash +```python import numpy as np - ``` -We shorten the imported name to np for better readability of code using NumPy. This is a widely adopted convention that makes your code more readable for everyone working on it. We recommend to always use import numpy as np. +Using `np` as an alias enhances code readability and is a widely adopted convention. From 9f482ac8181b47e80c5469025ab067bab96ae54a Mon Sep 17 00:00:00 2001 From: Hema Sree Date: Wed, 15 May 2024 17:58:33 +0530 Subject: [PATCH 057/656] intro numpy --- contrib/numpy/Introduction.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/numpy/Introduction.md b/contrib/numpy/Introduction.md index c437c481..c0f5f528 100644 --- a/contrib/numpy/Introduction.md +++ b/contrib/numpy/Introduction.md @@ -19,7 +19,7 @@ Ensure Python is installed in your system. If not you can install it from here([ pip install numpy ``` -# Importing NumPy +## Importing NumPy To access NumPy functions, import it with the alias `np`: From 2189d3914cb4194df2ca02eb33a2f0d11cd07844 Mon Sep 17 00:00:00 2001 From: Ritesh <112797055+Antiquely3059@users.noreply.github.com> Date: Wed, 15 May 2024 18:53:35 +0530 Subject: [PATCH 058/656] Create Rock Paper Scissors Game.md --- .../mini-projects/Rock Paper Scissors Game.md | 48 +++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 contrib/mini-projects/Rock Paper Scissors Game.md diff --git a/contrib/mini-projects/Rock Paper Scissors Game.md b/contrib/mini-projects/Rock Paper Scissors Game.md new file mode 100644 index 00000000..ed815d5a --- /dev/null +++ b/contrib/mini-projects/Rock Paper Scissors Game.md @@ -0,0 +1,48 @@ +```python +import tkinter as tk +import random + +def determine_winner(user_choice, computer_choice): + """Determine the winner of the game.""" + if user_choice == computer_choice: + return "It's a tie!", computer_choice + elif (user_choice == "rock" and computer_choice == "scissors") or \ + (user_choice == "paper" and computer_choice == "rock") or \ + (user_choice == "scissors" and computer_choice == "paper"): + return "You win!", computer_choice + else: + return "Computer wins!", computer_choice + +def play_game(): + """Play the game and display the result.""" + user_choice = user_var.get() + computer_choice = random.choice(["rock", "paper", "scissors"]) + result, computer_pick = determine_winner(user_choice, computer_choice) + result_label.config(text=result) + computer_label.config(text=f"Computer picked: {computer_pick}") + +# Create main window +root = tk.Tk() +root.title("Rock Paper Scissors") + +# User choice options +user_var = tk.StringVar() +user_var.set("rock") # Default choice +choices = ["rock", "paper", "scissors"] +for choice in choices: + rb = tk.Radiobutton(root, text=choice, variable=user_var, value=choice) + rb.pack() + +# Play button +play_button = tk.Button(root, text="Play", command=play_game) +play_button.pack() + +# Result label +result_label = tk.Label(root, text="", font=("Helvetica", 16)) +result_label.pack() + +# Computer pick label +computer_label = tk.Label(root, text="", font=("Helvetica", 12)) +computer_label.pack() + +root.mainloop() From 8f0c49efa87011c70be9d9c89889c2d9d63b8ec1 Mon Sep 17 00:00:00 2001 From: Ritesh <112797055+Antiquely3059@users.noreply.github.com> Date: Wed, 15 May 2024 18:59:12 +0530 Subject: [PATCH 059/656] Update Rock Paper Scissors Game.md --- contrib/mini-projects/Rock Paper Scissors Game.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/contrib/mini-projects/Rock Paper Scissors Game.md b/contrib/mini-projects/Rock Paper Scissors Game.md index ed815d5a..9cc17ee9 100644 --- a/contrib/mini-projects/Rock Paper Scissors Game.md +++ b/contrib/mini-projects/Rock Paper Scissors Game.md @@ -1,3 +1,12 @@ + +Here's how it looks: + +# Rock Paper Scissors Game + +This is a simple implementation of the classic rock-paper-scissors game in Python. + +## Code: + ```python import tkinter as tk import random From c79f13a28c7f21033c4c85d14a15fb675ee5bd84 Mon Sep 17 00:00:00 2001 From: Ritesh <112797055+Antiquely3059@users.noreply.github.com> Date: Wed, 15 May 2024 19:02:05 +0530 Subject: [PATCH 060/656] Update Rock Paper Scissors Game.md --- contrib/mini-projects/Rock Paper Scissors Game.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/contrib/mini-projects/Rock Paper Scissors Game.md b/contrib/mini-projects/Rock Paper Scissors Game.md index 9cc17ee9..b08a2683 100644 --- a/contrib/mini-projects/Rock Paper Scissors Game.md +++ b/contrib/mini-projects/Rock Paper Scissors Game.md @@ -1,6 +1,3 @@ - -Here's how it looks: - # Rock Paper Scissors Game This is a simple implementation of the classic rock-paper-scissors game in Python. From 25a319ff3cb82a26ddc8f0b272713bb68a8fea77 Mon Sep 17 00:00:00 2001 From: Ritesh <112797055+Antiquely3059@users.noreply.github.com> Date: Wed, 15 May 2024 19:54:12 +0530 Subject: [PATCH 061/656] Update Rock Paper Scissors Game.md --- .../mini-projects/Rock Paper Scissors Game.md | 36 +++++++++++++++++-- 1 file changed, 33 insertions(+), 3 deletions(-) diff --git a/contrib/mini-projects/Rock Paper Scissors Game.md b/contrib/mini-projects/Rock Paper Scissors Game.md index b08a2683..c1a5ecf0 100644 --- a/contrib/mini-projects/Rock Paper Scissors Game.md +++ b/contrib/mini-projects/Rock Paper Scissors Game.md @@ -2,8 +2,19 @@ This is a simple implementation of the classic rock-paper-scissors game in Python. -## Code: +## Code Explanation: +In this section, we import the required libraries (`tkinter` for GUI and `random` for generating computer choices) and define two functions: + +- `determine_winner(user_choice, computer_choice)`: + - This function determines the winner of the game based on the choices made by the user and the computer. + - It returns a tuple containing the result of the game and the computer's choice. + +- `play_game()`: + - This function handles the gameplay logic. + - It gets the user's choice from the radio buttons, generates a random choice for the computer, determines the winner using the `determine_winner()` function, and updates the result and computer pick labels accordingly. + +### Imports and Function Definitions: ```python import tkinter as tk import random @@ -26,7 +37,9 @@ def play_game(): result, computer_pick = determine_winner(user_choice, computer_choice) result_label.config(text=result) computer_label.config(text=f"Computer picked: {computer_pick}") - +``` +### GUI Setup: +```python # Create main window root = tk.Tk() root.title("Rock Paper Scissors") @@ -38,7 +51,14 @@ choices = ["rock", "paper", "scissors"] for choice in choices: rb = tk.Radiobutton(root, text=choice, variable=user_var, value=choice) rb.pack() - +``` +- Here, we create the main window for the game using `tkinter.Tk()`. We set the title to "Rock Paper Scissors". +- We define a StringVar to store the user's choice and set the default choice to "rock". +- We create radio buttons for the user to choose from ("rock", "paper", "scissors") and pack them into the main window. +``` +``` +### Play Button and Result Labels: +```python # Play button play_button = tk.Button(root, text="Play", command=play_game) play_button.pack() @@ -50,5 +70,15 @@ result_label.pack() # Computer pick label computer_label = tk.Label(root, text="", font=("Helvetica", 12)) computer_label.pack() +``` +- We create a "Play" button that triggers the play_game() function when clicked, using tkinter.Button. +- We create two labels to display the result of the game (result_label) and the computer's choice (computer_label). Both labels initially display no text and are packed into the main window. +``` +``` +### Mainloop: +```python root.mainloop() +``` +- Finally, we start the Tkinter event loop using root.mainloop(), which keeps the GUI window open and responsive until the user closes it. +- From 1196e6ee89d05deb8186e815838c90ab1b49caec Mon Sep 17 00:00:00 2001 From: Ritesh <112797055+Antiquely3059@users.noreply.github.com> Date: Wed, 15 May 2024 19:57:34 +0530 Subject: [PATCH 062/656] Update Rock Paper Scissors Game.md --- contrib/mini-projects/Rock Paper Scissors Game.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/contrib/mini-projects/Rock Paper Scissors Game.md b/contrib/mini-projects/Rock Paper Scissors Game.md index c1a5ecf0..36326b0d 100644 --- a/contrib/mini-projects/Rock Paper Scissors Game.md +++ b/contrib/mini-projects/Rock Paper Scissors Game.md @@ -53,7 +53,7 @@ for choice in choices: rb.pack() ``` - Here, we create the main window for the game using `tkinter.Tk()`. We set the title to "Rock Paper Scissors". -- We define a StringVar to store the user's choice and set the default choice to "rock". +- We define a `StringVar` to store the user's choice and set the default choice to "rock". - We create radio buttons for the user to choose from ("rock", "paper", "scissors") and pack them into the main window. ``` ``` @@ -71,8 +71,8 @@ result_label.pack() computer_label = tk.Label(root, text="", font=("Helvetica", 12)) computer_label.pack() ``` -- We create a "Play" button that triggers the play_game() function when clicked, using tkinter.Button. -- We create two labels to display the result of the game (result_label) and the computer's choice (computer_label). Both labels initially display no text and are packed into the main window. +- We create a "Play" button that triggers the `play_game()` function when clicked, using `tkinter.Button`. +- We create two labels to display the result of the game (`result_label`) and the computer's choice (`computer_label`). Both labels initially display no text and are packed into the main window. ``` ``` @@ -80,5 +80,5 @@ computer_label.pack() ```python root.mainloop() ``` -- Finally, we start the Tkinter event loop using root.mainloop(), which keeps the GUI window open and responsive until the user closes it. +- Finally, we start the Tkinter event loop using `root.mainloop()`, which keeps the GUI window open and responsive until the user closes it. - From 989278ebe3ac868d63f6f66b9461474fb2f8679e Mon Sep 17 00:00:00 2001 From: Hema Sree Date: Thu, 16 May 2024 13:08:37 +0530 Subject: [PATCH 063/656] change file name --- contrib/numpy/Introduction.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/numpy/Introduction.md b/contrib/numpy/Introduction.md index c0f5f528..eb3af67d 100644 --- a/contrib/numpy/Introduction.md +++ b/contrib/numpy/Introduction.md @@ -21,7 +21,7 @@ pip install numpy ## Importing NumPy -To access NumPy functions, import it with the alias `np`: +To access NumPy functions, import it with the alias `np`. ```python import numpy as np From 7ae451cc067a73adc6068b18ddb461cea84942c6 Mon Sep 17 00:00:00 2001 From: Hema Sree Date: Thu, 16 May 2024 13:12:41 +0530 Subject: [PATCH 064/656] change file name --- contrib/numpy/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index c6bf23c9..5d3f63b8 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -1,3 +1,3 @@ # List of sections -- [Introduction](Introduction.md) +- [Introduction](introduction.md) From 9bb50dcf528efb9cd1dbbd8970e5a52130c86482 Mon Sep 17 00:00:00 2001 From: HEMA SREE VEMULAMADA <109607812+Hemav009@users.noreply.github.com> Date: Thu, 16 May 2024 13:14:26 +0530 Subject: [PATCH 065/656] Rename Introduction.md to introduction.md --- contrib/numpy/{Introduction.md => introduction.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/numpy/{Introduction.md => introduction.md} (100%) diff --git a/contrib/numpy/Introduction.md b/contrib/numpy/introduction.md similarity index 100% rename from contrib/numpy/Introduction.md rename to contrib/numpy/introduction.md From bc4525a817f9f89d046cd9aafd1e496d147e0744 Mon Sep 17 00:00:00 2001 From: Niyonika Gaur <83643952+niyonikagaur@users.noreply.github.com> Date: Thu, 16 May 2024 14:22:43 +0530 Subject: [PATCH 066/656] Create Intro_SciPy.md --- Intro_SciPy.md | 136 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 136 insertions(+) create mode 100644 Intro_SciPy.md diff --git a/Intro_SciPy.md b/Intro_SciPy.md new file mode 100644 index 00000000..afd8b3a0 --- /dev/null +++ b/Intro_SciPy.md @@ -0,0 +1,136 @@ +# Introduction to SciPy +#### Python has a powerful library known as SciPy that is used for scientific and technical computing. It functions by extending the capabilities of NumPy and providing numerous operations for numerical data. Although the same library, SciPy is divided into distinct sections meant for various scientific computations. +## Installation of Scipy +### Install using the command: +#### C:\Users\Your Name>pip install scipy + You can also use a Python distribution that already has Scipy installed like Anaconda or Spyder. +### Importing SciPy +#### from scipy import constants +## Key Features of SciPy +### 1. Numerical Integration +#### It helps in computing definite or indefinite integrals of functions +``` +from scipy import integrate + +#Define the function to integrate +def f(x): + return x**2 + +#Compute definite integral of f from 0 to 1 +result, error = integrate.quad(f, 0, 1) +print(result) +``` +#### Output +``` +0.33333333333333337 +``` +### 2. Optimization +#### It can be used to minimize or maximize functions, here is an example of minimizing roots of an equation +``` +from scipy.optimize import minimize + +# Define an objective function to minimize +def objective(x): + return x**2 + 10*np.sin(x) + +# Minimize the objective function starting from x=0 +result = minimize(objective, x0=0) +print(result.x) +``` +#### Output +``` +array([-1.30644012]) +``` +### 3. Linear Algebra +#### Solving Linear computations +``` +from scipy import linalg +import numpy as np + +# Define a square matrix +A = np.array([[1, 2], [3, 4]]) + +# Define a vector +b = np.array([5, 6]) + +# Solve Ax = b for x +x = linalg.solve(A, b) +print(x) +``` +#### Output +``` +array([-4. , 4.5]) +``` +### 4. Statistics +#### Performing statistics functions, like here we'll be distributing the data +``` +from scipy import stats +import numpy as np + +# Generate random data from a normal distribution +data = stats.norm.rvs(loc=0, scale=1, size=1000) + +# Fit a normal distribution to the data +mean, std = stats.norm.fit(data) +``` +### 5. Signal Processing +#### To process spectral signals, like EEG or MEG +``` +from scipy import signal +import numpy as np + +# Create a signal (e.g., sine wave) +t = np.linspace(0, 1, 1000) +signal = np.sin(2 * np.pi * 5 * t) + 0.5 * np.random.randn(1000) + +# Apply a low-pass Butterworth filter +b, a = signal.butter(4, 0.1, 'low') +filtered_signal = signal.filtfilt(b, a, signal) +``` +The various filters applied that are applied here, are a part of signal analysis at a deeper level. +### 6. Sparse Matrix +#### The word ' sparse 'means less, i.e., the data is mostly unused during some operation or analysis. So, to handle this data, a Sparse Matrix is created +#### There are two types of Sparse Matrices: +##### 1. CSC: Compressed Sparse Column, it is used for efficient math functions and for column slicing +##### 2. CSR: Compressed Sparse Row, it is used for fast row slicing +#### In CSC format +``` +from scipy import sparse +import numpy as np + +data = np.array([[0, 0], [0, 1], [2, 0]]) + +row_indices = np.array([1, 2, 1]) +col_indices = np.array([1, 0, 2]) +values = np.array([1, 2, 1]) + +sparse_matrix_csc = sparse.csc_matrix((values, (row_indices, col_indices))) +``` +#### In CSR format +``` +from scipy import sparse +import numpy as np + +data = np.array([[0, 0], [0, 1], [2, 0]]) +sparse_matrix = sparse.csr_matrix(data) +``` +### 7. Image Processing +#### It is used to process the images, like changing dimensions or properties. For example, when you're doing a project on medical imaging, this library is mainly used. +``` +from scipy import ndimage +import matplotlib.pyplot as plt + +image = plt.imread('path/to/image.jpg') +plt.imshow(image) +plt.show() + +# Apply Gaussian blur to the image +blurred_image = ndimage.gaussian_filter(image, sigma=1) +plt.imshow(blurred_image) +plt.show() +``` +#### The gaussian blur is one of the properties of the ' ndimage ' package in SciPy libraries, it used for better understanding of the image. + + + + From 4c665090b7ebffd44593f90cf0cb3061ae25e390 Mon Sep 17 00:00:00 2001 From: Harish-2003 <76206815+Harish-2003@users.noreply.github.com> Date: Thu, 16 May 2024 17:05:02 +0530 Subject: [PATCH 067/656] Create TIC_TAC_TOE_GAME.md --- contrib/mini-projects/TIC_TAC_TOE_GAME.md | 91 +++++++++++++++++++++++ 1 file changed, 91 insertions(+) create mode 100644 contrib/mini-projects/TIC_TAC_TOE_GAME.md diff --git a/contrib/mini-projects/TIC_TAC_TOE_GAME.md b/contrib/mini-projects/TIC_TAC_TOE_GAME.md new file mode 100644 index 00000000..8589e64c --- /dev/null +++ b/contrib/mini-projects/TIC_TAC_TOE_GAME.md @@ -0,0 +1,91 @@ +# Python Code For The Tic Tac Toe Game +# Tic Tac Toe Game + +## Overview + +### Objective +- Get three of your symbols (X or O) in a row (horizontally, vertically, or diagonally) on a 3x3 grid. + +### Gameplay +- Two players take turns. +- Player 1 uses X, Player 2 uses O. +- Players mark an empty square in each turn. + +### Winning +- The first player to align three of their symbols wins. +- If all squares are filled without any player aligning three symbols, the game is a draw. + +```python +print("this game should be played by two people player1 takes x player2 takes o") +board = [['1','2','3'],['4','5','6'],['7','8','9']] +x = 'X' +o = 'O' +def displayBoard(): + print(f" {board[0][0]} | {board[0][1]} | {board[0][2]}") + print("----------------------------------------") + print(f" {board[1][0]} | {board[1][1]} | {board[1][2]}") + print("----------------------------------------") + print(f" {board[2][0]} | {board[2][1]} | {board[2][2]}") + print("----------------------------------------") +def updateBoard(character,position): + row = (position-1)//3 + column = (position-1)%3 + board[row][column] = character +def check_win(): + for i in range(3): + if board[i][0] == board[i][1] == board[i][2]: + return 1 + elif board[0][i] == board[1][i] == board[2][i]: + return 1 + if board[0][2] == board[1][1] == board[2][0]: + return 1 + elif board[0][0] == board[1][1] == board[2][2]: + return 1 + return 0 +def check_position(position): + row = (position-1)//3 + column = (position-1)%3 + if board[row][column] == x or board[row][column] == o: + return 0 + return 1 +print("==============================welcome to tic tac toe game =====================") +counter = 0 +while 1: + if counter % 2 == 0: + displayBoard() + while 1: + choice = int(input(f"player{(counter%2)+1},enter your position('{x}');")) + if choice < 1 or choice > 9: + print("invalid input oplease try againn") + if check_position(choice): + updateBoard(x,choice) + if check_win(): + print(f"Congratulations !!!!!!!!!!!Player {(counter % 2)+1} won !!!!!!!!!!") + exit(0) + else : + counter += 1 + break + else: + print(f"position{choice} is already occupied.Choose another position") + if counter == 9: + print("the match ended with draw better luck next time") + exit(0) + else: + displayBoard() + while 1: + choice = int(input(f"player{(counter%2)+1},enter your position('{o}'):")) + if choice < 1 or choice > 9: + print("invalid input please try again") + if check_position(choice): + updateBoard(o,choice) + if check_win(): + print(f"congratulations !!!!!!!!!!!!!!! player{(counter%2)+1} won !!!!!!!!!!!!!1") + exit(0) + else: + counter += 1 + break + else: + print(f"position {choice} is already occupied.choose another position") + print() +``` + From 12878fd655130f6b643b07c8b516a1265f7ef768 Mon Sep 17 00:00:00 2001 From: Harish-2003 <76206815+Harish-2003@users.noreply.github.com> Date: Thu, 16 May 2024 17:13:10 +0530 Subject: [PATCH 068/656] Update index.md --- contrib/mini-projects/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/mini-projects/index.md b/contrib/mini-projects/index.md index 82596a2f..187d0303 100644 --- a/contrib/mini-projects/index.md +++ b/contrib/mini-projects/index.md @@ -1,3 +1,3 @@ # List of sections -- [Section title](filename.md) +- [TIC_TAC_TOE_GAME](TIC_TAC_TOE_GAME.md) From 2ff1b14b5cb95f5280b709cff64dd399ce570de4 Mon Sep 17 00:00:00 2001 From: Ritesh <112797055+Antiquely3059@users.noreply.github.com> Date: Fri, 17 May 2024 08:45:09 +0530 Subject: [PATCH 069/656] Update index.md --- contrib/mini-projects/index.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/contrib/mini-projects/index.md b/contrib/mini-projects/index.md index 82596a2f..37ebc9b7 100644 --- a/contrib/mini-projects/index.md +++ b/contrib/mini-projects/index.md @@ -1,3 +1,4 @@ # List of sections -- [Section title](filename.md) +- [Rock Paper Scissors Game](Rock Paper Scissors Game.md) + From 7af22b698cca0a7bbf993319894d8ca69e62bf89 Mon Sep 17 00:00:00 2001 From: Ritesh <112797055+Antiquely3059@users.noreply.github.com> Date: Fri, 17 May 2024 08:45:55 +0530 Subject: [PATCH 070/656] Rename Rock Paper Scissors Game.md to Rock_Paper_Scissors_Game.md --- .../{Rock Paper Scissors Game.md => Rock_Paper_Scissors_Game.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/mini-projects/{Rock Paper Scissors Game.md => Rock_Paper_Scissors_Game.md} (100%) diff --git a/contrib/mini-projects/Rock Paper Scissors Game.md b/contrib/mini-projects/Rock_Paper_Scissors_Game.md similarity index 100% rename from contrib/mini-projects/Rock Paper Scissors Game.md rename to contrib/mini-projects/Rock_Paper_Scissors_Game.md From f0d0b515a21467b985766163a027f3a1d6cf9899 Mon Sep 17 00:00:00 2001 From: Ritesh <112797055+Antiquely3059@users.noreply.github.com> Date: Fri, 17 May 2024 08:46:20 +0530 Subject: [PATCH 071/656] Update index.md --- contrib/mini-projects/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/mini-projects/index.md b/contrib/mini-projects/index.md index 37ebc9b7..7d0285a4 100644 --- a/contrib/mini-projects/index.md +++ b/contrib/mini-projects/index.md @@ -1,4 +1,4 @@ # List of sections -- [Rock Paper Scissors Game](Rock Paper Scissors Game.md) +- [Rock Paper Scissors Game](Rock_Paper_Scissors_Game.md) From 78cfa1f238e2d0a6c90c8bfc73d151a7d4cc3da4 Mon Sep 17 00:00:00 2001 From: iABn0rma1 Date: Fri, 17 May 2024 16:47:41 +0530 Subject: [PATCH 072/656] Understanding and Building the ANNs from Scratch --- .../ArtificialNeuralNetwork.md | 172 ++++++++++++++++++ contrib/machine-learning/index.md | 1 + 2 files changed, 173 insertions(+) create mode 100644 contrib/machine-learning/ArtificialNeuralNetwork.md diff --git a/contrib/machine-learning/ArtificialNeuralNetwork.md b/contrib/machine-learning/ArtificialNeuralNetwork.md new file mode 100644 index 00000000..0fafc6c5 --- /dev/null +++ b/contrib/machine-learning/ArtificialNeuralNetwork.md @@ -0,0 +1,172 @@ +# Understanding the Neural Network + +## Table of Contents +
+Click to expand + +- [Introduciton](#introduction) +- [Neuron to Perceptron](#neuron-to-perceptron) +- [Key concepts](#key-concepts) + - [Layers](#layers) + - [Weights and Biases](#weights-and-biases) + - [Activation Function](#activation-functions) + - [Forward and Backward Pass](#forward-and-backward-propagation) +- [Implementation](#building-from-scratch) + +
+ + +## Introduction + +This guide will walk you through a fundamental neural network implementation in Python. We'll build a `Neural Network` from scratch, allowing you to grasp the core concepts of how neural networks learn and make predictions. + +### Let's start by Understanding the Basic Architecture of Neural Nets + +## Neuron to Perceptron + +| `Neuron` cells forming the humand nervous system | `Perceptron` inspired from human brain | +| :----------------------------------------------- | -------------------------------------: | +| | | +| Neurons are nerve cells that send messages all over your body to allow you to do everything from breathing to talking, eating, walking, and thinking. | The perceptron is a mathematical model of a biological neuron. Performing heavy computations to think like humans. | +| Neuron collects signals from dendrites. | The first layer is knownn as Input Layer, acting like dendritres to receive the input signal. | +| Synapses are the connections between neurons where signals are transmitted. | Weights represent synapses. | +The axon terminal releases neurotransmitters to transmit the signal to other neurons. | The output is the final result – between 1 & 0, representing classification or prediction. | +--- +> Human brain has a Network of Neurons, about 86 billion neurons and more than a 100 trillion synapses connections! + + +## **Key Concepts** + +Artificial neurons are the fundamental processing units in an ANN. They receive inputs, multiply them by weights (representing the strength of connections), sum those weighted inputs, and then apply an activation function to produce an output. + +### Layers +Neurons in ANNs are organized into layers: +* **Input Layer:** Receives the raw data. +* **(n) Hidden Layers:** (Optional) Intermediate layers where complex transformations occur. They learn to detect patterns and features in the data. +* **Output Layer:** Produces the final result (prediction or classification). +

+ +

+ +### Weights and Biases +- For each input $(x_i)$, a weight $(w_i)$ is associated with it. Weights, multiplied with input units $(w_i \cdot x_i)$, determine the influence of one neuron's output on another. +- A bias $(b_i)$ is added to help influence the end product, giving the equation as $(w_i \cdot x_i + b_i)$. +- During training, the network adjusts these weights and biases to minimize errors and improve its predictions. + +

+ +

+ +### Activation Functions +- An activation function is applied to the result to introduce non-linearity in the model, allowing ANNs to learn more complex relationships from the data. +- The resulting equation: $y = f(g(x))$, determines whether the neuron will "fire" or not, i.e., if its output will be used as input for the next neuron. +- Common activation functions include the sigmoid function, tanh (hyperbolic tangent), and ReLU (Rectified Linear Unit). + +

+ +

+ +### Forward and Backward Propagation +- **Flow of Information:** All the above steps are part of Forward Propagation. It gives the output equation as $y = f\left(\sum_{i=1}^n w_i x_i + b_i\right)$ +- **Error Correction:** Backpropagation is the algorithm used to train ANNs by calculating the gradient of error at the output layer and then propagating this error backward through the network. This allows the network to adjust its weights and biases in the direction that reduces the error. +- The chain rule of calculus is the foundational concept to compute the gradient of the error: + $ + \delta_{ij}(E) = \frac{\partial E}{\partial w_{ij}} = \frac{\partial E}{\partial \hat{y}_j} \cdot \frac{\partial \hat{y}_j}{\partial \theta_j} \cdot \frac{\partial \theta_j}{\partial w_{ij}} + $ + where $E$ is the error, $\hat{y}_j$ is the predicted output, $\theta_j$ is the input to the activation function of the $j^{th}$ neuron, and $w_{ij}$ is the weight from neuron $i$ to neuron $j$. + +

+ +

+ + +## Building From Scratch + +```python +# Import required libraries +import numpy as np +import matplotlib.pyplot as plt + +class SimpleNeuralNetwork: + def __init__(self, input_size, hidden_size, output_size): + self.input_size = input_size + self.hidden_size = hidden_size + self.output_size = output_size + + # Initialize weights and biases + self.weights_input_hidden = np.random.randn(input_size, hidden_size) + self.bias_hidden = np.random.randn(hidden_size) + self.weights_hidden_output = np.random.randn(hidden_size, output_size) + self.bias_output = np.random.randn(output_size) + + def sigmoid(self, x): + return 1 / (1 + np.exp(-x)) + + def sigmoid_derivative(self, x): + return x * (1 - x) + + def forward(self, X): + self.hidden_layer_input = np.dot(X, self.weights_input_hidden) + self.bias_hidden + self.hidden_layer_output = self.sigmoid(self.hidden_layer_input) + + self.output_layer_input = np.dot(self.hidden_layer_output, self.weights_hidden_output) + self.bias_output + self.output = self.sigmoid(self.output_layer_input) + + return self.output + + def backward(self, X, y, learning_rate): + output_error = y - self.output + output_delta = output_error * self.sigmoid_derivative(self.output) + + hidden_error = output_delta.dot(self.weights_hidden_output.T) + hidden_delta = hidden_error * self.sigmoid_derivative(self.hidden_layer_output) + + self.weights_hidden_output += self.hidden_layer_output.T.dot(output_delta) * learning_rate + self.bias_output += np.sum(output_delta, axis=0) * learning_rate + self.weights_input_hidden += X.T.dot(hidden_delta) * learning_rate + self.bias_hidden += np.sum(hidden_delta, axis=0) * learning_rate + + def train(self, X, y, epochs, learning_rate): + self.losses = [] + for epoch in range(epochs): + self.forward(X) + self.backward(X, y, learning_rate) + loss = np.mean(np.square(y - self.output)) + self.losses.append(loss) + if epoch % 1000 == 0: + print(f"Epoch {epoch}, Loss: {loss}") + + def plot_loss(self): + plt.plot(self.losses) + plt.xlabel('Epochs') + plt.ylabel('Loss') + plt.title('Training Loss Over Epochs') + plt.show() +``` + +### Creating the Input & Output Array +Let's create a dummy input and outpu dataset. Here, the first two columns will be useful, while the rest might be noise. +```python +X = np.array([[0,0], [0,1], [1,0], [1,1]]) +y = np.array([[0], [1], [1], [1]]) +``` + +### Defining the Neural Network +With our input and output data ready, we'll define a simple neural network with one hidden layer containing three neurons. +```python +# neural network architecture +input_size = 2 +hidden_layers = 1 +hidden_neurons = [2] +output_size = 1 +``` + +### Visualizing the Training Loss +To understand how well our model is learning, let's visualize the training loss over epochs. +```python +model = NeuralNetwork(input_size, hidden_layers, hidden_neurons, output_size) +model.train(X, y, 100) +``` +

+ +

diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 82596a2f..c6f4df26 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -1,3 +1,4 @@ # List of sections - [Section title](filename.md) +- [Artificial Neural Network from the Ground Up](ArtificialNeuralNetwork.md) From 04caabe01b99ba9337e158c9c1018335eed35bcc Mon Sep 17 00:00:00 2001 From: HimakarC <116370824+HimakarC@users.noreply.github.com> Date: Fri, 17 May 2024 17:06:47 +0530 Subject: [PATCH 073/656] Update index.md --- contrib/pandas/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 5c0f2b43..aadd970d 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -1,3 +1,4 @@ # List of sections - [Pandas Series Vs NumPy ndarray](pandas_series_vs_numpy_ndarray.md) +- [Excel Operations using Pandas DataFrame](excel_with_pandas.md) From 4ffc87b269773a98dfc5824abb32117684b85211 Mon Sep 17 00:00:00 2001 From: HimakarC <116370824+HimakarC@users.noreply.github.com> Date: Fri, 17 May 2024 17:07:17 +0530 Subject: [PATCH 074/656] Create excel_with_pandas.md --- contrib/pandas/excel_with_pandas.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 contrib/pandas/excel_with_pandas.md diff --git a/contrib/pandas/excel_with_pandas.md b/contrib/pandas/excel_with_pandas.md new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/contrib/pandas/excel_with_pandas.md @@ -0,0 +1 @@ + From 56170159e1ca6e88cfcee929e3316c7dac4432da Mon Sep 17 00:00:00 2001 From: unknown Date: Fri, 17 May 2024 18:09:43 +0530 Subject: [PATCH 075/656] written --- contrib/pandas/excel_with_pandas.md | 50 +++++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) diff --git a/contrib/pandas/excel_with_pandas.md b/contrib/pandas/excel_with_pandas.md index 8b137891..6902c3fc 100644 --- a/contrib/pandas/excel_with_pandas.md +++ b/contrib/pandas/excel_with_pandas.md @@ -1 +1,51 @@ +# Pandas DataFrame + +The Pandas DataFrame is a two-dimensional, size-mutable, and possibly heterogeneous tabular data format with labelled axes. A data frame is a two-dimensional data structure in which the data can be organised in rows and columns. Pandas DataFrames are comprised of three main components: data, rows, and columns. + +In the real world, Pandas DataFrames are formed by importing datasets from existing storage, which can be a Excel file, a SQL database or CSV file. Pandas DataFrames may be constructed from lists, dictionaries, or lists of dictionaries, etc. + + +### Installation of libraries + +`pip install pandas + pip install xlrd` + + +Example for reading data from an Excel File: + +```python +import pandas as pd + +l = pd.read_excel('example.xlsx') +d = pd.DataFrame(l) +print(d) +``` +Output: +'''python + Name Age +0 John 12 +''' + +- **Note:** The above program can also be used for loading/retrieving the data. + + +Example for Inserting Data into Excel File: + +```python +import pandas as pd + +l = pd.read_excel('file_name.xlsx') +d = {'Name': ['Bob', 'John'], 'Age': [12, 28]} +d = pd.DataFrame(d) +L = pd.concat([l, d], ignore_index = True) +L.to_excel('file_name.xlsx', index = False) +print(L) +``` + +Output: +'''python + Name Age +0 Bob 12 +1 John 28 +''' From 9eb63b951d03e283aa76ee0d9ce980dc4ea509ee Mon Sep 17 00:00:00 2001 From: unknown Date: Fri, 17 May 2024 18:11:53 +0530 Subject: [PATCH 076/656] updated --- contrib/pandas/excel_with_pandas.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/contrib/pandas/excel_with_pandas.md b/contrib/pandas/excel_with_pandas.md index 6902c3fc..4e679647 100644 --- a/contrib/pandas/excel_with_pandas.md +++ b/contrib/pandas/excel_with_pandas.md @@ -21,10 +21,10 @@ d = pd.DataFrame(l) print(d) ``` Output: -'''python +```python Name Age 0 John 12 -''' +``` - **Note:** The above program can also be used for loading/retrieving the data. @@ -43,9 +43,9 @@ print(L) ``` Output: -'''python +```python Name Age 0 Bob 12 1 John 28 -''' +``` From 0b6bd74a3b7daae3b28f8fc66576d23baee4bbc0 Mon Sep 17 00:00:00 2001 From: unknown Date: Fri, 17 May 2024 18:12:42 +0530 Subject: [PATCH 077/656] updated --- contrib/pandas/excel_with_pandas.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/pandas/excel_with_pandas.md b/contrib/pandas/excel_with_pandas.md index 4e679647..b09b8c3a 100644 --- a/contrib/pandas/excel_with_pandas.md +++ b/contrib/pandas/excel_with_pandas.md @@ -7,7 +7,7 @@ In the real world, Pandas DataFrames are formed by importing datasets from exis ### Installation of libraries -`pip install pandas +`pip install pandas
pip install xlrd` From f8044fb421f609c0ae67e65eefb4feabac914568 Mon Sep 17 00:00:00 2001 From: unknown Date: Fri, 17 May 2024 18:13:28 +0530 Subject: [PATCH 078/656] updated --- contrib/pandas/excel_with_pandas.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/contrib/pandas/excel_with_pandas.md b/contrib/pandas/excel_with_pandas.md index b09b8c3a..af87b7b7 100644 --- a/contrib/pandas/excel_with_pandas.md +++ b/contrib/pandas/excel_with_pandas.md @@ -7,8 +7,8 @@ In the real world, Pandas DataFrames are formed by importing datasets from exis ### Installation of libraries -`pip install pandas
- pip install xlrd` +`pip install pandas` +`pip install xlrd` Example for reading data from an Excel File: From 9fe61b8ec04b71037dff7506dd6169f259111bfa Mon Sep 17 00:00:00 2001 From: HimakarC <116370824+HimakarC@users.noreply.github.com> Date: Fri, 17 May 2024 18:15:20 +0530 Subject: [PATCH 079/656] Update excel_with_pandas.md --- contrib/pandas/excel_with_pandas.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/pandas/excel_with_pandas.md b/contrib/pandas/excel_with_pandas.md index af87b7b7..137ef451 100644 --- a/contrib/pandas/excel_with_pandas.md +++ b/contrib/pandas/excel_with_pandas.md @@ -7,7 +7,7 @@ In the real world, Pandas DataFrames are formed by importing datasets from exis ### Installation of libraries -`pip install pandas` +`pip install pandas`
`pip install xlrd` From e4e9ad812fe87d70862390074deba405c2f9fe13 Mon Sep 17 00:00:00 2001 From: HimakarC <116370824+HimakarC@users.noreply.github.com> Date: Fri, 17 May 2024 18:16:41 +0530 Subject: [PATCH 080/656] excel_with_pandas.md --- contrib/pandas/excel_with_pandas.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/contrib/pandas/excel_with_pandas.md b/contrib/pandas/excel_with_pandas.md index 137ef451..13218d65 100644 --- a/contrib/pandas/excel_with_pandas.md +++ b/contrib/pandas/excel_with_pandas.md @@ -26,8 +26,6 @@ Output: 0 John 12 ``` -- **Note:** The above program can also be used for loading/retrieving the data. - Example for Inserting Data into Excel File: From e830336a54f5f6bce6e597df9900ee35b7a71709 Mon Sep 17 00:00:00 2001 From: HimakarC <116370824+HimakarC@users.noreply.github.com> Date: Fri, 17 May 2024 18:25:40 +0530 Subject: [PATCH 081/656] Create excel_with_pandas.md --- contrib/pandas/excel_with_pandas.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/contrib/pandas/excel_with_pandas.md b/contrib/pandas/excel_with_pandas.md index 13218d65..57984446 100644 --- a/contrib/pandas/excel_with_pandas.md +++ b/contrib/pandas/excel_with_pandas.md @@ -5,11 +5,20 @@ The Pandas DataFrame is a two-dimensional, size-mutable, and possibly heterogene In the real world, Pandas DataFrames are formed by importing datasets from existing storage, which can be a Excel file, a SQL database or CSV file. Pandas DataFrames may be constructed from lists, dictionaries, or lists of dictionaries, etc. +Features of Pandas `DataFrame`: + +- **Size mutable**: DataFrames are mutable in size, meaning that new rows and columns can be added or removed as needed. +- **Labeled axes**: DataFrames have labeled axes, which makes it easy to keep track of the data. +- **Arithmetic operations**: DataFrames support arithmetic operations on rows and columns. +- **High performance**: DataFrames are highly performant, making them ideal for working with large datasets. + + ### Installation of libraries `pip install pandas`
`pip install xlrd` +- **Note**: The `xlrd` library is used for Excel operations. Example for reading data from an Excel File: From 7fb2baf809f2cd40c0ccdd406fd75f8cb3e29d1b Mon Sep 17 00:00:00 2001 From: HimakarC <116370824+HimakarC@users.noreply.github.com> Date: Fri, 17 May 2024 18:31:22 +0530 Subject: [PATCH 082/656] Update excel_with_pandas.md --- contrib/pandas/excel_with_pandas.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/contrib/pandas/excel_with_pandas.md b/contrib/pandas/excel_with_pandas.md index 57984446..d325e466 100644 --- a/contrib/pandas/excel_with_pandas.md +++ b/contrib/pandas/excel_with_pandas.md @@ -56,3 +56,8 @@ Output: 1 John 28 ``` +### Usage of Pandas DataFrame: + +- Can be used to store and analyze financial data, such as stock prices, trading data, and economic data. +- Can be used to store and analyze sensor data, such as data from temperature sensors, motion sensors, and GPS sensors. +- Can be used to store and analyze log data, such as web server logs, application logs, and system logs From fc55d32e87621268fa32e16f0c9bec55464f7ead Mon Sep 17 00:00:00 2001 From: Vrisha Shah <74671946+Vrisha213@users.noreply.github.com> Date: Fri, 17 May 2024 18:44:42 +0530 Subject: [PATCH 083/656] Create confusion-matrix.md Added content on Confusion Matrix with code example and heatmap visualization. --- contrib/machine-learning/confusion-matrix.md | 61 ++++++++++++++++++++ 1 file changed, 61 insertions(+) create mode 100644 contrib/machine-learning/confusion-matrix.md diff --git a/contrib/machine-learning/confusion-matrix.md b/contrib/machine-learning/confusion-matrix.md new file mode 100644 index 00000000..959df9a1 --- /dev/null +++ b/contrib/machine-learning/confusion-matrix.md @@ -0,0 +1,61 @@ +Confusion Matrix - A confusion matrix is a fundamental performance evaluation tool used in machine learning to assess the accuracy of a classification model. It is an N x N matrix, where N represents the number of target classes. + +For binary classification, it results in a 2 x 2 matrix that outlines four key parameters: +1. True Positive (TP) - The predicted value matches the actual value, or the predicted class matches the actual class. +For example - the actual value was positive, and the model predicted a positive value. +2. True Negative (TN) - The predicted value matches the actual value, or the predicted class matches the actual class. +For example - the actual value was negative, and the model predicted a negative value. +3. False Positive (FP)/Type I Error - The predicted value was falsely predicted. +For example - the actual value was negative, but the model predicted a positive value. +4. False Negative (FN)/Type II Error - The predicted value was falsely predicted. +For example - the actual value was positive, but the model predicted a negative value. + +The confusion matrix enables the calculation of various metrics like accuracy, precision, recall, F1-Score and specificity. +1. Accuracy - It represents the proportion of correctly classified instances out of the total number of instances in the dataset. +2. Precision - It quantifies the accuracy of positive predictions made by the model. +3. Recall - It quantifies the ability of a model to correctly identify all positive instances in the dataset and is also known as sensitivity or true positive rate. +4. F1-Score - It is a single measure that combines precision and recall, offering a balanced evaluation of a classification model's effectiveness. + +To implement the confusion matrix in Python, we can use the confusion_matrix() function from the sklearn.metrics module of the scikit-learn library. +The function returns a 2D array that represents the confusion matrix. +We can also visualize the confusion matrix using a heatmap. + +# Import necessary libraries +import numpy as np +from sklearn.metrics import confusion_matrix,classification_report +import seaborn as sns +import matplotlib.pyplot as plt + +# Create the NumPy array for actual and predicted labels +actual = np.array(['Apple', 'Apple', 'Apple', 'Not Apple', 'Apple', 'Not Apple', 'Apple', 'Apple', 'Not Apple', 'Not Apple']) +predicted = np.array(['Apple', 'Not Apple', 'Apple', 'Not Apple', 'Apple', 'Apple', 'Apple', 'Apple', 'Not Apple', 'Not Apple']) + +# Compute the confusion matrix +cm = confusion_matrix(actual,predicted) + +# Plot the confusion matrix with the help of the seaborn heatmap +sns.heatmap(cm, + annot=True, + fmt='g', + xticklabels=['Apple', 'Not Apple'], + yticklabels=['Apple', 'Not Apple']) +plt.xlabel('Prediction', fontsize=13) +plt.ylabel('Actual', fontsize=13) +plt.title('Confusion Matrix', fontsize=17) +plt.show() + +# Classifications Report based on Confusion Metrics +print(classification_report(actual, predicted)) + +# Results +1. Confusion Matrix: +[[5 1] +[1 3]] +2. Classification Report: + precision recall f1-score support +Apple 0.83 0.83 0.83 6 +Not Apple 0.75 0.75 0.75 4 + +accuracy 0.80 10 +macro avg 0.79 0.79 0.79 10 +weighted avg 0.80 0.80 0.80 10 From 1e543beeab7c458c3a22060e3c68c73b282fa874 Mon Sep 17 00:00:00 2001 From: Vrisha Shah <74671946+Vrisha213@users.noreply.github.com> Date: Fri, 17 May 2024 18:46:04 +0530 Subject: [PATCH 084/656] Update confusion-matrix.md --- contrib/machine-learning/confusion-matrix.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/machine-learning/confusion-matrix.md b/contrib/machine-learning/confusion-matrix.md index 959df9a1..ac5cd317 100644 --- a/contrib/machine-learning/confusion-matrix.md +++ b/contrib/machine-learning/confusion-matrix.md @@ -1,6 +1,6 @@ Confusion Matrix - A confusion matrix is a fundamental performance evaluation tool used in machine learning to assess the accuracy of a classification model. It is an N x N matrix, where N represents the number of target classes. -For binary classification, it results in a 2 x 2 matrix that outlines four key parameters: +For binary classification, it results in a 2 x 2 matrix that outlines four key parameters: 1. True Positive (TP) - The predicted value matches the actual value, or the predicted class matches the actual class. For example - the actual value was positive, and the model predicted a positive value. 2. True Negative (TN) - The predicted value matches the actual value, or the predicted class matches the actual class. From 08c59bff565efbfff5fa8553e50ec5190046f37d Mon Sep 17 00:00:00 2001 From: Vrisha Shah <74671946+Vrisha213@users.noreply.github.com> Date: Fri, 17 May 2024 18:58:33 +0530 Subject: [PATCH 085/656] Update index.md Added link to new content on Confusion Matrix. This includes the title "Confusion Matrix" and the corresponding markdown file link "confusion-matrix.md". --- contrib/machine-learning/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 82596a2f..2f27fd53 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -1,3 +1,3 @@ # List of sections -- [Section title](filename.md) +- [Confusion Matrix](confusion-matrix.md) From 37280da9d5fdb1ee4b9ac17f89b7c46229fc9cf4 Mon Sep 17 00:00:00 2001 From: Stuti_07 <110724092+StutiRajput07@users.noreply.github.com> Date: Fri, 17 May 2024 19:05:26 +0530 Subject: [PATCH 086/656] Create path-finder.md --- contrib/mini-projects/path-finder.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 contrib/mini-projects/path-finder.md diff --git a/contrib/mini-projects/path-finder.md b/contrib/mini-projects/path-finder.md new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/contrib/mini-projects/path-finder.md @@ -0,0 +1 @@ + From bb1e0e389b9261e8b04087973829f290ef05a44a Mon Sep 17 00:00:00 2001 From: Stuti_07 <110724092+StutiRajput07@users.noreply.github.com> Date: Fri, 17 May 2024 19:20:11 +0530 Subject: [PATCH 087/656] Update path-finder.md --- contrib/mini-projects/path-finder.md | 119 +++++++++++++++++++++++++++ 1 file changed, 119 insertions(+) diff --git a/contrib/mini-projects/path-finder.md b/contrib/mini-projects/path-finder.md index 8b137891..98e9b87d 100644 --- a/contrib/mini-projects/path-finder.md +++ b/contrib/mini-projects/path-finder.md @@ -1 +1,120 @@ +# Path Finder +This Python script uses the curses library to visualize the process of finding a path through a maze in real-time within a terminal window. The program represents the maze as a list of lists, where each list represents a row in the maze, and each string element in the lists represents a cell in the maze. The maze includes walls (#), a start point (O), and an end point (X), with empty spaces ( ) that can be traversed. +## The script includes the following main components: +- Visualization Functions:
+ print_maze(maze, stdscr, path=[]): This function is used to display the maze in the terminal. It utilizes color pairs to distinguish between the maze walls, the path, and unexplored spaces. The current path being explored is displayed with a different color to make it stand out. + +- Utility Functions:
+ find_start(maze, start): This function searches the maze for the starting point (marked as O) and returns its position as a tuple (row, col).
+ find_neighbors(maze, row, col): This function identifies the valid adjacent cells (up, down, left, right) that can be moved to from the current position, + ignoring any walls or out-of-bound positions. + +- Pathfinding Logic:
+ find_path(maze, stdscr): This function implements a Breadth-First Search (BFS) algorithm to find a path from the start point to the end point (X). It uses a + queue to explore each possible path sequentially. As it explores the maze, it updates the display in real-time, allowing the viewer to follow the progress + visually. Each visited position is marked and not revisited, ensuring the algorithm efficiently covers all possible paths without repetition. + +Overall, the script demonstrates an effective use of the curses library to create a dynamic visual representation of the BFS algorithm solving a maze, providing both an educational tool for understanding pathfinding and an example of real-time data visualization in a terminal. + +#### Below is the code of the path finder + + +```python +import curses +from curses import wrapper +import queue +import time + +# Define the structure of the maze as a list of lists where each inner list represents a row. +maze = [ + ["#", "O", "#", "#", "#", "#", "#", "#", "#"], + ["#", " ", " ", " ", " ", " ", " ", " ", "#"], + ["#", " ", "#", "#", " ", "#", "#", " ", "#"], + ["#", " ", "#", " ", " ", " ", "#", " ", "#"], + ["#", " ", "#", " ", "#", " ", "#", " ", "#"], + ["#", " ", "#", " ", "#", " ", "#", " ", "#"], + ["#", " ", "#", " ", "#", " ", "#", "#", "#"], + ["#", " ", " ", " ", " ", " ", " ", " ", "#"], + ["#", "#", "#", "#", "#", "#", "#", "X", "#"] +] + +# Function to print the current state of the maze in the terminal. +def print_maze(maze, stdscr, path=[]): + BLUE = curses.color_pair(1) # Color pair for walls and free paths + RED = curses.color_pair(2) # Color pair for the current path + + for i, row in enumerate(maze): + for j, value in enumerate(row): + if (i, j) in path: + stdscr.addstr(i, j*2, "X", RED) # Print path character with red color + else: + stdscr.addstr(i, j*2, value, BLUE) # Print walls and free paths with blue color + +# Function to locate the starting point (marked 'O') in the maze. +def find_start(maze, start): + for i, row in enumerate(maze): + for j, value in enumerate(row): + if value == start: + return i, j + return None + +# Function to find a path from start ('O') to end ('X') using BFS. +def find_path(maze, stdscr): + start = "O" + end = "X" + start_pos = find_start(maze, start) # Get the start position + + q = queue.Queue() + q.put((start_pos, [start_pos])) # Initialize the queue with the start position + + visited = set() # Set to keep track of visited positions + + while not q.empty(): + current_pos, path = q.get() # Get the current position and path + row, col = current_pos + + stdscr.clear() # Clear the screen + print_maze(maze, stdscr, path) # Print the current state of the maze + time.sleep(0.2) # Delay for visibility + stdscr.refresh() # Refresh the screen + + if maze[row][col] == end: # Check if the current position is the end + return path # Return the path if end is reached + + # Get neighbors (up, down, left, right) that are not walls + neighbors = find_neighbors(maze, row, col) + for neighbor in neighbors: + if neighbor not in visited: + r, c = neighbor + if maze[r][c] != "#": + new_path = path + [neighbor] + q.put((neighbor, new_path)) + visited.add(neighbor) + +# Function to find the valid neighboring cells (not walls or out of bounds). +def find_neighbors(maze, row, col): + neighbors = [] + if row > 0: # UP + neighbors.append((row - 1, col)) + if row + 1 < len(maze): # DOWN + neighbors.append((row + 1, col)) + if col > 0: # LEFT + neighbors.append((row, col - 1)) + if col + 1 < len(maze[0]): # RIGHT + neighbors.append((row, col + 1)) + return neighbors + +# Main function to setup curses and run the pathfinding algorithm. +def main(stdscr): + curses.init_pair(1, curses.COLOR_BLUE, curses.COLOR_BLACK) # Initialize color pair for blue + curses.init_pair(2, curses.COLOR_RED, curses.COLOR_BLACK) # Initialize color pair for red + + find_path(maze, stdscr) # Find the path using BFS + stdscr.getch() # Wait for a key press before exiting + +wrapper(main) # Use the wrapper to initialize and finalize curses automatically. + +``` + + From c339a064e55185b92f88dd6ec9c71d11b3755167 Mon Sep 17 00:00:00 2001 From: Stuti_07 <110724092+StutiRajput07@users.noreply.github.com> Date: Fri, 17 May 2024 19:21:04 +0530 Subject: [PATCH 088/656] Update index.md --- contrib/mini-projects/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/mini-projects/index.md b/contrib/mini-projects/index.md index 82596a2f..d99a43eb 100644 --- a/contrib/mini-projects/index.md +++ b/contrib/mini-projects/index.md @@ -1,3 +1,3 @@ # List of sections -- [Section title](filename.md) +- [Path Finder](path-finder.md) From 0ad50df483d3179134a5e3551a661f6473a3500b Mon Sep 17 00:00:00 2001 From: HimakarC <116370824+HimakarC@users.noreply.github.com> Date: Fri, 17 May 2024 19:28:54 +0530 Subject: [PATCH 089/656] Update index.md --- contrib/pandas/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index aadd970d..834d1f43 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -1,4 +1,4 @@ # List of sections - [Pandas Series Vs NumPy ndarray](pandas_series_vs_numpy_ndarray.md) -- [Excel Operations using Pandas DataFrame](excel_with_pandas.md) +- [Excel using Pandas DataFrame](excel_with_pandas.md) From a758ff832d439bb225dc7f292974204e8f500643 Mon Sep 17 00:00:00 2001 From: Yogesh Vishwakarma <103316955+Yogeshkarma@users.noreply.github.com> Date: Fri, 17 May 2024 19:43:23 +0530 Subject: [PATCH 090/656] Create Regression.md --- contrib/machine-learning/Regression.md | 171 +++++++++++++++++++++++++ 1 file changed, 171 insertions(+) create mode 100644 contrib/machine-learning/Regression.md diff --git a/contrib/machine-learning/Regression.md b/contrib/machine-learning/Regression.md new file mode 100644 index 00000000..6ff6d285 --- /dev/null +++ b/contrib/machine-learning/Regression.md @@ -0,0 +1,171 @@ +# Regression + + +* Regression is a supervised machine learning technique which is used to predict continuous values. + + +> Now, Supervised learning is a category of machine learning that uses labeled datasets to train algorithms to predict outcomes and recognize patterns. + +* Regression is a statistical method used to model the relationship between a dependent variable (often denoted as 'y') and one or more independent variables (often denoted as 'x'). The goal of regression analysis is to understand how the dependent variable changes as the independent variables change. + # Types Of Regression + +1. Linear Regression +2. Polynomial Regression +3. Stepwise Regression +4. Decision Tree Regression +5. Random Forest Regression +6. Ridge Regression +7. Lasso Regression +8. ElasticNet Regression +9. Bayesian Linear Regression +10. Support Vector Regression + +But, we'll first start with Linear Regression +# Linear Regression + +* Linear regression is a fundamental statistical method used to model the relationship between a dependent variable (often denoted as +𝑌) and one or more independent variables (often denoted as +𝑋). The relationship is assumed to be linear, meaning that changes in the independent variables are associated with changes in the dependent variable in a straight-line fashion. + +The basic form of linear regression for a single independent variable is: + +**𝑌=𝛽0+𝛽1𝑋+𝜖** + +Where: + +* Y is the dependent variable. +* X is the independent variable. +* 𝛽0 is the intercept, representing the value of Y when X is zero +* 𝛽1 is the slope coefficient, representing the change in Y for a one-unit change in X +* ϵ is the error term, representing the variability in Y that is not explained by the linear relationship with X. + +# Basic Code of Linear Regression + +* This line imports the numpy library, which is widely used for numerical operations in Python. We use np as an alias for numpy, making it easier to reference functions and objects from the library. +``` +import numpy as np +``` + +* This line imports the LinearRegression class from the linear_model module of the scikit-learn library.scikit-learn is a powerful library for machine learning tasks in Python, and LinearRegression is a class provided by it for linear regression. +``` +from sklearn.linear_model import LinearRegression +``` +* This line creates a NumPy array X containing the independent variable values. In this example, we have a simple one-dimensional array representing the independent variable. The reshape(-1, 1) method reshapes the array into a column vector, necessary for use with scikit-learn + +``` +X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1) +``` +* This line creates a NumPy array Y containing the corresponding dependent variable values. These are the observed values of the dependent variable corresponding to the independent variable values in X. +``` +Y = np.array([2, 4, 5, 8, 5]) +``` + +* This line creates an instance of the LinearRegression class, which represents the linear regression model. We'll use this object to train the model and make predictions. +``` +model = LinearRegression() +``` + +* This line fits the linear regression model to the data. The fit() method takes two arguments: the independent variable (X) and the dependent variable (Y). This method estimates the coefficients of the linear regression equation that best fit the given data. +``` +model.fit(X, Y) +``` +* These lines print out the intercept (beta_0) and coefficient (beta_1) of the linear regression model. model.intercept_ gives the intercept value, and model.coef_ gives an array of coefficients, where model.coef_[0] corresponds to the coefficient of the first independent variable (in this case, there's only one). +``` +print("Intercept:", model.intercept_) +print("Coefficient:", model.coef_[0]) +``` + +* These lines demonstrate how to use the trained model to make predictions for new data. +* We create a new NumPy array new_data containing the values of the independent variable for which we want to predict the dependent variable values. +* We then use the predict() method of the model to obtain the predictions for these new data points. Finally, we print out the predicted values. +``` +new_data = np.array([[6], [7]]) +predictions = model.predict(new_data) +print("Predictions:", predictions) +``` +# Assumptions of Linear Regression + +# Linearity: + +* To assess the linearity assumption, we can visually inspect a scatter plot of the observed values versus the predicted values. +* If the relationship between them appears linear, it suggests that the linearity assumption is reasonable. +``` +import matplotlib.pyplot as plt +predictions = model.predict(X) +plt.scatter(predictions,Y) +plt.xlabel("Predicted Values") +plt.ylabel("Observed Values") +plt.title("Linearity Check: Observed vs Predicted") +plt.show() +``` +# Homoscedasticity: +* Homoscedasticity refers to the constant variance of the residuals across all levels of the independent variable(s). We can visually inspect a plot of residuals versus predicted values to check for homoscedasticity. +``` +residuals = Y - predictions +plt.scatter(predictions, residuals) +plt.xlabel("Predicted Values") +plt.ylabel("Residuals") +plt.title("Homoscedasticity Check: Residuals vs Predicted Values") +plt.axhline(y=0, color='red', linestyle='--') # Add horizontal line at y=0 +plt.show() + +``` +# Normality of Residuals: +* To assess the normality of residuals, we can visually inspect a histogram or a Q-Q plot of the residuals. +``` +import seaborn as sns + +sns.histplot(residuals, kde=True) +plt.xlabel("Residuals") +plt.ylabel("Frequency") +plt.title("Normality of Residuals: Histogram") +plt.show() + +import scipy.stats as stats + +stats.probplot(residuals, dist="norm", plot=plt) +plt.title("Normal Q-Q Plot") +plt.show() + +``` +# Metrics for Regression + + +# Mean Absolute Error (MAE) + +* MAE measures the average magnitude of the errors in a set of predictions, without considering their direction. It is the average of the absolute differences between predicted and actual values. +``` +from sklearn.metrics import mean_absolute_error + +mae = mean_absolute_error(Y, predictions) +print(f"Mean Absolute Error (MAE): {mae}") + +``` +# Mean Squared Error (MSE) + +* MSE measures the average of the squares of the errors. It gives more weight to larger errors, making it sensitive to outliers. +``` +from sklearn.metrics import mean_squared_error + +mse = mean_squared_error(Y, predictions) +print(f"Mean Squared Error (MSE): {mse}") +``` +# Root Mean Squared Error (RMSE) +* RMSE is the square root of the MSE. It provides an error metric that is in the same units as the dependent variable, making it more interpretable. +``` +rmse = np.sqrt(mse) +print(f"Root Mean Squared Error (RMSE): {rmse}") + +``` +# R-squared (Coefficient of Determination) +* R-squared measures the proportion of the variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1, where 1 indicates a perfect fit. +``` +from sklearn.metrics import r2_score + +r2 = r2_score(Y, predictions) +print(f"R-squared (R^2): {r2}") +``` + +> In this tutorial, The sample dataset is there for learning purpose only + + From d51207d7c9966ee7122fc687661d6dc271742435 Mon Sep 17 00:00:00 2001 From: Yogesh Vishwakarma <103316955+Yogeshkarma@users.noreply.github.com> Date: Fri, 17 May 2024 19:44:58 +0530 Subject: [PATCH 091/656] Update index.md --- contrib/machine-learning/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 82596a2f..094b211c 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -1,3 +1,3 @@ # List of sections -- [Section title](filename.md) +- [Regression in Machine Learning](Regression.md) From 741bb87351bc533ddace81823ed506aa63188a59 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Fri, 17 May 2024 23:11:26 +0530 Subject: [PATCH 092/656] Update index.md --- contrib/pandas/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 5c0f2b43..f2a3fa72 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -1,3 +1,4 @@ # List of sections - [Pandas Series Vs NumPy ndarray](pandas_series_vs_numpy_ndarray.md) +- [Pandas Introduction and Dataframes in Pandas](Introduction_to_Pandas_Library_and_DataFrames.md) From 35baa355cb23b9f7dad9818ebc5cbe80fff35e84 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Fri, 17 May 2024 23:11:53 +0530 Subject: [PATCH 093/656] Add files via upload --- ...uction_to_Pandas_Library_and_DataFrames.md | 395 ++++++++++++++++++ 1 file changed, 395 insertions(+) create mode 100644 contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md diff --git a/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md b/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md new file mode 100644 index 00000000..5e7dcd3e --- /dev/null +++ b/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md @@ -0,0 +1,395 @@ +# Introduction_to_Pandas_Library_and_DataFrames + + +> Content Creator - Krishna Kaushik + +**As you have learnt Python Programming , now it's time for some applications.** + +- Machine Learning and Data Science is the emerging field of today's time , to work in this this field your first step should be `Data Science` as Machine Learning is all about data. +- To begin with Data Science your first tool will be `Pandas Library`. + +## Introduction of Pandas Library + +Pandas is a data analysis and manipulation tool, built on top of the python programming language. Pandas got its name from the term Panel data (‘Pa’ from Panel and ‘da’ from data). Panel data is a data which have rows and columns in it like excel spreadsheets, csv files etc. + +**To use Pandas, first we’ve to import it.** + +## Why pandas? + +* Pandas provides a simple-to-use but very capable set of functions that you can use on your data. +* It is also associate with other machine learning libraries , so it is important to learn it. + +* For example - It is highly used to transform tha data which will be use by machine learning model during the training. + + +```python +# Importing the pandas +import pandas as pd +``` + +*To import any module in Python use “import 'module name' ” command, I used “pd” as pandas abbreviation because we don’t need to type pandas every time only type “pd” to use pandas.* + + +```python +# To check available pandas version +print(f"Pandas Version is : {pd.__version__}") +``` + + Pandas Version is : 2.1.4 + + +## Understanding Pandas data types + +Pandas has two main data types : `Series` and `DataFrames` + +* `pandas.Series` is a 1-dimensional column of data. +* `pandas.DataFrames` is 2 -dimensional data table having rows and columns. + +### 1. Series datatype + +**To creeate a series you can use `pd.Series()` and passing a python list inside()**. + +Note: S in Series is capital if you use small s it will give you an error. + +> Let's create a series + + + +```python +# Creating a series of car companies +cars = pd.Series(["Honda","Audi","Thar","BMW"]) +cars +``` + + + + + 0 Honda + 1 Audi + 2 Thar + 3 BMW + dtype: object + + + +The above code creates a Series of cars companies the name of series is “cars” the code “pd.Series([“Honda” , “Audi” , “Thar”, "BMW"])” means Hey! pandas (pd) create a Series of cars named "Honda" , "Audi" , "Thar" and "BMW". + +The default index of a series is 0,1,2….(Remember it starts from 0) + +To change the index of any series set the “index” parameter accordingly. It takes the list of index values: + + +```python +cars = pd.Series(["Honda","Audi","Thar","BMW"],index = ["A" , "B" , "C" ,"D"]) +cars +``` + + + + + A Honda + B Audi + C Thar + D BMW + dtype: object + + + +You can see that the index has been changed from numbers to A, B ,C and D. + +And the mentioned ‘dtype’ tells us about the type of data we have in the series. + +### 2. DataFrames datatype + +DataFrame contains rows and columns like a csv file have. + +You can also create a DataFrame by using `pd.DataFrame()` and passing it a Python dictionary. + + +```python +# Let's create +cars_with_colours = pd.DataFrame({"Cars" : ["BMW","Audi","Thar","Honda"], + "Colour" : ["Black","White","Red","Green"]}) +cars_with_colours +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CarsColour
0BMWBlack
1AudiWhite
2TharRed
3HondaGreen
+
+ + + +The dictionary key is the `column name` and value are the `column data`. + +*You can also create a DataFrame with the help of series.* + + +```python +# Let's create two series +students = pd.Series(["Ram","Mohan","Krishna","Shivam"]) +age = pd.Series([19,20,21,24]) + +students +``` + + + + + 0 Ram + 1 Mohan + 2 Krishna + 3 Shivam + dtype: object + + + + +```python +age +``` + + + + + 0 19 + 1 20 + 2 21 + 3 24 + dtype: int64 + + + + +```python +# Now let's create a dataframe with the help of above series +# pass the series name to the dictionary value + +record = pd.DataFrame({"Student_Name":students , + "Age" :age}) +record +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Student_NameAge
0Ram19
1Mohan20
2Krishna21
3Shivam24
+
+ + + + +```python +# To print the list of columns names +record.columns +``` + + + + + Index(['Student_Name', 'Age'], dtype='object') + + + +### Describe Data + +**The good news is that pandas has many built-in functions which allow you to quickly get information about a DataFrame.** +Let's explore the `record` dataframe + +#### 1. Use `.dtypes` to find what datatype a column contains + + +```python +record.dtypes +``` + + + + + Student_Name object + Age int64 + dtype: object + + + +#### 2. use `.describe()` for statistical overview. + + +```python +record.describe() # It only display the results for numeric data +``` + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Age
count4.000000
mean21.000000
std2.160247
min19.000000
25%19.750000
50%20.500000
75%21.750000
max24.000000
+
+ + + +#### 3. Use `.info()` to find information about the dataframe + + +```python +record.info() +``` + + + RangeIndex: 4 entries, 0 to 3 + Data columns (total 2 columns): + # Column Non-Null Count Dtype + --- ------ -------------- ----- + 0 Student_Name 4 non-null object + 1 Age 4 non-null int64 + dtypes: int64(1), object(1) + memory usage: 196.0+ bytes + + + +```python + +``` From 6bee5482ad8b0b83273f1a8f6c3ddc8aee483d3c Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Fri, 17 May 2024 23:14:01 +0530 Subject: [PATCH 094/656] Update Introduction_to_Pandas_Library_and_DataFrames.md --- ...uction_to_Pandas_Library_and_DataFrames.md | 54 ------------------- 1 file changed, 54 deletions(-) diff --git a/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md b/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md index 5e7dcd3e..809a155a 100644 --- a/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md +++ b/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md @@ -112,24 +112,6 @@ cars_with_colours = pd.DataFrame({"Cars" : ["BMW","Audi","Thar","Honda"], "Colour" : ["Black","White","Red","Green"]}) cars_with_colours ``` - - - - -
- @@ -214,24 +196,6 @@ record = pd.DataFrame({"Student_Name":students , "Age" :age}) record ``` - - - - -
-
@@ -307,24 +271,6 @@ record.dtypes ```python record.describe() # It only display the results for numeric data ``` - - - - -
-
From 1834690a1064b0e280d1d8f4e6c31ca61f3f13b9 Mon Sep 17 00:00:00 2001 From: Niyonika Gaur <83643952+niyonikagaur@users.noreply.github.com> Date: Fri, 17 May 2024 23:37:59 +0530 Subject: [PATCH 095/656] Introduction_to_SciPy.md --- contrib/scipy/index.md | 132 ++++++++++++++++++++++++++++++++++++++++- 1 file changed, 130 insertions(+), 2 deletions(-) diff --git a/contrib/scipy/index.md b/contrib/scipy/index.md index 82596a2f..9c35f95c 100644 --- a/contrib/scipy/index.md +++ b/contrib/scipy/index.md @@ -1,3 +1,131 @@ -# List of sections +## Installation of Scipy +### Install using the command: +#### C:\Users\Your Name>pip install scipy + You can also use a Python distribution that already has Scipy installed like Anaconda, or Spyder. +### Importing SciPy +#### from scipy import constants +## Key Features of SciPy +### 1. Numerical Integration +#### It helps in computing definite or indefinite integrals of functions +``` +from scipy import integrate + +#Define the function to integrate +def f(x): + return x**2 + +#Compute definite integral of f from 0 to 1 +result, error = integrate.quad(f, 0, 1) +print(result) +``` +#### Output +``` +0.33333333333333337 +``` +### 2. Optimization +#### It can be used to minimize or maximize functions, here is an example of minimizing roots of an equation +``` +from scipy.optimize import minimize + +# Define an objective function to minimize +def objective(x): + return x**2 + 10*np.sin(x) + +# Minimize the objective function starting from x=0 +result = minimize(objective, x0=0) +print(result.x) +``` +#### Output +``` +array([-1.30644012]) +``` +### 3. Linear Algebra +#### Solving Linear computations +``` +from scipy import linalg +import numpy as np + +# Define a square matrix +A = np.array([[1, 2], [3, 4]]) + +# Define a vector +b = np.array([5, 6]) + +# Solve Ax = b for x +x = linalg.solve(A, b) +print(x) +``` +#### Output +``` +array([-4. , 4.5]) +``` +### 4. Statistics +#### Performing statistics functions, like here we'll be distributing the data +``` +from scipy import stats +import numpy as np + +# Generate random data from a normal distribution +data = stats.norm.rvs(loc=0, scale=1, size=1000) + +# Fit a normal distribution to the data +mean, std = stats.norm.fit(data) +``` +### 5. Signal Processing +#### To process spectral signals, like EEG or MEG +``` +from scipy import signal +import numpy as np + +# Create a signal (e.g., sine wave) +t = np.linspace(0, 1, 1000) +signal = np.sin(2 * np.pi * 5 * t) + 0.5 * np.random.randn(1000) + +# Apply a low-pass Butterworth filter +b, a = signal.butter(4, 0.1, 'low') +filtered_signal = signal.filtfilt(b, a, signal) +``` +The various filters applied that are applied here, are a part of signal analysis at a deeper level. +### 6. Sparse Matrix +#### The word ' sparse 'means less, i.e., the data is mostly unused during some operation or analysis. So, to handle this data, a Sparse Matrix is created +#### There are two types of Sparse Matrices: +##### 1. CSC: Compressed Sparse Column, it is used for efficient math functions and for column slicing +##### 2. CSR: Compressed Sparse Row, it is used for fast row slicing +#### In CSC format +``` +from scipy import sparse +import numpy as np + +data = np.array([[0, 0], [0, 1], [2, 0]]) + +row_indices = np.array([1, 2, 1]) +col_indices = np.array([1, 0, 2]) +values = np.array([1, 2, 1]) + +sparse_matrix_csc = sparse.csc_matrix((values, (row_indices, col_indices))) +``` +#### In CSR format +``` +from scipy import sparse +import numpy as np + +data = np.array([[0, 0], [0, 1], [2, 0]]) +sparse_matrix = sparse.csr_matrix(data) +``` +### 7. Image Processing +#### It is used to process the images, like changing dimensions or properties. For example, when you're doing a project on medical imaging, this library is mainly used. +``` +from scipy import ndimage +import matplotlib.pyplot as plt + +image = plt.imread('path/to/image.jpg') +plt.imshow(image) +plt.show() + +# Apply Gaussian blur to the image +blurred_image = ndimage.gaussian_filter(image, sigma=1) +plt.imshow(blurred_image) +plt.show() +``` +#### The gaussian blur is one of the properties of the ' ndimage ' package in SciPy libraries, it used for better understanding of the image. -- [Section title](filename.md) From c60a01491bfbb42a8a9d65b2f28a6e691a4b7e4e Mon Sep 17 00:00:00 2001 From: iABn0rma1 Date: Fri, 17 May 2024 23:38:40 +0530 Subject: [PATCH 096/656] updated content --- .../ArtificialNeuralNetwork.md | 19 ------------------- 1 file changed, 19 deletions(-) diff --git a/contrib/machine-learning/ArtificialNeuralNetwork.md b/contrib/machine-learning/ArtificialNeuralNetwork.md index 0fafc6c5..c577c945 100644 --- a/contrib/machine-learning/ArtificialNeuralNetwork.md +++ b/contrib/machine-learning/ArtificialNeuralNetwork.md @@ -26,7 +26,6 @@ This guide will walk you through a fundamental neural network implementation in | `Neuron` cells forming the humand nervous system | `Perceptron` inspired from human brain | | :----------------------------------------------- | -------------------------------------: | -| | | | Neurons are nerve cells that send messages all over your body to allow you to do everything from breathing to talking, eating, walking, and thinking. | The perceptron is a mathematical model of a biological neuron. Performing heavy computations to think like humans. | | Neuron collects signals from dendrites. | The first layer is knownn as Input Layer, acting like dendritres to receive the input signal. | | Synapses are the connections between neurons where signals are transmitted. | Weights represent synapses. | @@ -44,28 +43,17 @@ Neurons in ANNs are organized into layers: * **Input Layer:** Receives the raw data. * **(n) Hidden Layers:** (Optional) Intermediate layers where complex transformations occur. They learn to detect patterns and features in the data. * **Output Layer:** Produces the final result (prediction or classification). -

- -

### Weights and Biases - For each input $(x_i)$, a weight $(w_i)$ is associated with it. Weights, multiplied with input units $(w_i \cdot x_i)$, determine the influence of one neuron's output on another. - A bias $(b_i)$ is added to help influence the end product, giving the equation as $(w_i \cdot x_i + b_i)$. - During training, the network adjusts these weights and biases to minimize errors and improve its predictions. -

- -

- ### Activation Functions - An activation function is applied to the result to introduce non-linearity in the model, allowing ANNs to learn more complex relationships from the data. - The resulting equation: $y = f(g(x))$, determines whether the neuron will "fire" or not, i.e., if its output will be used as input for the next neuron. - Common activation functions include the sigmoid function, tanh (hyperbolic tangent), and ReLU (Rectified Linear Unit). -

- -

- ### Forward and Backward Propagation - **Flow of Information:** All the above steps are part of Forward Propagation. It gives the output equation as $y = f\left(\sum_{i=1}^n w_i x_i + b_i\right)$ - **Error Correction:** Backpropagation is the algorithm used to train ANNs by calculating the gradient of error at the output layer and then propagating this error backward through the network. This allows the network to adjust its weights and biases in the direction that reduces the error. @@ -75,10 +63,6 @@ Neurons in ANNs are organized into layers: $ where $E$ is the error, $\hat{y}_j$ is the predicted output, $\theta_j$ is the input to the activation function of the $j^{th}$ neuron, and $w_{ij}$ is the weight from neuron $i$ to neuron $j$. -

- -

- ## Building From Scratch @@ -167,6 +151,3 @@ To understand how well our model is learning, let's visualize the training loss model = NeuralNetwork(input_size, hidden_layers, hidden_neurons, output_size) model.train(X, y, 100) ``` -

- -

From 7c3b30466982483e69db3513e0afc438e982cad8 Mon Sep 17 00:00:00 2001 From: Niyonika Gaur <83643952+niyonikagaur@users.noreply.github.com> Date: Fri, 17 May 2024 23:58:14 +0530 Subject: [PATCH 097/656] Update index.md --- contrib/scipy/index.md | 131 +---------------------------------------- 1 file changed, 2 insertions(+), 129 deletions(-) diff --git a/contrib/scipy/index.md b/contrib/scipy/index.md index 9c35f95c..3ff5a7db 100644 --- a/contrib/scipy/index.md +++ b/contrib/scipy/index.md @@ -1,131 +1,4 @@ -## Installation of Scipy -### Install using the command: -#### C:\Users\Your Name>pip install scipy - You can also use a Python distribution that already has Scipy installed like Anaconda, or Spyder. -### Importing SciPy -#### from scipy import constants -## Key Features of SciPy -### 1. Numerical Integration -#### It helps in computing definite or indefinite integrals of functions -``` -from scipy import integrate +# List of sections -#Define the function to integrate -def f(x): - return x**2 - -#Compute definite integral of f from 0 to 1 -result, error = integrate.quad(f, 0, 1) -print(result) -``` -#### Output -``` -0.33333333333333337 -``` -### 2. Optimization -#### It can be used to minimize or maximize functions, here is an example of minimizing roots of an equation -``` -from scipy.optimize import minimize - -# Define an objective function to minimize -def objective(x): - return x**2 + 10*np.sin(x) - -# Minimize the objective function starting from x=0 -result = minimize(objective, x0=0) -print(result.x) -``` -#### Output -``` -array([-1.30644012]) -``` -### 3. Linear Algebra -#### Solving Linear computations -``` -from scipy import linalg -import numpy as np - -# Define a square matrix -A = np.array([[1, 2], [3, 4]]) - -# Define a vector -b = np.array([5, 6]) - -# Solve Ax = b for x -x = linalg.solve(A, b) -print(x) -``` -#### Output -``` -array([-4. , 4.5]) -``` -### 4. Statistics -#### Performing statistics functions, like here we'll be distributing the data -``` -from scipy import stats -import numpy as np - -# Generate random data from a normal distribution -data = stats.norm.rvs(loc=0, scale=1, size=1000) - -# Fit a normal distribution to the data -mean, std = stats.norm.fit(data) -``` -### 5. Signal Processing -#### To process spectral signals, like EEG or MEG -``` -from scipy import signal -import numpy as np - -# Create a signal (e.g., sine wave) -t = np.linspace(0, 1, 1000) -signal = np.sin(2 * np.pi * 5 * t) + 0.5 * np.random.randn(1000) - -# Apply a low-pass Butterworth filter -b, a = signal.butter(4, 0.1, 'low') -filtered_signal = signal.filtfilt(b, a, signal) -``` -The various filters applied that are applied here, are a part of signal analysis at a deeper level. -### 6. Sparse Matrix -#### The word ' sparse 'means less, i.e., the data is mostly unused during some operation or analysis. So, to handle this data, a Sparse Matrix is created -#### There are two types of Sparse Matrices: -##### 1. CSC: Compressed Sparse Column, it is used for efficient math functions and for column slicing -##### 2. CSR: Compressed Sparse Row, it is used for fast row slicing -#### In CSC format -``` -from scipy import sparse -import numpy as np - -data = np.array([[0, 0], [0, 1], [2, 0]]) - -row_indices = np.array([1, 2, 1]) -col_indices = np.array([1, 0, 2]) -values = np.array([1, 2, 1]) - -sparse_matrix_csc = sparse.csc_matrix((values, (row_indices, col_indices))) -``` -#### In CSR format -``` -from scipy import sparse -import numpy as np - -data = np.array([[0, 0], [0, 1], [2, 0]]) -sparse_matrix = sparse.csr_matrix(data) -``` -### 7. Image Processing -#### It is used to process the images, like changing dimensions or properties. For example, when you're doing a project on medical imaging, this library is mainly used. -``` -from scipy import ndimage -import matplotlib.pyplot as plt - -image = plt.imread('path/to/image.jpg') -plt.imshow(image) -plt.show() - -# Apply Gaussian blur to the image -blurred_image = ndimage.gaussian_filter(image, sigma=1) -plt.imshow(blurred_image) -plt.show() -``` -#### The gaussian blur is one of the properties of the ' ndimage ' package in SciPy libraries, it used for better understanding of the image. +- [Installation of Scipy and its key uses](pandas_series_vs_numpy_ndarray.md) From 0a63d2e17d69b8d8d3032bf36c51a183dc0e2450 Mon Sep 17 00:00:00 2001 From: Niyonika Gaur <83643952+niyonikagaur@users.noreply.github.com> Date: Fri, 17 May 2024 23:59:25 +0530 Subject: [PATCH 098/656] Installation_of_Scipy_&_its_key_uses.md --- .../Installation_of_Scipy_&_its_key_uses.md | 130 ++++++++++++++++++ 1 file changed, 130 insertions(+) create mode 100644 contrib/scipy/Installation_of_Scipy_&_its_key_uses.md diff --git a/contrib/scipy/Installation_of_Scipy_&_its_key_uses.md b/contrib/scipy/Installation_of_Scipy_&_its_key_uses.md new file mode 100644 index 00000000..33232dc0 --- /dev/null +++ b/contrib/scipy/Installation_of_Scipy_&_its_key_uses.md @@ -0,0 +1,130 @@ +## Installation of Scipy +### Install using the command: +#### C:\Users\Your Name>pip install scipy + You can also use a Python distribution that already has Scipy installed like Anaconda, or Spyder. +### Importing SciPy +#### from scipy import constants +## Key Features of SciPy +### 1. Numerical Integration +#### It helps in computing definite or indefinite integrals of functions +``` +from scipy import integrate + +#Define the function to integrate +def f(x): + return x**2 + +#Compute definite integral of f from 0 to 1 +result, error = integrate.quad(f, 0, 1) +print(result) +``` +#### Output +``` +0.33333333333333337 +``` +### 2. Optimization +#### It can be used to minimize or maximize functions, here is an example of minimizing roots of an equation +``` +from scipy.optimize import minimize + +# Define an objective function to minimize +def objective(x): + return x**2 + 10*np.sin(x) + +# Minimize the objective function starting from x=0 +result = minimize(objective, x0=0) +print(result.x) +``` +#### Output +``` +array([-1.30644012]) +``` +### 3. Linear Algebra +#### Solving Linear computations +``` +from scipy import linalg +import numpy as np + +# Define a square matrix +A = np.array([[1, 2], [3, 4]]) + +# Define a vector +b = np.array([5, 6]) + +# Solve Ax = b for x +x = linalg.solve(A, b) +print(x) +``` +#### Output +``` +array([-4. , 4.5]) +``` +### 4. Statistics +#### Performing statistics functions, like here we'll be distributing the data +``` +from scipy import stats +import numpy as np + +# Generate random data from a normal distribution +data = stats.norm.rvs(loc=0, scale=1, size=1000) + +# Fit a normal distribution to the data +mean, std = stats.norm.fit(data) +``` +### 5. Signal Processing +#### To process spectral signals, like EEG or MEG +``` +from scipy import signal +import numpy as np + +# Create a signal (e.g., sine wave) +t = np.linspace(0, 1, 1000) +signal = np.sin(2 * np.pi * 5 * t) + 0.5 * np.random.randn(1000) + +# Apply a low-pass Butterworth filter +b, a = signal.butter(4, 0.1, 'low') +filtered_signal = signal.filtfilt(b, a, signal) +``` +The various filters applied that are applied here, are a part of signal analysis at a deeper level. +### 6. Sparse Matrix +#### The word ' sparse 'means less, i.e., the data is mostly unused during some operation or analysis. So, to handle this data, a Sparse Matrix is created +#### There are two types of Sparse Matrices: +##### 1. CSC: Compressed Sparse Column, it is used for efficient math functions and for column slicing +##### 2. CSR: Compressed Sparse Row, it is used for fast row slicing +#### In CSC format +``` +from scipy import sparse +import numpy as np + +data = np.array([[0, 0], [0, 1], [2, 0]]) + +row_indices = np.array([1, 2, 1]) +col_indices = np.array([1, 0, 2]) +values = np.array([1, 2, 1]) + +sparse_matrix_csc = sparse.csc_matrix((values, (row_indices, col_indices))) +``` +#### In CSR format +``` +from scipy import sparse +import numpy as np + +data = np.array([[0, 0], [0, 1], [2, 0]]) +sparse_matrix = sparse.csr_matrix(data) +``` +### 7. Image Processing +#### It is used to process the images, like changing dimensions or properties. For example, when you're doing a project on medical imaging, this library is mainly used. +``` +from scipy import ndimage +import matplotlib.pyplot as plt + +image = plt.imread('path/to/image.jpg') +plt.imshow(image) +plt.show() + +# Apply Gaussian blur to the image +blurred_image = ndimage.gaussian_filter(image, sigma=1) +plt.imshow(blurred_image) +plt.show() +``` +#### The gaussian blur is one of the properties of the ' ndimage ' package in SciPy libraries, it used for better understanding of the image. From 2632f7c8a1a542b98616668311e24d344ca79736 Mon Sep 17 00:00:00 2001 From: Niyonika Gaur <83643952+niyonikagaur@users.noreply.github.com> Date: Fri, 17 May 2024 23:59:48 +0530 Subject: [PATCH 099/656] Update index.md --- contrib/scipy/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/scipy/index.md b/contrib/scipy/index.md index 3ff5a7db..abc2fb0b 100644 --- a/contrib/scipy/index.md +++ b/contrib/scipy/index.md @@ -1,4 +1,4 @@ # List of sections -- [Installation of Scipy and its key uses](pandas_series_vs_numpy_ndarray.md) +- [Installation of Scipy and its key uses]( Installation_of_Scipy_&_its_key_uses.md) From 6574b726fecffba739e6fc4d01776944b76333f7 Mon Sep 17 00:00:00 2001 From: Vrisha Shah <74671946+Vrisha213@users.noreply.github.com> Date: Sat, 18 May 2024 12:44:43 +0530 Subject: [PATCH 100/656] Update index.md --- contrib/machine-learning/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 2f27fd53..40a22370 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -1,3 +1,4 @@ # List of sections - [Confusion Matrix](confusion-matrix.md) +- [Support Vector Machine Algorithm](support-vector-machine.md) From 72909dc3abda33ad617b81007e9f1da69a15f5ba Mon Sep 17 00:00:00 2001 From: Yatharth Date: Sat, 18 May 2024 13:18:25 +0530 Subject: [PATCH 101/656] JSON MDODULE load loads dump dumps update and append --- contrib/advanced-python/index.md | 1 + contrib/advanced-python/json-module.md | 294 +++++++++++++++++++++++++ 2 files changed, 295 insertions(+) create mode 100644 contrib/advanced-python/json-module.md diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 5ea5081c..8f4f26a6 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -1,3 +1,4 @@ # List of sections - [Decorators/\*args/**kwargs](decorator-kwargs-args.md) +- [JSON module](json-module.md) diff --git a/contrib/advanced-python/json-module.md b/contrib/advanced-python/json-module.md new file mode 100644 index 00000000..20a94081 --- /dev/null +++ b/contrib/advanced-python/json-module.md @@ -0,0 +1,294 @@ +# JSON Module + +## What is JSON? + +- JSON (JavaScript Object Notation) is a format for structuring data. +- JSON is a lightweight, text-based data interchange format that is completely language-independent. +- Similar to XML, JSON is a format for structuring data commonly used by web applications to communicate with each other. + +## Why JSON? + +- Whenever we declare a variable and assign a value to it, the variable itself doesn't hold the value. Instead, the variable holds an address in memory where the value is stored. For example: + +```python +age = 21 +``` + +- When we use `age`, it gets replaced with `21`. However, age doesn't contain 21, it contains the address of the memory location where 21 is stored. + +- While this works locally, transferring this data, such as through an API, poses a challenge. Sending your computer’s entire memory with the addresses is impractical and insecure. This is where JSON comes to the rescue. + +### Example JSON + +- JSON supports most widely used data types including String + , Number, Boolean, Null, Array and Object. +- Here is an example of JSON file + +```json +{ + "name": "John Doe", + "age": 21, + "isStudent": true, + "address": null, + "courses": ["Math", "Science", "History"], + "grades": { + "Math": 95, + "Science": 89, + "History": 76 + } +} +``` + +# Python JSON + +Python too supports JSON with a built-in package called `json`. This package provides all the necessary tools for working with JSON Objects including `parsing, serializing, deserializing, and many more`. + +## 1. Python parse JSON string. + +- To parse JSON string Python firstly we import the JSON module. +- JSON string is converted to a Python object using `json.loads()` method of JSON module in Python. +- Example Code: + +```python +# Python program to convert JSON to Python +import json + +# JSON string +students ='{"id":"01", "name": "Yatharth", "department":"Computer Science Engineering"}' + +# Convert string to Python dict +students_dict = json.loads(students) +print(students_dict) + +print(students_dict['name']) + +``` + +- Ouput: + +```json +{'id': '01', 'name': 'Yatharth', 'department': 'Computer Science Engineering'} +Yatharth +``` + +## 2. Python load JSON file. + +- JSON data can also be directly fetch from a json file +- Example: + +```python +import json +# Opening JSON file +f = open('input.json',) + +# Returns JSON object as a dictionary +data = json.load(f) + +# Iterating through the json file +for i in data['students']: + print(i) + +# Closing file +f.close() +``` + +- JSON file + +```json +{ + "students":{ + { + "id": "01", + "name": "Yatharth", + "department": "Computer Science Engineering" + }, + { + "id": "02", + "name": "Raj", + "department": "Mechanical Engineering" + } + } +} +``` + +- Ouput + +```json +{'id': '01', 'name': 'Yatharth', 'department': 'Computer Science Engineering'} +{'id': '02', 'name': 'Raj', 'department': 'Mechanical Engineering'} +``` +- `json.load()`: Reads JSON data from a file object and deserializes it into a Python object. +- `json.loads()`: Deserializes JSON data from a string into a Python object. +
+ +### Addtiotnal Context +Relation between python data types and json data types is given in table below. +
+ +| Python Object | JSON Object | +|-----------------|-------------| +| Dict | object | +| list, tuple | array | +| str | string | +| int, long, float | numbers | +| True | true | +| False | false | +| None | null | + + + +## 3. Python Dictionary to JSON String +- Parsing python dictionary to json string using `json.dumps()`. +- Example Code: +```python +import json + +# Data to be written +dictionary ={ + "id": "03", + "name": "Suraj", + "department": "Civil Engineering" +} + +# Serializing json +json_object = json.dumps(dictionary, indent = 4) +print(json_object) +``` +- Output: +``` json +{ + "department": "Civil Engineering", + "id": "02", + "name": "Suraj" +} +``` +## 4. Python Dictionary to JSON file. +- - Parsing python dictionary to json string using `json.dump()`. +- Example Code: +``` python +import json + +# Data to be written +dictionary ={ + "name" : "Satyendra", + "rollno" : 51, + "cgpa" : 8.8, + "phonenumber" : "123456789" +} + +with open("sample.json", "w") as outfile: + json.dump(dictionary, outfile) + +``` +- Ouput: `sample.json` +``` json +{ + "name" : "Satyendra", + "rollno" : 51, + "cgpa" : 8.8, + "phonenumber" : "123456789" +} + +``` +## 5. Append Python Dictionary to JSON String. +- Append to an already existing string using `json.update()`. +- Example : +```python +import json +# JSON data: +x = { + "id": "03", + "name": "Suraj" +} + +# python object to be appended +y = { "department": "Civil Engineering"} + +# parsing JSON string: +z = json.loads(x) + +# appending the data +z.update(y) + +# the result is a JSON string: +print(json.dumps(z)) + +``` +- Ouput: +```json +{"id": "03", "name": "Suraj", "department": "Civil Engineering"} +``` + + +## 6. Append Python Dictionary to JSON File. +- There is no direct function to append in file. So, we will load file in a dictionary, update dictionary then update content and convert back to json file format. +- `data.json` +``` json +{ + "students":{ + { + "id": "01", + "name": "Yatharth", + "department": "Computer Science Engineering" + }, + { + "id": "02", + "name": "Raj", + "department": "Mechanical Engineering" + } + } +} +``` +- Example Code: +``` python +import json + +# function to add to JSON +def write_json(new_data, filename='data.json'): + with open(filename,'r+') as file: + # First we load existing data into a dict. + file_data = json.load(file) + # Join new_data with file_data inside students + file_data["students"].append(new_data) + # Sets file's current position at offset. + file.seek(0) + # convert back to json. + json.dump(file_data, file, indent = 4) + +# python object to be appended +y = { + "id": "03", + "name": "Suraj", + "department": "Civil Engineering" +} + +write_json(y) + +``` +- Output: +```json +{ + "students":{ + { + "id": "01", + "name": "Yatharth", + "department": "Computer Science Engineering" + }, + { + "id": "02", + "name": "Raj", + "department": "Mechanical Engineering" + }, + { + "id": "03", + "name": "Suraj", + "department": "Civil Engineering" + } + } +} +``` + +
+
+ +The Python json module simplifies the handling of JSON data, offering a bridge between Python data structures and JSON representations, vital for data exchange and storage in modern applications. \ No newline at end of file From a478e70c4bea7637ba6795ab1cf81c7d00d5ca2a Mon Sep 17 00:00:00 2001 From: Anushka Mokashi Date: Sat, 18 May 2024 14:10:53 +0530 Subject: [PATCH 102/656] Descriptive Statistics with Python and Pandas --- contrib/pandas/Descriptive_Statistics.md | 599 +++++++++++++++++++++++ contrib/pandas/index.md | 1 + 2 files changed, 600 insertions(+) create mode 100644 contrib/pandas/Descriptive_Statistics.md diff --git a/contrib/pandas/Descriptive_Statistics.md b/contrib/pandas/Descriptive_Statistics.md new file mode 100644 index 00000000..7775965b --- /dev/null +++ b/contrib/pandas/Descriptive_Statistics.md @@ -0,0 +1,599 @@ +## Descriptive Statistics + +In the realm of data science, understanding the characteristics of data is fundamental. Descriptive statistics provide the tools and techniques to succinctly summarize and present the key features of a dataset. It serves as the cornerstone for exploring, visualizing, and ultimately gaining insights from data. + +Descriptive statistics encompasses a range of methods designed to describe the central tendency, dispersion, and shape of a dataset. Through measures such as mean, median, mode, standard deviation, and variance, descriptive statistics offer a comprehensive snapshot of the data's distribution and variability. + +Data scientists utilize descriptive statistics to uncover patterns, identify outliers, and assess the overall structure of data before delving into more advanced analyses. By summarizing large and complex datasets into manageable and interpretable summaries, descriptive statistics facilitate informed decision-making and actionable insights. + + +```python +import pandas as pd +import numpy as np +``` + + +```python +df = pd.read_csv("Age-Income-Dataset.csv") +df +``` +| | Age | Income | +| --- | ----------- | ------ | +| 0 | Young | 25000 | +| 1 | Middle Age | 54000 | +| 2 | Old | 60000 | +| 3 | Young | 15000 | +| 4 | Young | 45000 | +| 5 | Young | 65000 | +| 6 | Young | 70000 | +| 7 | Young | 30000 | +| 8 | Middle Age | 27000 | +| 9 | Young | 23000 | +| 10 | Young | 48000 | +| 11 | Old | 52000 | +| 12 | Young | 33000 | +| 13 | Old | 80000 | +| 14 | Old | 75000 | +| 15 | Old | 35000 | +| 16 | Middle Age | 29000 | +| 17 | Middle Age | 57000 | +| 18 | Old | 43000 | +| 19 | Middle Age | 56000 | +| 20 | Old | 63000 | +| 21 | Old | 32000 | +| 22 | Old | 45000 | +| 23 | Old | 89000 | +| 24 | Middle Age | 90000 | +| 25 | Middle Age | 93000 | +| 26 | Young | 80000 | +| 27 | Young | 87000 | +| 28 | Young | 38000 | +| 29 | Young | 23000 | +| 30 | Middle Age | 38900 | +| 31 | Middle Age | 53200 | +| 32 | Old | 43800 | +| 33 | Middle Age | 25600 | +| 34 | Middle Age | 65400 | +| 35 | Old | 76800 | +| 36 | Old | 89700 | +| 37 | Old | 41800 | +| 38 | Young | 31900 | +| 39 | Old | 25600 | +| 40 | Middle Age | 45700 | +| 41 | Old | 35600 | +| 42 | Young | 54300 | +| 43 | Middle Age | 65400 | +| 44 | Old | 67800 | +| 45 | Old | 24500 | +| 46 | Middle Age | 34900 | +| 47 | Old | 45300 | +| 48 | Young | 68400 | +| 49 | Middle Age | 51700 | + + + +```python +df.describe() +``` + + + +| | Income | +|-------|-------------| +| count | 50.000000 | +| mean | 50966.000000 | +| std | 21096.683268 | +| min | 15000.000000 | +| 25% | 33475.000000 | +| 50% | 46850.000000 | +| 75% | 65400.000000 | +| max | 93000.000000 | + + +### Mean + +The mean, also known as the average, is a measure of central tendency in a dataset. It represents the typical value of a set of numbers. The formula to calculate the mean of a dataset is: + +$$ \overline{x} = \frac{\sum\limits_{i=1}^{n} x_i}{n} $$ + +* $\overline{x}$ (pronounced "x bar") represents the mean value. +* $x_i$ represents the individual value in the dataset (where i goes from 1 to n). +* $\sum$ (sigma) represents the summation symbol, indicating we add up all the values from i=1 to n. +* $n$ represents the total number of values in the dataset. + + + +```python +df['Income'].mean() +``` + + + + +50966.0 + + + + +```python +def mean_f(df): + for col in df.columns: + if df[col].dtype != 'O': + temp = 0 + for i in df[col]: + temp = temp +i + print("Without pandas Library -> ") + print("Average of {} is {}".format(col,(temp/len(df[col])))) + print() + print("With pandas Library -> ") + print(df[col].mean()) + +mean_f(df) +``` + +Without pandas Library -> +Average of Income is 50966.0 + +With pandas Library -> +50966.0 + + +### Median + + +The median is another measure of central tendency in a dataset. Unlike the mean, which is the average value of all data points, the median represents the middle value when the dataset is ordered from smallest to largest. If the dataset has an odd number of observations, the median is the middle value. If the dataset has an even number of observations, the median is the average of the two middle values. + +The median represents the "middle" value in a dataset. There are two cases to consider depending on whether the number of observations (n) is odd or even: + +**Odd number of observations (n):** + +In this case, the median (M) is the value located at the middle position when the data is ordered from least to greatest. We can calculate the position using the following formula: + +$$ M = x_{n+1/2} $$ + +**Even number of observations (n):** + +When we have an even number of observations, there isn't a single "middle" value. Instead, the median is the average of the two middle values after ordering the data. Here's the formula to find the median: + +$$ M = \frac{x_{n/2} + x_{(n/2)+1}}{2} $$ + +**Explanation:** + +* M represents the median value. +* n represents the total number of observations in the dataset. +* $x$ represents the individual value. + + + + + +```python +df['Income'].median() +``` + + + + +46850.0 + + + + +```python +def median_f(df): + for col in df.columns: + if df[col].dtype != 'O': + sorted_data = sorted(df[col]) + n = len(df[col]) + if n%2 == 0: + x1 =sorted_data[int((n/2))] + x2 =sorted_data[int((n/2))+1] + median=(x1+x2)/2 + else: + median = sorted_data[(n+1)/2] + print("Median without library ->") + print("Median of {} is {} ".format(col,median)) + print("Median with library ->") + print(df[col].median()) +median_f(df) +``` + +Median without library -> +Median of Income is 49850.0 \ +Median with library -> +46850.0 + + +### Mode + + +The mode is a measure of central tendency that represents the value or values that occur most frequently in a dataset. Unlike the mean and median, which focus on the average or middle value, the mode identifies the most common value(s) in the dataset. + + +```python +def mode_f(df): + for col in df.columns: + if df[col].dtype == 'O': + print("Column:", col) + arr = df[col].sort_values() + + prevcnt = 0 + cnt = 0 + ans = arr[0] + temp = arr[0] + + for i in arr: + if(temp == i) : + cnt += 1 + else: + prevcnt = cnt + cnt = 1 + temp = i + if(cnt > prevcnt): + ans = i + + print("Without pandas Library -> ") + print("Mode of {} is {}".format(col,ans)) + print() + print("With pandas Library -> ") + print(df[col].mode()) +mode_f(df) +``` + +Column: Age +Without pandas Library -> \ +Mode of Age is Old + +With pandas Library -> \ +0 Old \ +Name: Age, dtype: object + + + + + +### Standard Deviation + +Standard deviation is a measure of the dispersion or spread of a dataset. It quantifies the amount of variation or dispersion of a set of values from the mean. In other words, it indicates how much individual values in a dataset deviate from the mean. + +$$s = \sqrt{\frac{\sum(x_i-\overline{x})^{2}}{n-1}}$$ + +* $s$ represents the standard deviation. +* $\sum$ (sigma) represents the summation symbol, indicating we add up the values for all data points. +* $x_i$ represents the individual value in the dataset. +* $\overline{x}$ (x bar) represents the mean value of the dataset. +* $n$ represents the total number of values in the dataset. + + +```python +df['Income'].std() +``` + + + + +21096.683267707253 + + + + +```python +import math +def std_f(df): + for col in df.columns: + if len(df[col]) == 0: + print("Column is empty") + if df[col].dtype != 'O': + sum = 0 + mean = df[col].mean() + for i in df[col]: + sum = sum + (i - mean)**2 + + std = math.sqrt(sum/len(df[col])) + print("Without pandas library ->") + print("Std : " , std) + print("With pandas library: ->") + print("Std : {}".format(np.std(df[col]))) ##ddof = 1 + +std_f(df) +``` + +Without pandas library -> +Std : 20884.6509187968 \ +With pandas library: -> +Std : 20884.6509187968 + + + +```python +df['Income'].count() +``` + + + + +50 + + + +### Minimum + + +```python +df['Income'].min() +``` + + + + +15000 + + + + +```python +def min_f(df): + for col in df.columns: + if df[col].dtype != "O": + sorted_data = sorted(df[col]) + min = sorted_data[0] + print("Without pandas Library->",min) + print("With pandas Library->",df[col].min()) + +min_f(df) +``` + +Without pandas Library-> 15000 \ +With pandas Library-> 15000 + + +### Maximum + + +```python +df['Income'].max() +``` + + + + +93000 + + + + +```python +def max_f(df): + for col in df.columns: + if df[col].dtype != "O": + sorted_data = sorted(df[col]) + max = sorted_data[len(df[col])-1] + print("Without pandas Library->",max) + print("With pandas Library->",df[col].max()) + +max_f(df) +``` + + Without pandas Library-> 93000 + With pandas Library-> 93000 + + +### Percentile + + +```python +df['Income'].quantile(0.25) +``` + + + + +33475.0 + + + + +```python +df['Income'].quantile(0.75) +``` + + + + +65400.0 + + + + +```python +def percentile_f(df,percentile): + for col in df.columns: + if df[col].dtype != 'O': + sorted_data = sorted(df[col]) + index = int(percentile*len(df[col])) + percentile_result = sorted_data[index] + print(f"{percentile} Percentile is : ",percentile_result) + +percentile_f(df,0.25) +``` + +0.25 Percentile is : 33000 + + +We have used the method of nearest rank to calculate percentile manually. + +Pandas uses linear interpolation of data to calculate percentiles. + +## Correlation and Covariance + + +```python +df = pd.read_csv('Iris.csv') +df.head(5) +``` + + + + +| | Id | SepalLengthCm | SepalWidthCm | PetalLengthCm | PetalWidthCm | Species | +|---|----|---------------|--------------|---------------|--------------|-------------| +| 0 | 1 | 5.1 | 3.5 | 1.4 | 0.2 | Iris-setosa | +| 1 | 2 | 4.9 | 3.0 | 1.4 | 0.2 | Iris-setosa | +| 2 | 3 | 4.7 | 3.2 | 1.3 | 0.2 | Iris-setosa | +| 3 | 4 | 4.6 | 3.1 | 1.5 | 0.2 | Iris-setosa | +| 4 | 5 | 5.0 | 3.6 | 1.4 | 0.2 | Iris-setosa | + + + + +```python +df.drop(['Id','Species'],axis=1,inplace= True) +``` + +### Covarience + +Covariance measures the degree to which two variables change together. If the covariance between two variables is positive, it means that they tend to increase or decrease together. If the covariance is negative, it means that as one variable increases, the other tends to decrease. However, covariance does not provide a standardized measure, making it difficult to interpret the strength of the relationship between variables, especially if the variables are measured in different units. + +$$ COV(X,Y) = \frac{\sum\limits_{i=1}^{n} (X_i - \overline{X}) (Y_i - \overline{Y})}{n - 1}$$ + +**Explanation:** + +* $COV(X, Y)$ represents the covariance between variables X and Y. +* $X_i$ and $Y_i$ represent the individual values for variables X and Y in the i-th observation. +* $\overline{X}$ and $\overline{Y}$ represent the mean values for variables X and Y, respectively. +* $n$ represents the total number of observations in the dataset. + + + +```python +df.cov() +``` + + +| | SepalLengthCm | SepalWidthCm | PetalLengthCm | PetalWidthCm | +|-------------------|-------------- |---------------|-----------------|--------------| +| **SepalLengthCm** | 0.685694 | -0.039268 | 1.273682 | 0.516904 | +| **SepalWidthCm** | -0.039268 | 0.188004 | -0.321713 | -0.117981 | +| **PetalLengthCm** | 1.273682 | -0.321713 | 3.113179 | 1.296387 | +| **PetalWidthCm** | 0.516904 | -0.117981 | 1.296387 | 0.582414 | + + + +```python +def cov_f(df): + for x in df.columns: + for y in df.columns: + mean_x = df[x].mean() + mean_y = df[y].mean() + + sum = 0 + n = len(df[x]) + + for val in range(n): + sum += (df[x].iloc[val] - mean_x)*(df[y].iloc[val] - mean_y) + print("Covariance of {} and {} is : {}".format(x,y, sum/(n-1))) + print() +cov_f(df) +``` + +Covariance of SepalLengthCm and SepalLengthCm is : 0.6856935123042504 \ +Covariance of SepalLengthCm and SepalWidthCm is : -0.03926845637583892 \ +Covariance of SepalLengthCm and PetalLengthCm is : 1.2736823266219246 \ +Covariance of SepalLengthCm and PetalWidthCm is : 0.5169038031319911 + +Covariance of SepalWidthCm and SepalLengthCm is : -0.03926845637583892 \ +Covariance of SepalWidthCm and SepalWidthCm is : 0.1880040268456377 \ +Covariance of SepalWidthCm and PetalLengthCm is : -0.32171275167785235 \ +Covariance of SepalWidthCm and PetalWidthCm is : -0.11798120805369115 + +Covariance of PetalLengthCm and SepalLengthCm is : 1.2736823266219246 \ +Covariance of PetalLengthCm and SepalWidthCm is : -0.32171275167785235 \ +Covariance of PetalLengthCm and PetalLengthCm is : 3.113179418344519 \ +Covariance of PetalLengthCm and PetalWidthCm is : 1.2963874720357946 + +Covariance of PetalWidthCm and SepalLengthCm is : 0.5169038031319911 \ +Covariance of PetalWidthCm and SepalWidthCm is : -0.11798120805369115 \ +Covariance of PetalWidthCm and PetalLengthCm is : 1.2963874720357946 \ +Covariance of PetalWidthCm and PetalWidthCm is : 0.5824143176733781 + + + +### Correlation + +Correlation, on the other hand, standardizes the measure of relationship between two variables, making it easier to interpret. It measures both the strength and direction of the linear relationship between two variables. Correlation values range between -1 and 1, where: + +$$r = \frac{n(\sum xy) - (\sum x)(\sum y)}{\sqrt{n(\sum x^2) - (\sum x)^2} \cdot \sqrt{n(\sum y^2) - (\sum y)^2}}$$ + +* r represents the correlation coefficient. +* n is the number of data points. + + + + +```python +df.corr() +``` + +| | SepalLengthCm | SepalWidthCm | PetalLengthCm | PetalWidthCm | +|-------------------|---------------|--------------|---------------|--------------| +| **SepalLengthCm** | 1.000000 | -0.109369 | 0.871754 | 0.817954 | +| **SepalWidthCm** | -0.109369 | 1.000000 | -0.420516 | -0.356544 | +| **PetalLengthCm** | 0.871754 | -0.420516 | 1.000000 | 0.962757 | +| **PetalWidthCm** | 0.817954 | -0.356544 | 0.962757 | 1.000000 | + + +```python +import math +def corr_f(df): + for i in df.columns: + for j in df.columns: + n = len(df[i]) + + sumX = 0 + for x in df[i]: + sumX += x + sumY = 0 + for y in df[j]: + sumY += y + + sumXY = 0 + for xy in range(n): + sumXY += (df[i].iloc[xy] * df[j].iloc[xy]) + + sumX2 = 0 + for x in df[i]: + sumX2 += (x**2) + sumY2 = 0 + for y in df[j]: + sumY2 += (y**2) + + NR = (n * sumXY) - (sumX*sumY) + DR = math.sqrt( ( (n * sumX2) - (sumX**2))*( (n * sumY2) - (sumY ** 2) ) ) + + print("Correlation of {} and {} :{}".format(i,j,NR/DR)) + print() + +corr_f(df) +``` + +Correlation of SepalLengthCm and SepalLengthCm :1.0 \ +Correlation of SepalLengthCm and SepalWidthCm :-0.10936924995067286 \ +Correlation of SepalLengthCm and PetalLengthCm :0.8717541573048861 \ +Correlation of SepalLengthCm and PetalWidthCm :0.8179536333691775 + +Correlation of SepalWidthCm and SepalLengthCm :-0.10936924995067286 \ +Correlation of SepalWidthCm and SepalWidthCm :1.0 \ +Correlation of SepalWidthCm and PetalLengthCm :-0.42051609640118826 \ +Correlation of SepalWidthCm and PetalWidthCm :-0.3565440896138223 + +Correlation of PetalLengthCm and SepalLengthCm :0.8717541573048861 \ +Correlation of PetalLengthCm and SepalWidthCm :-0.42051609640118826 \ +Correlation of PetalLengthCm and PetalLengthCm :1.0 \ +Correlation of PetalLengthCm and PetalWidthCm :0.9627570970509656 + +Correlation of PetalWidthCm and SepalLengthCm :0.8179536333691775 \ +Correlation of PetalWidthCm and SepalWidthCm :-0.3565440896138223 \ +Correlation of PetalWidthCm and PetalLengthCm :0.9627570970509656 \ +Correlation of PetalWidthCm and PetalWidthCm :1.0 + + diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 5c0f2b43..ae907885 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -1,3 +1,4 @@ # List of sections - [Pandas Series Vs NumPy ndarray](pandas_series_vs_numpy_ndarray.md) +- [Pandas Descriptive Statistics](Descriptive_Statistics.md) From f322f740d2ed2fbb669733b07e6dfc939acb53a9 Mon Sep 17 00:00:00 2001 From: AmrutaJayanti <142327526+AmrutaJayanti@users.noreply.github.com> Date: Sat, 18 May 2024 18:24:03 +0530 Subject: [PATCH 103/656] Update index.md --- contrib/advanced-python/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 5ea5081c..6fe4a9f9 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -1,3 +1,4 @@ # List of sections - [Decorators/\*args/**kwargs](decorator-kwargs-args.md) +- [OOPs](OOPs.md) From 95c272e87edbdbce3593360d36bd1deae6e54432 Mon Sep 17 00:00:00 2001 From: AmrutaJayanti <142327526+AmrutaJayanti@users.noreply.github.com> Date: Sat, 18 May 2024 18:24:30 +0530 Subject: [PATCH 104/656] Added OOPs --- contrib/advanced-python/OOPs.md | 470 ++++++++++++++++++++++++++++++++ 1 file changed, 470 insertions(+) create mode 100644 contrib/advanced-python/OOPs.md diff --git a/contrib/advanced-python/OOPs.md b/contrib/advanced-python/OOPs.md new file mode 100644 index 00000000..7e355f24 --- /dev/null +++ b/contrib/advanced-python/OOPs.md @@ -0,0 +1,470 @@ +--- +jupyter: + colab: + kernelspec: + display_name: Python 3 + name: python3 + language_info: + name: python + nbformat: 4 + nbformat_minor: 0 +--- + +::: {.cell .markdown id="WpcHLEIgeEdz"} +**PYTHON OOP CONCEPTS** +::: + +::: {.cell .markdown id="3j1W5YpNeEmS"} +In Python object-oriented Programming (OOPs) is a programming paradigm +that uses objects and classes in programming. It aims to implement +real-world entities like inheritance, polymorphisms, encapsulation, etc. +in the programming. The main concept of object-oriented Programming +(OOPs) or oops concepts in Python is to bind the data and the functions +that work together as a single unit so that no other part of the code +can access this data. + +**OOPs Concepts in Python** + +1. Class in Python + +2. Objects in Python + +3. Polymorphism in Python + +4. Encapsulation in Python + +5. Inheritance in Python + +6. Data Abstraction in Python + ::: + +::: {.cell .markdown id="3KQXMPlVeEyT"} +Python Class A class is a collection of objects. A class contains the +blueprints or the prototype from which the objects are being created. It +is a logical entity that contains some attributes and methods. +::: + +::: {.cell .code execution_count="2" id="6CdZhHSMerae"} + +```python +#Simple Class in Python +class Dog: + pass +``` + +::: + +::: {.cell .markdown id="y5t7jyHAfAHl"} +**Python Objects** In object oriented programming Python, The object is +an entity that has a state and behavior associated with it. It may be +any real-world object like a mouse, keyboard, chair, table, pen, etc. +Integers, strings, floating-point numbers, even arrays, and +dictionaries, are all objects. +::: + +::: {.cell .code execution_count="3" id="u8Y0G-hse-lm"} + +```python +obj = Dog() +``` + +::: + +::: {.cell .markdown id="\_FtRAPlNfLkb"} +**The Python **init** Method ** + +The **init** method is similar to constructors in C++ and Java. It is +run as soon as an object of a class is instantiated. The method is +useful to do any initialization you want to do with your object. +::: + +::: {.cell .code execution_count="4" colab="{\"base_uri\":\"https://localhost:8080/\"}" id="aMmXUxLHfSz6" outputId="ed23bfbd-445a-4d02-ebba-c210471e0af2"} + +```python +class Dog: + + # class attribute + attr1 = "mammal" + + # Instance attribute + def __init__(self, name): + self.name = name + +# Object instantiation +Rodger = Dog("Rodger") +Tommy = Dog("Tommy") + +# Accessing class attributes +print("Rodger is a {}".format(Rodger.__class__.attr1)) +print("Tommy is also a {}".format(Tommy.__class__.attr1)) + +# Accessing instance attributes +print("My name is {}".format(Rodger.name)) +print("My name is {}".format(Tommy.name)) +``` + +::: {.output .stream .stdout} +Rodger is a mammal +Tommy is also a mammal +My name is Rodger +My name is Tommy +::: +::: + +::: {.cell .markdown id="YFMnh417fZI6"} +**Inheritance** + +In Python object oriented Programming, Inheritance is the capability of +one class to derive or inherit the properties from another class. The +class that derives properties is called the derived class or child class +and the class from which the properties are being derived is called the +base class or parent class. + +Types of Inheritances: + +- Single Inheritance + +- Multilevel Inheritance + +- Multiple Inheritance + +- Hierarchial Inheritance + ::: + +::: {.cell .code execution_count="6" colab="{\"base_uri\":\"https://localhost:8080/\"}" id="PeQoseXogE2f" outputId="9b3a069e-c6f0-41b0-b5f3-704aa0f95160"} + +```python +#Single Inheritance +# Parent class +class Animal: + def __init__(self, name, sound): + self.name = name + self.sound = sound + + def make_sound(self): + print(f"{self.name} says {self.sound}") + +# Child class inheriting from Animal +class Dog(Animal): + def __init__(self, name): + # Call the constructor of the parent class + super().__init__(name, "Woof") + +# Child class inheriting from Animal +class Cat(Animal): + def __init__(self, name): + # Call the constructor of the parent class + super().__init__(name, "Meow") + +# Creating objects of the derived classes +dog = Dog("Buddy") +cat = Cat("Whiskers") + +# Accessing methods of the parent class +dog.make_sound() +cat.make_sound() +``` + +::: {.output .stream .stdout} +Buddy says Woof +Whiskers says Meow +::: +::: + +::: {.cell .code execution_count="7" colab="{\"base_uri\":\"https://localhost:8080/\"}" id="CKEMq39BgSu9" outputId="b252ac43-6116-4a88-9ac9-0475f54e63c0"} + +```python +#Multilevel Inheritance +# Parent class +class Animal: + def __init__(self, name): + self.name = name + + def speak(self): + print(f"{self.name} speaks") + +# Child class inheriting from Animal +class Dog(Animal): + def bark(self): + print(f"{self.name} barks") + +# Grandchild class inheriting from Dog +class GermanShepherd(Dog): + def guard(self): + print(f"{self.name} guards") + +# Creating objects of the derived classes +german_shepherd = GermanShepherd("Rocky") + +# Accessing methods from all levels of inheritance +german_shepherd.speak() # Accessing method from the Animal class +german_shepherd.bark() # Accessing method from the Dog class +german_shepherd.guard() # Accessing method from the GermanShepherd class +``` + +::: {.output .stream .stdout} +Rocky speaks +Rocky barks +Rocky guards +::: +::: + +::: {.cell .code execution_count="8" colab="{\"base_uri\":\"https://localhost:8080/\"}" id="wZ5qxZXBgdQ6" outputId="cd1c9d75-790c-49b3-8040-3334b896d779"} + +```python +#Hierarchial Inheritance +# Parent class +class Animal: + def __init__(self, name): + self.name = name + + def speak(self): + print(f"{self.name} speaks") + +# Child class 1 inheriting from Animal +class Dog(Animal): + def bark(self): + print(f"{self.name} barks") + +# Child class 2 inheriting from Animal +class Cat(Animal): + def meow(self): + print(f"{self.name} meows") + +# Creating objects of the derived classes +dog = Dog("Buddy") +cat = Cat("Whiskers") + +# Accessing methods from the parent and child classes +dog.speak() # Accessing method from the Animal class +dog.bark() # Accessing method from the Dog class +cat.speak() # Accessing method from the Animal class +cat.meow() # Accessing method from the Cat class +``` + +::: {.output .stream .stdout} +Buddy speaks +Buddy barks +Whiskers speaks +Whiskers meows +::: +::: + +::: {.cell .code execution_count="9" colab="{\"base_uri\":\"https://localhost:8080/\"}" id="8nPMri12glO5" outputId="a3c93e8c-f10f-4cd7-c402-3500342c9e28"} + +```python +#Multiple Inheritance +# Parent class 1 +class Herbivore: + def eat_plants(self): + print("Eating plants") + +# Parent class 2 +class Carnivore: + def eat_meat(self): + print("Eating meat") + +# Child class inheriting from both Herbivore and Carnivore +class Omnivore(Herbivore, Carnivore): + def eat(self): + print("Eating everything") + +# Creating an object of the Omnivore class +omnivore = Omnivore() + +# Accessing methods from both parent classes +omnivore.eat_plants() # Accessing method from Herbivore +omnivore.eat_meat() # Accessing method from Carnivore +omnivore.eat() # Accessing method from Omnivore +``` + +::: {.output .stream .stdout} +Eating plants +Eating meat +Eating everything +::: +::: + +::: {.cell .markdown id="KSw-WMePgvCa"} +**Polymorphism** In object oriented Programming Python, Polymorphism +simply means having many forms +::: + +::: {.cell .code execution_count="10" colab="{\"base_uri\":\"https://localhost:8080/\"}" id="cIYtM2_Pg8Ja" outputId="e887848d-060a-4317-a805-cfb0ca4e187f"} + +```python +class Bird: + + def intro(self): + print("There are many types of birds.") + + def flight(self): + print("Most of the birds can fly but some cannot.") + +class sparrow(Bird): + + def flight(self): + print("Sparrows can fly.") + +class ostrich(Bird): + + def flight(self): + print("Ostriches cannot fly.") + +obj_bird = Bird() +obj_spr = sparrow() +obj_ost = ostrich() + +obj_bird.intro() +obj_bird.flight() + +obj_spr.intro() +obj_spr.flight() + +obj_ost.intro() +obj_ost.flight() +``` + +::: {.output .stream .stdout} +There are many types of birds. +Most of the birds can fly but some cannot. +There are many types of birds. +Sparrows can fly. +There are many types of birds. +Ostriches cannot fly. +::: +::: + +::: {.cell .markdown id="NzsPIifmg-FI"} +**Python Encapsulation** + +In Python object oriented programming, Encapsulation is one of the +fundamental concepts in object-oriented programming (OOP). It describes +the idea of wrapping data and the methods that work on data within one +unit. This puts restrictions on accessing variables and methods directly +and can prevent the accidental modification of data. To prevent +accidental change, an object's variable can only be changed by an +object's method. Those types of variables are known as private +variables. +::: + +::: {.cell .code execution_count="11" colab="{\"base_uri\":\"https://localhost:8080/\"}" id="etbhALMHhGb9" outputId="75f27d20-7cee-4c85-b722-b9beb5ffe2b8"} + +```python +class Car: + def __init__(self, make, model, year): + self._make = make # Encapsulated attribute with single underscore + self._model = model # Encapsulated attribute with single underscore + self._year = year # Encapsulated attribute with single underscore + self._odometer_reading = 0 # Encapsulated attribute with single underscore + + def get_make(self): + return self._make + + def get_model(self): + return self._model + + def get_year(self): + return self._year + + def get_odometer_reading(self): + return self._odometer_reading + + def update_odometer(self, mileage): + if mileage >= self._odometer_reading: + self._odometer_reading = mileage + else: + print("You can't roll back an odometer!") + + def increment_odometer(self, miles): + self._odometer_reading += miles + +# Creating an instance of the Car class +my_car = Car("Toyota", "Camry", 2021) + +# Accessing encapsulated attributes through methods +print("Make:", my_car.get_make()) +print("Model:", my_car.get_model()) +print("Year:", my_car.get_year()) + +# Modifying encapsulated attribute through method +my_car.update_odometer(100) +print("Odometer Reading:", my_car.get_odometer_reading()) + +# Incrementing odometer reading +my_car.increment_odometer(50) +print("Odometer Reading after increment:", my_car.get_odometer_reading()) +``` + +::: {.output .stream .stdout} +Make: Toyota +Model: Camry +Year: 2021 +Odometer Reading: 100 +Odometer Reading after increment: 150 +::: +::: + +::: {.cell .markdown id="hJkQ9Tn5hUEV"} +**Data Abstraction** It hides unnecessary code details from the user. +Also, when we do not want to give out sensitive parts of our code +implementation and this is where data abstraction came. +::: + +::: {.cell .code execution_count="12" colab="{\"base_uri\":\"https://localhost:8080/\"}" id="FoMRMWEEhc-Z" outputId="3a77fd0d-8116-4997-c35f-dfebfc786b72"} + +```python +from abc import ABC, abstractmethod + +# Abstract class defining the interface for a Shape +class Shape(ABC): + def __init__(self, name): + self.name = name + + @abstractmethod + def area(self): + pass + + @abstractmethod + def perimeter(self): + pass + +# Concrete class implementing the Shape interface for a Rectangle +class Rectangle(Shape): + def __init__(self, name, length, width): + super().__init__(name) + self.length = length + self.width = width + + def area(self): + return self.length * self.width + + def perimeter(self): + return 2 * (self.length + self.width) + +# Concrete class implementing the Shape interface for a Circle +class Circle(Shape): + def __init__(self, name, radius): + super().__init__(name) + self.radius = radius + + def area(self): + return 3.14 * self.radius * self.radius + + def perimeter(self): + return 2 * 3.14 * self.radius + +# Creating objects of the derived classes +rectangle = Rectangle("Rectangle", 5, 4) +circle = Circle("Circle", 3) + +# Accessing methods defined by the Shape interface +print(f"{rectangle.name}: Area = {rectangle.area()}, Perimeter = {rectangle.perimeter()}") +print(f"{circle.name}: Area = {circle.area()}, Perimeter = {circle.perimeter()}") +``` + +::: {.output .stream .stdout} +Rectangle: Area = 20, Perimeter = 18 +Circle: Area = 28.259999999999998, Perimeter = 18.84 +::: +::: From dd8db2ea7cca92d35f0398c82b8f021e3ed6ae2c Mon Sep 17 00:00:00 2001 From: AmrutaJayanti <142327526+AmrutaJayanti@users.noreply.github.com> Date: Sat, 18 May 2024 18:27:49 +0530 Subject: [PATCH 105/656] Update OOPs.md --- contrib/advanced-python/OOPs.md | 107 +++----------------------------- 1 file changed, 7 insertions(+), 100 deletions(-) diff --git a/contrib/advanced-python/OOPs.md b/contrib/advanced-python/OOPs.md index 7e355f24..d6d12560 100644 --- a/contrib/advanced-python/OOPs.md +++ b/contrib/advanced-python/OOPs.md @@ -1,20 +1,4 @@ ---- -jupyter: - colab: - kernelspec: - display_name: Python 3 - name: python3 - language_info: - name: python - nbformat: 4 - nbformat_minor: 0 ---- - -::: {.cell .markdown id="WpcHLEIgeEdz"} -**PYTHON OOP CONCEPTS** -::: -::: {.cell .markdown id="3j1W5YpNeEmS"} In Python object-oriented Programming (OOPs) is a programming paradigm that uses objects and classes in programming. It aims to implement real-world entities like inheritance, polymorphisms, encapsulation, etc. @@ -36,15 +20,15 @@ can access this data. 5. Inheritance in Python 6. Data Abstraction in Python - ::: -::: {.cell .markdown id="3KQXMPlVeEyT"} + + Python Class A class is a collection of objects. A class contains the blueprints or the prototype from which the objects are being created. It is a logical entity that contains some attributes and methods. -::: -::: {.cell .code execution_count="2" id="6CdZhHSMerae"} + + ```python #Simple Class in Python @@ -54,31 +38,25 @@ class Dog: ::: -::: {.cell .markdown id="y5t7jyHAfAHl"} + **Python Objects** In object oriented programming Python, The object is an entity that has a state and behavior associated with it. It may be any real-world object like a mouse, keyboard, chair, table, pen, etc. Integers, strings, floating-point numbers, even arrays, and dictionaries, are all objects. -::: -::: {.cell .code execution_count="3" id="u8Y0G-hse-lm"} ```python obj = Dog() ``` -::: -::: {.cell .markdown id="\_FtRAPlNfLkb"} **The Python **init** Method ** The **init** method is similar to constructors in C++ and Java. It is run as soon as an object of a class is instantiated. The method is useful to do any initialization you want to do with your object. -::: -::: {.cell .code execution_count="4" colab="{\"base_uri\":\"https://localhost:8080/\"}" id="aMmXUxLHfSz6" outputId="ed23bfbd-445a-4d02-ebba-c210471e0af2"} ```python class Dog: @@ -103,15 +81,7 @@ print("My name is {}".format(Rodger.name)) print("My name is {}".format(Tommy.name)) ``` -::: {.output .stream .stdout} -Rodger is a mammal -Tommy is also a mammal -My name is Rodger -My name is Tommy -::: -::: -::: {.cell .markdown id="YFMnh417fZI6"} **Inheritance** In Python object oriented Programming, Inheritance is the capability of @@ -129,10 +99,7 @@ Types of Inheritances: - Multiple Inheritance - Hierarchial Inheritance - ::: - -::: {.cell .code execution_count="6" colab="{\"base_uri\":\"https://localhost:8080/\"}" id="PeQoseXogE2f" outputId="9b3a069e-c6f0-41b0-b5f3-704aa0f95160"} - + ```python #Single Inheritance # Parent class @@ -165,13 +132,8 @@ dog.make_sound() cat.make_sound() ``` -::: {.output .stream .stdout} -Buddy says Woof -Whiskers says Meow -::: -::: -::: {.cell .code execution_count="7" colab="{\"base_uri\":\"https://localhost:8080/\"}" id="CKEMq39BgSu9" outputId="b252ac43-6116-4a88-9ac9-0475f54e63c0"} + ```python #Multilevel Inheritance @@ -202,14 +164,7 @@ german_shepherd.bark() # Accessing method from the Dog class german_shepherd.guard() # Accessing method from the GermanShepherd class ``` -::: {.output .stream .stdout} -Rocky speaks -Rocky barks -Rocky guards -::: -::: -::: {.cell .code execution_count="8" colab="{\"base_uri\":\"https://localhost:8080/\"}" id="wZ5qxZXBgdQ6" outputId="cd1c9d75-790c-49b3-8040-3334b896d779"} ```python #Hierarchial Inheritance @@ -242,15 +197,6 @@ cat.speak() # Accessing method from the Animal class cat.meow() # Accessing method from the Cat class ``` -::: {.output .stream .stdout} -Buddy speaks -Buddy barks -Whiskers speaks -Whiskers meows -::: -::: - -::: {.cell .code execution_count="9" colab="{\"base_uri\":\"https://localhost:8080/\"}" id="8nPMri12glO5" outputId="a3c93e8c-f10f-4cd7-c402-3500342c9e28"} ```python #Multiple Inheritance @@ -278,19 +224,9 @@ omnivore.eat_meat() # Accessing method from Carnivore omnivore.eat() # Accessing method from Omnivore ``` -::: {.output .stream .stdout} -Eating plants -Eating meat -Eating everything -::: -::: -::: {.cell .markdown id="KSw-WMePgvCa"} **Polymorphism** In object oriented Programming Python, Polymorphism simply means having many forms -::: - -::: {.cell .code execution_count="10" colab="{\"base_uri\":\"https://localhost:8080/\"}" id="cIYtM2_Pg8Ja" outputId="e887848d-060a-4317-a805-cfb0ca4e187f"} ```python class Bird: @@ -325,17 +261,7 @@ obj_ost.intro() obj_ost.flight() ``` -::: {.output .stream .stdout} -There are many types of birds. -Most of the birds can fly but some cannot. -There are many types of birds. -Sparrows can fly. -There are many types of birds. -Ostriches cannot fly. -::: -::: -::: {.cell .markdown id="NzsPIifmg-FI"} **Python Encapsulation** In Python object oriented programming, Encapsulation is one of the @@ -346,9 +272,7 @@ and can prevent the accidental modification of data. To prevent accidental change, an object's variable can only be changed by an object's method. Those types of variables are known as private variables. -::: -::: {.cell .code execution_count="11" colab="{\"base_uri\":\"https://localhost:8080/\"}" id="etbhALMHhGb9" outputId="75f27d20-7cee-4c85-b722-b9beb5ffe2b8"} ```python class Car: @@ -396,22 +320,10 @@ my_car.increment_odometer(50) print("Odometer Reading after increment:", my_car.get_odometer_reading()) ``` -::: {.output .stream .stdout} -Make: Toyota -Model: Camry -Year: 2021 -Odometer Reading: 100 -Odometer Reading after increment: 150 -::: -::: -::: {.cell .markdown id="hJkQ9Tn5hUEV"} **Data Abstraction** It hides unnecessary code details from the user. Also, when we do not want to give out sensitive parts of our code implementation and this is where data abstraction came. -::: - -::: {.cell .code execution_count="12" colab="{\"base_uri\":\"https://localhost:8080/\"}" id="FoMRMWEEhc-Z" outputId="3a77fd0d-8116-4997-c35f-dfebfc786b72"} ```python from abc import ABC, abstractmethod @@ -463,8 +375,3 @@ print(f"{rectangle.name}: Area = {rectangle.area()}, Perimeter = {rectangle.peri print(f"{circle.name}: Area = {circle.area()}, Perimeter = {circle.perimeter()}") ``` -::: {.output .stream .stdout} -Rectangle: Area = 20, Perimeter = 18 -Circle: Area = 28.259999999999998, Perimeter = 18.84 -::: -::: From c56bbb3d4733f0c6770a4bb5755ea03043778182 Mon Sep 17 00:00:00 2001 From: Aditi Bansal <142652964+Aditi22Bansal@users.noreply.github.com> Date: Sat, 18 May 2024 18:51:39 +0530 Subject: [PATCH 106/656] Update index.md --- contrib/mini-projects/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/mini-projects/index.md b/contrib/mini-projects/index.md index 82596a2f..0c76d72b 100644 --- a/contrib/mini-projects/index.md +++ b/contrib/mini-projects/index.md @@ -1,3 +1,4 @@ # List of sections - [Section title](filename.md) +- [Hangman Game Based on Movies](hangman_game.md) From 7375e91a255113141a9a6e729ceaa56e1898d5dd Mon Sep 17 00:00:00 2001 From: Yatharth Date: Sat, 18 May 2024 18:56:11 +0530 Subject: [PATCH 107/656] removed html tags, removed weird color at line 70 and 117 --- contrib/advanced-python/json-module.md | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/contrib/advanced-python/json-module.md b/contrib/advanced-python/json-module.md index 20a94081..47e1561b 100644 --- a/contrib/advanced-python/json-module.md +++ b/contrib/advanced-python/json-module.md @@ -2,7 +2,7 @@ ## What is JSON? -- JSON (JavaScript Object Notation) is a format for structuring data. +- [JSON]("https://www.json.org/json-en.html") (JavaScript Object Notation) is a format for structuring data. - JSON is a lightweight, text-based data interchange format that is completely language-independent. - Similar to XML, JSON is a format for structuring data commonly used by web applications to communicate with each other. @@ -14,7 +14,7 @@ age = 21 ``` -- When we use `age`, it gets replaced with `21`. However, age doesn't contain 21, it contains the address of the memory location where 21 is stored. +- When we use `age`, it gets replaced with `21`. However, *age doesn't contain 21, it contains the address of the memory location where 21 is stored*. - While this works locally, transferring this data, such as through an API, poses a challenge. Sending your computer’s entire memory with the addresses is impractical and insecure. This is where JSON comes to the rescue. @@ -67,7 +67,7 @@ print(students_dict['name']) - Ouput: ```json -{'id': '01', 'name': 'Yatharth', 'department': 'Computer Science Engineering'} +{"id": "01", "name": "Yatharth", "department": "Computer Science Engineering"} Yatharth ``` @@ -114,16 +114,15 @@ f.close() - Ouput ```json -{'id': '01', 'name': 'Yatharth', 'department': 'Computer Science Engineering'} -{'id': '02', 'name': 'Raj', 'department': 'Mechanical Engineering'} +{"id": "01", "name": "Yatharth", "department": "Computer Science Engineering"} +{"id": "02", "name": "Raj", "department": "Mechanical Engineering"} ``` - `json.load()`: Reads JSON data from a file object and deserializes it into a Python object. - `json.loads()`: Deserializes JSON data from a string into a Python object. -
-### Addtiotnal Context + +## Addtiotnal Context Relation between python data types and json data types is given in table below. -
| Python Object | JSON Object | |-----------------|-------------| @@ -288,7 +287,4 @@ write_json(y) } ``` -
-
- The Python json module simplifies the handling of JSON data, offering a bridge between Python data structures and JSON representations, vital for data exchange and storage in modern applications. \ No newline at end of file From bc5469772704fa51bae90feb259a2adbfcaa0563 Mon Sep 17 00:00:00 2001 From: Yatharth Date: Sat, 18 May 2024 18:57:26 +0530 Subject: [PATCH 108/656] line error 71 removed --- contrib/advanced-python/json-module.md | 1 - 1 file changed, 1 deletion(-) diff --git a/contrib/advanced-python/json-module.md b/contrib/advanced-python/json-module.md index 47e1561b..eed0393d 100644 --- a/contrib/advanced-python/json-module.md +++ b/contrib/advanced-python/json-module.md @@ -68,7 +68,6 @@ print(students_dict['name']) ```json {"id": "01", "name": "Yatharth", "department": "Computer Science Engineering"} -Yatharth ``` ## 2. Python load JSON file. From 6063f595bdfb82655580a13d0260d547b920cd1c Mon Sep 17 00:00:00 2001 From: Manmita Das <34617961+manmita@users.noreply.github.com> Date: Sat, 18 May 2024 19:03:18 +0530 Subject: [PATCH 109/656] Create recursion.md --- contrib/ds-algorithms/recursion.md | 107 +++++++++++++++++++++++++++++ 1 file changed, 107 insertions(+) create mode 100644 contrib/ds-algorithms/recursion.md diff --git a/contrib/ds-algorithms/recursion.md b/contrib/ds-algorithms/recursion.md new file mode 100644 index 00000000..7ab31364 --- /dev/null +++ b/contrib/ds-algorithms/recursion.md @@ -0,0 +1,107 @@ +# Introduction to Recursions + +When a function calls itself to solve smaller instances of the same problem until a specified condition is fulfilled is called recursion. It is used for tasks that can be divided into smaller sub-tasks. + +# How Recursion Works + +To solve a problem using recursion we must define: +- Base condition :- The condition under which recursion ends. +- Recursive case :- The part of function which calls itself to solve a smaller instance of problem. + +Steps of Recursion + +When a recursive function is called, the following sequence of events occurs: +- Function Call: The function is invoked with a specific argument. +- Base Condition Check: The function checks if the argument satisfies the base case. +- Recursive Call: If the base case is not met, the function performs some operations and makes a recursive call with a modified argument. +- Stack Management: Each recursive call is placed on the call stack. The stack keeps track of each function call, its argument, and the point to return to once the call completes. +- Unwinding the Stack: When the base case is eventually met, the function returns a value, and the stack starts unwinding, returning values to previous function calls until the initial call is resolved. + +# What is Stack Overflow in Recursion + +Stack overflow is an error that occurs when the call stack memory limit is exceeded. During execution of recursion calls they are simultaneously stored in a recursion stack waiting for the recursive function to be completed. Without a base case, the function would call itself indefinitely, leading to a stack overflow. + +# Example + +- Factorial of a Number + + The factorial of i natural numbers is nth integer multiplied by factorial of (i-1) numbers. The base case is if i=0 we return 1 as factorial of 0 is 1. + +```python +def factorial(i): + #base case + if i==0 : + return 1 + #recursive case + else : + return i * factorial(i-1) +i = 6 +print("Factorial of i is :", factorial(i)) # Output- Factorial of i is :720 +``` +# What is Backtracking + +Backtracking is a recursive algorithmic technique used to solve problems by exploring all possible solutions and discarding those that do not meet the problem's constraints. It is particularly useful for problems involving combinations, permutations, and finding paths in a grid. + +# How Backtracking Works + +- Incremental Solution Building: Solutions are built one step at a time. +- Feasibility Check: At each step, a check is made to see if the current partial solution is valid. +- Backtracking: If a partial solution is found to be invalid, the algorithm backtracks by removing the last added part of the solution and trying the next possibility. +- Exploration of All Possibilities: The process continues recursively, exploring all possible paths, until a solution is found or all possibilities are exhausted. + +# Example + +- Word Search + + Given a 2D grid of characters and a word, determine if the word exists in the grid. The word can be constructed from letters of sequentially adjacent cells, where "adjacent" cells are horizontally or vertically neighboring. The same letter cell may not be used more than once. + +Algorithm for Solving the Word Search Problem with Backtracking: +- Start at each cell: Attempt to find the word starting from each cell. +- Check all Directions: From each cell, try all four possible directions (up, down, left, right). +- Mark Visited Cells: Use a temporary marker to indicate cells that are part of the current path to avoid revisiting. +- Backtrack: If a path does not lead to a solution, backtrack by unmarking the visited cell and trying the next possibility. + +```python +def exist(board, word): + rows, cols = len(board), len(board[0]) + + def backtrack(r, c, suffix): + if not suffix: + return True + + if r < 0 or r >= rows or c < 0 or c >= cols or board[r][c] != suffix[0]: + return False + + # Mark the cell as visited by replacing its character with a placeholder + ret = False + board[r][c], temp = '#', board[r][c] + + # Explore the four possible directions + for row_offset, col_offset in [(0, 1), (1, 0), (0, -1), (-1, 0)]: + ret = backtrack(r + row_offset, c + col_offset, suffix[1:]) + if ret: + break + + # Restore the cell's original value + board[r][c] = temp + return ret + + for row in range(rows): + for col in range(cols): + if backtrack(row, col, word): + return True + + return False + +# Test case +board = [ + ['A','B','C','E'], + ['S','F','C','S'], + ['A','D','E','E'] +] +word = "ABCES" +print(exist(board, word)) # Output: True +``` + + + From 2c56f3ef278be8220a1a6a10499f21fc7e4be192 Mon Sep 17 00:00:00 2001 From: Manmita Das <34617961+manmita@users.noreply.github.com> Date: Sat, 18 May 2024 19:05:51 +0530 Subject: [PATCH 110/656] Update index.md --- contrib/ds-algorithms/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 5b521551..ab16ebe9 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -2,3 +2,4 @@ - [Section title](filename.md) - [Sorting Algorithms](sorting-algorithms.md) +- [Recursion and Backtracking](recursion.md) From acf7acdd8f347a8d762ee0fc912de5ea85d34447 Mon Sep 17 00:00:00 2001 From: Aditi Bansal <142652964+Aditi22Bansal@users.noreply.github.com> Date: Sat, 18 May 2024 19:10:19 +0530 Subject: [PATCH 111/656] Create hangman_game.md This Hangman game script is a simple Python program designed to let players guess movie titles. It starts by importing the random module to select a movie from a predefined list. The game displays the movie title as underscores and reveals correctly guessed letters. Players have six attempts to guess the entire title, entering one letter at a time. The script checks if the input is valid, updates the list of guessed letters, and adjusts the number of attempts based on the correctness of the guess. The game continues until the player either guesses the title correctly or runs out of attempts. Upon completion, it congratulates the player for a correct guess or reveals the movie title if the attempts are exhausted. The main execution block ensures the game runs only when the script is executed directly. --- contrib/mini-projects/hangman_game.md | 220 ++++++++++++++++++++++++++ 1 file changed, 220 insertions(+) create mode 100644 contrib/mini-projects/hangman_game.md diff --git a/contrib/mini-projects/hangman_game.md b/contrib/mini-projects/hangman_game.md new file mode 100644 index 00000000..d62db912 --- /dev/null +++ b/contrib/mini-projects/hangman_game.md @@ -0,0 +1,220 @@ +# Hangman - Movies Edition +The Hangman game script is a simple Python program designed to let players guess movie titles. It starts by importing the random module to select a movie from a predefined list. The game displays the movie title as underscores and reveals correctly guessed letters. Players have six attempts to guess the entire title, entering one letter at a time. The script checks if the input is valid, updates the list of guessed letters, and adjusts the number of attempts based on the correctness of the guess. The game continues until the player either guesses the title correctly or runs out of attempts. Upon completion, it congratulates the player for a correct guess or reveals the movie title if the attempts are exhausted. The main execution block ensures the game runs only when the script is executed directly.Below is first the code and then an explanation of the code and its components. + +## Code + +``` +import random + +def choose_movie(): + movies = ['avatar', 'titanic', 'inception', 'jurassicpark', 'thegodfather', 'forrestgump', 'interstellar', 'pulpfiction', 'shawshank'] + return random.choice(movies) + +def display_word(movie, guessed_letters): + display = "" + for letter in movie: + if letter in guessed_letters: + display += letter + " " + else: + display += "_ " + return display + +def hangman_movies(): + movie = choose_movie() + guessed_letters = [] + attempts = 6 + + print("Welcome to Hangman - Movies Edition!") + print("Try to guess the name of the movie. You have 6 attempts.") + + while attempts > 0: + print("\n" + display_word(movie, guessed_letters)) + guess = input("Guess a letter: ").lower() + + if len(guess) != 1 or not guess.isalpha(): + print("Please enter a single letter.") + continue + + if guess in guessed_letters: + print("You've already guessed that letter.") + continue + + guessed_letters.append(guess) + + if guess not in movie: + attempts -= 1 + print(f"Sorry, '{guess}' is not in the movie name. You have {attempts} attempts left.") + else: + print(f"Good guess! '{guess}' is in the movie name.") + + if "_" not in display_word(movie, guessed_letters): + print(f"\nCongratulations! You guessed the movie '{movie.capitalize()}' correctly!") + break + + if attempts == 0: + print(f"\nSorry, you ran out of attempts. The movie was '{movie.capitalize()}'.") + +if __name__ == "__main__": + hangman_movies() +``` + +## Code Explanation + +### Importing the Random Module + +```python + +import random + +``` + +The `random` module is imported to use the `choice` function, which will help in selecting a random movie from a predefined list. + +### Choosing a Movie + +```python + +def choose_movie(): + +movies = ['avatar', 'titanic', 'inception', 'jurassicpark', 'thegodfather', 'forrestgump', 'interstellar', 'pulpfiction', 'shawshank'] + +return random.choice(movies) + +``` + +The `choose_movie` function returns a random movie title from the `movies` list. + +### Displaying the Word + +```python + +def display_word(movie, guessed_letters): + +display = "" + +for letter in movie: + +if letter in guessed_letters: + +display += letter + " " + +else: + +display += "_ " + +return display + +``` + +The `display_word` function takes the movie title and a list of guessed letters as arguments. It constructs a string where correctly guessed letters are shown in their positions, and unknown letters are represented by underscores (`_`). + +### Hangman Game Logic + +```python + +def hangman_movies(): + +movie = choose_movie() + +guessed_letters = [] + +attempts = 6 + +print("Welcome to Hangman - Movies Edition!") + +print("Try to guess the name of the movie. You have 6 attempts.") + +while attempts > 0: + +print("\n" + display_word(movie, guessed_letters)) + +guess = input("Guess a letter: ").lower() + +if len(guess) != 1 or not guess.isalpha(): + +print("Please enter a single letter.") + +continue + +if guess in guessed_letters: + +print("You've already guessed that letter.") + +continue + +guessed_letters.append(guess) + +if guess not in movie: + +attempts -= 1 + +print(f"Sorry, '{guess}' is not in the movie name. You have {attempts} attempts left.") + +else: + +print(f"Good guess! '{guess}' is in the movie name.") + +if "_" not in display_word(movie, guessed_letters): + +print(f"\nCongratulations! You guessed the movie '{movie.capitalize()}' correctly!") + +break + +if attempts == 0: + +print(f"\nSorry, you ran out of attempts. The movie was '{movie.capitalize()}'.") + +``` + +The `hangman_movies` function manages the game's flow: + +1. It selects a random movie title using `choose_movie`. + +2. Initializes an empty list `guessed_letters` and sets the number of attempts to 6. + +3. Prints a welcome message and the initial game state. + +4. Enters a loop that continues until the player runs out of attempts or guesses the movie title. + +5. Displays the current state of the movie title with guessed letters revealed. + +6. Prompts the player to guess a letter. + +7. Validates the player's input: + +- Ensures it is a single alphabetic character. + +- Checks if the letter has already been guessed. + +8. Adds the guessed letter to `guessed_letters`. + +9. Updates the number of attempts if the guessed letter is not in the movie title. + +10. Congratulates the player if they guess the movie correctly. + +11. Informs the player of the correct movie title if they run out of attempts. + +### Main Execution Block + +```python + +if __name__ == "__main__": + +hangman_movies() + +``` +## Conclusion +This block ensures that the game runs only when the script is executed directly, not when it is imported as a module. + +## Output Screenshots: + +![image](https://github.com/Aditi22Bansal/learn-python/assets/142652964/a7af1f7e-c80e-4f83-b1f7-c7c5c72158b4) +![image](https://github.com/Aditi22Bansal/learn-python/assets/142652964/082e54dc-ce68-48fd-85da-3252d7629df8) + + + +## Conclusion + +This script provides a simple yet entertaining Hangman game focused on guessing movie titles. It demonstrates the use of functions, loops, conditionals, and user input handling in Python. + + From 391a0e591b6866c981e1e746473a2ad475b1315e Mon Sep 17 00:00:00 2001 From: Anshika Yadav <14anshika7yadav@gmail.com> Date: Sat, 18 May 2024 19:54:24 +0530 Subject: [PATCH 112/656] Closes #294 - Mini Project : Password Strength Checker --- contrib/mini-projects/index.md | 1 + .../password_strength_checker.md | 100 ++++++++++++++++++ 2 files changed, 101 insertions(+) create mode 100644 contrib/mini-projects/password_strength_checker.md diff --git a/contrib/mini-projects/index.md b/contrib/mini-projects/index.md index 82596a2f..71908f81 100644 --- a/contrib/mini-projects/index.md +++ b/contrib/mini-projects/index.md @@ -1,3 +1,4 @@ # List of sections - [Section title](filename.md) +- [Password strength checker](password_strength_checker.md) \ No newline at end of file diff --git a/contrib/mini-projects/password_strength_checker.md b/contrib/mini-projects/password_strength_checker.md new file mode 100644 index 00000000..ca65b8eb --- /dev/null +++ b/contrib/mini-projects/password_strength_checker.md @@ -0,0 +1,100 @@ +# about password strength + +> This code is a simple password strength checker. +It evaluates the strength of a user's password based on the presence of +uppercase letters, lowercase letters, digits, spaces, and special characters. + +### About the code: + +- The codebase is break down in two file `password_strength_checker.py` and `main.py`. + +`password_strength_checker.py` The function evaluates password strength based on character types (uppercase, lowercase, digits, spaces, special characters) and provides feedback on its security. +and `main.py` contains basic code. + +``` +import string + + +class password_checker: + def __init__(self, password): + self.password = password + + def check_password_strength(self): + """This function prompts the user to enter a password and then evaluates its strength.""" + + password_strength = 0 + upper_count = 0 + lower_count = 0 + num_count = 0 + space_count = 0 + specialcharacter_count = 0 + review = "" + + for char in list(password): + if char in string.ascii_uppercase: + upper_count += 1 + elif char in string.ascii_lowercase: + lower_count += 1 + elif char in string.digits: + num_count += 1 + elif char == " ": + space_count += 1 + else: + specialcharacter_count += 1 + + if upper_count >= 1: + password_strength += 1 + if lower_count >= 1: + password_strength += 1 + if num_count >= 1: + password_strength += 1 + if space_count >= 1: + password_strength += 1 + if specialcharacter_count >= 1: + password_strength += 1 + + if password_strength == 1: + review = "That's a very easy password, Not good for use" + elif password_strength == 2: + review = ( + "That's a weak password, You should change it to some strong password." + ) + elif password_strength == 3: + review = "Your password is just okay, you may change it." + elif password_strength == 4: + review = "Your password is hard to guess." + elif password_strength == 5: + review = "Its the strong password, No one can guess this password " + + about_password = { + "uppercase_letters ": upper_count, + "lowercase_letters": lower_count, + "space_count": space_count, + "specialcharacter_count": specialcharacter_count, + "password_strength": password_strength, + "about_password_strength": review, + } + print(about_password) + + def check_password(): + """This function prompts the user to decide if they want to check their password strength.""" + + choice = input("Do you want to check your password's strength? (Y/N): ") + if choice.upper() == "Y": + return True + elif choice.upper() == "N": + return False + else: + print("Invalid input. Please enter 'Y' for Yes or 'N' for No.") + return password_checker.check_password() + +``` +### Here's the implementation of 'main.py' +``` +import password_checker from password_strength_checker + +while password_checker.check_password(): + password = input("Enter your password: ") + p = password_checker(password) + p.check_password_strength() +``` \ No newline at end of file From 07d7643c6a0283db1b03c69e518a006a9d646f99 Mon Sep 17 00:00:00 2001 From: AmrutaJayanti <142327526+AmrutaJayanti@users.noreply.github.com> Date: Sat, 18 May 2024 20:01:02 +0530 Subject: [PATCH 113/656] Update OOPs.md --- contrib/advanced-python/OOPs.md | 22 ++++++++++++---------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/contrib/advanced-python/OOPs.md b/contrib/advanced-python/OOPs.md index d6d12560..0dbd855d 100644 --- a/contrib/advanced-python/OOPs.md +++ b/contrib/advanced-python/OOPs.md @@ -36,7 +36,7 @@ class Dog: pass ``` -::: + **Python Objects** In object oriented programming Python, The object is @@ -49,9 +49,9 @@ dictionaries, are all objects. ```python obj = Dog() ``` +This creates an instance for class Dog - -**The Python **init** Method ** +**The Python **init** Method** The **init** method is similar to constructors in C++ and Java. It is run as soon as an object of a class is instantiated. The method is @@ -80,7 +80,7 @@ print("Tommy is also a {}".format(Tommy.__class__.attr1)) print("My name is {}".format(Rodger.name)) print("My name is {}".format(Tommy.name)) ``` - +In the above mentioned code, init method is used to initialize the name. **Inheritance** @@ -131,7 +131,7 @@ cat = Cat("Whiskers") dog.make_sound() cat.make_sound() ``` - +The above code depicts the Single Inheritance, in case of single inheritance there's only a single base class and a derived class. Here, Dog and Cat are the derived classes with Animal as the parent class. They can access the methods of the base class or derive their own methods. @@ -163,7 +163,7 @@ german_shepherd.speak() # Accessing method from the Animal class german_shepherd.bark() # Accessing method from the Dog class german_shepherd.guard() # Accessing method from the GermanShepherd class ``` - +Multilevel inheritance is a concept in object-oriented programming where a class inherits properties and behaviors from another class, which itself may inherit from another class. In other words, it involves a chain of inheritance where a subclass inherits from a superclass, and that subclass can then become a superclass for another subclass.Its similar to GrandFather ,Father and Son .In the above code,Animal class is the superclass, Dog is derived from Animal and Dog is the parent of GermanShepherd. GermenShepherd is the child class of Dog. GermenShepherd can access methods of both Animal and Dog. ```python @@ -196,7 +196,7 @@ dog.bark() # Accessing method from the Dog class cat.speak() # Accessing method from the Animal class cat.meow() # Accessing method from the Cat class ``` - +Hierarchical inheritance is a type of inheritance in object-oriented programming where one class serves as a superclass for multiple subclasses. In this inheritance model, each subclass inherits properties and behaviors from the same superclass, creating a hierarchical tree-like structure. ```python #Multiple Inheritance @@ -223,7 +223,7 @@ omnivore.eat_plants() # Accessing method from Herbivore omnivore.eat_meat() # Accessing method from Carnivore omnivore.eat() # Accessing method from Omnivore ``` - +Multiple inheritance is a concept in object-oriented programming where a class can inherit properties and behaviors from more than one parent class. This means that a subclass can have multiple immediate parent classes, allowing it to inherit features from each of them. **Polymorphism** In object oriented Programming Python, Polymorphism simply means having many forms @@ -260,7 +260,7 @@ obj_spr.flight() obj_ost.intro() obj_ost.flight() ``` - +Poly stands for 'many' and morphism for 'forms'. In the above code, method flight() has many forms. **Python Encapsulation** @@ -374,4 +374,6 @@ circle = Circle("Circle", 3) print(f"{rectangle.name}: Area = {rectangle.area()}, Perimeter = {rectangle.perimeter()}") print(f"{circle.name}: Area = {circle.area()}, Perimeter = {circle.perimeter()}") ``` - +To implement Data Abstraction , we have to import abc . ABC stands for Abstract Base Class . All those classes which want to implement data abstraction have to inherit from ABC. +@abstractmethod is a decorator provided by the abc module, which stands for "abstract method". It's used to define abstract methods within abstract base classes (ABCs). An abstract method is a method declared in a class, but it does not contain an implementation. Instead, it serves as a placeholder, and its concrete implementation must be provided by subclasses. +Abstract methods can be implemented by the derived classes. From 2af70910214b943fa5df5a1f542389dec104ac43 Mon Sep 17 00:00:00 2001 From: AritraCh2005 <153842880+AritraCh2005@users.noreply.github.com> Date: Sat, 18 May 2024 20:28:17 +0530 Subject: [PATCH 114/656] Update index.md --- contrib/numpy/index.md | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index db6f0eb5..fb824850 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -1,12 +1,5 @@ # List of sections -* [Basic Mathematics](#BasicMaths) - * [What is a Matrix?](#Matrix-Intro) - * [Scalars and Vectors](#Scalars-Vectors) - * [Arrays in Python](#Arrays) - * [What is a Tensor?](#Tensors) - * [Addition and Subtraction in Matrices](#Arithmetic) - * [Transpose of Matrices & Vectors](#Transpose) - * [Dot Product](#DotProduct) - * [Dot Product of Matrices](#DotMatrices) +* [Basic Mathematics](basic_math.md) + From 2604ecd61a9911d1d73e764046ee09c78ab5fb05 Mon Sep 17 00:00:00 2001 From: rohit Date: Sat, 18 May 2024 21:51:53 +0530 Subject: [PATCH 115/656] Added TensorFlow,md File --- contrib/machine-learning/index.md | 2 +- contrib/machine-learning/tensorFlow.md | 64 ++++++++++++++++++++++++++ 2 files changed, 65 insertions(+), 1 deletion(-) create mode 100644 contrib/machine-learning/tensorFlow.md diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 82596a2f..1abe7077 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -1,3 +1,3 @@ # List of sections -- [Section title](filename.md) +- [TensorFlow.md](learn-python\contrib\machine-learning\tensorFlow.md) diff --git a/contrib/machine-learning/tensorFlow.md b/contrib/machine-learning/tensorFlow.md new file mode 100644 index 00000000..1d9357d0 --- /dev/null +++ b/contrib/machine-learning/tensorFlow.md @@ -0,0 +1,64 @@ +# TensorFlow + +Developed by the Google Brain team, TensorFlow is an open-source library that provides a comprehensive ecosystem for building and deploying machine learning models. It supports deep learning and neural networks and offers tools for both beginners and experts. + +## Key Features + +- **Flexible and comprehensive ecosystem** +- **Scalable for both production and research** +- **Supports CPUs, GPUs, and TPUs** + +## Basic Example: Linear Regression + +Let's start with a simple linear regression example in TensorFlow. + +```python +import tensorflow as tf +import numpy as np +import matplotlib.pyplot as plt + +# Generate synthetic data +X = np.array([1, 2, 3, 4, 5], dtype=np.float32) +Y = np.array([2, 4, 6, 8, 10], dtype=np.float32) + +# Define the model +model = tf.keras.Sequential([ + tf.keras.layers.Dense(units=1, input_shape=[1]) +]) + +# Compile the model +model.compile(optimizer='sgd', loss='mean_squared_error') + +# Train the model +history = model.fit(X, Y, epochs=500) + +# Predict +predictions = model.predict(X) + +# Plot the results +plt.plot(X, Y, 'ro', label='Original data') +plt.plot(X, predictions, 'b-', label='Fitted line') +plt.legend() +plt.show() +``` + +In this example: + +1. We define a simple dataset with a linear relationship. +2. We build a sequential model with one dense layer (linear regression). +3. We compile the model with stochastic gradient descent (SGD) optimizer and mean squared error loss. +4. We train the model for 500 epochs and then plot the original data and the fitted line. + +## When to Use TensorFlow + +TensorFlow is a great choice if you: + +- **Need to deploy machine learning models in production:** TensorFlow’s robust deployment options, including TensorFlow Serving, TensorFlow Lite, and TensorFlow.js, make it ideal for production environments. +- **Work on large-scale deep learning projects:** TensorFlow’s comprehensive ecosystem supports distributed training and has tools like TensorBoard for visualization. +- **Require high performance and scalability:** TensorFlow is optimized for performance and can leverage GPUs and TPUs for accelerated computing. +- **Want extensive support and documentation:** TensorFlow has a large community and extensive documentation, which can be very helpful for both beginners and advanced users. + +## Example Use Cases + +- Building and deploying complex neural networks for image recognition, natural language processing, or recommendation systems. +- Developing models that need to be run on mobile or embedded devices. \ No newline at end of file From ba9abd57a77ccfeab63e89f28720a97fb13980ae Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 18 May 2024 22:11:38 +0530 Subject: [PATCH 116/656] Add files via upload --- contrib/pandas/Titanic.csv | 1310 ++++++++++++++++++++++++++++++++++++ 1 file changed, 1310 insertions(+) create mode 100644 contrib/pandas/Titanic.csv diff --git a/contrib/pandas/Titanic.csv b/contrib/pandas/Titanic.csv new file mode 100644 index 00000000..f8d49dcf --- /dev/null +++ b/contrib/pandas/Titanic.csv @@ -0,0 +1,1310 @@ +"pclass","survived","name","sex","age","sibsp","parch","ticket","fare","cabin","embarked","boat","body","home.dest" +1,1,"Allen, Miss. Elisabeth Walton","female",29,0,0,"24160",211.3375,"B5","S","2",,"St Louis, MO" +1,1,"Allison, Master. Hudson Trevor","male",0.92,1,2,"113781",151.5500,"C22 C26","S","11",,"Montreal, PQ / Chesterville, ON" +1,0,"Allison, Miss. Helen Loraine","female",2,1,2,"113781",151.5500,"C22 C26","S",,,"Montreal, PQ / Chesterville, ON" +1,0,"Allison, Mr. Hudson Joshua Creighton","male",30,1,2,"113781",151.5500,"C22 C26","S",,"135","Montreal, PQ / Chesterville, ON" +1,0,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)","female",25,1,2,"113781",151.5500,"C22 C26","S",,,"Montreal, PQ / Chesterville, ON" +1,1,"Anderson, Mr. Harry","male",48,0,0,"19952",26.5500,"E12","S","3",,"New York, NY" +1,1,"Andrews, Miss. 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Hileni","female",14.5,1,0,"2665",14.4542,,"C",,"328", +3,0,"Zabour, Miss. Thamine","female",,1,0,"2665",14.4542,,"C",,, +3,0,"Zakarian, Mr. Mapriededer","male",26.5,0,0,"2656",7.2250,,"C",,"304", +3,0,"Zakarian, Mr. Ortin","male",27,0,0,"2670",7.2250,,"C",,, +3,0,"Zimmerman, Mr. Leo","male",29,0,0,"315082",7.8750,,"S",,, From d1e1fc5c9d637395e32235e45891698478929700 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 18 May 2024 22:37:38 +0530 Subject: [PATCH 117/656] Add files via upload --- .../Importing_and_Exporting_Data_in_Pandas.md | 291 ++++++++++++++++++ 1 file changed, 291 insertions(+) create mode 100644 contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md diff --git a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md new file mode 100644 index 00000000..a6949d28 --- /dev/null +++ b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md @@ -0,0 +1,291 @@ +# Importing_and_Exporting_Data_in_Pandas + +>Created by Krishna Kaushik + +- **Now we're able to create `Series` and `DataFrames` in pandas, but we usually do not do this , in practice we import the data which is in the form of .csv (Comma Seperated Values) , a spreadsheet file or something similar.** + +- *Good news is that pandas allows for easy importing of data like this through functions such as ``pd.read_csv()`` and ``pd.read_excel()`` for Microsoft Excel files.* + +## 1. Importing from a Google sheet to a pandas dataframe + +*Let's say that you wanted to get the information from Google Sheet document into a pandas DataFrame.*. + +*You could export it as a .csv file and then import it using ``pd.read_csv()``.* + +*In this case, the exported .csv file is called `Titanic.csv`* + + +```python +## Importing Titanic Data set +import pandas as pd + +titanic_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Titanic.csv") +titanic_df +``` + + + + +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest
011Allen, Miss. Elisabeth Waltonfemale29.000024160211.3375B5S2NaNSt Louis, MO
111Allison, Master. Hudson Trevormale0.9212113781151.5500C22 C26S11NaNMontreal, PQ / Chesterville, ON
210Allison, Miss. Helen Lorainefemale2.0012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
310Allison, Mr. Hudson Joshua Creightonmale30.0012113781151.5500C22 C26SNaN135.0Montreal, PQ / Chesterville, ON
410Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.0012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
.............................................
130430Zabour, Miss. Hilenifemale14.5010266514.4542NaNCNaN328.0NaN
130530Zabour, Miss. ThaminefemaleNaN10266514.4542NaNCNaNNaNNaN
130630Zakarian, Mr. Mapriededermale26.500026567.2250NaNCNaN304.0NaN
130730Zakarian, Mr. Ortinmale27.000026707.2250NaNCNaNNaNNaN
130830Zimmerman, Mr. Leomale29.00003150827.8750NaNSNaNNaNNaN
+

1309 rows × 14 columns

+
+ + + +The dataset I am using here for your reference is taken from the same repository i.e ``learn-python`` (https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Titanic.csv) I uploaded it you can use it from there. + +**Now we've got the same data from the Google Spreadsheet , but now available as ``pandas DataFrame`` which means we can now apply all pandas functionality over it.** + +#### Note: The quiet important thing i am telling is that ``pd.read_csv()`` takes the location of the file (which is in your current working directory) or the hyperlink of the dataset from the other source. + +#### But if you want to import the data from Github you can't directly use its link , you have to first convert it to raw by clicking on the raw button present in the repo . + +#### Also you can't use the data directly from `Kaggle` you have to use ``kaggle API`` + +## 2. The Anatomy of DataFrame + +**Different functions use different labels for different things, and can get a little confusing.** + +- Rows are refer as ``axis=0`` +- columns are refer as ``axis=1`` + +## 3. Exporting Data + +**OK, so after you've made a few changes to your data, you might want to export it and save it so someone else can access the changes.** + +**pandas allows you to export ``DataFrame's`` to ``.csv`` format using ``.to_csv()``, or to a spreadsheet format using .to_excel().** + +### Exporting a dataframe to a CSV + +**We haven't made any changes yet to the ``titanic_df`` DataFrame but let's try to export it.** + + +```python +#Export the titanic_df DataFrame to csv +titanic_df.to_csv("exported_titanic.csv") +``` + +Running this will save a file called ``exported_titanic.csv`` to the current folder. From bf4dcff08b1c47b0f15a63634879390156d2f995 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 18 May 2024 22:39:54 +0530 Subject: [PATCH 118/656] Update index.md --- contrib/pandas/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index f2a3fa72..84871dd8 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -2,3 +2,4 @@ - [Pandas Series Vs NumPy ndarray](pandas_series_vs_numpy_ndarray.md) - [Pandas Introduction and Dataframes in Pandas](Introduction_to_Pandas_Library_and_DataFrames.md) +- [Importing and Exportin data in pandas](Importing_and_Exporting_Data_in_Pandas.md) From 849f5abc6e072a7f96aec848fb8d0c5658518be8 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 18 May 2024 22:40:30 +0530 Subject: [PATCH 119/656] Update Importing_and_Exporting_Data_in_Pandas.md --- .../Importing_and_Exporting_Data_in_Pandas.md | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md index a6949d28..4d0ffadd 100644 --- a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md +++ b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md @@ -22,24 +22,6 @@ import pandas as pd titanic_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Titanic.csv") titanic_df ``` - - - - -
- From be8993b6164d12fab5d3ec6e94172b8da5deadef Mon Sep 17 00:00:00 2001 From: Vrisha Shah <74671946+Vrisha213@users.noreply.github.com> Date: Sun, 19 May 2024 09:28:58 +0530 Subject: [PATCH 120/656] Create support-vector-machine.md added conetnt on SVM --- .../support-vector-machine.md | 52 +++++++++++++++++++ 1 file changed, 52 insertions(+) create mode 100644 contrib/machine-learning/support-vector-machine.md diff --git a/contrib/machine-learning/support-vector-machine.md b/contrib/machine-learning/support-vector-machine.md new file mode 100644 index 00000000..90ee9223 --- /dev/null +++ b/contrib/machine-learning/support-vector-machine.md @@ -0,0 +1,52 @@ +Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning. +SVM can be of two types - +1. Linear SVM: Linear SVM is used for linearly separable data, which means if a dataset can be classified into two classes by using a single straight line, then such data is termed as linearly separable data, and classifier is used called as Linear SVM classifier. +2. Non-linear SVM: Non-Linear SVM is used for non-linearly separated data, which means if a dataset cannot be classified by using a straight line, then such data is termed as non-linear data and classifier used is called as Non-linear SVM classifier. + +Working of SVM - The goal of SVM is to find a hyperplane that separates the data points into different classes. A hyperplane is a line in 2D space, a plane in 3D space, or a higher-dimensional surface in n-dimensional space. The hyperplane is chosen in such a way that it maximizes the margin, which is the distance between the hyperplane and the closest data points of each class. The closest data points are called the support vectors. +The distance between the hyperplane and a data point "x" can be calculated using the formula − +distance = (w . x + b) / ||w|| +where "w" is the weight vector, "b" is the bias term, and "||w||" is the Euclidean norm of the weight vector. The weight vector "w" is perpendicular to the hyperplane and determines its orientation, while the bias term "b" determines its position. +The optimal hyperplane is found by solving an optimization problem, which is to maximize the margin subject to the constraint that all data points are correctly classified. In other words, we want to find the hyperplane that maximizes the margin between the two classes while ensuring that no data point is misclassified. This is a convex optimization problem that can be solved using quadratic programming. If the data points are not linearly separable, we can use a technique called kernel trick to map the data points into a higher-dimensional space where they become separable. The kernel function computes the inner product between the mapped data points without computing the mapping itself. This allows us to work with the data points in the higherdimensional space without incurring the computational cost of mapping them. + +1. Hyperplane: +There can be multiple lines/decision boundaries to segregate the classes in n-dimensional space, but we need to find out the best decision boundary that helps to classify the data points. This best boundary is known as the hyperplane of SVM. +The dimensions of the hyperplane depend on the features present in the dataset, which means if there are 2 features, then hyperplane will be a straight line. And if there are 3 features, then hyperplane will be a 2-dimension plane. We always create a hyperplane that has a maximum margin, which means the maximum distance between the data points. +2. Support Vectors: +The data points or vectors that are the closest to the hyperplane and which affect the position of the hyperplane are termed as Support Vector. Since these vectors support the hyperplane, hence called a Support vector. +3. Margin: +It may be defined as the gap between two lines on the closet data points of different classes. It can be calculated as the perpendicular distance from the line to the support vectors. Large margin is considered as a good margin and small margin is considered as a bad margin. + +We will use the famous Iris dataset, which contains the sepal length, sepal width, petal length, and petal width of three species of iris flowers: Iris setosa, Iris versicolor, and Iris virginica. The goal is to classify the flowers into their respective species based on these four features. We load the iris dataset using load_iris and split the data into training and testing sets using train_test_split. We use a test size of 0.2, which means that 20% of the data will be used for testing and 80% for training. We set the random state to 42 to ensure reproducibility of the results. + +# Implemetation of SVM in Python + +from sklearn.datasets import load_iris +from sklearn.model_selection import train_test_split +from sklearn.svm import SVC +from sklearn.metrics import accuracy_score + +# load the iris dataset +iris = load_iris() + +# split the data into training and testing sets +X_train, X_test, y_train, y_test = train_test_split(iris.data, +iris.target, test_size=0.2, random_state=42) + +# create an SVM classifier with a linear kernel +svm = SVC(kernel='linear') + +# train the SVM classifier on the training set +svm.fit(X_train, y_train) + +# make predictions on the testing set +y_pred = svm.predict(X_test) + +# calculate the accuracy of the classifier +accuracy = accuracy_score(y_test, y_pred) +print("Accuracy:", accuracy) + +# Output +Accuracy: 1 + + From 3386390f725b34ba1d52e55ccc150c18abbf24b3 Mon Sep 17 00:00:00 2001 From: Vrisha Shah <74671946+Vrisha213@users.noreply.github.com> Date: Sun, 19 May 2024 09:40:26 +0530 Subject: [PATCH 121/656] Update support-vector-machine.md Added content on SVM under machine learning From 4be728eb4764637fa8072aeebcaa9965e619d578 Mon Sep 17 00:00:00 2001 From: Ananyagra24 <155280507+Ananyagra24@users.noreply.github.com> Date: Sun, 19 May 2024 09:44:37 +0530 Subject: [PATCH 122/656] Update dice_roller.md --- contrib/mini-projects/dice_roller.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/contrib/mini-projects/dice_roller.md b/contrib/mini-projects/dice_roller.md index c0839343..6e1b793b 100644 --- a/contrib/mini-projects/dice_roller.md +++ b/contrib/mini-projects/dice_roller.md @@ -1,8 +1,15 @@ +Dice Roller +
+The aim of this project is to replicate a dice and generate a random number from the numbers 1 to 6.

+For this first we will import the random library which will help make random choices. ``` import random def dice(): dice_no = random.choice([1,2,3,4,5,6]) return "You got " + str(dice_no) +``` +The above snippet of code defines a function called "dice( )" which makes the random choice and returns the number that is generated. +``` def roll_dice(): print("Hey Guys, you will now roll a single dice using Python!") while True: @@ -17,3 +24,6 @@ def roll_dice(): print("Thanks for rolling the dice.") roll_dice() ``` +The above code defines a function called "roll_dice( )" which interacts with the user.
+It prompts the user to give an input and if the input is k,the code proceeds further to generate a random number or gives the message of invalid input and asks the user to try again.
+After the dice has been rolled once, the function asks the user whether they want a reroll in the form of a yes or no question.The dice is rolled again if the user gives 'yes' as an answer and exits the code if the user replies with anything other than yes. From 26053d5382c4f2e26160bba7ea1385b92c228c5c Mon Sep 17 00:00:00 2001 From: Eshparsi Date: Sun, 19 May 2024 12:08:10 +0530 Subject: [PATCH 123/656] graph --- contrib/Data-Structure-Graphs/graph.md | 220 +++++++++++++++++++++++++ contrib/Data-Structure-Graphs/index.md | 3 + 2 files changed, 223 insertions(+) create mode 100644 contrib/Data-Structure-Graphs/graph.md create mode 100644 contrib/Data-Structure-Graphs/index.md diff --git a/contrib/Data-Structure-Graphs/graph.md b/contrib/Data-Structure-Graphs/graph.md new file mode 100644 index 00000000..b9bd709d --- /dev/null +++ b/contrib/Data-Structure-Graphs/graph.md @@ -0,0 +1,220 @@ +# Graph Data Stucture + +Graph is a non-linear data structure consisting of vertices and edges. It is a powerful tool for representing and analyzing complex relationships between objects or entities. + +## Components of a Graph: + +**Vertices:** Vertices are the fundamental units of the graph. Sometimes, vertices are also known as vertex or nodes. Every node/vertex can be labeled or unlabeled. +**Edges:** Edges are drawn or used to connect two nodes of the graph. It can be ordered pair of nodes in a directed graph. Edges can connect any two nodes in any possible way. There are no rules. very edge can be labelled/unlabelled. + +## Basic Operations on Graphs: +- Insertion of Nodes/Edges in the graph +- Deletion of Nodes/Edges in the graph +- Searching on Graphs +- Traversal of Graphs + +## Types of Graph ## + + +**1.Undirected Graph:** In an undirected graph, edges have no direction, and they represent symmetric relationships between nodes. If there is an edge between node A and node B, you can travel from A to B and from B to A. + +**2. Directed Graph (Digraph):** In a directed graph, edges have a direction, indicating a one-way relationship between nodes. If there is an edge from node A to node B, you can travel from A to B but not necessarily from B to A. + +**3. Weighted Graph:** In a weighted graph, edges have associated weights or costs. These weights can represent various attributes such as distance, cost, or capacity. Weighted graphs are commonly used in applications like route planning or network optimization. + +**4. Cyclic Graph:** A cyclic graph contains at least one cycle, which is a path that starts and ends at the same node. In other words, you can traverse the graph and return to a previously visited node by following the edges. + +**5. Acyclic Graph:** An acyclic graph, as the name suggests, does not contain any cycles. This type of graph is often used in scenarios where a cycle would be nonsensical or undesirable, such as representing dependencies between tasks or events. + +**6. Tree:** A tree is a special type of acyclic graph where each node has a unique parent except for the root node, which has no parent. Trees have a hierarchical structure and are frequently used in data structures like binary trees or decision trees. + +## Representation of Graphs:## +There are two ways to store a graph: + +**1. Adjacency Matrix** +In this method, the graph is stored in the form of the 2D matrix where rows and columns denote vertices. Each entry in the matrix represents the weight of the edge between those vertices. + +```python +def create_adjacency_matrix(graph): + num_vertices = len(graph) + + adj_matrix = [[0] * num_vertices for _ in range(num_vertices)] + + for i in range(num_vertices): + for j in range(num_vertices): + if graph[i][j] == 1: + adj_matrix[i][j] = 1 + adj_matrix[j][i] = 1 + + return adj_matrix + + +graph = [ + [0, 1, 0, 0], + [1, 0, 1, 0], + [0, 1, 0, 1], + [0, 0, 1, 0] +] + +adj_matrix = create_adjacency_matrix(graph) + +for row in adj_matrix: + print(' '.join(map(str, row))) + +``` + +**2. Adjacency List** +This graph is represented as a collection of linked lists. There is an array of pointer which points to the edges connected to that vertex. + +```python +def create_adjacency_list(edges, num_vertices): + adj_list = [[] for _ in range(num_vertices)] + + for u, v in edges: + adj_list[u].append(v) + adj_list[v].append(u) + + return adj_list + +if __name__ == "__main__": + num_vertices = 4 + edges = [(0, 1), (0, 2), (1, 2), (2, 3), (3, 1)] + + adj_list = create_adjacency_list(edges, num_vertices) + + for i in range(num_vertices): + print(f"{i} -> {' '.join(map(str, adj_list[i]))}") +``` +`Output +0 -> 1 2 +1 -> 0 2 3 +2 -> 0 1 3 +3 -> 2 1 ` + + + +# Traversal Techniques # + +## Breadth First Search (BFS) ## +- It is a graph traversal algorithm that explores all the vertices in a graph at the current depth before moving on to the vertices at the next depth level. +- It starts at a specified vertex and visits all its neighbors before moving on to the next level of neighbors. +BFS is commonly used in algorithms for pathfinding, connected components, and shortest path problems in graphs. + +**Steps of BFS algorithms** + + +- **Step1:** Initially queue and visited arrays are empty. +- **Step2:** Push node 0 into queue and mark it visited. +- **Step 3:** Remove node 0 from the front of queue and visit the unvisited neighbours and push them into queue. +- **Step 4:** Remove node 1 from the front of queue and visit the unvisited neighbours and push them into queue. +- **Step 5:** Remove node 2 from the front of queue and visit the unvisited neighbours and push them into queue. +- **Step 6:** Remove node 3 from the front of queue and visit the unvisited neighbours and push them into queue. +- **Steps 7:** Remove node 4 from the front of queue and visit the unvisited neighbours and push them into queue. + +```python + +from collections import deque + +def bfs(adjList, startNode, visited): + q = deque() + + visited[startNode] = True + q.append(startNode) + + while q: + currentNode = q.popleft() + print(currentNode, end=" ") + + for neighbor in adjList[currentNode]: + if not visited[neighbor]: + visited[neighbor] = True + q.append(neighbor) + +def addEdge(adjList, u, v): + adjList[u].append(v) + +def main(): + vertices = 5 + + adjList = [[] for _ in range(vertices)] + + addEdge(adjList, 0, 1) + addEdge(adjList, 0, 2) + addEdge(adjList, 1, 3) + addEdge(adjList, 1, 4) + addEdge(adjList, 2, 4) + + visited = [False] * vertices + + print("Breadth First Traversal", end=" ") + bfs(adjList, 0, visited) + +if __name__ == "__main__": #Output : Breadth First Traversal 0 1 2 3 4 + main() + +``` + +**Time Complexity:** `O(V+E)`, where V is the number of nodes and E is the number of edges. +**Auxiliary Space:** `O(V)` + + +## Depth-first search ## + +Depth-first search is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root node (selecting some arbitrary node as the root node in the case of a graph) and explores as far as possible along each branch before backtracking. + +**Steps of DFS algorithms** +- **Step1:** Initially stack and visited arrays are empty. +- **Step 2:** Visit 0 and put its adjacent nodes which are not visited yet into the stack. +- **Step 3:** Now, Node 1 at the top of the stack, so visit node 1 and pop it from the stack and put all of its adjacent nodes which are not visited in the stack. +- **Step 4:** Now, Node 2 at the top of the stack, so visit node 2 and pop it from the stack and put all of its adjacent nodes which are not visited (i.e, 3, 4) in the stack. +- **Step 5:** Now, Node 4 at the top of the stack, so visit node 4 and pop it from the stack and put all of its adjacent nodes which are not visited in the stack. +- **Step 6:** Now, Node 3 at the top of the stack, so visit node 3 and pop it from the stack and put all of its adjacent nodes which are not visited in the stack. + + + +```python +from collections import defaultdict + +class Graph: + + def __init__(self): + + self.graph = defaultdict(list) + + def addEdge(self, u, v): + self.graph[u].append(v) + + def DFSUtil(self, v, visited): + + visited.add(v) + print(v, end=' ') + + for neighbour in self.graph[v]: + if neighbour not in visited: + self.DFSUtil(neighbour, visited) + + def DFS(self, v): + + visited = set() + + self.DFSUtil(v, visited) + +if __name__ == "__main__": + g = Graph() + g.addEdge(0, 1) + g.addEdge(0, 2) + g.addEdge(1, 2) + g.addEdge(2, 0) + g.addEdge(2, 3) + g.addEdge(3, 3) + + print("Depth First Traversal (starting from vertex 2): ",g.DFS(2)) #Output: Depth First Traversal (starting from vertex 2): 2 0 1 3 + +``` + +**Time complexity:** `O(V + E)`, where V is the number of vertices and E is the number of edges in the graph. +**Auxiliary Space:** `O(V + E)`, since an extra visited array of size V is required, And stack size for iterative call to DFS function. + +
+ + diff --git a/contrib/Data-Structure-Graphs/index.md b/contrib/Data-Structure-Graphs/index.md new file mode 100644 index 00000000..6471576d --- /dev/null +++ b/contrib/Data-Structure-Graphs/index.md @@ -0,0 +1,3 @@ +# List of sections + +- [Graphs](graph.md) \ No newline at end of file From 0db9cef68cc452409013ad9d94335d8e1e0c4124 Mon Sep 17 00:00:00 2001 From: Eshparsi Date: Sun, 19 May 2024 12:21:46 +0530 Subject: [PATCH 124/656] g --- contrib/Data-Structure-Graphs/graph.md | 61 +++++++++++++------------- 1 file changed, 30 insertions(+), 31 deletions(-) diff --git a/contrib/Data-Structure-Graphs/graph.md b/contrib/Data-Structure-Graphs/graph.md index b9bd709d..9837896b 100644 --- a/contrib/Data-Structure-Graphs/graph.md +++ b/contrib/Data-Structure-Graphs/graph.md @@ -2,21 +2,22 @@ Graph is a non-linear data structure consisting of vertices and edges. It is a powerful tool for representing and analyzing complex relationships between objects or entities. -## Components of a Graph: +## Components of a Graph -**Vertices:** Vertices are the fundamental units of the graph. Sometimes, vertices are also known as vertex or nodes. Every node/vertex can be labeled or unlabeled. -**Edges:** Edges are drawn or used to connect two nodes of the graph. It can be ordered pair of nodes in a directed graph. Edges can connect any two nodes in any possible way. There are no rules. very edge can be labelled/unlabelled. +1. **Vertices:** Vertices are the fundamental units of the graph. Sometimes, vertices are also known as vertex or nodes. Every node/vertex can be labeled or unlabeled. -## Basic Operations on Graphs: +2. **Edges:** Edges are drawn or used to connect two nodes of the graph. It can be ordered pair of nodes in a directed graph. Edges can connect any two nodes in any possible way. There are no rules. very edge can be labelled/unlabelled. + +## Basic Operations on Graphs - Insertion of Nodes/Edges in the graph - Deletion of Nodes/Edges in the graph - Searching on Graphs - Traversal of Graphs -## Types of Graph ## +## Types of Graph -**1.Undirected Graph:** In an undirected graph, edges have no direction, and they represent symmetric relationships between nodes. If there is an edge between node A and node B, you can travel from A to B and from B to A. +**1. Undirected Graph:** In an undirected graph, edges have no direction, and they represent symmetric relationships between nodes. If there is an edge between node A and node B, you can travel from A to B and from B to A. **2. Directed Graph (Digraph):** In a directed graph, edges have a direction, indicating a one-way relationship between nodes. If there is an edge from node A to node B, you can travel from A to B but not necessarily from B to A. @@ -28,10 +29,10 @@ Graph is a non-linear data structure consisting of vertices and edges. It is a p **6. Tree:** A tree is a special type of acyclic graph where each node has a unique parent except for the root node, which has no parent. Trees have a hierarchical structure and are frequently used in data structures like binary trees or decision trees. -## Representation of Graphs:## +## Representation of Graphs There are two ways to store a graph: -**1. Adjacency Matrix** +1. **Adjacency Matrix** In this method, the graph is stored in the form of the 2D matrix where rows and columns denote vertices. Each entry in the matrix represents the weight of the edge between those vertices. ```python @@ -63,8 +64,8 @@ for row in adj_matrix: ``` -**2. Adjacency List** -This graph is represented as a collection of linked lists. There is an array of pointer which points to the edges connected to that vertex. +2. **Adjacency List** +In this method, the graph is represented as a collection of linked lists. There is an array of pointer which points to the edges connected to that vertex. ```python def create_adjacency_list(edges, num_vertices): @@ -85,17 +86,17 @@ if __name__ == "__main__": for i in range(num_vertices): print(f"{i} -> {' '.join(map(str, adj_list[i]))}") ``` -`Output -0 -> 1 2 -1 -> 0 2 3 -2 -> 0 1 3 -3 -> 2 1 ` +`Output` +`0 -> 1 2` +`1 -> 0 2 3` +`2 -> 0 1 3` +`3 -> 2 1 ` -# Traversal Techniques # +# Traversal Techniques -## Breadth First Search (BFS) ## +## Breadth First Search (BFS) - It is a graph traversal algorithm that explores all the vertices in a graph at the current depth before moving on to the vertices at the next depth level. - It starts at a specified vertex and visits all its neighbors before moving on to the next level of neighbors. BFS is commonly used in algorithms for pathfinding, connected components, and shortest path problems in graphs. @@ -103,13 +104,13 @@ BFS is commonly used in algorithms for pathfinding, connected components, and sh **Steps of BFS algorithms** -- **Step1:** Initially queue and visited arrays are empty. -- **Step2:** Push node 0 into queue and mark it visited. +- **Step 1:** Initially queue and visited arrays are empty. +- **Step 2:** Push node 0 into queue and mark it visited. - **Step 3:** Remove node 0 from the front of queue and visit the unvisited neighbours and push them into queue. - **Step 4:** Remove node 1 from the front of queue and visit the unvisited neighbours and push them into queue. - **Step 5:** Remove node 2 from the front of queue and visit the unvisited neighbours and push them into queue. - **Step 6:** Remove node 3 from the front of queue and visit the unvisited neighbours and push them into queue. -- **Steps 7:** Remove node 4 from the front of queue and visit the unvisited neighbours and push them into queue. +- **Step 7:** Remove node 4 from the front of queue and visit the unvisited neighbours and push them into queue. ```python @@ -154,16 +155,17 @@ if __name__ == "__main__": #Output : Breadth First Traversal 0 1 2 3 4 ``` -**Time Complexity:** `O(V+E)`, where V is the number of nodes and E is the number of edges. -**Auxiliary Space:** `O(V)` +- **Time Complexity:** `O(V+E)`, where V is the number of nodes and E is the number of edges. +- **Auxiliary Space:** `O(V)` -## Depth-first search ## +## Depth-first search Depth-first search is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root node (selecting some arbitrary node as the root node in the case of a graph) and explores as far as possible along each branch before backtracking. **Steps of DFS algorithms** -- **Step1:** Initially stack and visited arrays are empty. + +- **Step 1:** Initially stack and visited arrays are empty. - **Step 2:** Visit 0 and put its adjacent nodes which are not visited yet into the stack. - **Step 3:** Now, Node 1 at the top of the stack, so visit node 1 and pop it from the stack and put all of its adjacent nodes which are not visited in the stack. - **Step 4:** Now, Node 2 at the top of the stack, so visit node 2 and pop it from the stack and put all of its adjacent nodes which are not visited (i.e, 3, 4) in the stack. @@ -178,14 +180,12 @@ from collections import defaultdict class Graph: def __init__(self): - self.graph = defaultdict(list) def addEdge(self, u, v): self.graph[u].append(v) def DFSUtil(self, v, visited): - visited.add(v) print(v, end=' ') @@ -194,9 +194,7 @@ class Graph: self.DFSUtil(neighbour, visited) def DFS(self, v): - visited = set() - self.DFSUtil(v, visited) if __name__ == "__main__": @@ -208,12 +206,13 @@ if __name__ == "__main__": g.addEdge(2, 3) g.addEdge(3, 3) - print("Depth First Traversal (starting from vertex 2): ",g.DFS(2)) #Output: Depth First Traversal (starting from vertex 2): 2 0 1 3 + print("Depth First Traversal (starting from vertex 2): ",g.DFS(2)) ``` +`Output: Depth First Traversal (starting from vertex 2): 2 0 1 3 ` -**Time complexity:** `O(V + E)`, where V is the number of vertices and E is the number of edges in the graph. -**Auxiliary Space:** `O(V + E)`, since an extra visited array of size V is required, And stack size for iterative call to DFS function. +- **Time complexity:** `O(V + E)`, where V is the number of vertices and E is the number of edges in the graph. +- **Auxiliary Space:** `O(V + E)`, since an extra visited array of size V is required, And stack size for iterative call to DFS function.
From 05071b50871a74cfa03420c70014f6a6c16d96fd Mon Sep 17 00:00:00 2001 From: Eshparsi <112681516+Eshparsi@users.noreply.github.com> Date: Sun, 19 May 2024 12:24:13 +0530 Subject: [PATCH 125/656] Update graph.md --- contrib/Data-Structure-Graphs/graph.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/contrib/Data-Structure-Graphs/graph.md b/contrib/Data-Structure-Graphs/graph.md index 9837896b..517c90d1 100644 --- a/contrib/Data-Structure-Graphs/graph.md +++ b/contrib/Data-Structure-Graphs/graph.md @@ -32,7 +32,7 @@ Graph is a non-linear data structure consisting of vertices and edges. It is a p ## Representation of Graphs There are two ways to store a graph: -1. **Adjacency Matrix** +1. **Adjacency Matrix:** In this method, the graph is stored in the form of the 2D matrix where rows and columns denote vertices. Each entry in the matrix represents the weight of the edge between those vertices. ```python @@ -64,7 +64,7 @@ for row in adj_matrix: ``` -2. **Adjacency List** +2. **Adjacency List:** In this method, the graph is represented as a collection of linked lists. There is an array of pointer which points to the edges connected to that vertex. ```python From 0b9c7823fac1eb920e4bda3cd7bb5667a7023601 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Sun, 19 May 2024 22:09:39 +0530 Subject: [PATCH 126/656] Create loading_arrays_from_files.md Added Introduction Added numpy.loadtxt method Added numpy.genfromtxt method Added numpy.fromfile method Added numpy.load method --- contrib/numpy/loading_arrays_from_files.md | 67 ++++++++++++++++++++++ 1 file changed, 67 insertions(+) create mode 100644 contrib/numpy/loading_arrays_from_files.md diff --git a/contrib/numpy/loading_arrays_from_files.md b/contrib/numpy/loading_arrays_from_files.md new file mode 100644 index 00000000..f1717c2d --- /dev/null +++ b/contrib/numpy/loading_arrays_from_files.md @@ -0,0 +1,67 @@ +# Loading Arrays From Files +The ability to load data from various file formats is a critical feature for scientific computing and data analysis. +NumPy provides several functions to read data from different file types and convert them into ndarrays. +This section will cover how to load ndarrays from common file formats, including CSV, TSV, and binary files. + +### Here are the methods available: + +`numpy.loadtxt`: The loadtxt function allows you to load data from a text file.You can specify various parameters such as the file name, data type, delimiter, +and more. It reads the file line by line, splits it at the specified delimiter, and converts the values into an array. + +- **Syntax:** + ```python + numpy.loadtxt(fname, dtype = float, delimiter=None, converters=None, skiprows=0, usecols=None) + ``` + + `fname` : Name of the file
+ `dtype` : Data type of the resulting array. (By default is float)
+ `delimiter`: String or character separating columns; default is any whitespace.
+ `converters`: Dictionary mapping column number to a function to convert that column's string to a float.
+ `skiprows`: Number of lines to skip at the beginning of the file.
+ `usecols`: Which columns to read starting from 0. + +- **Example for `loadtxt`:** + + **example.txt**
+ + ![image](https://github.com/Santhosh-Siddhardha/learn-python/assets/103999924/a0148d29-5fba-45fa-b3f4-058406b3016b) + + **Code**
+ ```python + import numpy as np + arr = np.loadtxt("loadtxt.txt", dtype=int) + print(arr) + ``` + + **Output**
+ ```python + [1 2 3 4 5] + ``` + + +`numpy.genfromtxt`: The genfromtxt function is similar to loadtxt but provides more flexibility. It handles missing values (such as NaNs), allows custom converters +for data parsing, and can handle different data types within the same file. It’s particularly useful for handling complex data formats. + +- **Syntax:** + ```python + numpy.genfromtxt(fname, dtype=float, delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None) + ``` + + `fname` : Name of the file
+ `dtype` : Data type of the resulting array. (By default is float)
+ `delimiter`: String or character separating columns; default is any whitespace.
+ `skip_header`: Number of lines to skip at the beginning of the file.
+ `skip_footer`: Number of lines to skip at the end of the file.
+ `converters`: Dictionary mapping column number to a function to convert that column's string to a float.
+ `missing_values`: Set of strings corresponding to missing data.
+ `filling_values`: Value used to fill in missing data. Default is NaN.
+ `usecols`: Which columns to read starting from 0. + +- **Examples for `genfromtxt`:** + + +`numpy.fromfile`: The fromfile function reads binary data directly from a file into a NumPy array. It doesn’t assume any specific format or delimiter; +instead, it interprets the raw binary data according to the specified data type. + +`numpy.load`: Load arrays saved in NumPy’s native binary format (.npy or .npz). These files preserve the array structure, data types, and metadata. +It’s an efficient way to store and load large arrays. From 09ad3d5d88d79ea4d509cf5822d5ddbef4f05962 Mon Sep 17 00:00:00 2001 From: Anushka Mokashi Date: Sun, 19 May 2024 22:12:47 +0530 Subject: [PATCH 127/656] GroupBy_Pandas --- contrib/pandas/GroupBy_Functions_Pandas.md | 391 +++++++++++++++++++++ contrib/pandas/index.md | 2 + 2 files changed, 393 insertions(+) create mode 100644 contrib/pandas/GroupBy_Functions_Pandas.md diff --git a/contrib/pandas/GroupBy_Functions_Pandas.md b/contrib/pandas/GroupBy_Functions_Pandas.md new file mode 100644 index 00000000..00bad796 --- /dev/null +++ b/contrib/pandas/GroupBy_Functions_Pandas.md @@ -0,0 +1,391 @@ +## Group By Functions + +GroupBy is a powerful function in pandas that allows you to split data into distinct groups based on one or more columns and perform operations on each group independently. It's a fundamental technique for data analysis and summarization. + +Here's a step-by-step breakdown of how groupby functions work in pandas: + +* __Splitting the Data:__ You can group your data based on one or more columns using the .groupby() method. This method takes a column name or a list of column names as input and splits the DataFrame into groups according to the values in those columns. + +* __Applying a Function:__ Once the data is grouped, you can apply various functions to each group. Pandas offers a variety of built-in aggregation functions like sum(), mean(), count(), etc., that can be used to summarize the data within each group. You can also use custom functions or lambda functions for more specific operations. + +* __Combining the Results:__ After applying the function to each group, the results are combined into a new DataFrame or Series, depending on the input data and the function used. This new data structure summarizes the data by group. + + +```python +import pandas as pd +import seaborn as sns +import numpy as np +``` + + +```python +iris_data = sns.load_dataset('iris') +``` + +This code loads the built-in Iris dataset from seaborn and stores it in a pandas DataFrame named iris_data. The Iris dataset contains measurements of flower sepal and petal dimensions for three Iris species (Setosa, Versicolor, Virginica). + + +```python +iris_data +``` + +| | sepal_length | sepal_width | petal_length | petal_width | species | +|----|--------------|-------------|--------------|-------------|-----------| +| 0 | 5.1 | 3.5 | 1.4 | 0.2 | setosa | +| 1 | 4.9 | 3.0 | 1.4 | 0.2 | setosa | +| 2 | 4.7 | 3.2 | 1.3 | 0.2 | setosa | +| 3 | 4.6 | 3.1 | 1.5 | 0.2 | setosa | +| 4 | 5.0 | 3.6 | 1.4 | 0.2 | setosa | +| ...| ... | ... | ... | ... | ... | +| 145| 6.7 | 3.0 | 5.2 | 2.3 | virginica | +| 146| 6.3 | 2.5 | 5.0 | 1.9 | virginica | +| 147| 6.5 | 3.0 | 5.2 | 2.0 | virginica | +| 148| 6.2 | 3.4 | 5.4 | 2.3 | virginica | +| 149| 5.9 | 3.0 | 5.1 | 1.8 | virginica | + + + + + +```python +iris_data.groupby(['species']).count() +``` + + + + +| species | sepal_length | sepal_width | petal_length | petal_width | +|------------|--------------|-------------|--------------|-------------| +| setosa | 50 | 50 | 50 | 50 | +| versicolor | 50 | 50 | 50 | 50 | +| virginica | 50 | 50 | 50 | 50 | + + + + +* We group the data by the 'species' column. +count() is applied to each group, which counts the number of occurrences (rows) in each species category. +* The output (species_counts) is a DataFrame showing the count of each species in the dataset. + + +```python +iris_data.groupby(["species"])["sepal_length"].mean() +``` + + + + + species + setosa 5.006\ + versicolor 5.936\ + virginica 6.588\ + Name: sepal_length, dtype: float64 + + + +* This groups the data by 'species' and selects the 'sepal_length' column. +mean() calculates the average sepal length for each species group. +* The output (species_means) is a Series containing the mean sepal length for each species. + + +```python +iris_data.groupby(["species"])["sepal_length"].std() +``` + + + + + species + setosa 0.352490\ + versicolor 0.516171\ + virginica 0.635880\ + Name: sepal_length, dtype: float64 + + + +* Similar to the previous, this groups by 'species' and selects the 'sepal_length' column. +However, it calculates the standard deviation (spread) of sepal length for each species group using std(). +* The output (species_std) is a Series containing the standard deviation of sepal length for each species + + +```python +iris_data.groupby(["species"])["sepal_length"].describe() +``` + + + +| species | count | mean | std | min | 25% | 50% | 75% | max | +|------------|-------|-------|----------|------|--------|------|------|------| +| setosa | 50.0 | 5.006 | 0.352490 | 4.3 | 4.800 | 5.0 | 5.2 | 5.8 | +| versicolor | 50.0 | 5.936 | 0.516171 | 4.9 | 5.600 | 5.9 | 6.3 | 7.0 | +| virginica | 50.0 | 6.588 | 0.635880 | 4.9 | 6.225 | 6.5 | 6.9 | 7.9 | + + + + +* We have used describe() to generate a more comprehensive summary of sepal length for each species group. +* It provides statistics like count, mean, standard deviation, minimum, maximum, percentiles, etc. +The output (species_descriptions) is a DataFrame containing these descriptive statistics for each species. + + +```python +iris_data.groupby(["species"])["sepal_length"].quantile(q=0.25) +``` + + + + + species\ + setosa 4.800\ + versicolor 5.600\ + virginica 6.225\ + Name: sepal_length, dtype: float64 + + + + +```python +iris_data.groupby(["species"])["sepal_length"].quantile(q=0.75) +``` + + + + + species\ + setosa 5.2\ + versicolor 6.3\ + virginica 6.9\ + Name: sepal_length, dtype: float64 + + + +* To calculate the quartiles (25th percentile and 75th percentile) of sepal length for each species group. +* quantile(q=0.25) gives the 25th percentile, which represents the value below which 25% of the data points lie. +* quantile(q=0.75) gives the 75th percentile, which represents the value below which 75% of the data points lie. +* The outputs (species_q1 and species_q3) are Series containing the respective quartile values for each species. + +## Custom Function For Group By + + +```python +nc = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width','species'] +``` + + +```python +nc +``` + + + + + ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species'] + + + + +```python +nc = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] +def species_stats(species_data,species_name): + print("Species Name: {}".format(species_name)) + print() + print("Mean:\n",species_data[nc].mean()) + print() + print("Median:\n",species_data[nc].median()) + print() + print("std:\n",species_data[nc].std()) + print() + print("25% percentile:\n",species_data[nc].quantile(0.25)) + print() + print("75% percentile:\n",species_data[nc].quantile(0.75)) + print() + print("Min:\n",species_data[nc].min()) + print() + print("Max:\n",species_data[nc].max()) + print() +``` + + +```python +setosa_data = iris_data[iris_data['species'] == 'setosa'] +``` + + +```python +versicolor_data = iris_data[iris_data['species'] == 'versicolor'] +``` + + +```python +virginica_data = iris_data[iris_data['species'] == 'virginica'] +``` + + +```python +species_data_names = ['setosa_data','viginica_data','versicolor_data'] +for data in species_data_names: + print("************** Species name {} *****************".format(data)) + species_stats(setosa_data,data) + print("------------------------------------") +``` + + ************** Species name setosa_data *****************\ + Species Name: setosa_data + + Mean:\ + sepal_length 5.006\ + sepal_width 3.428\ + petal_length 1.462\ + petal_width 0.246\ + dtype: float64 + + Median:\ + sepal_length 5.0\ + sepal_width 3.4\ + petal_length 1.5\ + petal_width 0.2\ + dtype: float64 + + std:\ + sepal_length 0.352490\ + sepal_width 0.379064\ + petal_length 0.173664\ + petal_width 0.105386\ + dtype: float64 + + 25% percentile:\ + sepal_length 4.8\ + sepal_width 3.2\ + petal_length 1.4\ + petal_width 0.2\ + Name: 0.25, dtype: float64 + + 75% percentile:\ + sepal_length 5.200\ + sepal_width 3.675\ + petal_length 1.575\ + petal_width 0.300\ + Name: 0.75, dtype: float64 + + Min:\ + sepal_length 4.3\ + sepal_width 2.3\ + petal_length 1.0\ + petal_width 0.1\ + dtype: float64 + + Max: + sepal_length 5.8\ + sepal_width 4.4\ + petal_length 1.9\ + petal_width 0.6\ + dtype: float64 + + ------------------------------------\ + ************** Species name viginica_data *****************\ + Species Name: viginica_data + + Mean:\ + sepal_length 5.006\ + sepal_width 3.428\ + petal_length 1.462\ + petal_width 0.246\ + dtype: float64 + + Median:\ + sepal_length 5.0\ + sepal_width 3.4\ + petal_length 1.5\ + petal_width 0.2\ + dtype: float64 + + std:\ + sepal_length 0.352490\ + sepal_width 0.379064\ + petal_length 0.173664\ + petal_width 0.105386\ + dtype: float64 + + 25% percentile:\ + sepal_length 4.8\ + sepal_width 3.2\ + petal_length 1.4\ + petal_width 0.2\ + Name: 0.25, dtype: float64 + + 75% percentile:\ + sepal_length 5.200\ + sepal_width 3.675\ + petal_length 1.575\ + petal_width 0.300\ + Name: 0.75, dtype: float64 + + Min:\ + sepal_length 4.3\ + sepal_width 2.3\ + petal_length 1.0\ + petal_width 0.1\ + dtype: float64 + + Max: + sepal_length 5.8 + sepal_width 4.4 + petal_length 1.9 + petal_width 0.6 + dtype: float64 + + ------------------------------------\ + ************** Species name versicolor_data *****************\ + Species Name: versicolor_data + + Mean:\ + sepal_length 5.006\ + sepal_width 3.428\ + petal_length 1.462\ + petal_width 0.246\ + dtype: float64 + + Median:\ + sepal_length 5.0\ + sepal_width 3.4\ + petal_length 1.5\ + petal_width 0.2\ + dtype: float64 + + std:\ + sepal_length 0.352490\ + sepal_width 0.379064\ + petal_length 0.173664\ + petal_width 0.105386\ + dtype: float64 + + 25% percentile:\ + sepal_length 4.8\ + sepal_width 3.2\ + petal_length 1.4\ + petal_width 0.2\ + Name: 0.25, dtype: float64 + + 75% percentile:\ + sepal_length 5.200\ + sepal_width 3.675\ + petal_length 1.575\ + petal_width 0.300\ + Name: 0.75, dtype: float64 + + Min: + sepal_length 4.3\ + sepal_width 2.3\ + petal_length 1.0\ + petal_width 0.1\ + dtype: float64 + + Max:\ + sepal_length 5.8\ + sepal_width 4.4\ + petal_length 1.9\ + petal_width 0.6\ + dtype: float64 + + ------------------------------------ + diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 5c0f2b43..e975b03d 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -1,3 +1,5 @@ # List of sections - [Pandas Series Vs NumPy ndarray](pandas_series_vs_numpy_ndarray.md) + +- [Group By Functions with Pandas](GroupBy_Functions_Pandas.md) From 39ed0f3e85deb55d68897f41e20d998eb8963246 Mon Sep 17 00:00:00 2001 From: Mohammed Ahmed Majid <109688855+PilotAxis@users.noreply.github.com> Date: Sun, 19 May 2024 22:59:32 +0530 Subject: [PATCH 128/656] Create Queues.md Added Introduction, Real Life Examples, Operations. --- contrib/ds-algorithms/Queues.md | 47 +++++++++++++++++++++++++++++++++ 1 file changed, 47 insertions(+) create mode 100644 contrib/ds-algorithms/Queues.md diff --git a/contrib/ds-algorithms/Queues.md b/contrib/ds-algorithms/Queues.md new file mode 100644 index 00000000..918c9343 --- /dev/null +++ b/contrib/ds-algorithms/Queues.md @@ -0,0 +1,47 @@ +# Introduction to Queues + +A queue is a linear data structure where elements are added at the back (enqueue) and removed from the front (dequeue). Imagine a line at a coffee shop, the first in line (front) gets served first, and new customers join at the back. This FIFO approach ensures order and fairness in processing elements. + +Queues offer efficient implementations for various scenarios. They are often used in: +- Task Scheduling - Operating systems utilize queues to manage processes waiting for CPU time. +- Breadth-first search algorithms - Traversing a tree or graph involves exploring neighbouring nodes level by level, often achieved using a queue. +- Message passing - Communication protocols leverage queues to buffer messages between applications for reliable delivery. + +
+ +Screenshot 2024-05-18 at 10 16 30 PM + + +## Real Life Examples of Queues +- Customer Service - Consider how a customer service phone line works. Customers calling are put into a queue. The first customer to call is the first one to be served (FIFO). As more customers call, they are added to the end of the queue, and as customers are served, they are removed from the front. The entire process follows the queue data structure. + +- Printers - Printers operate using a queue to manage print jobs. When a user sends a document to the printer, the job is added to the queue (enqueue). Once a job completes printing, it's removed from the queue (dequeue), and the next job in line starts. This sequential order of handling tasks perfectly exhibits the queue data structure. + +- Computer Memory - Certain types of computer memory use a queue data structure to hold and process instructions. For example, in a computer's cache memory, the fetch-decode-execute cycle of an instruction follows a queue. The first instruction fetched is the first one to be decoded and executed, while new instructions fetched are added to the rear. + +
+ +# Important Terminologies in Queues + +Understanding these terms is crucial for working with queues: + +- Enqueue - Adding an element to the back of the queue. +- Dequeue - Removing the element at the front of the queue. +- Front - The first element in the queue, to be removed next. +- Rear/Back - The last element in the queue, where new elements are added. +- Empty Queue - A queue with no elements. +- Overflow - Attempting to enqueue an element when the queue is full. +- Underflow - Attempting to dequeue an element from an empty queue. + +
+ +# Operations on a Queue + +There are some key operations in a queue that include - + +- isFULL - This operation is used to check if a queue is full. +- isEMPTY - This operation is used to check if a queue is empty. +- Display - This operation is used to display the queue elements. +- Peek - This operation is the process of getting the front value of a queue, without removing it. (i.e., Value at the front). + +image From 6c50c6e498bc3308fdd3a29c926ff76062744531 Mon Sep 17 00:00:00 2001 From: Mohammed Ahmed Majid <109688855+PilotAxis@users.noreply.github.com> Date: Sun, 19 May 2024 23:01:28 +0530 Subject: [PATCH 129/656] Update Queues.md --- contrib/ds-algorithms/Queues.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/ds-algorithms/Queues.md b/contrib/ds-algorithms/Queues.md index 918c9343..ead6ce42 100644 --- a/contrib/ds-algorithms/Queues.md +++ b/contrib/ds-algorithms/Queues.md @@ -9,7 +9,7 @@ Queues offer efficient implementations for various scenarios. They are often use
-Screenshot 2024-05-18 at 10 16 30 PM +Screenshot 2024-05-18 at 10 16 30 PM ## Real Life Examples of Queues From cbf87b0b5d9dc768dfdd8a03878789514cf75d4a Mon Sep 17 00:00:00 2001 From: Mohammed Ahmed Majid <109688855+PilotAxis@users.noreply.github.com> Date: Sun, 19 May 2024 23:03:44 +0530 Subject: [PATCH 130/656] Update Queues.md --- contrib/ds-algorithms/Queues.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/ds-algorithms/Queues.md b/contrib/ds-algorithms/Queues.md index ead6ce42..8ba2ce32 100644 --- a/contrib/ds-algorithms/Queues.md +++ b/contrib/ds-algorithms/Queues.md @@ -9,7 +9,7 @@ Queues offer efficient implementations for various scenarios. They are often use
-Screenshot 2024-05-18 at 10 16 30 PM +Types of Data Structure ## Real Life Examples of Queues From 811382bfca0d9097c78cff56b9e1fb10655b4088 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Sun, 19 May 2024 23:55:40 +0530 Subject: [PATCH 131/656] Update loading_arrays_from_files.md Added syntax for all methods Formatted Content Added examples for all methods Removed fromfile method --- contrib/numpy/loading_arrays_from_files.md | 103 +++++++++++++++------ 1 file changed, 75 insertions(+), 28 deletions(-) diff --git a/contrib/numpy/loading_arrays_from_files.md b/contrib/numpy/loading_arrays_from_files.md index f1717c2d..bd3648f5 100644 --- a/contrib/numpy/loading_arrays_from_files.md +++ b/contrib/numpy/loading_arrays_from_files.md @@ -1,26 +1,25 @@ # Loading Arrays From Files -The ability to load data from various file formats is a critical feature for scientific computing and data analysis. -NumPy provides several functions to read data from different file types and convert them into ndarrays. -This section will cover how to load ndarrays from common file formats, including CSV, TSV, and binary files. +Scientific computing and data analysis require the critical feature of being able to load data from different file formats. NumPy has several functionalities for reading data from various file types and converting them into arrays. This part of the content will show how one can load arrays from standard file formats. -### Here are the methods available: +## Here are the methods available: -`numpy.loadtxt`: The loadtxt function allows you to load data from a text file.You can specify various parameters such as the file name, data type, delimiter, +### 1. numpy.loadtxt(): +The loadtxt function allows you to load data from a text file.You can specify various parameters such as the file name, data type, delimiter, and more. It reads the file line by line, splits it at the specified delimiter, and converts the values into an array. -- **Syntax:** +- #### Syntax: ```python numpy.loadtxt(fname, dtype = float, delimiter=None, converters=None, skiprows=0, usecols=None) ``` - `fname` : Name of the file
- `dtype` : Data type of the resulting array. (By default is float)
- `delimiter`: String or character separating columns; default is any whitespace.
- `converters`: Dictionary mapping column number to a function to convert that column's string to a float.
- `skiprows`: Number of lines to skip at the beginning of the file.
- `usecols`: Which columns to read starting from 0. + **fname** : Name of the file
+ **dtype** : Data type of the resulting array. (By default is float)
+ **delimiter**: String or character separating columns; default is any whitespace.
+ **converters**: Dictionary mapping column number to a function to convert that column's string to a float.
+ **skiprows**: Number of lines to skip at the beginning of the file.
+ **usecols**: Which columns to read starting from 0. -- **Example for `loadtxt`:** +- #### Example for `loadtxt`: **example.txt**
@@ -38,30 +37,78 @@ and more. It reads the file line by line, splits it at the specified delimiter, [1 2 3 4 5] ``` +
-`numpy.genfromtxt`: The genfromtxt function is similar to loadtxt but provides more flexibility. It handles missing values (such as NaNs), allows custom converters +### 2. numpy.genfromtxt: +The `genfromtxt` function is similar to loadtxt but provides more flexibility. It handles missing values (such as NaNs), allows custom converters for data parsing, and can handle different data types within the same file. It’s particularly useful for handling complex data formats. -- **Syntax:** +- #### Syntax: ```python numpy.genfromtxt(fname, dtype=float, delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None) ``` - `fname` : Name of the file
- `dtype` : Data type of the resulting array. (By default is float)
- `delimiter`: String or character separating columns; default is any whitespace.
- `skip_header`: Number of lines to skip at the beginning of the file.
- `skip_footer`: Number of lines to skip at the end of the file.
- `converters`: Dictionary mapping column number to a function to convert that column's string to a float.
- `missing_values`: Set of strings corresponding to missing data.
- `filling_values`: Value used to fill in missing data. Default is NaN.
- `usecols`: Which columns to read starting from 0. + **fname** : Name of the file
+ **dtype** : Data type of the resulting array. (By default is float)
+ **delimiter**: String or character separating columns; default is any whitespace.
+ **skip_header**: Number of lines to skip at the beginning of the file.
+ **skip_footer**: Number of lines to skip at the end of the file.
+ **converters**: Dictionary mapping column number to a function to convert that column's string to a float.
+ **missing_values**: Set of strings corresponding to missing data.
+ **filling_values**: Value used to fill in missing data. Default is NaN.
+ **usecols**: Which columns to read starting from 0. -- **Examples for `genfromtxt`:** +- #### Example for `genfromtxt`: + **example.txt**
+ + ![image](https://github.com/Santhosh-Siddhardha/learn-python/assets/103999924/3f9cdd91-4255-4e30-923d-f29c5f237798) + + + **Code**
+ ```python + import numpy as np + arr = np.genfromtxt("example.txt", dtype='str', usecols=1) + print(arr) + ``` + + **Output**
+ ```python + ['Name' 'Kohli' 'Dhoni' 'Rohit'] + ``` + +
-`numpy.fromfile`: The fromfile function reads binary data directly from a file into a NumPy array. It doesn’t assume any specific format or delimiter; -instead, it interprets the raw binary data according to the specified data type. -`numpy.load`: Load arrays saved in NumPy’s native binary format (.npy or .npz). These files preserve the array structure, data types, and metadata. +### 3. numpy.load +`load` method is used to load arrays saved in NumPy’s native binary format (.npy or .npz). These files preserve the array structure, data types, and metadata. It’s an efficient way to store and load large arrays. + +- #### Syntax: + ```python + numpy.load(fname, mmap_mode=None, encoding='ASCII') + ``` + + **fname** : Name of the file
+ **mmap_mode** : Memory-map the file using the given mode (r, r+, w+, c).(By Default None)
+ **encoding**:Encoding is used when reading Python2 strings only. (By Default ASCII)
+ +- #### Example for `load`: + + **Code**
+ ```python + import numpy as np + arr = np.array(['a','b','c']) + np.savez('data.npz', array=arr) + # stores arr in data.npz in NumPy's native binary format + data = np.load('data.npz') + print(data['array']) + ``` + + **Output**
+ ```python + ['a' 'b' 'c'] + ``` +
+ +These methods empower users to seamlessly integrate data into their scientific workflows, whether from text files or binary formats. From efb68d962d83cda93705e6e2c35fefb7622d54a4 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Sun, 19 May 2024 23:57:34 +0530 Subject: [PATCH 132/656] Update index.md Added Loading Arrays from Files Section Linked it to its md file --- contrib/numpy/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 82596a2f..5c8cfa65 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -1,3 +1,3 @@ # List of sections -- [Section title](filename.md) +- [Loading Arrays from Files](loading_arrays_from_files.md) From 53269a5ad335eb89bb768ca26165f447a3f46d22 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Sun, 19 May 2024 23:58:31 +0530 Subject: [PATCH 133/656] Update loading_arrays_from_files.md changed format --- contrib/numpy/loading_arrays_from_files.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/numpy/loading_arrays_from_files.md b/contrib/numpy/loading_arrays_from_files.md index bd3648f5..85105a37 100644 --- a/contrib/numpy/loading_arrays_from_files.md +++ b/contrib/numpy/loading_arrays_from_files.md @@ -4,7 +4,7 @@ Scientific computing and data analysis require the critical feature of being abl ## Here are the methods available: ### 1. numpy.loadtxt(): -The loadtxt function allows you to load data from a text file.You can specify various parameters such as the file name, data type, delimiter, +The `loadtxt` function allows you to load data from a text file.You can specify various parameters such as the file name, data type, delimiter, and more. It reads the file line by line, splits it at the specified delimiter, and converts the values into an array. - #### Syntax: From cfc731c2acb0b85011b47f3a4b3fe3fc2ea0b91b Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Mon, 20 May 2024 09:50:49 +0530 Subject: [PATCH 134/656] Update loading_arrays_from_files.md Changed introduction content formatted code formatted content --- contrib/numpy/loading_arrays_from_files.md | 27 +++++++++++----------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/contrib/numpy/loading_arrays_from_files.md b/contrib/numpy/loading_arrays_from_files.md index 85105a37..c0011213 100644 --- a/contrib/numpy/loading_arrays_from_files.md +++ b/contrib/numpy/loading_arrays_from_files.md @@ -14,7 +14,7 @@ and more. It reads the file line by line, splits it at the specified delimiter, **fname** : Name of the file
**dtype** : Data type of the resulting array. (By default is float)
- **delimiter**: String or character separating columns; default is any whitespace.
+ **delimiter**: String or character separating columns. (By default is whitespace)
**converters**: Dictionary mapping column number to a function to convert that column's string to a float.
**skiprows**: Number of lines to skip at the beginning of the file.
**usecols**: Which columns to read starting from 0. @@ -27,8 +27,9 @@ and more. It reads the file line by line, splits it at the specified delimiter, **Code**
```python - import numpy as np - arr = np.loadtxt("loadtxt.txt", dtype=int) + import numpy as np + + arr = np.loadtxt("example.txt", dtype=int) print(arr) ``` @@ -39,20 +40,18 @@ and more. It reads the file line by line, splits it at the specified delimiter,
-### 2. numpy.genfromtxt: +### 2. numpy.genfromtxt(): The `genfromtxt` function is similar to loadtxt but provides more flexibility. It handles missing values (such as NaNs), allows custom converters for data parsing, and can handle different data types within the same file. It’s particularly useful for handling complex data formats. - #### Syntax: ```python - numpy.genfromtxt(fname, dtype=float, delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None) + numpy.genfromtxt(fname, dtype=float, delimiter=None, converters=None, missing_values=None, filling_values=None, usecols=None) ``` **fname** : Name of the file
**dtype** : Data type of the resulting array. (By default is float)
**delimiter**: String or character separating columns; default is any whitespace.
- **skip_header**: Number of lines to skip at the beginning of the file.
- **skip_footer**: Number of lines to skip at the end of the file.
**converters**: Dictionary mapping column number to a function to convert that column's string to a float.
**missing_values**: Set of strings corresponding to missing data.
**filling_values**: Value used to fill in missing data. Default is NaN.
@@ -67,7 +66,8 @@ for data parsing, and can handle different data types within the same file. It **Code**
```python - import numpy as np + import numpy as np + arr = np.genfromtxt("example.txt", dtype='str', usecols=1) print(arr) ``` @@ -80,7 +80,7 @@ for data parsing, and can handle different data types within the same file. It
-### 3. numpy.load +### 3. numpy.load(): `load` method is used to load arrays saved in NumPy’s native binary format (.npy or .npz). These files preserve the array structure, data types, and metadata. It’s an efficient way to store and load large arrays. @@ -90,7 +90,7 @@ It’s an efficient way to store and load large arrays. ``` **fname** : Name of the file
- **mmap_mode** : Memory-map the file using the given mode (r, r+, w+, c).(By Default None)
+ **mmap_mode** : Memory-map the file using the given mode (r, r+, w+, c)(By Default None).Memory-mapping only works with arrays stored in a binary file on disk, not with compressed archives like .npz.
**encoding**:Encoding is used when reading Python2 strings only. (By Default ASCII)
- #### Example for `load`: @@ -98,10 +98,11 @@ It’s an efficient way to store and load large arrays. **Code**
```python import numpy as np + arr = np.array(['a','b','c']) - np.savez('data.npz', array=arr) - # stores arr in data.npz in NumPy's native binary format - data = np.load('data.npz') + np.savez('example.npz', array=arr) # stores arr in data.npz in NumPy's native binary format + + data = np.load('example.npz') print(data['array']) ``` From d314d760af6ddcabef554cf07f317a690bff51cb Mon Sep 17 00:00:00 2001 From: Rupa-Rd Date: Mon, 20 May 2024 21:46:30 +0530 Subject: [PATCH 135/656] Datatypes.md file is added to the index.md --- contrib/numpy/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 82596a2f..6d02025a 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -1,3 +1,4 @@ # List of sections - [Section title](filename.md) +- [Numpy Datatypes](datatypes.md) \ No newline at end of file From 2e33a9b997189dc81f0dc73abd1f0c8ff8793cf0 Mon Sep 17 00:00:00 2001 From: Rupa-Rd Date: Mon, 20 May 2024 21:47:17 +0530 Subject: [PATCH 136/656] NumPy datatypes list is added --- contrib/numpy/datatypes.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 contrib/numpy/datatypes.md diff --git a/contrib/numpy/datatypes.md b/contrib/numpy/datatypes.md new file mode 100644 index 00000000..768add18 --- /dev/null +++ b/contrib/numpy/datatypes.md @@ -0,0 +1,35 @@ +# Numpy Data Types +In NumPy, data types play a crcial role in representing and manipulating numerical data. + +Numpy supports the following data types: + +- `i` - integer +- `b` - boolean +- `u` - unsigned integer +- `f` - float +- `c` - complex float +- `m` - timedelta +- `M` - datetime +- `O` - object +- `S` - string +- `U` - unicode string + +## Example for integer type + +## Example for float type + +## Example for boolean type + +## Example for unsigned integer type + +## Example for complex type + +## Example for datetime type + +## Example for string type + +## Example for object type + +## Example for unicode string type + +## Example for timedelta type From 68a2219c6830f8af7026853854ff6338d3ffbdbf Mon Sep 17 00:00:00 2001 From: Rithish S Date: Mon, 20 May 2024 21:51:47 +0530 Subject: [PATCH 137/656] binomial distribution added --- .../machine-learning/binomial_distribution.md | 123 ++++++++++++++++++ contrib/machine-learning/index.md | 1 + 2 files changed, 124 insertions(+) create mode 100644 contrib/machine-learning/binomial_distribution.md diff --git a/contrib/machine-learning/binomial_distribution.md b/contrib/machine-learning/binomial_distribution.md new file mode 100644 index 00000000..2fddc315 --- /dev/null +++ b/contrib/machine-learning/binomial_distribution.md @@ -0,0 +1,123 @@ +# Binomial Distribution + +## Introduction + +The binomial distribution is a discrete probability distribution that describes the number of successes in a fixed number of independent Bernoulli trials, each with the same probability of success. It is commonly used in statistics and probability theory. + +### Key Characteristics + +- **Number of trials (n):** The number of independent experiments or trials. +- **Probability of success (p):** The probability of success on an individual trial. +- **Number of successes (k):** The number of successful outcomes in n trials. + +The binomial distribution is defined by the probability mass function (PMF): + +\[ P(X = k) = \binom{n}{k} p^k (1 - p)^{n - k} \] + +where: +- \(\binom{n}{k}\) is the binomial coefficient, calculated as \(\frac{n!}{k!(n-k)!}\). + +## Properties of Binomial Distribution + +- **Mean:** \( \mu = np \) +- **Variance:** \( \sigma^2 = np(1 - p) \) +- **Standard Deviation:** \( \sigma = \sqrt{np(1 - p)} \) + +## Python Implementation + +Let's implement the binomial distribution using Python. We'll use the `scipy.stats` library to compute the binomial PMF and CDF, and `matplotlib` to visualize it. + +### Step-by-Step Implementation + +1. **Import necessary libraries:** + + ```python + import numpy as np + import matplotlib.pyplot as plt + from scipy.stats import binom + ``` + +2. **Define parameters:** + + ```python + # Number of trials + n = 10 + # Probability of success + p = 0.5 + # Number of successes + k = np.arange(0, n + 1) + ``` + +3. **Compute the PMF:** + + ```python + pmf = binom.pmf(k, n, p) + ``` + +4. **Plot the PMF:** + + ```python + plt.bar(k, pmf, color='blue') + plt.xlabel('Number of Successes') + plt.ylabel('Probability') + plt.title('Binomial Distribution PMF') + plt.show() + ``` + +5. **Compute the CDF:** + + ```python + cdf = binom.cdf(k, n, p) + ``` + +6. **Plot the CDF:** + + ```python + plt.plot(k, cdf, marker='o', linestyle='--', color='blue') + plt.xlabel('Number of Successes') + plt.ylabel('Cumulative Probability') + plt.title('Binomial Distribution CDF') + plt.grid(True) + plt.show() + ``` + +### Complete Code + +Here is the complete code for the binomial distribution implementation: + +```python +import numpy as np +import matplotlib.pyplot as plt +from scipy.stats import binom + +# Parameters +n = 10 # Number of trials +p = 0.5 # Probability of success + +# Number of successes +k = np.arange(0, n + 1) + +# Compute PMF +pmf = binom.pmf(k, n, p) + +# Plot PMF +plt.figure(figsize=(12, 6)) +plt.subplot(1, 2, 1) +plt.bar(k, pmf, color='blue') +plt.xlabel('Number of Successes') +plt.ylabel('Probability') +plt.title('Binomial Distribution PMF') + +# Compute CDF +cdf = binom.cdf(k, n, p) + +# Plot CDF +plt.subplot(1, 2, 2) +plt.plot(k, cdf, marker='o', linestyle='--', color='blue') +plt.xlabel('Number of Successes') +plt.ylabel('Cumulative Probability') +plt.title('Binomial Distribution CDF') +plt.grid(True) + +plt.tight_layout() +plt.show() diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 82596a2f..ec4dfc3a 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -1,3 +1,4 @@ # List of sections - [Section title](filename.md) +- [Binomial Distribution](binomial_distribution.md) From 3f731e7f211facfc725a08d455c90dc6c27b8df6 Mon Sep 17 00:00:00 2001 From: Rithish S <123345316+Rithish5513U@users.noreply.github.com> Date: Mon, 20 May 2024 21:53:31 +0530 Subject: [PATCH 138/656] Update binomial_distribution.md --- contrib/machine-learning/binomial_distribution.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/contrib/machine-learning/binomial_distribution.md b/contrib/machine-learning/binomial_distribution.md index 2fddc315..94e4c419 100644 --- a/contrib/machine-learning/binomial_distribution.md +++ b/contrib/machine-learning/binomial_distribution.md @@ -1,3 +1,7 @@ + + # Binomial Distribution ## Introduction From 92beeba6c21b6cb27242d91fb6f6bd4ed3b019ba Mon Sep 17 00:00:00 2001 From: Rupa-Rd Date: Mon, 20 May 2024 21:54:39 +0530 Subject: [PATCH 139/656] dtype() definition and example is added --- contrib/numpy/datatypes.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/contrib/numpy/datatypes.md b/contrib/numpy/datatypes.md index 768add18..39dd46b5 100644 --- a/contrib/numpy/datatypes.md +++ b/contrib/numpy/datatypes.md @@ -14,6 +14,17 @@ Numpy supports the following data types: - `S` - string - `U` - unicode string +## dtype() Function +The `dtype()` function returns the type of the NumPy array object. +``` python + import numpy as np + + arr = np.array([1, 2, 3, 4]) + + print(arr.dtype) + + # Output: int64 +``` ## Example for integer type ## Example for float type @@ -33,3 +44,6 @@ Numpy supports the following data types: ## Example for unicode string type ## Example for timedelta type + +# Data Type Conversion + From 7d800d598046394c7409eab176750f0468198e5f Mon Sep 17 00:00:00 2001 From: Rithish S <123345316+Rithish5513U@users.noreply.github.com> Date: Mon, 20 May 2024 21:57:32 +0530 Subject: [PATCH 140/656] Update binomial_distribution.md --- contrib/machine-learning/binomial_distribution.md | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/contrib/machine-learning/binomial_distribution.md b/contrib/machine-learning/binomial_distribution.md index 94e4c419..0d1d3280 100644 --- a/contrib/machine-learning/binomial_distribution.md +++ b/contrib/machine-learning/binomial_distribution.md @@ -1,7 +1,3 @@ - - # Binomial Distribution ## Introduction @@ -16,16 +12,16 @@ The binomial distribution is a discrete probability distribution that describes The binomial distribution is defined by the probability mass function (PMF): -\[ P(X = k) = \binom{n}{k} p^k (1 - p)^{n - k} \] +P(X = k) = (n choose k) p^k (1 - p)^(n - k) where: -- \(\binom{n}{k}\) is the binomial coefficient, calculated as \(\frac{n!}{k!(n-k)!}\). +- (n choose k) is the binomial coefficient, calculated as n! / (k!(n-k)!). ## Properties of Binomial Distribution -- **Mean:** \( \mu = np \) -- **Variance:** \( \sigma^2 = np(1 - p) \) -- **Standard Deviation:** \( \sigma = \sqrt{np(1 - p)} \) +- **Mean:** μ = np +- **Variance:** σ² = np(1 - p) +- **Standard Deviation:** σ = √(np(1 - p)) ## Python Implementation From 8c8544540948ec757d5b2cc97df09278fcaf9e61 Mon Sep 17 00:00:00 2001 From: Rupa-Rd Date: Mon, 20 May 2024 22:08:40 +0530 Subject: [PATCH 141/656] Example for integer type is added --- contrib/numpy/datatypes.md | 36 +++++++++++++++++++++++++++++++++++- 1 file changed, 35 insertions(+), 1 deletion(-) diff --git a/contrib/numpy/datatypes.md b/contrib/numpy/datatypes.md index 39dd46b5..c70e810a 100644 --- a/contrib/numpy/datatypes.md +++ b/contrib/numpy/datatypes.md @@ -16,17 +16,51 @@ Numpy supports the following data types: ## dtype() Function The `dtype()` function returns the type of the NumPy array object. + +Example 1 ``` python import numpy as np arr = np.array([1, 2, 3, 4]) print(arr.dtype) - + # Output: int64 ``` + +Example 2 +``` python + import numpy as np + + arr = np.array(['apple', 'banana', 'cherry']) + + print(arr.dtype) + + # Output: Date: Mon, 20 May 2024 22:17:35 +0530 Subject: [PATCH 142/656] Example for float type is added --- contrib/numpy/datatypes.md | 40 ++++++++++++++++++++++++++++++++++++-- 1 file changed, 38 insertions(+), 2 deletions(-) diff --git a/contrib/numpy/datatypes.md b/contrib/numpy/datatypes.md index c70e810a..1a355d2a 100644 --- a/contrib/numpy/datatypes.md +++ b/contrib/numpy/datatypes.md @@ -46,8 +46,10 @@ Way 1: Using function `int_()` import numpy as np arr = np.int_([2,4,6]) - + # Size: int8, int16, int32, int64 + print(arr.dtype()) + # Output: int64 ``` @@ -56,13 +58,47 @@ Way 2: Using `dtype()` import numpy as np arr = np.array([2,4,6], dtype='i4') + # Size: i1, i2, i4, i8 print(arr.dtype) - # Output: int8 + # Output: int32 ``` + ## Example for float type +Way 1: Using function `float_()` +``` python + import numpy as np + + arr = np.float_(1) + # Size: float8, float16, float32, float64 + + print(arr) + print(arr.dtype()) + + # Output: + # 1.0 + # float64 +``` + +Way 2: Using `dtype()` +``` python + import numpy as np + + arr = np.array([2,4,6], dtype='f4') + # Size: f1, f2, f4, f8 + + print(arr) + print(arr.dtype) + + # Output: + # [1. 2. 3. 4.] + # float32 +``` + +Note: `np.single()` has the same function as `float32()`. + ## Example for boolean type ## Example for unsigned integer type From c64b262eda0bc8a2e74fbb63f9ba9df5423a91a3 Mon Sep 17 00:00:00 2001 From: Rupa-Rd Date: Mon, 20 May 2024 22:28:30 +0530 Subject: [PATCH 143/656] Example for unsigned integer type is added --- contrib/numpy/datatypes.md | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/contrib/numpy/datatypes.md b/contrib/numpy/datatypes.md index 1a355d2a..ea9430b9 100644 --- a/contrib/numpy/datatypes.md +++ b/contrib/numpy/datatypes.md @@ -65,6 +65,7 @@ Way 2: Using `dtype()` # Output: int32 ``` +Note: `np.intc()` has the same function as `int32()`. ## Example for float type Way 1: Using function `float_()` @@ -101,8 +102,32 @@ Note: `np.single()` has the same function as `float32()`. ## Example for boolean type +``` python + import numpy as np + + x = np.bool_(1) + + print(x) + print(x.dtype) + + # Output: + # True + # bool +``` ## Example for unsigned integer type +``` python + import numpy as np + + x = np.uintc(1) + + print(x) + print(x.dtype) + + # Output: + # 1 + # uint32 +``` ## Example for complex type ## Example for datetime type From 092c550b67fb80bcd63cb813babe4d7bb4471547 Mon Sep 17 00:00:00 2001 From: Rupa-Rd Date: Mon, 20 May 2024 22:32:10 +0530 Subject: [PATCH 144/656] Example for complex type is added --- contrib/numpy/datatypes.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/contrib/numpy/datatypes.md b/contrib/numpy/datatypes.md index ea9430b9..8bf52ad0 100644 --- a/contrib/numpy/datatypes.md +++ b/contrib/numpy/datatypes.md @@ -128,7 +128,22 @@ Note: `np.single()` has the same function as `float32()`. # 1 # uint32 ``` + ## Example for complex type +Complex type is a combination of real number + imaginary number. The `complex_()` is used to define the complex type NumPy object. +``` python + import numpy as np + + x = np.complex_(1) + # Size: complex64, complex128 + + print(x) + print(x.dtype) + + # Output: + # (1+0j) + # complex128 +``` ## Example for datetime type From 13c485e7dee1b7563c5c8b4e3f5e17343066d4f7 Mon Sep 17 00:00:00 2001 From: Rupa-Rd Date: Mon, 20 May 2024 22:39:24 +0530 Subject: [PATCH 145/656] Example for datetime type is added --- contrib/numpy/datatypes.md | 20 +++++++++++++++++++- 1 file changed, 19 insertions(+), 1 deletion(-) diff --git a/contrib/numpy/datatypes.md b/contrib/numpy/datatypes.md index 8bf52ad0..bf18837c 100644 --- a/contrib/numpy/datatypes.md +++ b/contrib/numpy/datatypes.md @@ -136,7 +136,7 @@ Complex type is a combination of real number + imaginary number. The `complex_() x = np.complex_(1) # Size: complex64, complex128 - + print(x) print(x.dtype) @@ -146,6 +146,24 @@ Complex type is a combination of real number + imaginary number. The `complex_() ``` ## Example for datetime type +The `datetime64()` is used to define the date, month and year. + +``` python + import numpy as np + + x = np.datetime64('1970-05') + y = np.datetime64('2024-20-05') + z = np.datetime64('2024') + + print(x,x.dtype) + print(y,y.dtype) + print(z,z.dtype) + + # Output: + # 1970-05 datetime64[M] + # 1970-01-11 datetime64[D] + # 1970 datetime64[Y] +``` ## Example for string type From 722d34de63e7ef058cd3275fdefcbc726dc60f35 Mon Sep 17 00:00:00 2001 From: Rupa-Rd Date: Mon, 20 May 2024 22:46:00 +0530 Subject: [PATCH 146/656] Example for timedelta type is added --- contrib/numpy/datatypes.md | 25 ++++++++++++++++++++----- 1 file changed, 20 insertions(+), 5 deletions(-) diff --git a/contrib/numpy/datatypes.md b/contrib/numpy/datatypes.md index bf18837c..616d6c3d 100644 --- a/contrib/numpy/datatypes.md +++ b/contrib/numpy/datatypes.md @@ -151,8 +151,8 @@ The `datetime64()` is used to define the date, month and year. ``` python import numpy as np - x = np.datetime64('1970-05') - y = np.datetime64('2024-20-05') + x = np.datetime64('2024-05') + y = np.datetime64('2024-05-20') z = np.datetime64('2024') print(x,x.dtype) @@ -160,9 +160,9 @@ The `datetime64()` is used to define the date, month and year. print(z,z.dtype) # Output: - # 1970-05 datetime64[M] - # 1970-01-11 datetime64[D] - # 1970 datetime64[Y] + # 2024-05 datetime64[M] + # 2024-20-05 datetime64[D] + # 2024 datetime64[Y] ``` ## Example for string type @@ -172,6 +172,21 @@ The `datetime64()` is used to define the date, month and year. ## Example for unicode string type ## Example for timedelta type +The `timedelta64()` used to find the difference between the `datetime64()`. The arguments for timedelta64 are a number, to represent the number of units, and a date/time unit, such as (D)ay, (M)onth, (Y)ear, (h)ours, (m)inutes, or (s)econds. The timedelta64 data type also accepts the string “NAT” in place of the number for a “Not A Time” value. +``` python + import numpy as np + + x = np.datetime64('2024-05-20') + y = np.datetime64('2023-05-20') + res = x - y + + print(res) + print(res.dtype) + + # Output: + # 366 days + # timedelta64[D] +``` # Data Type Conversion From 26a0dc814d76da7c6fd9be4ed40d22ea1c0e04fc Mon Sep 17 00:00:00 2001 From: Rupa-Rd Date: Mon, 20 May 2024 22:48:00 +0530 Subject: [PATCH 147/656] Example for object type is added --- contrib/numpy/datatypes.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/contrib/numpy/datatypes.md b/contrib/numpy/datatypes.md index 616d6c3d..6107d8de 100644 --- a/contrib/numpy/datatypes.md +++ b/contrib/numpy/datatypes.md @@ -168,7 +168,18 @@ The `datetime64()` is used to define the date, month and year. ## Example for string type ## Example for object type +``` python + import numpy as np + + arr = np.object_([1, 2, 3, 4]) + + print(arr) + print(arr.dtype) + # Output: + # [1, 2, 3, 4] + # object +``` ## Example for unicode string type ## Example for timedelta type From cf4a924c0eedfa9672377d967c6ea93efd25b146 Mon Sep 17 00:00:00 2001 From: Rupa-Rd Date: Mon, 20 May 2024 22:56:17 +0530 Subject: [PATCH 148/656] Example for String type is added --- contrib/numpy/datatypes.md | 20 ++++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/contrib/numpy/datatypes.md b/contrib/numpy/datatypes.md index 6107d8de..d365623a 100644 --- a/contrib/numpy/datatypes.md +++ b/contrib/numpy/datatypes.md @@ -166,6 +166,15 @@ The `datetime64()` is used to define the date, month and year. ``` ## Example for string type +``` python + import numpy as np + + arr = np.str_("roopa") + + print(arr.dtype) + + # Output: Date: Mon, 20 May 2024 23:03:59 +0530 Subject: [PATCH 149/656] Example type conversion is added --- contrib/numpy/datatypes.md | 33 ++++++++++++++++++++++++++++++++- 1 file changed, 32 insertions(+), 1 deletion(-) diff --git a/contrib/numpy/datatypes.md b/contrib/numpy/datatypes.md index d365623a..12690b9b 100644 --- a/contrib/numpy/datatypes.md +++ b/contrib/numpy/datatypes.md @@ -216,4 +216,35 @@ The `timedelta64()` used to find the difference between the `datetime64()`. The # 366 days # timedelta64[D] ``` -# Data Type Conversion \ No newline at end of file +# Data Type Conversion +`astype()` function is used to the NumPy object from one type to another type. + +It creates a copy of the array and allows to specify the data type of our choice. + +## Example 1 + +``` python + import numpy as np + + x = np.array([1.2, 3.4, 5.6]) + y = x.astype(int) + + print(y,y.dtype) + + # Output: + # [1 3 5] int64 +``` + +## Example 2 + +``` python + import numpy as np + + x = np.array([1, 3, 0]) + y = x.astype(bool) + + print(y,y.dtype) + + # Output: + # [True True False] bool +``` \ No newline at end of file From 5effa72de84fae6d7ee5d4cbba07d0f7a4d2f8e6 Mon Sep 17 00:00:00 2001 From: Rupa-Rd Date: Mon, 20 May 2024 23:08:12 +0530 Subject: [PATCH 150/656] index.md is modified --- contrib/numpy/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 6d02025a..fa6ae9ed 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -1,4 +1,4 @@ # List of sections -- [Section title](filename.md) -- [Numpy Datatypes](datatypes.md) \ No newline at end of file +- [NumPy Data Types](datatypes.md) +- [Section title](filename.md) \ No newline at end of file From bfdd5dbc41e60d3193991d3687456515f1c17b39 Mon Sep 17 00:00:00 2001 From: Rupa-Rd Date: Mon, 20 May 2024 23:14:25 +0530 Subject: [PATCH 151/656] Additional datatype longdouble is added --- contrib/numpy/datatypes.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/contrib/numpy/datatypes.md b/contrib/numpy/datatypes.md index 12690b9b..f58cee59 100644 --- a/contrib/numpy/datatypes.md +++ b/contrib/numpy/datatypes.md @@ -216,6 +216,20 @@ The `timedelta64()` used to find the difference between the `datetime64()`. The # 366 days # timedelta64[D] ``` +## Additional Data Type (`longdouble`) +`longdouble` is a data type that provides higher precision than the standard double-precision floating-point (`float64`) type. + +``` python + import numpy as np + + arr = np.longdouble([1.222222, 4.44, 45.55]) + + print(arr, arr.dtype) + + # Output: + # [1.222222 4.44 45.55] float128 +``` + # Data Type Conversion `astype()` function is used to the NumPy object from one type to another type. From 4e36263a1cf3cf117f51af3643c1e75e2617e329 Mon Sep 17 00:00:00 2001 From: Rupa-Rd Date: Mon, 20 May 2024 23:23:29 +0530 Subject: [PATCH 152/656] datatypes.md modified --- contrib/numpy/datatypes.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/contrib/numpy/datatypes.md b/contrib/numpy/datatypes.md index f58cee59..fb9df47b 100644 --- a/contrib/numpy/datatypes.md +++ b/contrib/numpy/datatypes.md @@ -14,6 +14,9 @@ Numpy supports the following data types: - `S` - string - `U` - unicode string + +_Referred from: W3schools_ + ## dtype() Function The `dtype()` function returns the type of the NumPy array object. @@ -261,4 +264,4 @@ It creates a copy of the array and allows to specify the data type of our choice # Output: # [True True False] bool -``` \ No newline at end of file +``` From 749aa23485d565a52eee2ec025d3139d2e2b41f9 Mon Sep 17 00:00:00 2001 From: shivam navadiya Date: Tue, 21 May 2024 18:17:04 +0530 Subject: [PATCH 153/656] Updated index.md --- contrib/ds-algorithms/index.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 5b521551..0e1f2567 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -2,3 +2,8 @@ - [Section title](filename.md) - [Sorting Algorithms](sorting-algorithms.md) +- [Divide and Conquer Algorithm](divide-and-conquer-algorithm) +- [Searching Algorithms](searching-algorithms.md) +- [Greedy Algorithms](greedy-algorithms.md) +- [Dynamic Programming](dynamic-programming.md) + From ec1e51c677b2a533352542d00ce34cc2579bf585 Mon Sep 17 00:00:00 2001 From: shivam navadiya Date: Tue, 21 May 2024 18:20:05 +0530 Subject: [PATCH 154/656] Created Divide and Conquer Algorithm --- .../divide-and-conquer-algorithm.md | 54 +++++++++++++++++++ 1 file changed, 54 insertions(+) create mode 100644 contrib/ds-algorithms/divide-and-conquer-algorithm.md diff --git a/contrib/ds-algorithms/divide-and-conquer-algorithm.md b/contrib/ds-algorithms/divide-and-conquer-algorithm.md new file mode 100644 index 00000000..b5a356ea --- /dev/null +++ b/contrib/ds-algorithms/divide-and-conquer-algorithm.md @@ -0,0 +1,54 @@ +# Divide and Conquer Algorithms + +Divide and Conquer is a paradigm for solving problems that involves breaking a problem into smaller sub-problems, solving the sub-problems recursively, and then combining their solutions to solve the original problem. + +## Merge Sort + +Merge Sort is a popular sorting algorithm that follows the divide and conquer strategy. It divides the input array into two halves, recursively sorts the halves, and then merges them. + +**Algorithm Overview:** +- **Divide:** Divide the unsorted list into two sublists of about half the size. +- **Conquer:** Recursively sort each sublist. +- **Combine:** Merge the sorted sublists back into one sorted list. + +```python +def merge_sort(arr): + if len(arr) > 1: + mid = len(arr) // 2 + left_half = arr[:mid] + right_half = arr[mid:] + + merge_sort(left_half) + merge_sort(right_half) + + i = j = k = 0 + + while i < len(left_half) and j < len(right_half): + if left_half[i] < right_half[j]: + arr[k] = left_half[i] + i += 1 + else: + arr[k] = right_half[j] + j += 1 + k += 1 + + while i < len(left_half): + arr[k] = left_half[i] + i += 1 + k += 1 + + while j < len(right_half): + arr[k] = right_half[j] + j += 1 + k += 1 + +arr = [12, 11, 13, 5, 6, 7] +merge_sort(arr) +print("Sorted array:", arr) +``` + +## Complexity Analysis +- **Time Complexity:** O(n log n) in all cases +- **Space Complexity:** O(n) additional space for the merge operation + +--- From bd2103dac33ccbc0fc5f4efa0c797fca1916879c Mon Sep 17 00:00:00 2001 From: shivam navadiya Date: Tue, 21 May 2024 18:21:22 +0530 Subject: [PATCH 155/656] Created Searching Algorithms --- contrib/ds-algorithms/searching-algorithms.md | 161 ++++++++++++++++++ 1 file changed, 161 insertions(+) create mode 100644 contrib/ds-algorithms/searching-algorithms.md diff --git a/contrib/ds-algorithms/searching-algorithms.md b/contrib/ds-algorithms/searching-algorithms.md new file mode 100644 index 00000000..78b86d14 --- /dev/null +++ b/contrib/ds-algorithms/searching-algorithms.md @@ -0,0 +1,161 @@ +# Searching Algorithms + +Searching algorithms are techniques used to locate specific items within a collection of data. These algorithms are fundamental in computer science and are employed in various applications, from databases to web search engines. + +## Real Life Example of Searching +- Searching for a word in a dictionary +- Searching for a specific book in a library +- Searching for a contact in your phone's address book +- Searching for a file on your computer, etc. + +# Some common searching techniques + +# 1. Linear Search + +Linear search, also known as sequential search, is a straightforward searching algorithm that checks each element in a collection until the target element is found or the entire collection has been traversed. It is simple to implement but becomes inefficient for large datasets. + +**Algorithm Overview:** +- **Sequential Checking:** The algorithm iterates through each element in the collection, starting from the first element. +- **Comparing Elements:** At each iteration, it compares the current element with the target element. +- **Finding the Target:** If the current element matches the target, the search terminates, and the index of the element is returned. +- **Completing the Search:** If the entire collection is traversed without finding the target, the algorithm indicates that the element is not present. + +## Linear Search Code in Python + +```python +def linear_search(arr, target): + for i in range(len(arr)): + if arr[i] == target: + return i + return -1 + +arr = [5, 3, 8, 1, 2] +target = 8 +result = linear_search(arr, target) +if result != -1: + print(f"Element {target} found at index {result}.") +else: + print(f"Element {target} not found.") +``` + +## Complexity Analysis +- **Time Complexity**: O(n) +- **Space Complexity**: O(1) + +
+
+
+ +# 2. Binary Search + +Binary search is an efficient searching algorithm that works on sorted collections. It repeatedly divides the search interval in half until the target element is found or the interval is empty. Binary search is significantly faster than linear search but requires the collection to be sorted beforehand. + +**Algorithm Overview:** +- **Initial State:** Binary search starts with the entire collection as the search interval. +- **Divide and Conquer:** At each step, it calculates the middle element of the current interval and compares it with the target. +- **Narrowing Down the Interval:** If the middle element is equal to the target, the search terminates successfully. Otherwise, it discards half of the search interval based on the comparison result. +- **Repeating the Process:** The algorithm repeats this process on the remaining half of the interval until the target is found or the interval is empty. + +## Binary Search Code in Python (Iterative) + +```python +def binary_search(arr, target): + low = 0 + high = len(arr) - 1 + while low <= high: + mid = (low + high) // 2 + if arr[mid] == target: + return mid + elif arr[mid] < target: + low = mid + 1 + else: + high = mid - 1 + return -1 + +arr = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19] +target = 13 +result = binary_search(arr, target) +if result != -1: + print(f"Element {target} found at index {result}.") +else: + print(f"Element {target} not found.") +``` + +## Binary Search Code in Python (Recursive) + +```python +def binary_search_recursive(arr, target, low, high): + if low <= high: + mid = (low + high) // 2 + if arr[mid] == target: + return mid + elif arr[mid] < target: + return binary_search_recursive(arr, target, mid + 1, high) + else: + return binary_search_recursive(arr, target, low, mid - 1) + else: + return -1 + +arr = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19] +target = 13 +result = binary_search_recursive(arr, target, 0, len(arr) - 1) +if result != -1: + print(f"Element {target} found at index {result}.") +else: + print(f"Element {target} not found.") +``` + +## Complexity Analysis +- **Time Complexity**: O(log n) +- **Space Complexity**: O(1) (Iterative), O(log n) (Recursive) + +
+
+
+ +# 3. Interpolation Search + +Interpolation search is an improved version of binary search, especially useful when the elements in the collection are uniformly distributed. Instead of always dividing the search interval in half, interpolation search estimates the position of the target element based on its value and the values of the endpoints of the search interval. + +**Algorithm Overview:** +- **Estimating Position:** Interpolation search calculates an approximate position of the target element within the search interval based on its value and the values of the endpoints. +- **Refining the Estimate:** It adjusts the estimated position based on whether the target value is likely to be closer to the beginning or end of the search interval. +- **Updating the Interval:** Using the refined estimate, it narrows down the search interval iteratively until the target is found or the interval becomes empty. + +## Interpolation Search Code in Python + +```python +def interpolation_search(arr, target): + low = 0 + high = len(arr) - 1 + while low <= high and arr[low] <= target <= arr[high]: + if low == high: + if arr[low] == target: + return low + return -1 + pos = low + ((target - arr[low]) * (high - low)) // (arr[high] - arr[low]) + if arr[pos] == target: + return pos + elif arr[pos] < target: + low = pos + 1 + else: + high = pos - 1 + return -1 + +arr = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] +target = 60 +result = interpolation_search(arr, target) +if result != -1: + print(f"Element {target} found at index {result}.") +else: + print(f"Element {target} not found.") +``` + +## Complexity Analysis +- **Time Complexity**: O(log log n) (Average) +- **Space Complexity**: O(1) + +
+
+
+ From dbd9fd9081ec04b13c2a2e33889a7ce18b8e5f94 Mon Sep 17 00:00:00 2001 From: shivam navadiya Date: Tue, 21 May 2024 18:22:18 +0530 Subject: [PATCH 156/656] Created Greedy Algorithms --- contrib/ds-algorithms/greedy-algorithms.md | 135 +++++++++++++++++++++ 1 file changed, 135 insertions(+) create mode 100644 contrib/ds-algorithms/greedy-algorithms.md diff --git a/contrib/ds-algorithms/greedy-algorithms.md b/contrib/ds-algorithms/greedy-algorithms.md new file mode 100644 index 00000000..c79ee991 --- /dev/null +++ b/contrib/ds-algorithms/greedy-algorithms.md @@ -0,0 +1,135 @@ +# Greedy Algorithms + +Greedy algorithms are simple, intuitive algorithms that make a sequence of choices at each step with the hope of finding a global optimum. They are called "greedy" because at each step, they choose the most advantageous option without considering the future consequences. Despite their simplicity, greedy algorithms are powerful tools for solving optimization problems, especially when the problem exhibits the greedy-choice property. + +## Real-Life Examples of Greedy Algorithms +- **Coin Change:** Finding the minimum number of coins to make a certain amount of change. +- **Job Scheduling:** Assigning tasks to machines to minimize completion time. +- **Huffman Coding:** Constructing an optimal prefix-free binary code for data compression. +- **Fractional Knapsack:** Selecting items to maximize the value within a weight limit. + +# Some Common Greedy Algorithms + +# 1. Coin Change Problem + +The coin change problem is a classic example of a greedy algorithm. Given a set of coin denominations and a target amount, the objective is to find the minimum number of coins required to make up that amount. + +**Algorithm Overview:** +- **Greedy Strategy:** At each step, the algorithm selects the largest denomination coin that is less than or equal to the remaining amount. +- **Repeat Until Amount is Zero:** The process continues until the remaining amount becomes zero. + +## Coin Change Code in Python + +```python +def coin_change(coins, amount): + coins.sort(reverse=True) + num_coins = 0 + for coin in coins: + num_coins += amount // coin + amount %= coin + if amount == 0: + return num_coins + else: + return -1 + +coins = [1, 5, 10, 25] +amount = 63 +result = coin_change(coins, amount) +if result != -1: + print(f"Minimum number of coins required: {result}.") +else: + print("It is not possible to make the amount with the given denominations.") +``` + +## Complexity Analysis +- **Time Complexity**: O(n log n) for sorting (if not pre-sorted), O(n) for iteration +- **Space Complexity**: O(1) + +
+
+
+ +# 2. Activity Selection Problem + +The activity selection problem involves selecting the maximum number of mutually compatible activities that can be performed by a single person or machine, assuming that a person can only work on one activity at a time. + +**Algorithm Overview:** +- **Greedy Strategy:** Sort the activities based on their finish times. +- **Selecting Activities:** Iterate through the sorted activities, selecting each activity if it doesn't conflict with the previously selected ones. + +## Activity Selection Code in Python + +```python +def activity_selection(start, finish): + n = len(start) + activities = [] + i = 0 + activities.append(i) + for j in range(1, n): + if start[j] >= finish[i]: + activities.append(j) + i = j + return activities + +start = [1, 3, 0, 5, 8, 5] +finish = [2, 4, 6, 7, 9, 9] +selected_activities = activity_selection(start, finish) +print("Selected activities:", selected_activities) +``` + +## Complexity Analysis +- **Time Complexity**: O(n log n) for sorting (if not pre-sorted), O(n) for iteration +- **Space Complexity**: O(1) + +
+
+
+ +# 3. Huffman Coding + +Huffman coding is a method of lossless data compression that efficiently represents characters or symbols in a file. It uses variable-length codes to represent characters, with shorter codes assigned to more frequent characters. + +**Algorithm Overview:** +- **Frequency Analysis:** Determine the frequency of each character in the input data. +- **Building the Huffman Tree:** Construct a binary tree where each leaf node represents a character and the path to the leaf node determines its code. +- **Assigning Codes:** Traverse the Huffman tree to assign codes to each character, with shorter codes for more frequent characters. + +## Huffman Coding Code in Python + +```python +from heapq import heappush, heappop, heapify +from collections import defaultdict + +def huffman_coding(data): + frequency = defaultdict(int) + for char in data: + frequency[char] += 1 + + heap = [[weight, [symbol, ""]] for symbol, weight in frequency.items()] + heapify(heap) + + while len(heap) > 1: + lo = heappop(heap) + hi = heappop(heap) + for pair in lo[1:]: + pair[1] = '0' + pair[1] + for pair in hi[1:]: + pair[1] = '1' + pair[1] + heappush(heap, [lo[0] + hi[0]] + lo[1:] + hi[1:]) + + return sorted(heappop(heap)[1:], key=lambda p: (len(p[-1]), p)) + +data = "Huffman coding is a greedy algorithm" +encoded_data = huffman_coding(data) +print("Huffman Codes:") +for symbol, code in encoded_data: + print(f"{symbol}: {code}") +``` + +## Complexity Analysis +- **Time Complexity**: O(n log n) for heap operations, where n is the number of unique characters +- **Space Complexity**: O(n) for the heap + +
+
+
From 20009b0e888e778a112ef18b96c322f1532ec1b8 Mon Sep 17 00:00:00 2001 From: shivam navadiya Date: Tue, 21 May 2024 18:23:16 +0530 Subject: [PATCH 157/656] Created Dynamic Programming --- contrib/ds-algorithms/dynamic-programming.md | 132 +++++++++++++++++++ 1 file changed, 132 insertions(+) create mode 100644 contrib/ds-algorithms/dynamic-programming.md diff --git a/contrib/ds-algorithms/dynamic-programming.md b/contrib/ds-algorithms/dynamic-programming.md new file mode 100644 index 00000000..43149f86 --- /dev/null +++ b/contrib/ds-algorithms/dynamic-programming.md @@ -0,0 +1,132 @@ +# Dynamic Programming + +Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems and solving each subproblem only once. It stores the solutions to subproblems to avoid redundant computations, making it particularly useful for optimization problems where the solution can be obtained by combining solutions to smaller subproblems. + +## Real-Life Examples of Dynamic Programming +- **Fibonacci Sequence:** Computing the nth Fibonacci number efficiently. +- **Shortest Path:** Finding the shortest path in a graph from a source to a destination. +- **String Edit Distance:** Calculating the minimum number of operations required to transform one string into another. +- **Knapsack Problem:** Maximizing the value of items in a knapsack without exceeding its weight capacity. + +# Some Common Dynamic Programming Techniques + +# 1. Fibonacci Sequence + +The Fibonacci sequence is a classic example used to illustrate dynamic programming. It is a series of numbers where each number is the sum of the two preceding ones, usually starting with 0 and 1. + +**Algorithm Overview:** +- **Base Cases:** The first two numbers in the Fibonacci sequence are defined as 0 and 1. +- **Memoization:** Store the results of previously computed Fibonacci numbers to avoid redundant computations. +- **Recurrence Relation:** Compute each Fibonacci number by adding the two preceding numbers. + +## Fibonacci Sequence Code in Python (Top-Down Approach with Memoization) + +```python +def fibonacci(n, memo={}): + if n in memo: + return memo[n] + if n <= 1: + return n + memo[n] = fibonacci(n-1, memo) + fibonacci(n-2, memo) + return memo[n] + +n = 10 +print(f"The {n}th Fibonacci number is: {fibonacci(n)}.") +``` + +## Fibonacci Sequence Code in Python (Bottom-Up Approach) + +```python +def fibonacci(n): + fib = [0, 1] + for i in range(2, n + 1): + fib.append(fib[i - 1] + fib[i - 2]) + return fib[n] + +n = 10 +print(f"The {n}th Fibonacci number is: {fibonacci(n)}.") +``` + +## Complexity Analysis +- **Time Complexity**: O(n) for both approaches +- **Space Complexity**: O(n) for the top-down approach (due to memoization), O(1) for the bottom-up approach + +
+
+
+ +# 2. Longest Common Subsequence + +The longest common subsequence (LCS) problem is to find the longest subsequence common to two sequences. A subsequence is a sequence that appears in the same relative order but not necessarily contiguous. + +**Algorithm Overview:** +- **Base Cases:** If one of the sequences is empty, the LCS is empty. +- **Memoization:** Store the results of previously computed LCS lengths to avoid redundant computations. +- **Recurrence Relation:** Compute the LCS length by comparing characters of the sequences and making decisions based on whether they match. + +## Longest Common Subsequence Code in Python (Top-Down Approach with Memoization) + +```python +def longest_common_subsequence(X, Y, m, n, memo={}): + if (m, n) in memo: + return memo[(m, n)] + if m == 0 or n == 0: + return 0 + if X[m - 1] == Y[n - 1]: + memo[(m, n)] = 1 + longest_common_subsequence(X, Y, m - 1, n - 1, memo) + else: + memo[(m, n)] = max(longest_common_subsequence(X, Y, m, n - 1, memo), + longest_common_subsequence(X, Y, m - 1, n, memo)) + return memo[(m, n)] + +X = "AGGTAB" +Y = "GXTXAYB" +print("Length of Longest Common Subsequence:", longest_common_subsequence(X, Y, len(X), len(Y))) +``` + +## Complexity Analysis +- **Time Complexity**: O(m * n) for the top-down approach, where m and n are the lengths of the input sequences +- **Space Complexity**: O(m * n) for the memoization table + +
+
+
+ +# 3. 0-1 Knapsack Problem + +The 0-1 knapsack problem is a classic optimization problem where the goal is to maximize the total value of items selected while keeping the total weight within a specified limit. + +**Algorithm Overview:** +- **Base Cases:** If the capacity of the knapsack is 0 or there are no items to select, the total value is 0. +- **Memoization:** Store the results of previously computed subproblems to avoid redundant computations. +- **Recurrence Relation:** Compute the maximum value by considering whether to include the current item or not. + +## 0-1 Knapsack Problem Code in Python (Top-Down Approach with Memoization) + +```python +def knapsack(weights, values, capacity, n, memo={}): + if (capacity, n) in memo: + return memo[(capacity, n)] + if n == 0 or capacity == 0: + return 0 + if weights[n - 1] > capacity: + memo[(capacity, n)] = knapsack(weights, values, capacity, n - 1, memo) + else: + memo[(capacity, n)] = max(values[n - 1] + knapsack(weights, values, capacity - weights[n - 1], n - 1, memo), + knapsack(weights, values, capacity, n - 1, memo)) + return memo[(capacity, n)] + +weights = [10, 20, 30] +values = [60, 100, 120] +capacity = 50 +n = len(weights) +print("Maximum value that can be obtained:", knapsack(weights, values, capacity, n)) +``` + +## Complexity Analysis +- **Time Complexity**: O(n * W) for the top-down approach, where n is the number of items and W is the capacity of the knapsack +- **Space Complexity**: O(n * W) for the memoization table + +
+
+
\ No newline at end of file From a50c8c9a90a5f4c739a24fd752add4af8627afd0 Mon Sep 17 00:00:00 2001 From: Mohammed Ahmed Majid <109688855+PilotAxis@users.noreply.github.com> Date: Tue, 21 May 2024 19:13:40 +0530 Subject: [PATCH 158/656] Update Queues.md --- contrib/ds-algorithms/Queues.md | 131 ++++++++++++++++++++++++++------ 1 file changed, 107 insertions(+), 24 deletions(-) diff --git a/contrib/ds-algorithms/Queues.md b/contrib/ds-algorithms/Queues.md index 8ba2ce32..2c5c0f0f 100644 --- a/contrib/ds-algorithms/Queues.md +++ b/contrib/ds-algorithms/Queues.md @@ -1,23 +1,33 @@ -# Introduction to Queues +# Queues in Python A queue is a linear data structure where elements are added at the back (enqueue) and removed from the front (dequeue). Imagine a line at a coffee shop, the first in line (front) gets served first, and new customers join at the back. This FIFO approach ensures order and fairness in processing elements. Queues offer efficient implementations for various scenarios. They are often used in: -- Task Scheduling - Operating systems utilize queues to manage processes waiting for CPU time. -- Breadth-first search algorithms - Traversing a tree or graph involves exploring neighbouring nodes level by level, often achieved using a queue. -- Message passing - Communication protocols leverage queues to buffer messages between applications for reliable delivery. +- **Task Scheduling** - Operating systems utilize queues to manage processes waiting for CPU time. +- **Breadth-first search algorithms** - Traversing a tree or graph involves exploring neighbouring nodes level by level, often achieved using a queue. +- **Message passing** - Communication protocols leverage queues to buffer messages between applications for reliable delivery. -
+## Types of Queue -Types of Data Structure +A queue can be classified into 4 types - +- **Simple Queue** - A simple queue is a queue, where we can only insert an element at the back and remove the element from the front of the queue, this type of queue follows the FIFO principle. +- **Double-Ended Queue (Dequeue)** - In this type of queue, insertions and deletions of elements can be performed from both ends of the queue.
+Double-ended queues can be classified into 2 types -> + - **Input-Restricted Queue** + - **Output-Restricted Queue** +- **Circular Queue** - It is a special type of queue where the back is connected to the front, where the operations follow the FIFO principle. +- **Priority Queue** - In this type of queue, elements are accessed based on their priority in the queue.
+Priority queues are of 2 types -> + - **Ascending Priority Queue** + - **Descending Priority Queue** ## Real Life Examples of Queues -- Customer Service - Consider how a customer service phone line works. Customers calling are put into a queue. The first customer to call is the first one to be served (FIFO). As more customers call, they are added to the end of the queue, and as customers are served, they are removed from the front. The entire process follows the queue data structure. +- **Customer Service** - Consider how a customer service phone line works. Customers calling are put into a queue. The first customer to call is the first one to be served (FIFO). As more customers call, they are added to the end of the queue, and as customers are served, they are removed from the front. The entire process follows the queue data structure. -- Printers - Printers operate using a queue to manage print jobs. When a user sends a document to the printer, the job is added to the queue (enqueue). Once a job completes printing, it's removed from the queue (dequeue), and the next job in line starts. This sequential order of handling tasks perfectly exhibits the queue data structure. +- **Printers** - Printers operate using a queue to manage print jobs. When a user sends a document to the printer, the job is added to the queue (enqueue). Once a job completes printing, it's removed from the queue (dequeue), and the next job in line starts. This sequential order of handling tasks perfectly exhibits the queue data structure. -- Computer Memory - Certain types of computer memory use a queue data structure to hold and process instructions. For example, in a computer's cache memory, the fetch-decode-execute cycle of an instruction follows a queue. The first instruction fetched is the first one to be decoded and executed, while new instructions fetched are added to the rear. +- **Computer Memory** - Certain types of computer memory use a queue data structure to hold and process instructions. For example, in a computer's cache memory, the fetch-decode-execute cycle of an instruction follows a queue. The first instruction fetched is the first one to be decoded and executed, while new instructions fetched are added to the rear.
@@ -25,23 +35,96 @@ Queues offer efficient implementations for various scenarios. They are often use Understanding these terms is crucial for working with queues: -- Enqueue - Adding an element to the back of the queue. -- Dequeue - Removing the element at the front of the queue. -- Front - The first element in the queue, to be removed next. -- Rear/Back - The last element in the queue, where new elements are added. -- Empty Queue - A queue with no elements. -- Overflow - Attempting to enqueue an element when the queue is full. -- Underflow - Attempting to dequeue an element from an empty queue. - -
+- **Enqueue** - Adding an element to the back of the queue. +- **Dequeue** - Removing the element at the front of the queue. +- **Front** - The first element in the queue, to be removed next. +- **Rear/Back** - The last element in the queue, where new elements are added. +- **Empty Queue** - A queue with no elements. +- **Overflow** - Attempting to enqueue an element when the queue is full. +- **Underflow** - Attempting to dequeue an element from an empty queue. -# Operations on a Queue +## Operations on a Queue There are some key operations in a queue that include - -- isFULL - This operation is used to check if a queue is full. -- isEMPTY - This operation is used to check if a queue is empty. -- Display - This operation is used to display the queue elements. -- Peek - This operation is the process of getting the front value of a queue, without removing it. (i.e., Value at the front). +- **isFULL** - This operation checks if a queue is full. +- **isEMPTY** - This operation checks if a queue is empty. +- **Display** - This operation displays the queue elements. +- **Peek** - This operation is the process of getting the front value of a queue, without removing it. (i.e., Value at the front). + +
-image +# Implementation of Queue + +```python +def isEmpty(Qu): + if Qu == []: + return True + else: + return False + +def Enqueue(Qu, item) : + Qu.append(item) + if len(Qu) == 1: + front = rear = 0 + else: + rear = len(Qu) - 1 + print(item, "enqueued to queue") +def Dequeue(Qu): + if isEmpty(Qu): + print("Underflow") + else: + item = Qu.pop(0) + if len(Qu) == 0: #if it was single-element queue + front = rear = None + print(item, "dequeued from queue") + +def Peek(Qu): + if isEmpty(Qu): + print("Underflow") + else: + front = 0 + print("Frontmost item is :", Qu[front]) + +def Display(Qu): + if isEmpty(Qu): + print("Queue Empty!") + elif len(Qu) == 1: + print(Qu[0], "<== front, rear") + else: + front = 0 + rear = len(Qu) - 1 + print(Qu[front], "<-front") + for a in range(1, rear): + print(Qu[a]) + print(Qu[rear], "<-rear") + +queue = [] #initially queue is empty +front = None + +# Example Usage +Enqueue(queue, 1) +Enqueue(queue, 2) +Enqueue(queue, 3) +Dequeue(queue) +Peek(queue) +Display(queue) +``` + +## Output + +``` +1 enqueued to queue +2 enqueued to queue +3 enqueued to queue +1 dequeued from queue +Frontmost item is : 2 +2 <-front +3 <-rear +``` + +## Complexity Analysis + +- **Worst case**: `O(n^2)` This occurs when the code performs lots of display operations. +- **Best case**: `O(n)` If the code mostly performs enqueue, dequeue and peek operations. +- **Average case**: `O(n^2)` It occurs when the number of operations in display are more than the operations in enqueue, dequeue and peek. From 0f0a658a61f9c90eb503ec575b11f1656e3436e9 Mon Sep 17 00:00:00 2001 From: Mohammed Ahmed Majid <109688855+PilotAxis@users.noreply.github.com> Date: Tue, 21 May 2024 19:19:15 +0530 Subject: [PATCH 159/656] Update index.md Added reference to the file Queues.md --- contrib/ds-algorithms/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 5b521551..e88fab13 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -2,3 +2,4 @@ - [Section title](filename.md) - [Sorting Algorithms](sorting-algorithms.md) +- [Queues in Python](Queues.md) From 4ee5b32b8e217eac48799391a2f8182ac2abae35 Mon Sep 17 00:00:00 2001 From: Pradnya <116059908+PradnyaGaitonde@users.noreply.github.com> Date: Tue, 21 May 2024 21:51:48 +0530 Subject: [PATCH 160/656] Create dates_and_times.md Added content for Working with dates & times in python --- contrib/advanced-python/dates_and_times.md | 129 +++++++++++++++++++++ 1 file changed, 129 insertions(+) create mode 100644 contrib/advanced-python/dates_and_times.md diff --git a/contrib/advanced-python/dates_and_times.md b/contrib/advanced-python/dates_and_times.md new file mode 100644 index 00000000..f599e266 --- /dev/null +++ b/contrib/advanced-python/dates_and_times.md @@ -0,0 +1,129 @@ +## Working with Dates and Times in Python +Handling dates and times is an essential aspect of many programming tasks. +Python provides robust modules to work with dates and times, making it easier to perform operations like formatting, parsing, and arithmetic. +This guide provides an overview of these modules and their key functionalities. + +## 1. 'datetime' Module +The datetime module supplies classes for manipulating dates and times. The main classes in the datetime module are: + +* date: Represents a date (year, month, day). +* time: Represents a time (hour, minute, second, microsecond). +* datetime: Combines date and time information. +* timedelta: Represents the difference between two dates or times. +* tzinfo: Provides time zone information objects. + +**Key Concepts:** + +* Naive vs. Aware: Naive datetime objects do not contain time zone information, while aware datetime objects do. +* Immutability: date and time objects are immutable; once created, they cannot be changed. + +Example: +```bash +python +Copy code +import datetime +# Get the current date and time +now = datetime.datetime.now() +print("Current date and time:", now) +``` + +## 2. Formatting Dates and Times +Formatting involves converting datetime objects into human-readable strings. This is achieved using the strftime method, which stands for "string format time." +You can specify various format codes to dictate how the output string should be structured. + +**Common Format Codes:** + +* %Y: Year with century (e.g., 2024) +* %m: Month as a zero-padded decimal number (e.g., 01) +* %d: Day of the month as a zero-padded decimal number (e.g., 15) +* %H: Hour (24-hour clock) as a zero-padded decimal number (e.g., 13) +* %M: Minute as a zero-padded decimal number (e.g., 45) +* %S: Second as a zero-padded decimal number (e.g., 30) + +Example: +```bash +python +Copy code +import datetime + +now = datetime.datetime.now() +formatted_now = now.strftime("%Y-%m-%d %H:%M:%S") +print("Formatted current date and time:", formatted_now) +``` + +## 3. Parsing Dates and Times +Parsing is the process of converting strings representing dates and times into datetime objects. The strptime method, which stands for "string parse time," +allows you to specify the format of the input string. + +Example: +```bash +python +Copy code +import datetime + +date_string = "2024-05-15 13:45:30" +date_object = datetime.datetime.strptime(date_string, "%Y-%m-%d %H:%M:%S") +print("Parsed date and time:", date_object) +``` + +## 4. Working with Time Differences +The timedelta class is used to represent the difference between two datetime objects. This is useful for calculations involving durations, such as finding the +number of days between two dates or adding a certain period to a date. + +Example: +```bash +python +Copy code +import datetime + +date1 = datetime.datetime(2024, 5, 15, 12, 0, 0) +date2 = datetime.datetime(2024, 5, 20, 14, 30, 0) + +difference = date2 - date1 +print("Difference:", difference) +print("Days:", difference.days) +print("Total seconds:", difference.total_seconds()) +``` + +## 5. Time Zones +Time zone handling in Python is facilitated by the pytz library. It allows you to convert naive datetime objects into timezone-aware objects and perform +operations across different time zones. + +**Key Concepts:** + +* Timezone-aware: A datetime object that includes timezone information. +* Localization: The process of associating a naive datetime with a time zone. + +Example: +```bash +python +Copy code +import datetime +import pytz + +# Define a timezone +tz = pytz.timezone('Asia/Kolkata') + +# Get the current time in a specific timezone +now = datetime.datetime.now(tz) +print("Current time in Asia/Kolkata:", now) +``` + +## 6. Date Arithmetic +Date arithmetic involves performing operations like addition or subtraction on date or datetime objects using timedelta. This is useful for calculating future +or past dates based on a given date. + +Example: +```bash +python +Copy code +import datetime + +today = datetime.date.today() +future_date = today + datetime.timedelta(days=10) +print("Date after 10 days:", future_date) +``` + +## Summary +Python’s datetime module and the pytz library provide comprehensive tools for working with dates, times, and time zones. They enable you to perform a wide range +of operations, from basic date manipulations to complex time zone conversions. From affc013943c380df51e90c08553573a6da46a143 Mon Sep 17 00:00:00 2001 From: Pradnya <116059908+PradnyaGaitonde@users.noreply.github.com> Date: Tue, 21 May 2024 21:55:23 +0530 Subject: [PATCH 161/656] Update index.md Added index for dates and times --- contrib/advanced-python/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 5ea5081c..bf377fce 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -1,3 +1,4 @@ # List of sections - [Decorators/\*args/**kwargs](decorator-kwargs-args.md) +- [Working with Dates & Times in Python](dates_and_times.md) From 2967206c16c69227da131aa560efe0831148bc12 Mon Sep 17 00:00:00 2001 From: Pradnya <116059908+PradnyaGaitonde@users.noreply.github.com> Date: Tue, 21 May 2024 22:01:17 +0530 Subject: [PATCH 162/656] Create regular_expressions.md Added content for regular expressions in python --- .../advanced-python/regular_expressions.md | 96 +++++++++++++++++++ 1 file changed, 96 insertions(+) create mode 100644 contrib/advanced-python/regular_expressions.md diff --git a/contrib/advanced-python/regular_expressions.md b/contrib/advanced-python/regular_expressions.md new file mode 100644 index 00000000..65ff2c2b --- /dev/null +++ b/contrib/advanced-python/regular_expressions.md @@ -0,0 +1,96 @@ +## Regular Expressions in Python +Regular expressions (regex) are a powerful tool for pattern matching and text manipulation. +Python's re module provides comprehensive support for regular expressions, enabling efficient text processing and validation. + +## 1. Introduction to Regular Expressions +A regular expression is a sequence of characters defining a search pattern. Common use cases include validating input, searching within text, and extracting +specific patterns. + +## 2. Basic Syntax +Literal Characters: Match exact characters (e.g., abc matches "abc"). +Metacharacters: Special characters like ., *, ?, +, ^, $, [ ], and | used to build patterns. + +**Common Metacharacters:** + +* .: Any character except newline. +* ^: Start of the string. +* $: End of the string. +* *: 0 or more repetitions. +* +: 1 or more repetitions. +* ?: 0 or 1 repetition. +* []: Any one character inside brackets (e.g., [a-z]). +* |: Either the pattern before or after. + +## 3. Using the re Module + +**Key functions in the re module:** + +* re.match(): Checks for a match at the beginning of the string. +* re.search(): Searches for a match anywhere in the string. +* re.findall(): Returns a list of all matches. +* re.sub(): Replaces matches with a specified string. + +Examples: +```bash +import re + +# Match at the beginning +print(re.match(r'\d+', '123abc').group()) # Output: 123 + +# Search anywhere +print(re.search(r'\d+', 'abc123').group()) # Output: 123 + +# Find all matches +print(re.findall(r'\d+', 'abc123def456')) # Output: ['123', '456'] + +# Substitute matches +print(re.sub(r'\d+', '#', 'abc123def456')) # Output: abc#def# +``` + +## 4. Compiling Regular Expressions +Compiling regular expressions improves performance for repeated use. + +Example: +```bash +import re + +pattern = re.compile(r'\d+') +print(pattern.match('123abc').group()) # Output: 123 +print(pattern.search('abc123').group()) # Output: 123 +print(pattern.findall('abc123def456')) # Output: ['123', '456'] +``` + +## 5. Groups and Capturing +Parentheses () group and capture parts of the match. + +Example: +```bash +import re + +match = re.match(r'(\d{3})-(\d{2})-(\d{4})', '123-45-6789') +if match: + print(match.group()) # Output: 123-45-6789 + print(match.group(1)) # Output: 123 + print(match.group(2)) # Output: 45 + print(match.group(3)) # Output: 6789 +``` + +## 6. Special Sequences +Special sequences are shortcuts for common patterns: + +* \d: Any digit. +* \D: Any non-digit. +* \w: Any alphanumeric character. +* \W: Any non-alphanumeric character. +* \s: Any whitespace character. +* \S: Any non-whitespace character. +Example: +```bash +import re + +print(re.search(r'\w+@\w+\.\w+', 'Contact: support@example.com').group()) # Output: support@example.com +``` + +## Summary +Regular expressions are a versatile tool for text processing in Python. The re module offers powerful functions and metacharacters for pattern matching, +searching, and manipulation, making it an essential skill for handling complex text processing tasks. From d44f8fc253ae6a74af3894ecd77ceb2ef5f01adf Mon Sep 17 00:00:00 2001 From: Pradnya <116059908+PradnyaGaitonde@users.noreply.github.com> Date: Tue, 21 May 2024 22:03:27 +0530 Subject: [PATCH 163/656] Update index.md added index for regular --- contrib/advanced-python/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 5ea5081c..ebb567a2 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -1,3 +1,4 @@ # List of sections - [Decorators/\*args/**kwargs](decorator-kwargs-args.md) +- [Regular Expressions in Python](regular_expressions.md) From d7dc67b678124cd54a93c5b917af9d7a903fb82d Mon Sep 17 00:00:00 2001 From: Pradnya <116059908+PradnyaGaitonde@users.noreply.github.com> Date: Tue, 21 May 2024 22:05:01 +0530 Subject: [PATCH 164/656] Update dates_and_times.md --- contrib/advanced-python/dates_and_times.md | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/contrib/advanced-python/dates_and_times.md b/contrib/advanced-python/dates_and_times.md index f599e266..983f6b23 100644 --- a/contrib/advanced-python/dates_and_times.md +++ b/contrib/advanced-python/dates_and_times.md @@ -19,8 +19,6 @@ The datetime module supplies classes for manipulating dates and times. The main Example: ```bash -python -Copy code import datetime # Get the current date and time now = datetime.datetime.now() @@ -42,8 +40,6 @@ You can specify various format codes to dictate how the output string should be Example: ```bash -python -Copy code import datetime now = datetime.datetime.now() @@ -57,8 +53,6 @@ allows you to specify the format of the input string. Example: ```bash -python -Copy code import datetime date_string = "2024-05-15 13:45:30" @@ -72,8 +66,6 @@ number of days between two dates or adding a certain period to a date. Example: ```bash -python -Copy code import datetime date1 = datetime.datetime(2024, 5, 15, 12, 0, 0) @@ -96,8 +88,6 @@ operations across different time zones. Example: ```bash -python -Copy code import datetime import pytz @@ -115,8 +105,6 @@ or past dates based on a given date. Example: ```bash -python -Copy code import datetime today = datetime.date.today() From d443fba280e9f1271fd9c54168631f9ff306b8a5 Mon Sep 17 00:00:00 2001 From: Jaya-Prakash-17 Date: Wed, 22 May 2024 22:20:21 +0530 Subject: [PATCH 165/656] "Added Content Decision Trees" --- contrib/machine-learning/Decision-Tree.md | 257 ++++++++++++++++++++++ contrib/machine-learning/index.md | 2 +- 2 files changed, 258 insertions(+), 1 deletion(-) create mode 100644 contrib/machine-learning/Decision-Tree.md diff --git a/contrib/machine-learning/Decision-Tree.md b/contrib/machine-learning/Decision-Tree.md new file mode 100644 index 00000000..6563a223 --- /dev/null +++ b/contrib/machine-learning/Decision-Tree.md @@ -0,0 +1,257 @@ +# Decision Trees +Decision trees are a type of supervised machine learning algorithm that is mostly used in classification problems. They work for both categorical and continuous input and output variables. + +It is also interpreted as acyclic graph that can be utilized for decision-making is called a decision tree. Every branching node in the graph looks at a particular feature (j) of the feature vector. The left branch is taken when the feature's value is less than a certain threshold; the right branch is taken when it is higher. The class to which the example belongs is decided upon as soon as the leaf node is reached. + +## Key Components of a Decision Tree +**Root Node:** This is the decision tree's first node, and it symbolizes the whole population or sample. + +**Internal Nodes:** These are the nodes that make decisions and they stand in for the characteristics or features. + +**Leaf Nodes:** These are the nodes that make decisions and they stand in for the characteristics or features. + +**Branches:** These are the lines that connect the nodes, and they show how the choice was made depending on the feature value. + +### Example: Predicting Loan Approval + +In this example, we will use a decision tree to forecast the approval or denial of a loan application based on a number of features, including job status, credit score, and income. + +``` + Root Node + (All Applications) + / \ + Internal Node Internal Node + (Credit Score) (Employment Status) + / \ / \ + Leaf Node Leaf Node Leaf Node Leaf Node +(Approve Loan) (Deny Loan) (Approve Loan) (Deny Loan) +``` +> There are various formulations of the decision tree learning algorithm. Here, we consider just one, called ID3. + +## Appropriate Problems For Decision Tree Learning +In general, decision tree learning works best on issues that have the following characteristics: +1. ***Instances*** are represented by ***key-value pairs*** +2. The ***output values of the target function are discrete***. Each sample is given a Boolean categorization (yes or no) by the decision tree. Learning functions with multiple possible output values can be effortlessly integrated into decision tree approaches. +3. ***Disjunctive descriptions may be required*** +4. The ***training data may contain errors*** – ***Decision tree learning methods are robust to errors,*** both errors in classifications of the training examples and errors in the attribute +values that describe these examples. +5. ***Missing attribute values could be present in the training data.*** Using decision tree approaches is possible even in cases where some training examples have missing values. + +# Decision Tree Algorithm +The decision tree method classifies the data according to a tree structure. The root node, that holds the complete dataset, is where it all begins. The algorithm then determines which feature, according to a certain criterion like information gain or Gini impurity, is appropriate for splitting the dataset. Subsets of the dataset are then created according to the values of the chosen feature. Until a halting condition is satisfied—for example, obtaining a minimal number of samples per leaf node or a maximum tree depth—this procedure is repeated recursively for every subset. + + +### Which Attribute Is the Best Classifier? +- The ID3 algorithm's primary idea is choose which characteristic to test at each tree node. +- Information gain, a statistical feature that quantifies how well a certain attribute divides the training samples into groups based on the target classification. +- When building the tree, ID3 chooses a candidate attribute  using the information gain metric. + +## Entropy & Information + +**Entropy** is a metric that quantifies the level of impurity or uncertainty present in a given dataset. When it comes to decision trees, entropy measures how similar the target variable is within a specific node or subset of the data. It is utilized for assessing the quality of potential splits during the tree construction process. + +The entropy of a node is calculated as: +__Entropy = -Σ(pi * log2(pi))__ + +where `p``i` is the proportion of instances belonging to class `i` in the current node. The entropy is at its maximum when all classes are equally represented in the node, indicating maximum impurity or uncertainty. + +**Information Gain** is a measure used to estimate the possible reduction in entropy achieved by separating the data according to a certain attribute. It quantifies the projected decrease in impurity or uncertainty after the separation. + +The information gain for a feature `A` is calculated as: +__Information Gain = Entropy(parent) - Σ(weight(child) * Entropy(child))__ + +### Example of a Decision Tree +Let us look at a basic decision tree example that predicts a person's likelihood of playing tennis based on climate conditions + +**Data Set:** +--- +| Day | Outlook | Temperature | Humidity | Wind | PlayTennis | +|-----|---------|-------------|----------|------|------------| +| D1 | Sunny | Hot | High | Weak | No | +| D2 | Sunny | Hot | High | Strong | No | +| D3 | Overcast| Hot | High | Weak | Yes | +| D4 | Rain | Mild | High | Weak | Yes | +| D5 | Rain | Cool | Normal | Weak | Yes | +| D6 | Rain | Cool | Normal | Strong | No | +| D7 | Overcast| Cool | Normal | Strong | Yes | +| D8 | Sunny | Mild | High | Weak | No | +| D9 | Sunny | Cool | Normal | Weak | Yes | +| D10 | Rain | Mild | Normal | Weak | Yes | +| D11 | Sunny | Mild | Normal | Strong | Yes | +| D12 | Overcast| Mild | High | Strong | Yes | +| D13 | Overcast| Hot | Normal | Weak | Yes | +| D14 | Rain | Mild | High | Strong | No | +--- + + +1. Calculate the entropy of the entire dataset. +2. For each feature, calculate the information gain by splitting the data based on that feature. +3. Select the feature with the highest information gain to create the root node. +4. Repeat steps 1-3 for each child node until a stopping criterion is met (e.g., all instances in a node belong to the same class, or the maximum depth is reached). + +Let's start with calculating the entropy of the entire dataset: +Total instances: 14 +No instances: 5 +Yes instances: 9 + +**Entropy** = -((5/14) * log2(5/14) + (9/14) * log2(9/14)) = 0.940 + +Now, we'll calculate the information gain for each feature: + +**Outlook**: +- Sunny: 2 No, 3 Yes (Entropy = 0.971) +- Overcast: 0 No, 4 Yes (Entropy = 0) +- Rain: 3 No, 2 Yes (Entropy = 0.971) + +Information Gain = 0.940 - ((5/14) * 0.971 + (4/14) * 0 + (5/14) * 0.971) = 0.246 + +**Temperature**: +- Hot: 2 No, 2 Yes (Entropy = 1) +- Mild: 2 No, 4 Yes (Entropy = 0.811) +- Cool: 1 No, 3 Yes (Entropy = 0.918) + +Information Gain = 0.940 - ((4/14) * 1 + (6/14) * 0.811 + (4/14) * 0.918) = 0.029 + +**Humidity**: +- High: 3 No, 4 Yes (Entropy = 0.985) +- Normal: 2 No, 5 Yes (Entropy = 0.971) + +Information Gain = 0.940 - ((7/14) * 0.985 + (7/14) * 0.971) = 0.012 + +**Wind**: +- Weak: 2 No, 6 Yes (Entropy = 0.811) +- Strong: 3 No, 3 Yes (Entropy = 1) + +Information Gain = 0.940 - ((8/14) * 0.811 + (6/14) * 1) = 0.048 + +The feature with the highest information gain is Outlook, so we'll create the root node based on that. + +**Step 1: Root Node (Outlook)** +``` + Root Node (Outlook) + / | \ + Sunny Overcast Rain + Entropy: 0.971 Entropy: 0 Entropy: 0.971 + 5 instances 4 instances 5 instances + +``` + +Now, we'll continue building the tree by recursively splitting the child nodes based on the feature with the highest information gain within each subset. + +**Step 2: Splitting Sunny and Rain Nodes** + +For the Sunny node: +- Temperature: + - Hot: 2 No, 0 Yes (Entropy = 0) + - Mild: 0 No, 3 Yes (Entropy = 0) + - Cool: 0 No, 0 Yes (Entropy = 0) + Information Gain = 0.971 + +- Humidity: + - High: 1 No, 2 Yes (Entropy = 0.918) + - Normal: 1 No, 1 Yes (Entropy = 1) + Information Gain = 0.153 + +- Wind: + - Weak: 1 No, 2 Yes (Entropy = 0.918) + - Strong: 1 No, 1 Yes (Entropy = 1) + Information Gain = 0.153 + +The highest information gain is achieved by splitting on Temperature, so we'll create child nodes for Sunny based on Temperature. + +For the Rain node: +- Wind: + - Weak: 1 No, 3 Yes (Entropy = 0.918) + - Strong: 2 No, 0 Yes (Entropy = 0) + Information Gain = 0.153 + +Since there is only one feature left (Wind), we'll create child nodes for Rain based on Wind. + +**Step 3: Updated Decision Tree** +``` + Root Node (Outlook) + / | \ + Sunny Overcast Rain + / | \ Entropy: 0 / \ + Hot Mild Cool 4 instances Weak Strong + Entropy: 0 Entropy: 0 Entropy: 0.918 Entropy: 0 + 2 instances 3 instances 4 instances 1 instance +``` +At this point, all leaf nodes are either pure (entropy = 0) or have instances belonging to a single class. Therefore, we can stop the tree construction process. + +**Step 4: Pruning the Decision Tree** + +The decision tree we constructed in the previous steps is a complete tree that perfectly classifies the training data. However, this can lead to overfitting, meaning the tree may perform poorly on new, unseen data due to its complexity and memorization of noise in the training set. + +To address this, we can prune the tree by removing some of the leaf nodes or branches that contribute little to the overall classification accuracy. Pruning helps to generalize the tree and improve its performance on unseen data. + +There are various pruning techniques, such as: + +1. **Pre-pruning**: Stopping the tree growth based on a pre-defined criterion (e.g., maximum depth, minimum instances in a node, etc.). +2. **Post-pruning**: Growing the tree to its full depth and then removing subtrees or branches based on a pruning criterion. + +>We can observe that the "Cool" node under the "Sunny" branch has no instances in the training data. Removing this node will not affect the classification accuracy on the training set, and it may help generalize the tree better. + +**Step 5: Pruned Decision Tree** +``` + Root Node (Outlook) + / | \ + / | \ + Sunny Overcast Rain + / \ Entropy: 0 / \ + Hot Mild 4 instances Weak Strong +Entropy: 0 Entropy: 0.918 Entropy: 0 Entropy: 0 + 2 instances 4 instances 3 instances 2 instances +``` + +**Step 6: Visualizing the Decision Tree** + +Decision trees can be visualized graphically to provide a clear representation of the hierarchical structure and the decision rules. This visualization can aid in understanding the tree's logic and interpreting the results. + +There are various tools and libraries available for visualizing decision trees. One popular library in Python is `graphviz`, which can create tree-like diagrams and visualizations. + +Here's an example of how to visualize our pruned decision tree using `graphviz` in Python: + +```python +import graphviz +from sklearn import tree + +# Create a decision tree classifier +decision_tree_classifier = tree.DecisionTreeClassifier() + +# Train the classifier on the dataset X and labels y +decision_tree_classifier.fit(X, y) + +# Visualize the decision tree +tree_dot_data = tree.export_graphviz(decision_tree_classifier, out_file=None, + feature_names=['Outlook', 'Temperature', 'Humidity', 'Wind'], + class_names=['No', 'Yes'], filled=True, rounded=True, special_characters=True) + +# Create a graph from the DOT data +graph = graphviz.Source(tree_dot_data) + +# Render and save the decision tree as an image file +graph.render("decision_tree") + +``` +``` + Outlook + / | \ + Sunny Overcast Rain + / | / \ + Humidity Yes Wind Wind + / \ / \ +High Normal Weak Strong + No Yes Yes No +``` + +The final decision tree classifies instances based on the following rules: + +- If Outlook is Overcast, PlayTennis is Yes +- If Outlook is Sunny and Temperature is Hot, PlayTennis is No +- If Outlook is Sunny and Temperature is Mild, PlayTennis is Yes +- If Outlook is Sunny and Temperature is Cool, PlayTennis is Yes (no instances in the dataset) +- If Outlook is Rain and Wind is Weak, PlayTennis is Yes +- If Outlook is Rain and Wind is Strong, PlayTennis is No + +> Note that the calculated entropies and information gains may vary slightly depending on the specific implementation and rounding methods used. \ No newline at end of file diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 82596a2f..971efaf6 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -1,3 +1,3 @@ # List of sections -- [Section title](filename.md) +- [Decision Tree Learning](Decision-Tree.md) From 5e95059765b6cb99498c1302b94f68c8d5ee482c Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Thu, 23 May 2024 02:57:11 +0530 Subject: [PATCH 166/656] Update dice_roller.md --- contrib/mini-projects/dice_roller.md | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/contrib/mini-projects/dice_roller.md b/contrib/mini-projects/dice_roller.md index 6e1b793b..0f4e5f93 100644 --- a/contrib/mini-projects/dice_roller.md +++ b/contrib/mini-projects/dice_roller.md @@ -1,14 +1,18 @@ -Dice Roller -
-The aim of this project is to replicate a dice and generate a random number from the numbers 1 to 6.

+## Dice Roller + +The aim of this project is to replicate a dice and generate a random number from the numbers 1 to 6. + For this first we will import the random library which will help make random choices. + ``` import random def dice(): dice_no = random.choice([1,2,3,4,5,6]) return "You got " + str(dice_no) ``` -The above snippet of code defines a function called "dice( )" which makes the random choice and returns the number that is generated. + +The above snippet of code defines a function called `dice()` which makes the random choice and returns the number that is generated. + ``` def roll_dice(): print("Hey Guys, you will now roll a single dice using Python!") @@ -24,6 +28,9 @@ def roll_dice(): print("Thanks for rolling the dice.") roll_dice() ``` -The above code defines a function called "roll_dice( )" which interacts with the user.
-It prompts the user to give an input and if the input is k,the code proceeds further to generate a random number or gives the message of invalid input and asks the user to try again.
-After the dice has been rolled once, the function asks the user whether they want a reroll in the form of a yes or no question.The dice is rolled again if the user gives 'yes' as an answer and exits the code if the user replies with anything other than yes. + +The above code defines a function called `roll_dice()` which interacts with the user. + +It prompts the user to give an input and if the input is `k`,the code proceeds further to generate a random number or gives the message of invalid input and asks the user to try again. + +After the dice has been rolled once, the function asks the user whether they want a reroll in the form of a `yes` or `no` question. The dice is rolled again if the user gives `yes` as an answer and exits the code if the user replies with anything other than yes. From 4d86a0af83d35990a71144e88c8979e5146529e8 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Thu, 23 May 2024 04:15:56 +0530 Subject: [PATCH 167/656] Update index.md --- contrib/machine-learning/index.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 40a22370..44b39d4e 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -1,4 +1,6 @@ # List of sections +- [Section title](filename.md) - [Confusion Matrix](confusion-matrix.md) - [Support Vector Machine Algorithm](support-vector-machine.md) + From 428bd62cd219830b77dde615f5a0efb3432afbb1 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Thu, 23 May 2024 04:20:12 +0530 Subject: [PATCH 168/656] Update confusion-matrix.md --- contrib/machine-learning/confusion-matrix.md | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/contrib/machine-learning/confusion-matrix.md b/contrib/machine-learning/confusion-matrix.md index ac5cd317..4bedf667 100644 --- a/contrib/machine-learning/confusion-matrix.md +++ b/contrib/machine-learning/confusion-matrix.md @@ -1,4 +1,6 @@ -Confusion Matrix - A confusion matrix is a fundamental performance evaluation tool used in machine learning to assess the accuracy of a classification model. It is an N x N matrix, where N represents the number of target classes. +## Confusion Matrix + +A confusion matrix is a fundamental performance evaluation tool used in machine learning to assess the accuracy of a classification model. It is an N x N matrix, where N represents the number of target classes. For binary classification, it results in a 2 x 2 matrix that outlines four key parameters: 1. True Positive (TP) - The predicted value matches the actual value, or the predicted class matches the actual class. @@ -20,15 +22,18 @@ To implement the confusion matrix in Python, we can use the confusion_matrix() f The function returns a 2D array that represents the confusion matrix. We can also visualize the confusion matrix using a heatmap. +```python # Import necessary libraries import numpy as np -from sklearn.metrics import confusion_matrix,classification_report +from sklearn.metrics import confusion_matrix, classification_report import seaborn as sns import matplotlib.pyplot as plt # Create the NumPy array for actual and predicted labels -actual = np.array(['Apple', 'Apple', 'Apple', 'Not Apple', 'Apple', 'Not Apple', 'Apple', 'Apple', 'Not Apple', 'Not Apple']) -predicted = np.array(['Apple', 'Not Apple', 'Apple', 'Not Apple', 'Apple', 'Apple', 'Apple', 'Apple', 'Not Apple', 'Not Apple']) +actual = np.array(['Apple', 'Apple', 'Apple', 'Not Apple', 'Apple', + 'Not Apple', 'Apple', 'Apple', 'Not Apple', 'Not Apple']) +predicted = np.array(['Apple', 'Not Apple', 'Apple', 'Not Apple', 'Apple', + 'Apple', 'Apple', 'Apple', 'Not Apple', 'Not Apple']) # Compute the confusion matrix cm = confusion_matrix(actual,predicted) @@ -46,8 +51,11 @@ plt.show() # Classifications Report based on Confusion Metrics print(classification_report(actual, predicted)) +``` + +### Results -# Results +``` 1. Confusion Matrix: [[5 1] [1 3]] @@ -59,3 +67,4 @@ Not Apple 0.75 0.75 0.75 4 accuracy 0.80 10 macro avg 0.79 0.79 0.79 10 weighted avg 0.80 0.80 0.80 10 +``` From 34131322a2753f32d44630e1c127a6716b099fca Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Thu, 23 May 2024 04:22:17 +0530 Subject: [PATCH 169/656] Update support-vector-machine.md --- .../support-vector-machine.md | 20 ++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/contrib/machine-learning/support-vector-machine.md b/contrib/machine-learning/support-vector-machine.md index 90ee9223..0117e9f4 100644 --- a/contrib/machine-learning/support-vector-machine.md +++ b/contrib/machine-learning/support-vector-machine.md @@ -1,12 +1,19 @@ +## Support Vector Machine + Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning. + SVM can be of two types - 1. Linear SVM: Linear SVM is used for linearly separable data, which means if a dataset can be classified into two classes by using a single straight line, then such data is termed as linearly separable data, and classifier is used called as Linear SVM classifier. 2. Non-linear SVM: Non-Linear SVM is used for non-linearly separated data, which means if a dataset cannot be classified by using a straight line, then such data is termed as non-linear data and classifier used is called as Non-linear SVM classifier. -Working of SVM - The goal of SVM is to find a hyperplane that separates the data points into different classes. A hyperplane is a line in 2D space, a plane in 3D space, or a higher-dimensional surface in n-dimensional space. The hyperplane is chosen in such a way that it maximizes the margin, which is the distance between the hyperplane and the closest data points of each class. The closest data points are called the support vectors. -The distance between the hyperplane and a data point "x" can be calculated using the formula − +Working of SVM - The goal of SVM is to find a hyperplane that separates the data points into different classes. A hyperplane is a line in 2D space, a plane in 3D space, or a higher-dimensional surface in n-dimensional space. The hyperplane is chosen in such a way that it maximizes the margin, which is the distance between the hyperplane and the closest data points of each class. The closest data points are called the support vectors. + +The distance between the hyperplane and a data point "x" can be calculated using the formula − +``` distance = (w . x + b) / ||w|| +``` where "w" is the weight vector, "b" is the bias term, and "||w||" is the Euclidean norm of the weight vector. The weight vector "w" is perpendicular to the hyperplane and determines its orientation, while the bias term "b" determines its position. + The optimal hyperplane is found by solving an optimization problem, which is to maximize the margin subject to the constraint that all data points are correctly classified. In other words, we want to find the hyperplane that maximizes the margin between the two classes while ensuring that no data point is misclassified. This is a convex optimization problem that can be solved using quadratic programming. If the data points are not linearly separable, we can use a technique called kernel trick to map the data points into a higher-dimensional space where they become separable. The kernel function computes the inner product between the mapped data points without computing the mapping itself. This allows us to work with the data points in the higherdimensional space without incurring the computational cost of mapping them. 1. Hyperplane: @@ -19,8 +26,9 @@ It may be defined as the gap between two lines on the closet data points of diff We will use the famous Iris dataset, which contains the sepal length, sepal width, petal length, and petal width of three species of iris flowers: Iris setosa, Iris versicolor, and Iris virginica. The goal is to classify the flowers into their respective species based on these four features. We load the iris dataset using load_iris and split the data into training and testing sets using train_test_split. We use a test size of 0.2, which means that 20% of the data will be used for testing and 80% for training. We set the random state to 42 to ensure reproducibility of the results. -# Implemetation of SVM in Python +### Implemetation of SVM in Python +```python from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.svm import SVC @@ -45,8 +53,10 @@ y_pred = svm.predict(X_test) # calculate the accuracy of the classifier accuracy = accuracy_score(y_test, y_pred) print("Accuracy:", accuracy) +``` -# Output +#### Output +``` Accuracy: 1 - +``` From 5b74a4be6581161649b2056588b93ac4702ba3d2 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Thu, 23 May 2024 04:54:25 +0530 Subject: [PATCH 170/656] Update Descriptive_Statistics.md --- contrib/pandas/Descriptive_Statistics.md | 190 ++++++++++------------- 1 file changed, 82 insertions(+), 108 deletions(-) diff --git a/contrib/pandas/Descriptive_Statistics.md b/contrib/pandas/Descriptive_Statistics.md index 7775965b..abdb33b2 100644 --- a/contrib/pandas/Descriptive_Statistics.md +++ b/contrib/pandas/Descriptive_Statistics.md @@ -10,13 +10,11 @@ Data scientists utilize descriptive statistics to uncover patterns, identify out ```python import pandas as pd import numpy as np -``` - -```python df = pd.read_csv("Age-Income-Dataset.csv") df ``` + | | Age | Income | | --- | ----------- | ------ | | 0 | Young | 25000 | @@ -70,14 +68,10 @@ df | 48 | Young | 68400 | | 49 | Middle Age | 51700 | - - ```python df.describe() ``` - - | | Income | |-------|-------------| | count | 50.000000 | @@ -101,18 +95,17 @@ $$ \overline{x} = \frac{\sum\limits_{i=1}^{n} x_i}{n} $$ * $\sum$ (sigma) represents the summation symbol, indicating we add up all the values from i=1 to n. * $n$ represents the total number of values in the dataset. - - ```python df['Income'].mean() ``` +#### Result - - +``` 50966.0 +``` - +#### Without pandas ```python @@ -131,12 +124,10 @@ def mean_f(df): mean_f(df) ``` -Without pandas Library -> -Average of Income is 50966.0 +Average of Income: -With pandas Library -> -50966.0 - +- Without pandas Library -> 50966.0 +- With pandas Library -> 50966.0 ### Median @@ -163,21 +154,17 @@ $$ M = \frac{x_{n/2} + x_{(n/2)+1}}{2} $$ * n represents the total number of observations in the dataset. * $x$ represents the individual value. - - - - ```python df['Income'].median() ``` +#### Result - - +``` 46850.0 +``` - - +#### Without pandas ```python def median_f(df): @@ -198,18 +185,15 @@ def median_f(df): median_f(df) ``` -Median without library -> -Median of Income is 49850.0 \ -Median with library -> -46850.0 - +Median of Income: -### Mode +- Median without library -> 49850.0 +- Median with library -> 46850.0 +### Mode The mode is a measure of central tendency that represents the value or values that occur most frequently in a dataset. Unlike the mean and median, which focus on the average or middle value, the mode identifies the most common value(s) in the dataset. - ```python def mode_f(df): for col in df.columns: @@ -240,17 +224,17 @@ def mode_f(df): mode_f(df) ``` +#### Result + +``` Column: Age -Without pandas Library -> \ +Without pandas Library -> Mode of Age is Old -With pandas Library -> \ -0 Old \ +With pandas Library -> +0 Old Name: Age, dtype: object - - - - +``` ### Standard Deviation @@ -264,18 +248,17 @@ $$s = \sqrt{\frac{\sum(x_i-\overline{x})^{2}}{n-1}}$$ * $\overline{x}$ (x bar) represents the mean value of the dataset. * $n$ represents the total number of values in the dataset. - ```python df['Income'].std() ``` +#### Result - - +``` 21096.683267707253 +``` - - +#### Without pandas ```python import math @@ -304,17 +287,17 @@ With pandas library: -> Std : 20884.6509187968 +### Count ```python df['Income'].count() ``` +#### Result - - +``` 50 - - +``` ### Minimum @@ -323,13 +306,13 @@ df['Income'].count() df['Income'].min() ``` +#### Result - - +``` 15000 +``` - - +#### Without pandas ```python def min_f(df): @@ -354,13 +337,13 @@ With pandas Library-> 15000 df['Income'].max() ``` +#### Result - - +``` 93000 +``` - - +#### Without pandas ```python def max_f(df): @@ -374,8 +357,8 @@ def max_f(df): max_f(df) ``` - Without pandas Library-> 93000 - With pandas Library-> 93000 +Without pandas Library-> 93000 +With pandas Library-> 93000 ### Percentile @@ -385,25 +368,23 @@ max_f(df) df['Income'].quantile(0.25) ``` +#### Result - - +``` 33475.0 - - - +``` ```python df['Income'].quantile(0.75) ``` +#### Result - - +``` 65400.0 +``` - - +#### Without pandas ```python def percentile_f(df,percentile): @@ -432,9 +413,6 @@ df = pd.read_csv('Iris.csv') df.head(5) ``` - - - | | Id | SepalLengthCm | SepalWidthCm | PetalLengthCm | PetalWidthCm | Species | |---|----|---------------|--------------|---------------|--------------|-------------| | 0 | 1 | 5.1 | 3.5 | 1.4 | 0.2 | Iris-setosa | @@ -443,9 +421,6 @@ df.head(5) | 3 | 4 | 4.6 | 3.1 | 1.5 | 0.2 | Iris-setosa | | 4 | 5 | 5.0 | 3.6 | 1.4 | 0.2 | Iris-setosa | - - - ```python df.drop(['Id','Species'],axis=1,inplace= True) ``` @@ -463,13 +438,10 @@ $$ COV(X,Y) = \frac{\sum\limits_{i=1}^{n} (X_i - \overline{X}) (Y_i - \overline{ * $\overline{X}$ and $\overline{Y}$ represent the mean values for variables X and Y, respectively. * $n$ represents the total number of observations in the dataset. - - ```python df.cov() ``` - | | SepalLengthCm | SepalWidthCm | PetalLengthCm | PetalWidthCm | |-------------------|-------------- |---------------|-----------------|--------------| | **SepalLengthCm** | 0.685694 | -0.039268 | 1.273682 | 0.516904 | @@ -477,7 +449,7 @@ df.cov() | **PetalLengthCm** | 1.273682 | -0.321713 | 3.113179 | 1.296387 | | **PetalWidthCm** | 0.516904 | -0.117981 | 1.296387 | 0.582414 | - +#### Without pandas ```python def cov_f(df): @@ -496,27 +468,29 @@ def cov_f(df): cov_f(df) ``` -Covariance of SepalLengthCm and SepalLengthCm is : 0.6856935123042504 \ -Covariance of SepalLengthCm and SepalWidthCm is : -0.03926845637583892 \ -Covariance of SepalLengthCm and PetalLengthCm is : 1.2736823266219246 \ +#### Result + +``` +Covariance of SepalLengthCm and SepalLengthCm is : 0.6856935123042504 +Covariance of SepalLengthCm and SepalWidthCm is : -0.03926845637583892 +Covariance of SepalLengthCm and PetalLengthCm is : 1.2736823266219246 Covariance of SepalLengthCm and PetalWidthCm is : 0.5169038031319911 -Covariance of SepalWidthCm and SepalLengthCm is : -0.03926845637583892 \ -Covariance of SepalWidthCm and SepalWidthCm is : 0.1880040268456377 \ -Covariance of SepalWidthCm and PetalLengthCm is : -0.32171275167785235 \ +Covariance of SepalWidthCm and SepalLengthCm is : -0.03926845637583892 +Covariance of SepalWidthCm and SepalWidthCm is : 0.1880040268456377 +Covariance of SepalWidthCm and PetalLengthCm is : -0.32171275167785235 Covariance of SepalWidthCm and PetalWidthCm is : -0.11798120805369115 -Covariance of PetalLengthCm and SepalLengthCm is : 1.2736823266219246 \ -Covariance of PetalLengthCm and SepalWidthCm is : -0.32171275167785235 \ -Covariance of PetalLengthCm and PetalLengthCm is : 3.113179418344519 \ +Covariance of PetalLengthCm and SepalLengthCm is : 1.2736823266219246 +Covariance of PetalLengthCm and SepalWidthCm is : -0.32171275167785235 +Covariance of PetalLengthCm and PetalLengthCm is : 3.113179418344519 Covariance of PetalLengthCm and PetalWidthCm is : 1.2963874720357946 -Covariance of PetalWidthCm and SepalLengthCm is : 0.5169038031319911 \ -Covariance of PetalWidthCm and SepalWidthCm is : -0.11798120805369115 \ -Covariance of PetalWidthCm and PetalLengthCm is : 1.2963874720357946 \ +Covariance of PetalWidthCm and SepalLengthCm is : 0.5169038031319911 +Covariance of PetalWidthCm and SepalWidthCm is : -0.11798120805369115 +Covariance of PetalWidthCm and PetalLengthCm is : 1.2963874720357946 Covariance of PetalWidthCm and PetalWidthCm is : 0.5824143176733781 - - +```` ### Correlation @@ -527,9 +501,6 @@ $$r = \frac{n(\sum xy) - (\sum x)(\sum y)}{\sqrt{n(\sum x^2) - (\sum x)^2} \cdot * r represents the correlation coefficient. * n is the number of data points. - - - ```python df.corr() ``` @@ -541,6 +512,7 @@ df.corr() | **PetalLengthCm** | 0.871754 | -0.420516 | 1.000000 | 0.962757 | | **PetalWidthCm** | 0.817954 | -0.356544 | 0.962757 | 1.000000 | +#### Without using pandas ```python import math @@ -576,24 +548,26 @@ def corr_f(df): corr_f(df) ``` -Correlation of SepalLengthCm and SepalLengthCm :1.0 \ -Correlation of SepalLengthCm and SepalWidthCm :-0.10936924995067286 \ -Correlation of SepalLengthCm and PetalLengthCm :0.8717541573048861 \ +#### Result + +``` +Correlation of SepalLengthCm and SepalLengthCm :1.0 +Correlation of SepalLengthCm and SepalWidthCm :-0.10936924995067286 +Correlation of SepalLengthCm and PetalLengthCm :0.8717541573048861 Correlation of SepalLengthCm and PetalWidthCm :0.8179536333691775 -Correlation of SepalWidthCm and SepalLengthCm :-0.10936924995067286 \ -Correlation of SepalWidthCm and SepalWidthCm :1.0 \ -Correlation of SepalWidthCm and PetalLengthCm :-0.42051609640118826 \ +Correlation of SepalWidthCm and SepalLengthCm :-0.10936924995067286 +Correlation of SepalWidthCm and SepalWidthCm :1.0 +Correlation of SepalWidthCm and PetalLengthCm :-0.42051609640118826 Correlation of SepalWidthCm and PetalWidthCm :-0.3565440896138223 -Correlation of PetalLengthCm and SepalLengthCm :0.8717541573048861 \ -Correlation of PetalLengthCm and SepalWidthCm :-0.42051609640118826 \ -Correlation of PetalLengthCm and PetalLengthCm :1.0 \ +Correlation of PetalLengthCm and SepalLengthCm :0.8717541573048861 +Correlation of PetalLengthCm and SepalWidthCm :-0.42051609640118826 +Correlation of PetalLengthCm and PetalLengthCm :1.0 Correlation of PetalLengthCm and PetalWidthCm :0.9627570970509656 -Correlation of PetalWidthCm and SepalLengthCm :0.8179536333691775 \ -Correlation of PetalWidthCm and SepalWidthCm :-0.3565440896138223 \ -Correlation of PetalWidthCm and PetalLengthCm :0.9627570970509656 \ +Correlation of PetalWidthCm and SepalLengthCm :0.8179536333691775 +Correlation of PetalWidthCm and SepalWidthCm :-0.3565440896138223 +Correlation of PetalWidthCm and PetalLengthCm :0.9627570970509656 Correlation of PetalWidthCm and PetalWidthCm :1.0 - - +``` From 519a5052ec83364fc2f74b4345f65dab63650e1d Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Thu, 23 May 2024 05:10:19 +0530 Subject: [PATCH 171/656] Update index.md --- contrib/machine-learning/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 1abe7077..7a247eba 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -1,3 +1,3 @@ # List of sections -- [TensorFlow.md](learn-python\contrib\machine-learning\tensorFlow.md) +- [TensorFlow.md](tensorFlow.md) From eefc5d342a7b08a1bbad81ca802e2837b6735171 Mon Sep 17 00:00:00 2001 From: Yodha Sudarsi <101962069+Yodha-Sudarsi@users.noreply.github.com> Date: Thu, 23 May 2024 05:36:06 +0530 Subject: [PATCH 172/656] Installing numpy (#463) * Updated List of sections * Updated index.md Added info about how to install NumPy in a system. * Added installing numpy.md file and updated the index.md * Added installing-numpy.md file and updated the index.md * Create installing-numpy.md for installation * Update index.md Added operations-on-arrays.md * Create operations-on-arrays.md * Update index.md * Update index.md * Rename installing-numpy.md to installing_numpy.md * Rename operations-on-arrays.md to operations_on_arrays.md * Update installing_numpy.md * Update index.md * Rename installing_numpy.md to installing-numpy.md * Rename operations_on_arrays.md to operations-on-arrays.md * Update operations-on-arrays.md * Update operations-on-arrays.md * Revert "Update operations-on-arrays.md" * Delete contrib/numpy/operations-on-arrays.md * Update index.md --------- Co-authored-by: Ankit Mahato --- contrib/numpy/index.md | 1 + contrib/numpy/installing-numpy.md | 82 +++++++++++++++++++++++++++++++ 2 files changed, 83 insertions(+) create mode 100644 contrib/numpy/installing-numpy.md diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 5d3f63b8..ff0f25bf 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -1,3 +1,4 @@ # List of sections +- [Installing NumPy](installing-numpy.md) - [Introduction](introduction.md) diff --git a/contrib/numpy/installing-numpy.md b/contrib/numpy/installing-numpy.md new file mode 100644 index 00000000..7339e8cf --- /dev/null +++ b/contrib/numpy/installing-numpy.md @@ -0,0 +1,82 @@ +# Installing NumPy + +NumPy is the fundamental package for scientific computing in Python. +NumPy is used for working with arrays. + +The only prerequisite for installing NumPy is Python itself. +# +**Step 1: Check if PIP is Installed** + +Before installing NumPy, it's essential to ensure that PIP (Python Package Installer) is installed on your system. PIP is a package management system used to install and manage Python packages. You can verify if PIP is installed by running a simple command in your terminal or command prompt. + +```bash +pip --version +``` + +If PIP is not currently installed on your system, you can install it by visiting the [pypi.org](https://pypi.org/project/pip/) webpage. + +# + +**Step 2: Installing PIP** + +**get-pip.py** + +This is a Python script that uses some bootstrapping logic to install pip. + +Open a terminal / command prompt and run: + +**Linux** +```bash +python get-pip.py +``` + +**Windows** +```bash +py get-pip.py +``` + +**MacOS** +```bash +python get-pip.py +``` + +# + +**Step 3: Installing NumPy** + +NumPy can be installed either through conda or pip. + +If you use pip, you can install NumPy with: + +```bash +pip install numpy +``` + +If you use conda, you can install NumPy from the defaults or conda-forge channels: + +``` +# Best practice, use an environment rather than install in the base env +conda create -n my-env +conda activate my-env +``` + +``` +# If you want to install from conda-forge +conda config --env --add channels conda-forge +``` + +``` +# The actual install command +conda install numpy +``` + +You can find more information about how to install [NumPy](https://numpy.org/install/) on numpy.org. + +# + +**Step 4: Check if NumPy is Installed** + +We can utilize the "pip show" command not only to display the version but also to determine whether NumPy is installed on the system. +```bash +pip show numpy +``` From aa0a00137b48b0251ff421602c46aa905fb3ef5c Mon Sep 17 00:00:00 2001 From: Yodha Sudarsi <101962069+Yodha-Sudarsi@users.noreply.github.com> Date: Thu, 23 May 2024 05:56:34 +0530 Subject: [PATCH 173/656] Operation on numpy (#464) * Updated List of sections * Updated index.md Added info about how to install NumPy in a system. * Added installing numpy.md file and updated the index.md * Added installing-numpy.md file and updated the index.md * Create installing-numpy.md for installation * Update index.md Added operations-on-arrays.md * Create operations-on-arrays.md * Update index.md * Update index.md * Rename installing-numpy.md to installing_numpy.md * Rename operations-on-arrays.md to operations_on_arrays.md * Update installing_numpy.md * Update index.md * Rename installing_numpy.md to installing-numpy.md * Rename operations_on_arrays.md to operations-on-arrays.md * Update operations-on-arrays.md * Update operations-on-arrays.md * Revert "Update operations-on-arrays.md" * Delete contrib/numpy/installing-numpy.md * Update operations-on-arrays.md --------- Co-authored-by: Ankit Mahato --- contrib/numpy/index.md | 1 + contrib/numpy/operations-on-arrays.md | 281 ++++++++++++++++++++++++++ 2 files changed, 282 insertions(+) create mode 100644 contrib/numpy/operations-on-arrays.md diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index b38006cd..6ee2e9de 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -3,3 +3,4 @@ - [Installing NumPy](installing-numpy.md) - [Introduction](introduction.md) - [NumPy Data Types](datatypes.md) +- [Operations on Arrays in NumPy](operations-on-arrays.md) diff --git a/contrib/numpy/operations-on-arrays.md b/contrib/numpy/operations-on-arrays.md new file mode 100644 index 00000000..e3966d27 --- /dev/null +++ b/contrib/numpy/operations-on-arrays.md @@ -0,0 +1,281 @@ +# Operations on Arrays + +## NumPy Arithmetic Operations + +NumPy offers a broad array of operations for arrays, including arithmetic functions. + +The arithmetic operations in NumPy are popular for their simplicity and efficiency in handling array calculations. + +**Addition** + +we can use the `+` operator to perform element-wise addition between two or more NumPy arrays. + +**Code** +```python +import numpy as np +array_1 = np.array([9, 10, 11, 12]) +array_2 = np.array([1, 3, 5, 7]) +result_1 = array_1 + array_2 +print("Utilizing the + operator:", result_1) +``` + +**Output:** +``` +Utilizing the + operator: [10 13 16 19] +``` + +**Subtraction** + +we can use the `-` operator to perform element-wise subtraction between two or more NumPy arrays. + +**Code** +```python +import numpy as np +array_1 = np.array([9, 10, 11, 12]) +array_2 = np.array([1, 3, 5, 7]) +result_1 = array_1 - array_2 +print("Utilizing the - operator:", result_1) +``` + +**Output:** +``` +Utilizing the - operator: [8 7 6 5] +``` + +**Multiplication** + +we can use the `*` operator to perform element-wise multiplication between two or more NumPy arrays. + +**Code** +```python +import numpy as np +array_1 = np.array([9, 10, 11, 12]) +array_2 = np.array([1, 3, 5, 7]) +result_1 = array_1 * array_2 +print("Utilizing the * operator:", result_1) +``` + +**Output:** +``` +Utilizing the * operator: [9 30 55 84] +``` + +**Division** + +we can use the `/` operator to perform element-wise division between two or more NumPy arrays. + +**Code** +```python +import numpy as np +array_1 = np.array([9, 10, 11, 12]) +array_2 = np.array([1, 3, 5, 7]) +result_1 = array_1 / array_2 +print("Utilizing the / operator:", result_1) +``` + +**Output:** +``` +Utilizing the / operator: [9. 3.33333333 2.2 1.71428571] +``` + +**Exponentiation** + +we can use the `**` operator to perform element-wise exponentiation between two or more NumPy arrays. + +**Code** +```python +import numpy as np +array_1 = np.array([9, 10, 11, 12]) +array_2 = np.array([1, 3, 5, 7]) +result_1 = array_1 ** array_2 +print("Utilizing the ** operator:", result_1) +``` + +**Output:** +``` +Utilizing the ** operator: [9 1000 161051 35831808] +``` + +**Modulus** + +We can use the `%` operator to perform element-wise modulus operations between two or more NumPy arrays. + +**Code** +```python +import numpy as np +array_1 = np.array([9, 10, 11, 12]) +array_2 = np.array([1, 3, 5, 7]) +result_1 = array_1 % array_2 +print("Utilizing the % operator:", result_1) +``` + +**Output:** +``` +Utilizing the % operator: [0 1 1 5] +``` + +
+ +## NumPy Comparision Operations + +
+ +NumPy provides various comparison operators that can compare elements across multiple NumPy arrays. + +**less than operator** + +The `<` operator returns `True` if the value of operand on left is less than the value of operand on right. + +**Code** +```python +import numpy as np +array_1 = np.array([12,15,20]) +array_2 = np.array([20,15,12]) +result_1 = array_1 < array_2 +print("array_1 < array_2:",result_1) +``` +**Output:** +``` +array_1 < array_2 : [True False False] +``` + +**less than or equal to operator** + +The `<=` operator returns `True` if the value of operand on left is lesser than or equal to the value of operand on right. + +**Code** +```python +import numpy as np +array_1 = np.array([12,15,20]) +array_2 = np.array([20,15,12]) +result_1 = array_1 <= array_2 +print("array_1 <= array_2:",result_1) +``` +**Output:** +``` +array_1 <= array_2: [True True False] +``` + +**greater than operator** + +The `>` operator returns `True` if the value of operand on left is greater than the value of operand on right. + +**Code** +```python +import numpy as np +array_1 = np.array([12,15,20]) +array_2 = np.array([20,15,12]) +result_2 = array_1 > array_2 +print("array_1 > array_2:",result_2) +``` +**Output:** +``` +array_1 > array_2 : [False False True] +``` + +**greater than or equal to operator** + +The `>=` operator returns `True` if the value of operand on left is greater than or equal to the value of operand on right. + +**Code** +```python +import numpy as np +array_1 = np.array([12,15,20]) +array_2 = np.array([20,15,12]) +result_2 = array_1 >= array_2 +print("array_1 >= array_2:",result_2) +``` +**Output:** +``` +array_1 >= array_2: [False True True] +``` + +**equal to operator** + +The `==` operator returns `True` if the value of operand on left is same as the value of operand on right. + +**Code** +```python +import numpy as np +array_1 = np.array([12,15,20]) +array_2 = np.array([20,15,12]) +result_3 = array_1 == array_2 +print("array_1 == array_2:",result_3) +``` +**Output:** +``` +array_1 == array_2: [False True False] +``` + +**not equal to operator** + +The `!=` operator returns `True` if the value of operand on left is not equal to the value of operand on right. + +**Code** +```python +import numpy as np +array_1 = np.array([12,15,20]) +array_2 = np.array([20,15,12]) +result_3 = array_1 != array_2 +print("array_1 != array_2:",result_3) +``` +**Output:** +``` +array_1 != array_2: [True False True] +``` + +
+ +## NumPy Logical Operations + +Logical operators perform Boolean algebra. A branch of algebra that deals with `True` and `False` statements. + +It illustrates the logical operations of AND, OR, and NOT using np.logical_and(), np.logical_or(), and np.logical_not() functions, respectively. + +**Logical AND** + +Evaluates the element-wise truth value of `array_1` AND `array_2` + +**Code** +```python +import numpy as np +array_1 = np.array([True, False, True]) +array_2 = np.array([False, False, True]) +print(np.logical_and(array_1, array_2)) +``` +**Output:** +``` +[False False True] +``` + +**Logical OR** + +Evaluates the element-wise truth value of `array_1` OR `array_2` + +**Code** +```python +import numpy as np +array_1 = np.array([True, False, True]) +array_2 = np.array([False, False, True]) +print(np.logical_or(array_1, array_2)) +``` +**Output:** +``` +[True False True] +``` + +**Logical NOT** + +Evaluates the element-wise truth value of `array_1` NOT `array_2` + +**Code** +```python +import numpy as np +array_1 = np.array([True, False, True]) +array_2 = np.array([False, False, True]) +print(np.logical_not(array_1)) +``` +**Output:** +``` +[False True False] +``` From 1ed2c2f5d8566616b54563c2130d37dbee05569d Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Thu, 23 May 2024 06:18:13 +0530 Subject: [PATCH 174/656] Rename TIC_TAC_TOE_GAME.md to tic-tac-toe.md --- contrib/mini-projects/{TIC_TAC_TOE_GAME.md => tic-tac-toe.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/mini-projects/{TIC_TAC_TOE_GAME.md => tic-tac-toe.md} (100%) diff --git a/contrib/mini-projects/TIC_TAC_TOE_GAME.md b/contrib/mini-projects/tic-tac-toe.md similarity index 100% rename from contrib/mini-projects/TIC_TAC_TOE_GAME.md rename to contrib/mini-projects/tic-tac-toe.md From 7d98fe81f13817199ba58f2f2709423bebfe35b7 Mon Sep 17 00:00:00 2001 From: Eshparsi Date: Thu, 23 May 2024 07:20:00 +0530 Subject: [PATCH 175/656] shifted content to ds-algorithms --- contrib/Data-Structure-Graphs/index.md | 3 --- contrib/{Data-Structure-Graphs => ds-algorithms}/graph.md | 0 contrib/ds-algorithms/index.md | 1 + 3 files changed, 1 insertion(+), 3 deletions(-) delete mode 100644 contrib/Data-Structure-Graphs/index.md rename contrib/{Data-Structure-Graphs => ds-algorithms}/graph.md (100%) diff --git a/contrib/Data-Structure-Graphs/index.md b/contrib/Data-Structure-Graphs/index.md deleted file mode 100644 index 6471576d..00000000 --- a/contrib/Data-Structure-Graphs/index.md +++ /dev/null @@ -1,3 +0,0 @@ -# List of sections - -- [Graphs](graph.md) \ No newline at end of file diff --git a/contrib/Data-Structure-Graphs/graph.md b/contrib/ds-algorithms/graph.md similarity index 100% rename from contrib/Data-Structure-Graphs/graph.md rename to contrib/ds-algorithms/graph.md diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 5b521551..347a9d02 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -2,3 +2,4 @@ - [Section title](filename.md) - [Sorting Algorithms](sorting-algorithms.md) +- [Graphs](graph.md) From f52fa729cffddbeed121826024ca972ff97cd1a9 Mon Sep 17 00:00:00 2001 From: Eshparsi <112681516+Eshparsi@users.noreply.github.com> Date: Thu, 23 May 2024 07:28:59 +0530 Subject: [PATCH 176/656] Update index.md --- contrib/ds-algorithms/index.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 347a9d02..adec6aa3 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -1,5 +1,11 @@ # List of sections -- [Section title](filename.md) -- [Sorting Algorithms](sorting-algorithms.md) +- [Queues in Python](Queues.md) - [Graphs](graph.md) +- [Sorting Algorithms](sorting-algorithms.md) +- [Recursion and Backtracking](recursion.md) +- [Divide and Conquer Algorithm](divide-and-conquer-algorithm.md) +- [Searching Algorithms](searching-algorithms.md) +- [Greedy Algorithms](greedy-algorithms.md) +- [Dynamic Programming](dynamic-programming.md) + From bc18bfed040ddae6301112acda317672950291fa Mon Sep 17 00:00:00 2001 From: Eshparsi Date: Thu, 23 May 2024 07:32:15 +0530 Subject: [PATCH 177/656] . --- .../divide-and-conquer-algorithm.md | 54 ++++++ contrib/ds-algorithms/dynamic-programming.md | 132 ++++++++++++++ contrib/ds-algorithms/greedy-algorithms.md | 135 +++++++++++++++ contrib/ds-algorithms/recursion.md | 107 ++++++++++++ contrib/ds-algorithms/searching-algorithms.md | 161 ++++++++++++++++++ 5 files changed, 589 insertions(+) create mode 100644 contrib/ds-algorithms/divide-and-conquer-algorithm.md create mode 100644 contrib/ds-algorithms/dynamic-programming.md create mode 100644 contrib/ds-algorithms/greedy-algorithms.md create mode 100644 contrib/ds-algorithms/recursion.md create mode 100644 contrib/ds-algorithms/searching-algorithms.md diff --git a/contrib/ds-algorithms/divide-and-conquer-algorithm.md b/contrib/ds-algorithms/divide-and-conquer-algorithm.md new file mode 100644 index 00000000..b5a356ea --- /dev/null +++ b/contrib/ds-algorithms/divide-and-conquer-algorithm.md @@ -0,0 +1,54 @@ +# Divide and Conquer Algorithms + +Divide and Conquer is a paradigm for solving problems that involves breaking a problem into smaller sub-problems, solving the sub-problems recursively, and then combining their solutions to solve the original problem. + +## Merge Sort + +Merge Sort is a popular sorting algorithm that follows the divide and conquer strategy. It divides the input array into two halves, recursively sorts the halves, and then merges them. + +**Algorithm Overview:** +- **Divide:** Divide the unsorted list into two sublists of about half the size. +- **Conquer:** Recursively sort each sublist. +- **Combine:** Merge the sorted sublists back into one sorted list. + +```python +def merge_sort(arr): + if len(arr) > 1: + mid = len(arr) // 2 + left_half = arr[:mid] + right_half = arr[mid:] + + merge_sort(left_half) + merge_sort(right_half) + + i = j = k = 0 + + while i < len(left_half) and j < len(right_half): + if left_half[i] < right_half[j]: + arr[k] = left_half[i] + i += 1 + else: + arr[k] = right_half[j] + j += 1 + k += 1 + + while i < len(left_half): + arr[k] = left_half[i] + i += 1 + k += 1 + + while j < len(right_half): + arr[k] = right_half[j] + j += 1 + k += 1 + +arr = [12, 11, 13, 5, 6, 7] +merge_sort(arr) +print("Sorted array:", arr) +``` + +## Complexity Analysis +- **Time Complexity:** O(n log n) in all cases +- **Space Complexity:** O(n) additional space for the merge operation + +--- diff --git a/contrib/ds-algorithms/dynamic-programming.md b/contrib/ds-algorithms/dynamic-programming.md new file mode 100644 index 00000000..43149f86 --- /dev/null +++ b/contrib/ds-algorithms/dynamic-programming.md @@ -0,0 +1,132 @@ +# Dynamic Programming + +Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems and solving each subproblem only once. It stores the solutions to subproblems to avoid redundant computations, making it particularly useful for optimization problems where the solution can be obtained by combining solutions to smaller subproblems. + +## Real-Life Examples of Dynamic Programming +- **Fibonacci Sequence:** Computing the nth Fibonacci number efficiently. +- **Shortest Path:** Finding the shortest path in a graph from a source to a destination. +- **String Edit Distance:** Calculating the minimum number of operations required to transform one string into another. +- **Knapsack Problem:** Maximizing the value of items in a knapsack without exceeding its weight capacity. + +# Some Common Dynamic Programming Techniques + +# 1. Fibonacci Sequence + +The Fibonacci sequence is a classic example used to illustrate dynamic programming. It is a series of numbers where each number is the sum of the two preceding ones, usually starting with 0 and 1. + +**Algorithm Overview:** +- **Base Cases:** The first two numbers in the Fibonacci sequence are defined as 0 and 1. +- **Memoization:** Store the results of previously computed Fibonacci numbers to avoid redundant computations. +- **Recurrence Relation:** Compute each Fibonacci number by adding the two preceding numbers. + +## Fibonacci Sequence Code in Python (Top-Down Approach with Memoization) + +```python +def fibonacci(n, memo={}): + if n in memo: + return memo[n] + if n <= 1: + return n + memo[n] = fibonacci(n-1, memo) + fibonacci(n-2, memo) + return memo[n] + +n = 10 +print(f"The {n}th Fibonacci number is: {fibonacci(n)}.") +``` + +## Fibonacci Sequence Code in Python (Bottom-Up Approach) + +```python +def fibonacci(n): + fib = [0, 1] + for i in range(2, n + 1): + fib.append(fib[i - 1] + fib[i - 2]) + return fib[n] + +n = 10 +print(f"The {n}th Fibonacci number is: {fibonacci(n)}.") +``` + +## Complexity Analysis +- **Time Complexity**: O(n) for both approaches +- **Space Complexity**: O(n) for the top-down approach (due to memoization), O(1) for the bottom-up approach + +
+
+
+ +# 2. Longest Common Subsequence + +The longest common subsequence (LCS) problem is to find the longest subsequence common to two sequences. A subsequence is a sequence that appears in the same relative order but not necessarily contiguous. + +**Algorithm Overview:** +- **Base Cases:** If one of the sequences is empty, the LCS is empty. +- **Memoization:** Store the results of previously computed LCS lengths to avoid redundant computations. +- **Recurrence Relation:** Compute the LCS length by comparing characters of the sequences and making decisions based on whether they match. + +## Longest Common Subsequence Code in Python (Top-Down Approach with Memoization) + +```python +def longest_common_subsequence(X, Y, m, n, memo={}): + if (m, n) in memo: + return memo[(m, n)] + if m == 0 or n == 0: + return 0 + if X[m - 1] == Y[n - 1]: + memo[(m, n)] = 1 + longest_common_subsequence(X, Y, m - 1, n - 1, memo) + else: + memo[(m, n)] = max(longest_common_subsequence(X, Y, m, n - 1, memo), + longest_common_subsequence(X, Y, m - 1, n, memo)) + return memo[(m, n)] + +X = "AGGTAB" +Y = "GXTXAYB" +print("Length of Longest Common Subsequence:", longest_common_subsequence(X, Y, len(X), len(Y))) +``` + +## Complexity Analysis +- **Time Complexity**: O(m * n) for the top-down approach, where m and n are the lengths of the input sequences +- **Space Complexity**: O(m * n) for the memoization table + +
+
+
+ +# 3. 0-1 Knapsack Problem + +The 0-1 knapsack problem is a classic optimization problem where the goal is to maximize the total value of items selected while keeping the total weight within a specified limit. + +**Algorithm Overview:** +- **Base Cases:** If the capacity of the knapsack is 0 or there are no items to select, the total value is 0. +- **Memoization:** Store the results of previously computed subproblems to avoid redundant computations. +- **Recurrence Relation:** Compute the maximum value by considering whether to include the current item or not. + +## 0-1 Knapsack Problem Code in Python (Top-Down Approach with Memoization) + +```python +def knapsack(weights, values, capacity, n, memo={}): + if (capacity, n) in memo: + return memo[(capacity, n)] + if n == 0 or capacity == 0: + return 0 + if weights[n - 1] > capacity: + memo[(capacity, n)] = knapsack(weights, values, capacity, n - 1, memo) + else: + memo[(capacity, n)] = max(values[n - 1] + knapsack(weights, values, capacity - weights[n - 1], n - 1, memo), + knapsack(weights, values, capacity, n - 1, memo)) + return memo[(capacity, n)] + +weights = [10, 20, 30] +values = [60, 100, 120] +capacity = 50 +n = len(weights) +print("Maximum value that can be obtained:", knapsack(weights, values, capacity, n)) +``` + +## Complexity Analysis +- **Time Complexity**: O(n * W) for the top-down approach, where n is the number of items and W is the capacity of the knapsack +- **Space Complexity**: O(n * W) for the memoization table + +
+
+
\ No newline at end of file diff --git a/contrib/ds-algorithms/greedy-algorithms.md b/contrib/ds-algorithms/greedy-algorithms.md new file mode 100644 index 00000000..c79ee991 --- /dev/null +++ b/contrib/ds-algorithms/greedy-algorithms.md @@ -0,0 +1,135 @@ +# Greedy Algorithms + +Greedy algorithms are simple, intuitive algorithms that make a sequence of choices at each step with the hope of finding a global optimum. They are called "greedy" because at each step, they choose the most advantageous option without considering the future consequences. Despite their simplicity, greedy algorithms are powerful tools for solving optimization problems, especially when the problem exhibits the greedy-choice property. + +## Real-Life Examples of Greedy Algorithms +- **Coin Change:** Finding the minimum number of coins to make a certain amount of change. +- **Job Scheduling:** Assigning tasks to machines to minimize completion time. +- **Huffman Coding:** Constructing an optimal prefix-free binary code for data compression. +- **Fractional Knapsack:** Selecting items to maximize the value within a weight limit. + +# Some Common Greedy Algorithms + +# 1. Coin Change Problem + +The coin change problem is a classic example of a greedy algorithm. Given a set of coin denominations and a target amount, the objective is to find the minimum number of coins required to make up that amount. + +**Algorithm Overview:** +- **Greedy Strategy:** At each step, the algorithm selects the largest denomination coin that is less than or equal to the remaining amount. +- **Repeat Until Amount is Zero:** The process continues until the remaining amount becomes zero. + +## Coin Change Code in Python + +```python +def coin_change(coins, amount): + coins.sort(reverse=True) + num_coins = 0 + for coin in coins: + num_coins += amount // coin + amount %= coin + if amount == 0: + return num_coins + else: + return -1 + +coins = [1, 5, 10, 25] +amount = 63 +result = coin_change(coins, amount) +if result != -1: + print(f"Minimum number of coins required: {result}.") +else: + print("It is not possible to make the amount with the given denominations.") +``` + +## Complexity Analysis +- **Time Complexity**: O(n log n) for sorting (if not pre-sorted), O(n) for iteration +- **Space Complexity**: O(1) + +
+
+
+ +# 2. Activity Selection Problem + +The activity selection problem involves selecting the maximum number of mutually compatible activities that can be performed by a single person or machine, assuming that a person can only work on one activity at a time. + +**Algorithm Overview:** +- **Greedy Strategy:** Sort the activities based on their finish times. +- **Selecting Activities:** Iterate through the sorted activities, selecting each activity if it doesn't conflict with the previously selected ones. + +## Activity Selection Code in Python + +```python +def activity_selection(start, finish): + n = len(start) + activities = [] + i = 0 + activities.append(i) + for j in range(1, n): + if start[j] >= finish[i]: + activities.append(j) + i = j + return activities + +start = [1, 3, 0, 5, 8, 5] +finish = [2, 4, 6, 7, 9, 9] +selected_activities = activity_selection(start, finish) +print("Selected activities:", selected_activities) +``` + +## Complexity Analysis +- **Time Complexity**: O(n log n) for sorting (if not pre-sorted), O(n) for iteration +- **Space Complexity**: O(1) + +
+
+
+ +# 3. Huffman Coding + +Huffman coding is a method of lossless data compression that efficiently represents characters or symbols in a file. It uses variable-length codes to represent characters, with shorter codes assigned to more frequent characters. + +**Algorithm Overview:** +- **Frequency Analysis:** Determine the frequency of each character in the input data. +- **Building the Huffman Tree:** Construct a binary tree where each leaf node represents a character and the path to the leaf node determines its code. +- **Assigning Codes:** Traverse the Huffman tree to assign codes to each character, with shorter codes for more frequent characters. + +## Huffman Coding Code in Python + +```python +from heapq import heappush, heappop, heapify +from collections import defaultdict + +def huffman_coding(data): + frequency = defaultdict(int) + for char in data: + frequency[char] += 1 + + heap = [[weight, [symbol, ""]] for symbol, weight in frequency.items()] + heapify(heap) + + while len(heap) > 1: + lo = heappop(heap) + hi = heappop(heap) + for pair in lo[1:]: + pair[1] = '0' + pair[1] + for pair in hi[1:]: + pair[1] = '1' + pair[1] + heappush(heap, [lo[0] + hi[0]] + lo[1:] + hi[1:]) + + return sorted(heappop(heap)[1:], key=lambda p: (len(p[-1]), p)) + +data = "Huffman coding is a greedy algorithm" +encoded_data = huffman_coding(data) +print("Huffman Codes:") +for symbol, code in encoded_data: + print(f"{symbol}: {code}") +``` + +## Complexity Analysis +- **Time Complexity**: O(n log n) for heap operations, where n is the number of unique characters +- **Space Complexity**: O(n) for the heap + +
+
+
diff --git a/contrib/ds-algorithms/recursion.md b/contrib/ds-algorithms/recursion.md new file mode 100644 index 00000000..7ab31364 --- /dev/null +++ b/contrib/ds-algorithms/recursion.md @@ -0,0 +1,107 @@ +# Introduction to Recursions + +When a function calls itself to solve smaller instances of the same problem until a specified condition is fulfilled is called recursion. It is used for tasks that can be divided into smaller sub-tasks. + +# How Recursion Works + +To solve a problem using recursion we must define: +- Base condition :- The condition under which recursion ends. +- Recursive case :- The part of function which calls itself to solve a smaller instance of problem. + +Steps of Recursion + +When a recursive function is called, the following sequence of events occurs: +- Function Call: The function is invoked with a specific argument. +- Base Condition Check: The function checks if the argument satisfies the base case. +- Recursive Call: If the base case is not met, the function performs some operations and makes a recursive call with a modified argument. +- Stack Management: Each recursive call is placed on the call stack. The stack keeps track of each function call, its argument, and the point to return to once the call completes. +- Unwinding the Stack: When the base case is eventually met, the function returns a value, and the stack starts unwinding, returning values to previous function calls until the initial call is resolved. + +# What is Stack Overflow in Recursion + +Stack overflow is an error that occurs when the call stack memory limit is exceeded. During execution of recursion calls they are simultaneously stored in a recursion stack waiting for the recursive function to be completed. Without a base case, the function would call itself indefinitely, leading to a stack overflow. + +# Example + +- Factorial of a Number + + The factorial of i natural numbers is nth integer multiplied by factorial of (i-1) numbers. The base case is if i=0 we return 1 as factorial of 0 is 1. + +```python +def factorial(i): + #base case + if i==0 : + return 1 + #recursive case + else : + return i * factorial(i-1) +i = 6 +print("Factorial of i is :", factorial(i)) # Output- Factorial of i is :720 +``` +# What is Backtracking + +Backtracking is a recursive algorithmic technique used to solve problems by exploring all possible solutions and discarding those that do not meet the problem's constraints. It is particularly useful for problems involving combinations, permutations, and finding paths in a grid. + +# How Backtracking Works + +- Incremental Solution Building: Solutions are built one step at a time. +- Feasibility Check: At each step, a check is made to see if the current partial solution is valid. +- Backtracking: If a partial solution is found to be invalid, the algorithm backtracks by removing the last added part of the solution and trying the next possibility. +- Exploration of All Possibilities: The process continues recursively, exploring all possible paths, until a solution is found or all possibilities are exhausted. + +# Example + +- Word Search + + Given a 2D grid of characters and a word, determine if the word exists in the grid. The word can be constructed from letters of sequentially adjacent cells, where "adjacent" cells are horizontally or vertically neighboring. The same letter cell may not be used more than once. + +Algorithm for Solving the Word Search Problem with Backtracking: +- Start at each cell: Attempt to find the word starting from each cell. +- Check all Directions: From each cell, try all four possible directions (up, down, left, right). +- Mark Visited Cells: Use a temporary marker to indicate cells that are part of the current path to avoid revisiting. +- Backtrack: If a path does not lead to a solution, backtrack by unmarking the visited cell and trying the next possibility. + +```python +def exist(board, word): + rows, cols = len(board), len(board[0]) + + def backtrack(r, c, suffix): + if not suffix: + return True + + if r < 0 or r >= rows or c < 0 or c >= cols or board[r][c] != suffix[0]: + return False + + # Mark the cell as visited by replacing its character with a placeholder + ret = False + board[r][c], temp = '#', board[r][c] + + # Explore the four possible directions + for row_offset, col_offset in [(0, 1), (1, 0), (0, -1), (-1, 0)]: + ret = backtrack(r + row_offset, c + col_offset, suffix[1:]) + if ret: + break + + # Restore the cell's original value + board[r][c] = temp + return ret + + for row in range(rows): + for col in range(cols): + if backtrack(row, col, word): + return True + + return False + +# Test case +board = [ + ['A','B','C','E'], + ['S','F','C','S'], + ['A','D','E','E'] +] +word = "ABCES" +print(exist(board, word)) # Output: True +``` + + + diff --git a/contrib/ds-algorithms/searching-algorithms.md b/contrib/ds-algorithms/searching-algorithms.md new file mode 100644 index 00000000..78b86d14 --- /dev/null +++ b/contrib/ds-algorithms/searching-algorithms.md @@ -0,0 +1,161 @@ +# Searching Algorithms + +Searching algorithms are techniques used to locate specific items within a collection of data. These algorithms are fundamental in computer science and are employed in various applications, from databases to web search engines. + +## Real Life Example of Searching +- Searching for a word in a dictionary +- Searching for a specific book in a library +- Searching for a contact in your phone's address book +- Searching for a file on your computer, etc. + +# Some common searching techniques + +# 1. Linear Search + +Linear search, also known as sequential search, is a straightforward searching algorithm that checks each element in a collection until the target element is found or the entire collection has been traversed. It is simple to implement but becomes inefficient for large datasets. + +**Algorithm Overview:** +- **Sequential Checking:** The algorithm iterates through each element in the collection, starting from the first element. +- **Comparing Elements:** At each iteration, it compares the current element with the target element. +- **Finding the Target:** If the current element matches the target, the search terminates, and the index of the element is returned. +- **Completing the Search:** If the entire collection is traversed without finding the target, the algorithm indicates that the element is not present. + +## Linear Search Code in Python + +```python +def linear_search(arr, target): + for i in range(len(arr)): + if arr[i] == target: + return i + return -1 + +arr = [5, 3, 8, 1, 2] +target = 8 +result = linear_search(arr, target) +if result != -1: + print(f"Element {target} found at index {result}.") +else: + print(f"Element {target} not found.") +``` + +## Complexity Analysis +- **Time Complexity**: O(n) +- **Space Complexity**: O(1) + +
+
+
+ +# 2. Binary Search + +Binary search is an efficient searching algorithm that works on sorted collections. It repeatedly divides the search interval in half until the target element is found or the interval is empty. Binary search is significantly faster than linear search but requires the collection to be sorted beforehand. + +**Algorithm Overview:** +- **Initial State:** Binary search starts with the entire collection as the search interval. +- **Divide and Conquer:** At each step, it calculates the middle element of the current interval and compares it with the target. +- **Narrowing Down the Interval:** If the middle element is equal to the target, the search terminates successfully. Otherwise, it discards half of the search interval based on the comparison result. +- **Repeating the Process:** The algorithm repeats this process on the remaining half of the interval until the target is found or the interval is empty. + +## Binary Search Code in Python (Iterative) + +```python +def binary_search(arr, target): + low = 0 + high = len(arr) - 1 + while low <= high: + mid = (low + high) // 2 + if arr[mid] == target: + return mid + elif arr[mid] < target: + low = mid + 1 + else: + high = mid - 1 + return -1 + +arr = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19] +target = 13 +result = binary_search(arr, target) +if result != -1: + print(f"Element {target} found at index {result}.") +else: + print(f"Element {target} not found.") +``` + +## Binary Search Code in Python (Recursive) + +```python +def binary_search_recursive(arr, target, low, high): + if low <= high: + mid = (low + high) // 2 + if arr[mid] == target: + return mid + elif arr[mid] < target: + return binary_search_recursive(arr, target, mid + 1, high) + else: + return binary_search_recursive(arr, target, low, mid - 1) + else: + return -1 + +arr = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19] +target = 13 +result = binary_search_recursive(arr, target, 0, len(arr) - 1) +if result != -1: + print(f"Element {target} found at index {result}.") +else: + print(f"Element {target} not found.") +``` + +## Complexity Analysis +- **Time Complexity**: O(log n) +- **Space Complexity**: O(1) (Iterative), O(log n) (Recursive) + +
+
+
+ +# 3. Interpolation Search + +Interpolation search is an improved version of binary search, especially useful when the elements in the collection are uniformly distributed. Instead of always dividing the search interval in half, interpolation search estimates the position of the target element based on its value and the values of the endpoints of the search interval. + +**Algorithm Overview:** +- **Estimating Position:** Interpolation search calculates an approximate position of the target element within the search interval based on its value and the values of the endpoints. +- **Refining the Estimate:** It adjusts the estimated position based on whether the target value is likely to be closer to the beginning or end of the search interval. +- **Updating the Interval:** Using the refined estimate, it narrows down the search interval iteratively until the target is found or the interval becomes empty. + +## Interpolation Search Code in Python + +```python +def interpolation_search(arr, target): + low = 0 + high = len(arr) - 1 + while low <= high and arr[low] <= target <= arr[high]: + if low == high: + if arr[low] == target: + return low + return -1 + pos = low + ((target - arr[low]) * (high - low)) // (arr[high] - arr[low]) + if arr[pos] == target: + return pos + elif arr[pos] < target: + low = pos + 1 + else: + high = pos - 1 + return -1 + +arr = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] +target = 60 +result = interpolation_search(arr, target) +if result != -1: + print(f"Element {target} found at index {result}.") +else: + print(f"Element {target} not found.") +``` + +## Complexity Analysis +- **Time Complexity**: O(log log n) (Average) +- **Space Complexity**: O(1) + +
+
+
+ From 8b11aa5ade20c585a25ff48e34e08af59742d34b Mon Sep 17 00:00:00 2001 From: Eshparsi Date: Thu, 23 May 2024 07:34:09 +0530 Subject: [PATCH 178/656] . --- contrib/ds-algorithms/index.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 347a9d02..706729e5 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -1,5 +1,10 @@ # List of sections -- [Section title](filename.md) -- [Sorting Algorithms](sorting-algorithms.md) +- [Queues in Python](Queues.md) - [Graphs](graph.md) +- [Sorting Algorithms](sorting-algorithms.md) +- [Recursion and Backtracking](recursion.md) +- [Divide and Conquer Algorithm](divide-and-conquer-algorithm.md) +- [Searching Algorithms](searching-algorithms.md) +- [Greedy Algorithms](greedy-algorithms.md) +- [Dynamic Programming](dynamic-programming.md) \ No newline at end of file From b39147fb12bb40ce2652dc03737d60e3ab3ce23d Mon Sep 17 00:00:00 2001 From: Niyonika Gaur <83643952+niyonikagaur@users.noreply.github.com> Date: Thu, 23 May 2024 09:38:49 +0530 Subject: [PATCH 179/656] Delete Intro_SciPy.md --- Intro_SciPy.md | 136 ------------------------------------------------- 1 file changed, 136 deletions(-) delete mode 100644 Intro_SciPy.md diff --git a/Intro_SciPy.md b/Intro_SciPy.md deleted file mode 100644 index afd8b3a0..00000000 --- a/Intro_SciPy.md +++ /dev/null @@ -1,136 +0,0 @@ -# Introduction to SciPy -#### Python has a powerful library known as SciPy that is used for scientific and technical computing. It functions by extending the capabilities of NumPy and providing numerous operations for numerical data. Although the same library, SciPy is divided into distinct sections meant for various scientific computations. -## Installation of Scipy -### Install using the command: -#### C:\Users\Your Name>pip install scipy - You can also use a Python distribution that already has Scipy installed like Anaconda or Spyder. -### Importing SciPy -#### from scipy import constants -## Key Features of SciPy -### 1. Numerical Integration -#### It helps in computing definite or indefinite integrals of functions -``` -from scipy import integrate - -#Define the function to integrate -def f(x): - return x**2 - -#Compute definite integral of f from 0 to 1 -result, error = integrate.quad(f, 0, 1) -print(result) -``` -#### Output -``` -0.33333333333333337 -``` -### 2. Optimization -#### It can be used to minimize or maximize functions, here is an example of minimizing roots of an equation -``` -from scipy.optimize import minimize - -# Define an objective function to minimize -def objective(x): - return x**2 + 10*np.sin(x) - -# Minimize the objective function starting from x=0 -result = minimize(objective, x0=0) -print(result.x) -``` -#### Output -``` -array([-1.30644012]) -``` -### 3. Linear Algebra -#### Solving Linear computations -``` -from scipy import linalg -import numpy as np - -# Define a square matrix -A = np.array([[1, 2], [3, 4]]) - -# Define a vector -b = np.array([5, 6]) - -# Solve Ax = b for x -x = linalg.solve(A, b) -print(x) -``` -#### Output -``` -array([-4. , 4.5]) -``` -### 4. Statistics -#### Performing statistics functions, like here we'll be distributing the data -``` -from scipy import stats -import numpy as np - -# Generate random data from a normal distribution -data = stats.norm.rvs(loc=0, scale=1, size=1000) - -# Fit a normal distribution to the data -mean, std = stats.norm.fit(data) -``` -### 5. Signal Processing -#### To process spectral signals, like EEG or MEG -``` -from scipy import signal -import numpy as np - -# Create a signal (e.g., sine wave) -t = np.linspace(0, 1, 1000) -signal = np.sin(2 * np.pi * 5 * t) + 0.5 * np.random.randn(1000) - -# Apply a low-pass Butterworth filter -b, a = signal.butter(4, 0.1, 'low') -filtered_signal = signal.filtfilt(b, a, signal) -``` -The various filters applied that are applied here, are a part of signal analysis at a deeper level. -### 6. Sparse Matrix -#### The word ' sparse 'means less, i.e., the data is mostly unused during some operation or analysis. So, to handle this data, a Sparse Matrix is created -#### There are two types of Sparse Matrices: -##### 1. CSC: Compressed Sparse Column, it is used for efficient math functions and for column slicing -##### 2. CSR: Compressed Sparse Row, it is used for fast row slicing -#### In CSC format -``` -from scipy import sparse -import numpy as np - -data = np.array([[0, 0], [0, 1], [2, 0]]) - -row_indices = np.array([1, 2, 1]) -col_indices = np.array([1, 0, 2]) -values = np.array([1, 2, 1]) - -sparse_matrix_csc = sparse.csc_matrix((values, (row_indices, col_indices))) -``` -#### In CSR format -``` -from scipy import sparse -import numpy as np - -data = np.array([[0, 0], [0, 1], [2, 0]]) -sparse_matrix = sparse.csr_matrix(data) -``` -### 7. Image Processing -#### It is used to process the images, like changing dimensions or properties. For example, when you're doing a project on medical imaging, this library is mainly used. -``` -from scipy import ndimage -import matplotlib.pyplot as plt - -image = plt.imread('path/to/image.jpg') -plt.imshow(image) -plt.show() - -# Apply Gaussian blur to the image -blurred_image = ndimage.gaussian_filter(image, sigma=1) -plt.imshow(blurred_image) -plt.show() -``` -#### The gaussian blur is one of the properties of the ' ndimage ' package in SciPy libraries, it used for better understanding of the image. - - - - From f012e8c481de8f98a8a32e822268602bfe93f6b6 Mon Sep 17 00:00:00 2001 From: Labqari Date: Thu, 23 May 2024 15:38:35 +0530 Subject: [PATCH 180/656] adding flask --- contrib/advanced-python/index.md | 1 + ...duction-to-flask-a-python-web-framework.md | 440 ++++++++++++++++++ 2 files changed, 441 insertions(+) create mode 100644 contrib/advanced-python/introduction-to-flask-a-python-web-framework.md diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 5ea5081c..027f9892 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -1,3 +1,4 @@ # List of sections - [Decorators/\*args/**kwargs](decorator-kwargs-args.md) +- [Decorators/\*args/**kwargs](introduction-to-flask-a-python-web-framework.md) diff --git a/contrib/advanced-python/introduction-to-flask-a-python-web-framework.md b/contrib/advanced-python/introduction-to-flask-a-python-web-framework.md new file mode 100644 index 00000000..957f39b4 --- /dev/null +++ b/contrib/advanced-python/introduction-to-flask-a-python-web-framework.md @@ -0,0 +1,440 @@ +Sure, here's the guide without Markdown formatting: + +--- + +# Introduction to Flask: A Python Web Framework + +## Table of Contents +1. Introduction +2. Prerequisites +3. Setting Up Your Environment +4. Creating Your First Flask Application + - Project Structure + - Hello World Application +5. Routing +6. Templates and Static Files + - Jinja2 Templating Engine + - Serving Static Files +7. Working with Forms + - Handling Form Data +8. Database Integration + - Setting Up SQLAlchemy + - Performing CRUD Operations +9. Error Handling +10. Testing Your Application +11. Deploying Your Flask Application + - Using Gunicorn + - Deploying to Render +12. Conclusion +13. Further Reading and Resources + +--- + +## 1. Introduction +Flask is a lightweight WSGI web application framework in Python. It is designed with simplicity and flexibility in mind, allowing developers to create web applications with minimal setup. Flask was created by Armin Ronacher as part of the Pocoo project and has gained popularity for its ease of use and extensive documentation. + +## 2. Prerequisites +Before starting with Flask, ensure you have the following: +- Basic knowledge of Python. +- Understanding of web development concepts (HTML, CSS, JavaScript). +- Python installed on your machine (version 3.6 or higher). +- pip (Python package installer) installed. + +## 3. Setting Up Your Environment +1. **Install Python**: Download and install Python from python.org. +2. **Create a Virtual Environment**: + ``` + python -m venv venv + ``` +3. **Activate the Virtual Environment**: + - On Windows: + ``` + venv\Scripts\activate + ``` + - On macOS/Linux: + ``` + source venv/bin/activate + ``` +4. **Install Flask**: + ``` + pip install Flask + ``` + +## 4. Creating Your First Flask Application +### Project Structure +A typical Flask project structure might look like this: +``` +my_flask_app/ + app/ + __init__.py + routes.py + templates/ + static/ + venv/ + run.py +``` + +### Hello World Application +1. **Create a Directory for Your Project**: + ``` + mkdir my_flask_app + cd my_flask_app + ``` +2. **Initialize the Application**: + - Create `app/__init__.py`: + ```python + from flask import Flask + + def create_app(): + app = Flask(__name__) + + with app.app_context(): + from . import routes + return app + ``` + - Create `run.py`: + ```python + from app import create_app + + app = create_app() + + if __name__ == "__main__": + app.run(debug=True) + ``` + - Create `app/routes.py`: + ```python + from flask import current_app as app + + @app.route('/') + def hello_world(): + return 'Hello, World!' + ``` + +3. **Run the Application**: + ``` + python run.py + ``` + Navigate to `http://127.0.0.1:5000` in your browser to see "Hello, World!". + +## 5. Routing +In Flask, routes are defined using the `@app.route` decorator. Here's an example of different routes: + +```python +from flask import Flask + +app = Flask(__name__) + +@app.route('/') +def home(): + return 'Home Page' + +@app.route('/about') +def about(): + return 'About Page' + +@app.route('/user/') +def show_user_profile(username): + return f'User: {username}' +``` + +- **Explanation**: + - The `@app.route('/')` decorator binds the URL `'/'` to the `home` function, which returns 'Home Page'. + - The `@app.route('/about')` decorator binds the URL `/about` to the `about` function. + - The `@app.route('/user/')` decorator binds the URL `/user/` to the `show_user_profile` function, capturing the part of the URL as the `username` variable. + +## 6. Templates and Static Files +### Jinja2 Templating Engine +Jinja2 is Flask's templating engine. Templates are HTML files that can include dynamic content. + +- **Create a Template**: + - `app/templates/index.html`: + ```html + + + + {{ title }} + + +

{{ heading }}

+

{{ content }}

+ + + ``` + +- **Render the Template**: + ```python + from flask import Flask, render_template + + app = Flask(__name__) + + @app.route('/') + def home(): + return render_template('index.html', title='Home', heading='Welcome to Flask', content='This is a Flask application.') + ``` + +### Serving Static Files +Static files like CSS, JavaScript, and images are placed in the `static` directory. + +- **Create Static Files**: + - `app/static/style.css`: + ```css + body { + font-family: Arial, sans-serif; + } + ``` + +- **Include Static Files in Templates**: + ```html + + + + {{ title }} + + + +

{{ heading }}

+

{{ content }}

+ + + ``` + +## 7. Working with Forms +### Handling Form Data +Forms are used to collect user input. Flask provides utilities to handle form submissions. + +- **Create a Form**: + - `app/templates/form.html`: + ```html + + + + Form + + +
+ + + + + + + ``` + +- **Handle Form Submission**: + ```python + from flask import Flask, request, render_template + + app = Flask(__name__) + + @app.route('/form') + def form(): + return render_template('form.html') + + @app.route('/submit', methods=['POST']) + def submit(): + name = request.form['name'] + return f'Hello, {name}!' + ``` + +- **Explanation**: + - The `@app.route('/form')` route renders the form. + - The `@app.route('/submit', methods=['POST'])` route handles the form submission and displays the input name. + +## 8. Database Integration +### Setting Up SQLAlchemy +SQLAlchemy is an ORM that allows you to interact with databases using Python objects. + +- **Install SQLAlchemy**: + ``` + pip install flask_sqlalchemy + ``` + +- **Configure SQLAlchemy**: + - `app/__init__.py`: + ```python + from flask import Flask + from flask_sqlalchemy import SQLAlchemy + + db = SQLAlchemy() + + def create_app(): + app = Flask(__name__) + app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///site.db' + db.init_app(app) + return app + ``` + +### Performing CRUD Operations +Define models and perform CRUD operations. + +- **Define a Model**: + - `app/models.py`: + ```python + from app import db + + class User(db.Model): + id = db.Column(db.Integer, primary key=True) + username = db.Column(db.String(80), unique=True, nullable=False) + + def __repr__(self): + return f'' + ``` + +- **Create the Database**: + ```python + from app import create_app, db + from app.models import User + + app = create_app() + with app.app_context(): + db.create_all() + ``` + +- **Perform CRUD Operations**: + ```python + from app import db + from app.models import User + + # Create + new_user = User(username='new_user') + db.session.add(new_user) + db.session.commit() + + # Read + user = User.query.first() + + # Update + user.username = 'updated_user' + db.session.commit() + + # Delete + db.session.delete(user) + db.session.commit() + ``` + +## 9. Error Handling +Error handling in Flask can be managed by defining error handlers for different HTTP status codes. + +- **Define an Error Handler**: + ```python + from flask import Flask, render_template + + app = Flask(__name__) + + @app.errorhandler(404) + def page_not_found(e): + return render_template('404.html'), 404 + + @app.errorhandler(500) + def internal_server_error(e): + return render_template('500.html'), 500 + ``` + + - **Create Error Pages**: + `app/templates/404.html`: + + + + + Page Not Found + + +

404 - Page Not Found

+

The page you are looking for does not exist.

+ + + + + + - **app/templates/500.html:** + + + + + Internal Server Error + + +

500 - Internal Server Error

+

Something went wrong on our end. Please try again later.

+ + + + +## 10. Testing Your Application +Flask applications can be tested using Python's built-in `unittest` framework. + +- **Write a Test Case**: + - `tests/test_app.py`: + ```python + import unittest + from app import create_app + + class BasicTestCase(unittest.TestCase): + def setUp(self): + self.app = create_app() + self.app.config['TESTING'] = True + self.client = self.app.test_client() + + def test_home(self): + response = self.client.get('/') + self.assertEqual(response.status_code, 200) + self.assertIn(b'Hello, World!', response.data) + + if __name__ == '__main__': + unittest.main() + ``` + + - **Run the Tests**: + ``` + python -m unittest discover -s tests + ``` + +## 11. Deploying Your Flask Application +### Using Gunicorn +Gunicorn is a Python WSGI HTTP Server for UNIX. It’s a pre-fork worker model, meaning that it forks multiple worker processes to handle requests. + +- **Install Gunicorn**: + ``` + pip install gunicorn + ``` + +- **Run Your Application with Gunicorn**: + ``` + gunicorn -w 4 run:app + ``` + +### Deploying to Render +Render is a cloud platform for deploying web applications. + +- **Create a `requirements.txt` File**: + ``` + Flask + gunicorn + flask_sqlalchemy + ``` + +- **Create a `render.yaml` File**: + ```yaml + services: + - type: web + name: my-flask-app + env: python + plan: free + buildCommand: pip install -r requirements.txt + startCommand: gunicorn -w 4 run:app + ``` + +- **Deploy Your Application**: + 1. Push your code to a Git repository. + 2. Sign in to Render and create a new Web Service. + 3. Connect your repository and select the branch to deploy. + 4. Render will automatically use the `render.yaml` file to configure and deploy your application. + +## 12. Conclusion +Flask is a powerful and flexible framework for building web applications in Python. It offers simplicity and ease of use, making it a great choice for both beginners and experienced developers. This guide covered the basics of setting up a Flask application, routing, templating, working with forms, integrating databases, error handling, testing, and deployment. + +## 13. Further Reading and Resources +- Flask Documentation: https://flask.palletsprojects.com/en/latest/ +- Jinja2 Documentation: https://jinja.palletsprojects.com/en/latest/ +- SQLAlchemy Documentation: https://docs.sqlalchemy.org/en/latest/ +- Render Documentation: https://render.com/docs \ No newline at end of file From 0a2ca67628da43f74aa1dda7278b11fbadc4a2ac Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Thu, 23 May 2024 23:07:21 +0530 Subject: [PATCH 181/656] Create saving_numpy_arrays_to_files.md Added introduction Added save method --- contrib/numpy/saving_numpy_arrays_to_files.md | 40 +++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 contrib/numpy/saving_numpy_arrays_to_files.md diff --git a/contrib/numpy/saving_numpy_arrays_to_files.md b/contrib/numpy/saving_numpy_arrays_to_files.md new file mode 100644 index 00000000..8856a0c6 --- /dev/null +++ b/contrib/numpy/saving_numpy_arrays_to_files.md @@ -0,0 +1,40 @@ +# Saving NumPy Arrays to Files + +- Saving arrays in NumPy is important due to its efficiency in storage and speed, maintaining data integrity and precision, and offering convenience and interoperability. +- NumPy provides several methods to save arrays efficiently, either in binary or text formats. +- The primary methods are `save`, `savez`, and `savetxt`. + +### 1. numpy.save(): + +The `np.save` function saves a single NumPy array to a binary file with a `.npy` extension. This format is efficient and preserves the array's data type and shape. + +#### Syntax : + + ```python + numpy.save(file, arr, allow_pickle=True, fix_imports=True) + ``` +- **file** : Name of the file. +- **arr** : Array to be saved. +- **allow_pickle** : This is an Optional parameter, Allows saving object arrays using Python pickles.(By Default True) +- **fix_imports** : This is an Optional parameter, Fixes issues for Python 2 to Python 3 compatibility.(By Default True) + +#### Example : + +```python +import numpy as np + +arr = np.array([1,2,3,4,5]) +np.save("example.npy",arr) #saves arr into example.npy file in binary format +``` + +Inorder to load the array from example.npy + +```python +arr1 = np.load("example.npy") +print(arr1) +``` +**Output** : + +```python +[1,2,3,4,5] +``` From e734ba6a3ef7eb4e6a3fa746be6b2211cf724380 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Fri, 24 May 2024 01:09:23 +0530 Subject: [PATCH 182/656] Update saving_numpy_arrays_to_files.md Added savez method --- contrib/numpy/saving_numpy_arrays_to_files.md | 54 +++++++++++++++++++ 1 file changed, 54 insertions(+) diff --git a/contrib/numpy/saving_numpy_arrays_to_files.md b/contrib/numpy/saving_numpy_arrays_to_files.md index 8856a0c6..0d45e5d0 100644 --- a/contrib/numpy/saving_numpy_arrays_to_files.md +++ b/contrib/numpy/saving_numpy_arrays_to_files.md @@ -38,3 +38,57 @@ print(arr1) ```python [1,2,3,4,5] ``` +### 2. numpy.savez(): + +The `np.savez` function saves multiple NumPy arrays into a single file with a `.npz` extension. Each array is stored with a unique name. + +#### Syntax : + + ```python +numpy.savez(file, *args, **kwds) + ``` +- **file** : Name of the file. +- **args** : Arrays to be saved.( If arrays are unnamed, they are stored with default names like arr_0, arr_1, etc.) +- **kwds** : Named arrays to be saved. + +#### Example : + +```python +import numpy as np + +arr1 = np.array([1,2,3,4,5]) +arr2 = np.array(['a','b','c','d']) +arr3 = np.array([1.2,3.4,5]) +np.savez('example.npz', a1=arr1, a2=arr2, a3 = arr3) #saves arrays in npz format + +``` + +Inorder to load the array from example.npz + +```python + +arr = np.load('example.npz') +print(arr['a1']) +print(arr['a2']) +print(arr['a3']) + +``` +**Output** : +```python +[1 2 3 4 5] +['a' 'b' 'c' 'd'] +[1.2 3.4 5. ] +``` + +### 3. np.savetxt() + +The `np.savetxt` function saves a NumPy array to a text file, such as `.txt` or `.csv`. This format is human-readable and can be used for interoperability with other tools. + +#### Syntax : + + ```python +numpy.savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', header='', footer='', comments='# ', encoding=None) + ``` +- **fname** : Name of the file. +- **X** : Array to be saved. +- **kwds** : Named arrays to be saved. From f715f2751c58659a254e7a18905e38c89e1558af Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Fri, 24 May 2024 09:06:55 +0530 Subject: [PATCH 183/656] Update saving_numpy_arrays_to_files.md Added examples Added savetxt method --- contrib/numpy/saving_numpy_arrays_to_files.md | 36 +++++++++++++++++-- 1 file changed, 34 insertions(+), 2 deletions(-) diff --git a/contrib/numpy/saving_numpy_arrays_to_files.md b/contrib/numpy/saving_numpy_arrays_to_files.md index 0d45e5d0..c8935b8f 100644 --- a/contrib/numpy/saving_numpy_arrays_to_files.md +++ b/contrib/numpy/saving_numpy_arrays_to_files.md @@ -87,8 +87,40 @@ The `np.savetxt` function saves a NumPy array to a text file, such as `.txt` or #### Syntax : ```python -numpy.savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', header='', footer='', comments='# ', encoding=None) +numpy.savetxt(fname, X, delimiter=' ', newline='\n', header='', footer='', encoding=None) ``` - **fname** : Name of the file. - **X** : Array to be saved. -- **kwds** : Named arrays to be saved. +- **delimiter** : It is a Optional parameter,This is a character or string that is used to separate columns.(By Default it is " ") +- **newline** : It is a Optional parameter, Character for seperating lines.(By Default it is "\n") +- **header** : It is a Optional parameter, String that is written at beginning of the file. +- **footer** : It is a Optional parameter, String that is written at ending of the file. +- **encoding** : It is a Optional parameter, Encoding of the output file. (By Default it is None) + +#### Example : + +```python +import numpy as np + +arr = np.array([1.1,2.2,3,4.4,5]) +np.savetxt("example.txt",arr) #saves the array in example.txt + +``` + +Inorder to load the array from example.txt + +```python + +arr1 = np.loadtxt("example.txt") +print(arr1) + +``` +**Output** : +```python +[1.1 2.2 3. 4.4 5. ] +``` + + +By using these methods, you can efficiently save and load NumPy arrays in various formats suitable for your needs. + + From 1306c8e37ec17ba45166587f781b9036b5e03b2b Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Fri, 24 May 2024 09:19:01 +0530 Subject: [PATCH 184/656] Update index.md Added saving numpy arrays into files section --- contrib/numpy/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index f784798c..18ed17af 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -6,3 +6,4 @@ - [Basic Mathematics](basic_math.md) - [Operations on Arrays in NumPy](operations-on-arrays.md) - [Loading Arrays from Files](loading_arrays_from_files.md) +- [Saving Numpy Arrays into FIles](saving_numpy_arrays_to_files.md) From 091a5daeda4ec4afb9d7c90ad249ad27fa0fdcf6 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 24 May 2024 21:07:23 +0530 Subject: [PATCH 185/656] Rename Installation_of_Scipy_&_its_key_uses.md to installation_features.md --- ...lation_of_Scipy_&_its_key_uses.md => installation_features.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/scipy/{Installation_of_Scipy_&_its_key_uses.md => installation_features.md} (100%) diff --git a/contrib/scipy/Installation_of_Scipy_&_its_key_uses.md b/contrib/scipy/installation_features.md similarity index 100% rename from contrib/scipy/Installation_of_Scipy_&_its_key_uses.md rename to contrib/scipy/installation_features.md From bcf4df6d7d13627d33a1419178db4f42dca12581 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 24 May 2024 21:08:03 +0530 Subject: [PATCH 186/656] Update index.md --- contrib/scipy/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/scipy/index.md b/contrib/scipy/index.md index abc2fb0b..57371141 100644 --- a/contrib/scipy/index.md +++ b/contrib/scipy/index.md @@ -1,4 +1,4 @@ # List of sections -- [Installation of Scipy and its key uses]( Installation_of_Scipy_&_its_key_uses.md) +- [Installation of Scipy and its key uses](installation_features.md) From cdb57900d40905a8cb0685bba807e4a6484475d2 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Fri, 24 May 2024 21:11:33 +0530 Subject: [PATCH 187/656] Delete contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md --- .../Importing_and_Exporting_Data_in_Pandas.md | 273 ------------------ 1 file changed, 273 deletions(-) delete mode 100644 contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md diff --git a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md deleted file mode 100644 index 4d0ffadd..00000000 --- a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md +++ /dev/null @@ -1,273 +0,0 @@ -# Importing_and_Exporting_Data_in_Pandas - ->Created by Krishna Kaushik - -- **Now we're able to create `Series` and `DataFrames` in pandas, but we usually do not do this , in practice we import the data which is in the form of .csv (Comma Seperated Values) , a spreadsheet file or something similar.** - -- *Good news is that pandas allows for easy importing of data like this through functions such as ``pd.read_csv()`` and ``pd.read_excel()`` for Microsoft Excel files.* - -## 1. Importing from a Google sheet to a pandas dataframe - -*Let's say that you wanted to get the information from Google Sheet document into a pandas DataFrame.*. - -*You could export it as a .csv file and then import it using ``pd.read_csv()``.* - -*In this case, the exported .csv file is called `Titanic.csv`* - - -```python -## Importing Titanic Data set -import pandas as pd - -titanic_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Titanic.csv") -titanic_df -``` -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
pclasssurvivednamesexagesibspparchticketfarecabinembarkedboatbodyhome.dest
011Allen, Miss. Elisabeth Waltonfemale29.000024160211.3375B5S2NaNSt Louis, MO
111Allison, Master. Hudson Trevormale0.9212113781151.5500C22 C26S11NaNMontreal, PQ / Chesterville, ON
210Allison, Miss. Helen Lorainefemale2.0012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
310Allison, Mr. Hudson Joshua Creightonmale30.0012113781151.5500C22 C26SNaN135.0Montreal, PQ / Chesterville, ON
410Allison, Mrs. Hudson J C (Bessie Waldo Daniels)female25.0012113781151.5500C22 C26SNaNNaNMontreal, PQ / Chesterville, ON
.............................................
130430Zabour, Miss. Hilenifemale14.5010266514.4542NaNCNaN328.0NaN
130530Zabour, Miss. ThaminefemaleNaN10266514.4542NaNCNaNNaNNaN
130630Zakarian, Mr. Mapriededermale26.500026567.2250NaNCNaN304.0NaN
130730Zakarian, Mr. Ortinmale27.000026707.2250NaNCNaNNaNNaN
130830Zimmerman, Mr. Leomale29.00003150827.8750NaNSNaNNaNNaN
-

1309 rows × 14 columns

-
- - - -The dataset I am using here for your reference is taken from the same repository i.e ``learn-python`` (https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Titanic.csv) I uploaded it you can use it from there. - -**Now we've got the same data from the Google Spreadsheet , but now available as ``pandas DataFrame`` which means we can now apply all pandas functionality over it.** - -#### Note: The quiet important thing i am telling is that ``pd.read_csv()`` takes the location of the file (which is in your current working directory) or the hyperlink of the dataset from the other source. - -#### But if you want to import the data from Github you can't directly use its link , you have to first convert it to raw by clicking on the raw button present in the repo . - -#### Also you can't use the data directly from `Kaggle` you have to use ``kaggle API`` - -## 2. The Anatomy of DataFrame - -**Different functions use different labels for different things, and can get a little confusing.** - -- Rows are refer as ``axis=0`` -- columns are refer as ``axis=1`` - -## 3. Exporting Data - -**OK, so after you've made a few changes to your data, you might want to export it and save it so someone else can access the changes.** - -**pandas allows you to export ``DataFrame's`` to ``.csv`` format using ``.to_csv()``, or to a spreadsheet format using .to_excel().** - -### Exporting a dataframe to a CSV - -**We haven't made any changes yet to the ``titanic_df`` DataFrame but let's try to export it.** - - -```python -#Export the titanic_df DataFrame to csv -titanic_df.to_csv("exported_titanic.csv") -``` - -Running this will save a file called ``exported_titanic.csv`` to the current folder. From bd52a3a43bf558f158b3bf2880d8212a93ad8a83 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Fri, 24 May 2024 21:12:23 +0530 Subject: [PATCH 188/656] Delete contrib/pandas/Titanic.csv --- contrib/pandas/Titanic.csv | 1310 ------------------------------------ 1 file changed, 1310 deletions(-) delete mode 100644 contrib/pandas/Titanic.csv diff --git a/contrib/pandas/Titanic.csv b/contrib/pandas/Titanic.csv deleted file mode 100644 index f8d49dcf..00000000 --- a/contrib/pandas/Titanic.csv +++ /dev/null @@ -1,1310 +0,0 @@ -"pclass","survived","name","sex","age","sibsp","parch","ticket","fare","cabin","embarked","boat","body","home.dest" -1,1,"Allen, Miss. Elisabeth Walton","female",29,0,0,"24160",211.3375,"B5","S","2",,"St Louis, MO" -1,1,"Allison, Master. Hudson Trevor","male",0.92,1,2,"113781",151.5500,"C22 C26","S","11",,"Montreal, PQ / Chesterville, ON" -1,0,"Allison, Miss. Helen Loraine","female",2,1,2,"113781",151.5500,"C22 C26","S",,,"Montreal, PQ / Chesterville, ON" -1,0,"Allison, Mr. Hudson Joshua Creighton","male",30,1,2,"113781",151.5500,"C22 C26","S",,"135","Montreal, PQ / Chesterville, ON" -1,0,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)","female",25,1,2,"113781",151.5500,"C22 C26","S",,,"Montreal, PQ / Chesterville, ON" -1,1,"Anderson, Mr. Harry","male",48,0,0,"19952",26.5500,"E12","S","3",,"New York, NY" -1,1,"Andrews, Miss. Kornelia Theodosia","female",63,1,0,"13502",77.9583,"D7","S","10",,"Hudson, NY" -1,0,"Andrews, Mr. Thomas Jr","male",39,0,0,"112050",0.0000,"A36","S",,,"Belfast, NI" -1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)","female",53,2,0,"11769",51.4792,"C101","S","D",,"Bayside, Queens, NY" -1,0,"Artagaveytia, Mr. Ramon","male",71,0,0,"PC 17609",49.5042,,"C",,"22","Montevideo, Uruguay" -1,0,"Astor, Col. John Jacob","male",47,1,0,"PC 17757",227.5250,"C62 C64","C",,"124","New York, NY" -1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)","female",18,1,0,"PC 17757",227.5250,"C62 C64","C","4",,"New York, NY" -1,1,"Aubart, Mme. Leontine Pauline","female",24,0,0,"PC 17477",69.3000,"B35","C","9",,"Paris, France" -1,1,"Barber, Miss. Ellen ""Nellie""","female",26,0,0,"19877",78.8500,,"S","6",, -1,1,"Barkworth, Mr. Algernon Henry Wilson","male",80,0,0,"27042",30.0000,"A23","S","B",,"Hessle, Yorks" -1,0,"Baumann, Mr. John D","male",,0,0,"PC 17318",25.9250,,"S",,,"New York, NY" -1,0,"Baxter, Mr. Quigg Edmond","male",24,0,1,"PC 17558",247.5208,"B58 B60","C",,,"Montreal, PQ" -1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)","female",50,0,1,"PC 17558",247.5208,"B58 B60","C","6",,"Montreal, PQ" -1,1,"Bazzani, Miss. Albina","female",32,0,0,"11813",76.2917,"D15","C","8",, -1,0,"Beattie, Mr. Thomson","male",36,0,0,"13050",75.2417,"C6","C","A",,"Winnipeg, MN" -1,1,"Beckwith, Mr. Richard Leonard","male",37,1,1,"11751",52.5542,"D35","S","5",,"New York, NY" -1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)","female",47,1,1,"11751",52.5542,"D35","S","5",,"New York, NY" -1,1,"Behr, Mr. Karl Howell","male",26,0,0,"111369",30.0000,"C148","C","5",,"New York, NY" -1,1,"Bidois, Miss. Rosalie","female",42,0,0,"PC 17757",227.5250,,"C","4",, -1,1,"Bird, Miss. Ellen","female",29,0,0,"PC 17483",221.7792,"C97","S","8",, -1,0,"Birnbaum, Mr. Jakob","male",25,0,0,"13905",26.0000,,"C",,"148","San Francisco, CA" -1,1,"Bishop, Mr. Dickinson H","male",25,1,0,"11967",91.0792,"B49","C","7",,"Dowagiac, MI" -1,1,"Bishop, Mrs. Dickinson H (Helen Walton)","female",19,1,0,"11967",91.0792,"B49","C","7",,"Dowagiac, MI" -1,1,"Bissette, Miss. Amelia","female",35,0,0,"PC 17760",135.6333,"C99","S","8",, -1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan","male",28,0,0,"110564",26.5500,"C52","S","D",,"Stockholm, Sweden / Washington, DC" -1,0,"Blackwell, Mr. Stephen Weart","male",45,0,0,"113784",35.5000,"T","S",,,"Trenton, NJ" -1,1,"Blank, Mr. Henry","male",40,0,0,"112277",31.0000,"A31","C","7",,"Glen Ridge, NJ" -1,1,"Bonnell, Miss. Caroline","female",30,0,0,"36928",164.8667,"C7","S","8",,"Youngstown, OH" -1,1,"Bonnell, Miss. Elizabeth","female",58,0,0,"113783",26.5500,"C103","S","8",,"Birkdale, England Cleveland, Ohio" -1,0,"Borebank, Mr. John James","male",42,0,0,"110489",26.5500,"D22","S",,,"London / Winnipeg, MB" -1,1,"Bowen, Miss. Grace Scott","female",45,0,0,"PC 17608",262.3750,,"C","4",,"Cooperstown, NY" -1,1,"Bowerman, Miss. Elsie Edith","female",22,0,1,"113505",55.0000,"E33","S","6",,"St Leonards-on-Sea, England Ohio" -1,1,"Bradley, Mr. George (""George Arthur Brayton"")","male",,0,0,"111427",26.5500,,"S","9",,"Los Angeles, CA" -1,0,"Brady, Mr. John Bertram","male",41,0,0,"113054",30.5000,"A21","S",,,"Pomeroy, WA" -1,0,"Brandeis, Mr. Emil","male",48,0,0,"PC 17591",50.4958,"B10","C",,"208","Omaha, NE" -1,0,"Brewe, Dr. Arthur Jackson","male",,0,0,"112379",39.6000,,"C",,,"Philadelphia, PA" -1,1,"Brown, Mrs. James Joseph (Margaret Tobin)","female",44,0,0,"PC 17610",27.7208,"B4","C","6",,"Denver, CO" -1,1,"Brown, Mrs. John Murray (Caroline Lane Lamson)","female",59,2,0,"11769",51.4792,"C101","S","D",,"Belmont, MA" -1,1,"Bucknell, Mrs. William Robert (Emma Eliza Ward)","female",60,0,0,"11813",76.2917,"D15","C","8",,"Philadelphia, PA" -1,1,"Burns, Miss. Elizabeth Margaret","female",41,0,0,"16966",134.5000,"E40","C","3",, -1,0,"Butt, Major. Archibald Willingham","male",45,0,0,"113050",26.5500,"B38","S",,,"Washington, DC" -1,0,"Cairns, Mr. Alexander","male",,0,0,"113798",31.0000,,"S",,, -1,1,"Calderhead, Mr. Edward Pennington","male",42,0,0,"PC 17476",26.2875,"E24","S","5",,"New York, NY" -1,1,"Candee, Mrs. Edward (Helen Churchill Hungerford)","female",53,0,0,"PC 17606",27.4458,,"C","6",,"Washington, DC" -1,1,"Cardeza, Mr. Thomas Drake Martinez","male",36,0,1,"PC 17755",512.3292,"B51 B53 B55","C","3",,"Austria-Hungary / Germantown, Philadelphia, PA" -1,1,"Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake)","female",58,0,1,"PC 17755",512.3292,"B51 B53 B55","C","3",,"Germantown, Philadelphia, PA" -1,0,"Carlsson, Mr. Frans Olof","male",33,0,0,"695",5.0000,"B51 B53 B55","S",,,"New York, NY" -1,0,"Carrau, Mr. Francisco M","male",28,0,0,"113059",47.1000,,"S",,,"Montevideo, Uruguay" -1,0,"Carrau, Mr. Jose Pedro","male",17,0,0,"113059",47.1000,,"S",,,"Montevideo, Uruguay" -1,1,"Carter, Master. 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Hileni","female",14.5,1,0,"2665",14.4542,,"C",,"328", -3,0,"Zabour, Miss. Thamine","female",,1,0,"2665",14.4542,,"C",,, -3,0,"Zakarian, Mr. Mapriededer","male",26.5,0,0,"2656",7.2250,,"C",,"304", -3,0,"Zakarian, Mr. Ortin","male",27,0,0,"2670",7.2250,,"C",,, -3,0,"Zimmerman, Mr. Leo","male",29,0,0,"315082",7.8750,,"S",,, From f8f8686d6e127743dcfcc017422a2d701b5abe12 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Fri, 24 May 2024 21:12:48 +0530 Subject: [PATCH 189/656] Update index.md --- contrib/pandas/index.md | 1 - 1 file changed, 1 deletion(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index d194bfa1..c578adef 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -2,7 +2,6 @@ - [Pandas Series Vs NumPy ndarray](pandas_series_vs_numpy_ndarray.md) - [Pandas Introduction and Dataframes in Pandas](Introduction_to_Pandas_Library_and_DataFrames.md) -- [Importing and Exportin data in pandas](Importing_and_Exporting_Data_in_Pandas.md) - [Pandas Descriptive Statistics](Descriptive_Statistics.md) - [Group By Functions with Pandas](GroupBy_Functions_Pandas.md) - [Excel using Pandas DataFrame](excel_with_pandas.md) From e6a7ceee0cf230f7d8377f5f19b96a20ac1bfadd Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 24 May 2024 21:12:54 +0530 Subject: [PATCH 190/656] Update installation_features.md --- contrib/scipy/installation_features.md | 89 +++++++++++++++++++------- 1 file changed, 66 insertions(+), 23 deletions(-) diff --git a/contrib/scipy/installation_features.md b/contrib/scipy/installation_features.md index 33232dc0..d5410869 100644 --- a/contrib/scipy/installation_features.md +++ b/contrib/scipy/installation_features.md @@ -1,13 +1,25 @@ ## Installation of Scipy -### Install using the command: -#### C:\Users\Your Name>pip install scipy - You can also use a Python distribution that already has Scipy installed like Anaconda, or Spyder. + +You can install scipy using the command: + +``` +$ pip install scipy +``` + +You can also use a Python distribution that already has Scipy installed like Anaconda, or Spyder. + ### Importing SciPy -#### from scipy import constants + +```python +from scipy import constants +``` + ## Key Features of SciPy ### 1. Numerical Integration -#### It helps in computing definite or indefinite integrals of functions -``` + +It helps in computing definite or indefinite integrals of functions + +```python from scipy import integrate #Define the function to integrate @@ -18,13 +30,18 @@ def f(x): result, error = integrate.quad(f, 0, 1) print(result) ``` + #### Output + ``` 0.33333333333333337 ``` + ### 2. Optimization -#### It can be used to minimize or maximize functions, here is an example of minimizing roots of an equation -``` + +It can be used to minimize or maximize functions, here is an example of minimizing roots of an equation + +```python from scipy.optimize import minimize # Define an objective function to minimize @@ -35,13 +52,18 @@ def objective(x): result = minimize(objective, x0=0) print(result.x) ``` + #### Output + ``` array([-1.30644012]) ``` + ### 3. Linear Algebra -#### Solving Linear computations -``` + +Solving Linear computations + +```python from scipy import linalg import numpy as np @@ -55,13 +77,18 @@ b = np.array([5, 6]) x = linalg.solve(A, b) print(x) ``` + #### Output + ``` array([-4. , 4.5]) ``` + ### 4. Statistics -#### Performing statistics functions, like here we'll be distributing the data -``` + +Performing statistics functions, like here we'll be distributing the data + +```python from scipy import stats import numpy as np @@ -71,9 +98,12 @@ data = stats.norm.rvs(loc=0, scale=1, size=1000) # Fit a normal distribution to the data mean, std = stats.norm.fit(data) ``` + ### 5. Signal Processing -#### To process spectral signals, like EEG or MEG -``` + +To process spectral signals, like EEG or MEG + +```python from scipy import signal import numpy as np @@ -85,14 +115,21 @@ signal = np.sin(2 * np.pi * 5 * t) + 0.5 * np.random.randn(1000) b, a = signal.butter(4, 0.1, 'low') filtered_signal = signal.filtfilt(b, a, signal) ``` + The various filters applied that are applied here, are a part of signal analysis at a deeper level. + ### 6. Sparse Matrix -#### The word ' sparse 'means less, i.e., the data is mostly unused during some operation or analysis. So, to handle this data, a Sparse Matrix is created -#### There are two types of Sparse Matrices: -##### 1. CSC: Compressed Sparse Column, it is used for efficient math functions and for column slicing -##### 2. CSR: Compressed Sparse Row, it is used for fast row slicing + +The word ' sparse 'means less, i.e., the data is mostly unused during some operation or analysis. So, to handle this data, a Sparse Matrix is created + +There are two types of Sparse Matrices: + +1. CSC: Compressed Sparse Column, it is used for efficient math functions and for column slicing +2. CSR: Compressed Sparse Row, it is used for fast row slicing + #### In CSC format -``` + +```python from scipy import sparse import numpy as np @@ -104,17 +141,22 @@ values = np.array([1, 2, 1]) sparse_matrix_csc = sparse.csc_matrix((values, (row_indices, col_indices))) ``` + #### In CSR format -``` + +```python from scipy import sparse import numpy as np data = np.array([[0, 0], [0, 1], [2, 0]]) sparse_matrix = sparse.csr_matrix(data) ``` + ### 7. Image Processing -#### It is used to process the images, like changing dimensions or properties. For example, when you're doing a project on medical imaging, this library is mainly used. -``` + +It is used to process the images, like changing dimensions or properties. For example, when you're doing a project on medical imaging, this library is mainly used. + +```python from scipy import ndimage import matplotlib.pyplot as plt @@ -127,4 +169,5 @@ blurred_image = ndimage.gaussian_filter(image, sigma=1) plt.imshow(blurred_image) plt.show() ``` -#### The gaussian blur is one of the properties of the ' ndimage ' package in SciPy libraries, it used for better understanding of the image. + +The gaussian blur is one of the properties of the ' ndimage ' package in SciPy libraries, it used for better understanding of the image. From ab6428fd59dfae627ff94613cc55b19472b6c500 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Fri, 24 May 2024 21:40:43 +0530 Subject: [PATCH 191/656] Update Introduction_to_Pandas_Library_and_DataFrames.md --- ...uction_to_Pandas_Library_and_DataFrames.md | 147 +++--------------- 1 file changed, 25 insertions(+), 122 deletions(-) diff --git a/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md b/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md index 809a155a..3552437e 100644 --- a/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md +++ b/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md @@ -1,8 +1,5 @@ # Introduction_to_Pandas_Library_and_DataFrames - -> Content Creator - Krishna Kaushik - **As you have learnt Python Programming , now it's time for some applications.** - Machine Learning and Data Science is the emerging field of today's time , to work in this this field your first step should be `Data Science` as Machine Learning is all about data. @@ -110,42 +107,15 @@ You can also create a DataFrame by using `pd.DataFrame()` and passing it a Pytho # Let's create cars_with_colours = pd.DataFrame({"Cars" : ["BMW","Audi","Thar","Honda"], "Colour" : ["Black","White","Red","Green"]}) -cars_with_colours +print(cars_with_colours) ``` - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
CarsColour
0BMWBlack
1AudiWhite
2TharRed
3HondaGreen
- - + Cars Colour + 0 BMW Black + 1 Audi White + 2 Thar Red + 3 Honda Green + The dictionary key is the `column name` and value are the `column data`. @@ -194,42 +164,15 @@ age record = pd.DataFrame({"Student_Name":students , "Age" :age}) -record +print(record) ``` - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Student_NameAge
0Ram19
1Mohan20
2Krishna21
3Shivam24
- - + Student_Name Age + 0 Ram 19 + 1 Mohan 20 + 2 Krishna 21 + 3 Shivam 24 + ```python @@ -269,53 +212,19 @@ record.dtypes ```python -record.describe() # It only display the results for numeric data +print(record.describe()) # It only display the results for numeric data ``` - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Age
count4.000000
mean21.000000
std2.160247
min19.000000
25%19.750000
50%20.500000
75%21.750000
max24.000000
- - + Age + count 4.000000 + mean 21.000000 + std 2.160247 + min 19.000000 + 25% 19.750000 + 50% 20.500000 + 75% 21.750000 + max 24.000000 + #### 3. Use `.info()` to find information about the dataframe @@ -333,9 +242,3 @@ record.info() 1 Age 4 non-null int64 dtypes: int64(1), object(1) memory usage: 196.0+ bytes - - - -```python - -``` From 5ccb3f2b0fc0650b26eba3b99fc7f780df6a0fe8 Mon Sep 17 00:00:00 2001 From: Pranshu shah <97401387+shahpranshu27@users.noreply.github.com> Date: Fri, 24 May 2024 22:11:05 +0530 Subject: [PATCH 192/656] create lambda-function.md --- contrib/advanced-python/index.md | 1 + contrib/advanced-python/lambda-function.md | 88 ++++++++++++++++++++++ 2 files changed, 89 insertions(+) create mode 100644 contrib/advanced-python/lambda-function.md diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 5ea5081c..b884dde3 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -1,3 +1,4 @@ # List of sections - [Decorators/\*args/**kwargs](decorator-kwargs-args.md) +- [Lambda Function](lambda-function.md) diff --git a/contrib/advanced-python/lambda-function.md b/contrib/advanced-python/lambda-function.md new file mode 100644 index 00000000..93a53300 --- /dev/null +++ b/contrib/advanced-python/lambda-function.md @@ -0,0 +1,88 @@ +# Lambda Function + +Lambda functions in Python are small, anonymous functions that can be created on-the-fly. They are defined using the `lambda` keyword instead of the `def` keyword used for regular functions. Lambda functions are typically used for simple tasks where a full-blown function definition is not necessary. + +Here's an example of a lambda function that adds two numbers: + +```python +add = lambda x, y: x + y +print(add(3, 5)) # Output: 8 +``` + +The above lambda function is equivalent to the following regular function: + +```python +def add(x, y): + return x + y + +print(add(3, 5)) # Output: 8 +``` + +The difference between a regular function and a lambda function lies mainly in syntax and usage. Here are some key distinctions: + +1. **Syntax**: Lambda functions are defined using the `lambda` keyword, followed by parameters and a colon, while regular functions use the `def` keyword, followed by the function name, parameters, and a colon. + +2. **Name**: Lambda functions are anonymous; they do not have a name like regular functions. Regular functions are defined with a name. + +3. **Complexity**: Lambda functions are suitable for simple, one-liner tasks. They are not meant for complex operations or tasks that require multiple lines of code. Regular functions can handle more complex logic and can contain multiple statements and lines of code. + +4. **Usage**: Lambda functions are often used in situations where a function is needed as an argument to another function (e.g., sorting, filtering, mapping), or when you want to write concise code without defining a separate function. + +Lambda functions are used primarily for convenience and brevity in situations where a full function definition would be overkill or too cumbersome. They are handy for tasks that require a small, one-time function and can improve code readability when used judiciously. + +## Use Cases + +1. **Sorting**: Lambda functions are often used as key functions for sorting lists, dictionaries, or other data structures based on specific criteria. For example: + + ```python + students = [ + {"name": "Alice", "age": 20}, + {"name": "Bob", "age": 18}, + {"name": "Charlie", "age": 22} + ] + sorted_students = sorted(students, key=lambda x: x["age"]) + ``` + +2. **Filtering**: Lambda functions can be used with filter() to selectively include elements from a collection based on a condition. For instance: + + ```python + numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + even_numbers = list(filter(lambda x: x % 2 == 0, numbers)) + ``` + +3. **Mapping**: Lambda functions are useful with map() to apply a transformation to each element of a collection. For example: + + ```python + numbers = [1, 2, 3, 4, 5] + squared_numbers = list(map(lambda x: x**2, numbers)) + ``` + +4. **Event Handling**: In GUI programming or event-driven systems, lambda functions can be used as event handlers to execute specific actions when an event occurs. For instance: + + ```python + button.clicked.connect(lambda: self.on_button_click(argument)) + ``` + +5. **Callback Functions**: Lambda functions can be passed as callback functions to other functions, especially when a simple operation needs to be performed in response to an event. For example: + + ```python + def process_data(data, callback): + # Process data + result = ... + # Execute callback function + callback(result) + + process_data(data, lambda x: print("Result:", x)) + ``` + +6. **Anonymous Functions in Higher-Order Functions**: Lambda functions are commonly used with higher-order functions such as reduce(), which applies a rolling computation to sequential pairs of values in a list. For example: + + ```python + from functools import reduce + numbers = [1, 2, 3, 4, 5] + sum_of_numbers = reduce(lambda x, y: x + y, numbers) + ``` + +These are just a few examples of how lambda functions can be applied in Python to simplify code and make it more expressive. They are particularly useful in situations where a small, one-time function is needed and defining a separate named function would be excessive. + +In conclusion, **lambda functions** in Python offer a concise and powerful way to handle simple tasks without the need for full function definitions. Their versatility, especially in scenarios like sorting, filtering, and event handling, makes them valuable tools for improving code readability and efficiency. By mastering lambda functions, you can enhance your Python programming skills and tackle various tasks with elegance and brevity. \ No newline at end of file From 855c10a724b480b8f75462b90a1a7eb5a0be73c6 Mon Sep 17 00:00:00 2001 From: Yatharth Date: Fri, 24 May 2024 22:37:19 +0530 Subject: [PATCH 193/656] time space complexity --- contrib/ds-algorithms/index.md | 1 + .../ds-algorithms/time-space-complexity.md | 227 ++++++++++++++++++ 2 files changed, 228 insertions(+) create mode 100644 contrib/ds-algorithms/time-space-complexity.md diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 5b521551..9a0d556a 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -1,4 +1,5 @@ # List of sections - [Section title](filename.md) +- [Time and Space Complexity](time-space-complexity.md) - [Sorting Algorithms](sorting-algorithms.md) diff --git a/contrib/ds-algorithms/time-space-complexity.md b/contrib/ds-algorithms/time-space-complexity.md new file mode 100644 index 00000000..79a072f2 --- /dev/null +++ b/contrib/ds-algorithms/time-space-complexity.md @@ -0,0 +1,227 @@ +# Time and Space Complexity + +We can solve a problem using one or more algorithms. It's essential to learn how to compare the performance of different algorithms and select the best one for a specific task. +Therefore, it is highly required to use a method to compare the solutions in order to judge which one is more optimal.
The method must be: + +- Regardless of the system or its settings on which the algorithm is executing. +- Demonstrate a direct relationship with the quantity of inputs. +- Able to discriminate between two methods with clarity and precision. + +Two such methods use to analyze algorithms are `time complexity` and `space complexity`. + +## What is Time Complexity? + +The _number of operations an algorithm performs in proportion to the quantity of the input_ is measured by time complexity. It facilitates our investigation of how the performance of the algorithm scales with increasing input size. But in real life, **_time complexity does not refer to the time taken by the machine to execute a particular code_**. + +## Order of Growth and Asymptotic Notations + +The Order of Growth explains how an algorithm's space or running time expands as the amount of the input does. This increase is described via asymptotic language, such Big O notation, which concentrates on the dominating term as the input size approaches infinity and is independent of lower-order terms and machine-specific constants. + +### Common Asymptotic Notation + +1. `Big Oh (O)`: Provides the worst-case scenario for describing the upper bound of an algorithm's execution time. +2. `Big Omega (Ω)`: Provides the best-case scenario and describes the lower bound. +3. `Big Theta (Θ)`: Gives a tight constraint on the running time by describing both the upper and lower bounds. + +### 1. Big Oh (O) Notation + +Big O notation describes how an algorithm behaves as the input size gets closer to infinity and provides an upper bound on the time or space complexity of the method. It helps developers and computer scientists to evaluate the effectiveness of various algorithms without regard to the software or hardware environment. + +To denote asymptotic upper bound, we use O-notation. For a given function `g(n)`, we denote by `O(g(n))` (pronounced "big-oh of g of n") the set of functions: + +$$ +O(g(n)) = \{ f(n) : \exists \text{ positive constants } c \text{ and } n_0 \text{ such that } 0 \leq f(n) \leq c \cdot g(n) \text{ for all } n \geq n_0 \} +$$ + +Graphical representation of Big Oh: + +![BigOh Notation Graph](./images/Time-And-Space-Complexity-BigOh.png) + +Image Credit: www.programiz.com + +### 2. Big Omega (Ω) Notation + +Big Omega (Ω) notation is used to describe the lower bound of an algorithm's running time. It provides a way to express the minimum time complexity that an algorithm will take to complete. In other words, Big Omega gives us a guarantee that the algorithm will take at least a certain amount of time to run, regardless of other factors. + +To denote asymptotic lower bound, we use Omega-notation. For a given function `g(n)`, we denote by `Ω(g(n))` (pronounced "big-omega of g of n") the set of functions: + +$$ +\Omega(g(n)) = \{ f(n) : \exists \text{ positive constants } c \text{ and } n_0 \text{ such that } 0 \leq c \cdot g(n) \leq f(n) \text{ for all } n \geq n_0 \} +$$ + +Graphical representation of Big Omega: + +![BigOmega Notation Graph](./images/Time-And-Space-Complexity-BigOmega.png) + +Image Credit: www.programiz.com + +### 3. Big Theta (Θ) Notation + +Big Theta (Θ) notation provides a way to describe the asymptotic tight bound of an algorithm's running time. It offers a precise measure of the time complexity by establishing both an upper and lower bound, indicating that the running time of an algorithm grows at the same rate as a given function, up to constant factors. + +To denote asymptotic tight bound, we use Theta-notation. For a given function `g(n)`, we denote by `Θ(g(n))` (pronounced "big-theta of g of n") the set of functions: + +$$ +\Theta(g(n)) = \{ f(n) : \exists \text{ positive constants } c_1, c_2, \text{ and } n_0 \text{ such that } 0 \leq c_1 \cdot g(n) \leq f(n) \leq c_2 \cdot g(n) \text{ for all } n \geq n_0 \} +$$ + +Graphical representation of Big Theta: + +![Big Theta Notation Graph](./images/Time-And-Space-Complexity-BigTheta.png) + + +Image Credit: www.programiz.com + + +## Best Case, Worst Case and Average Case +### 1. Best-Case Scenario: +The best-case scenario refers to the situation where an algorithm performs optimally, achieving the lowest possible time or space complexity. It represents the most favorable conditions under which an algorithm operates. + +#### Characteristics: +- Represents the minimum time or space required by an algorithm to solve a problem. +- Occurs when the input data is structured in such a way that the algorithm can exploit its strengths fully. +- Often used to analyze the lower bound of an algorithm's performance. + +#### Example: +Consider the `linear search algorithm` where we're searching for a `target element` in an array. The best-case scenario occurs when the target element is found `at the very beginning of the array`. In this case, the algorithm would only need to make one comparison, resulting in a time complexity of O(1). + + +### 2. Worst-Case Scenario: +The worst-case scenario refers to the situation where an algorithm performs at its poorest, achieving the highest possible time or space complexity. It represents the most unfavorable conditions under which an algorithm operates. + +#### Characteristics: +- Represents the maximum time or space required by an algorithm to solve a problem. +- Occurs when the input data is structured in such a way that the algorithm encounters the most challenging conditions. +- Often used to analyze the upper bound of an algorithm's performance. + +#### Example: +Continuing with the `linear search algorithm`, the worst-case scenario occurs when the `target element` is either not present in the array or located `at the very end`. In this case, the algorithm would need to iterate through the entire array, resulting in a time complexity of O(n), where n is the size of the array. + + +### 3. Average-Case Scenario: +The average-case scenario refers to the expected performance of an algorithm over all possible inputs, typically calculated as the arithmetic mean of the time or space complexity. + +#### Characteristics: +- Represents the typical performance of an algorithm across a range of input data. +- Takes into account the distribution of inputs and their likelihood of occurrence. +- Provides a more realistic measure of an algorithm's performance compared to the best-case or worst-case scenarios. + +#### Example: +- For the `linear search algorithm`, the average-case scenario considers the probability distribution of the target element's position within the array. If the `target element is equally likely to be found at any position in the array`, the average-case time complexity would be O(n/2), as the algorithm would, on average, need to search halfway through the array. + + +## Space Complexity +The memory space that a code utilizes as it is being run is often referred to as space complexity. Additionally, space complexity depends on the machine, therefore rather than using the typical memory units like MB, GB, etc., we will express space complexity using the Big O notation. + +#### Examples of Space Complexity +1. `Constant Space Complexity (O(1))`: Algorithms that operate on a fixed-size array or use a constant number of variables have O(1) space complexity. + +2. `Linear Space Complexity (O(n))`: Algorithms that store each element of the input array in a separate variable or data structure have O(n) space complexity. + +3. `Quadratic Space Complexity (O(n^2))`: Algorithms that create a two-dimensional array or matrix with dimensions based on the input size have O(n^2) space complexity. + + +#### Analyzing Space Complexity +To analyze space complexity: + +- Identify the variables, data structures, and recursive calls used by the algorithm. +- Determine how the space requirements scale with the input size. +- Express the space complexity using Big O notation, considering the dominant terms that contribute most to the overall space usage. + +## Examples to calculate time and space complexity + +#### 1. Print all elements of given array : +Consider each line takes one unit of time to run. So, to simply iterate over an array to print all elements it will take `O(n)` time, where n is the size of array. +Code: +```python +arr = [1,2,3,4] #1 +for x in arr: #2 + print(x) #3 +``` +Here, the 1st statement executes only once. So, it takes one unit of time to run. The for loop consisting of 2nd and 3rd statements executes 4 times. +Also, as the code dosen't take any additional space except the input arr its Space Complexity is O(1) constant. + +#### 2. Linear Search +Linear search is a simple algorithm for finding an element in an array by sequentially checking each element until a match is found or the end of the array is reached. Here's an example of calculating the time and space complexity of linear search: + +```python +def linear_search(arr, target): + for x in arr: # n iterations in worst case + if x == target: # 1 + return True # 1 + return False # If element not found + +# Example usage +arr = [1, 3, 5, 7, 9] +target = 5 +print(linear_search(arr, target)) +``` +`Time Complexity Analysis:` +The for loop iterates through the entire array, which takes O(n) time in the worst case, where n is the size of the array. +Inside the loop, each operation takes constant time (O(1)). +Therefore, the time complexity of linear search is `O(n)`. + + +`Space Complexity Analysis:` +The space complexity of linear search is `O(1)` since it only uses a constant amount of additional space for variables regardless of the input size. + + +#### 3. Binary Search +Binary search is an efficient algorithm for finding an element in a sorted array by repeatedly dividing the search interval in half. Here's an example of calculating the time and space complexity of binary search: + +```python +def binary_search(arr, target): + left = 0 # 1 + right = len(arr) - 1 # 1 + + while left <= right: # log(n) iterations in worst case + mid = (left + right) // 2 # log(n) + + if arr[mid] == target: # 1 + return mid # 1 + elif arr[mid] < target: # 1 + left = mid + 1 # 1 + else: + right = mid - 1 # 1 + + return -1 # If element not found + +# Example usage +arr = [1, 3, 5, 7, 9] +target = 5 +print(binary_search(arr, target)) + +``` +`Time Complexity Analysis:` +The initialization of left and right takes constant time (O(1)). +The while loop runs for log(n) iterations in the worst case, where n is the size of the array. +Inside the loop, each operation takes constant time (O(1)). +Therefore, the time complexity of binary search is `O(log n)`. + +`Space Complexity Analysis:` +The space complexity of binary search is `O(1)` since it only uses a constant amount of additional space for variables regardless of the input size. + +#### 4. Fibbonaci Sequence +Let's consider an example of a function that generates Fibonacci numbers up to a given index and stores them in a list. In this case, the space complexity will not be constant because the size of the list grows with the Fibonacci sequence. + +```python +def fibonacci_sequence(n): + fib_list = [0, 1] # Initial Fibonacci sequence with first two numbers + + while len(fib_list) < n: # O(n) iterations in worst case + next_fib = fib_list[-1] + fib_list[-2] # Calculating next Fibonacci number + fib_list.append(next_fib) # Appending next Fibonacci number to list + + return fib_list + +# Example usage +n = 10 +fib_sequence = fibonacci_sequence(n) +print(fib_sequence) +``` +`Time Complexity Analysis:` +The while loop iterates until the length of the Fibonacci sequence list reaches n, so it takes `O(n)` iterations in the `worst case`.Inside the loop, each operation takes constant time (O(1)). + +`Space Complexity Analysis:` +The space complexity of this function is not constant because it creates and stores a list of Fibonacci numbers. +As n grows, the size of the list also grows, so the space complexity is O(n), 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zvqtBq`*k7U&$*pT0P7$#0~HR+@%NNHZA`&3x6q1gyJFSVP=%lg6k$Tz zi)xFzs@b3dze8` IYPlr-ALFsiXaE2J literal 0 HcmV?d00001 From 6db3d06e0f50d1c7bf28b43d6149c39ddff818d3 Mon Sep 17 00:00:00 2001 From: Yatharth Date: Fri, 24 May 2024 22:46:22 +0530 Subject: [PATCH 195/656] graph queues in index --- contrib/ds-algorithms/index.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 65698689..4f458f7c 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -2,6 +2,8 @@ - [Section title](filename.md) - [Time Space Complexity](time-space-complexity.md) +- [Queues in Python](Queues.md) +- [Graphs](graph.md) - [Sorting Algorithms](sorting-algorithms.md) - [Recursion and Backtracking](recursion.md) - [Divide and Conquer Algorithm](divide-and-conquer-algorithm.md) From 534475bf7a9e19bd4cd432f6ec4e54f5ae825b4d Mon Sep 17 00:00:00 2001 From: manishh12 Date: Fri, 24 May 2024 22:57:17 +0530 Subject: [PATCH 196/656] Added types of optimizers issue#527 --- .../machine-learning/Types_of_optimizers.md | 357 ++++++++++++++++++ contrib/machine-learning/index.md | 1 + 2 files changed, 358 insertions(+) create mode 100644 contrib/machine-learning/Types_of_optimizers.md diff --git a/contrib/machine-learning/Types_of_optimizers.md b/contrib/machine-learning/Types_of_optimizers.md new file mode 100644 index 00000000..7d0a6172 --- /dev/null +++ b/contrib/machine-learning/Types_of_optimizers.md @@ -0,0 +1,357 @@ +Sure, here's a more detailed explanation for each optimizer, including the mathematical formulation, intuition, advantages, and disadvantages, along with the Python implementation. + +--- + +# Optimizers in Machine Learning + +Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses. Optimization algorithms help to minimize (or maximize) an objective function (also called a loss function) which is simply a mathematical function dependent on the model's internal learnable parameters which are used in computing the target values from the set of features. + +## Types of Optimizers + +### 1. Gradient Descent + +**Explanation:** +Gradient Descent is the simplest and most commonly used optimization algorithm. It works by iteratively updating the model parameters in the opposite direction of the gradient of the objective function with respect to the parameters. The idea is to find the minimum of a function by taking steps proportional to the negative of the gradient of the function at the current point. + +**Mathematical Formulation:** + +The update rule for the parameter vector θ in gradient descent is represented by the equation: + +- \(theta_new = theta_old - alpha * gradient/) + +Where: +- theta_old is the old parameter vector. +- theta_new is the updated parameter vector. +- alpha is the learning rate. +- gradient is the gradient of the objective function with respect to the parameters. + + +**Intuition:** +- At each iteration, we calculate the gradient of the cost function. +- The parameters are updated in the opposite direction of the gradient. +- The size of the step is controlled by the learning rate \( \alpha \). + +**Advantages:** +- Simple to implement. +- Suitable for convex problems. + +**Disadvantages:** +- Can be slow for large datasets. +- May get stuck in local minima for non-convex problems. +- Requires careful tuning of the learning rate. + +**Python Implementation:** +```python +import numpy as np + +def gradient_descent(X, y, lr=0.01, epochs=1000): + m, n = X.shape + theta = np.zeros(n) + for epoch in range(epochs): + gradient = np.dot(X.T, (np.dot(X, theta) - y)) / m + theta -= lr * gradient + return theta +``` + +### 2. Stochastic Gradient Descent (SGD) + +**Explanation:** +SGD is a variation of gradient descent where we use only one training example to calculate the gradient and update the parameters. This introduces noise into the parameter updates, which can help to escape local minima but may cause the loss to fluctuate. + +**Mathematical Formulation:** + +- \(theta = theta - alpha * dJ(theta; x_i, y_i) / d(theta)/) + +\( x_i, y_i \) are a single training example and its target. + +**Intuition:** +- At each iteration, a random training example is selected. +- The gradient is calculated and the parameters are updated for this single example. +- This process is repeated for a specified number of epochs. + +**Advantages:** +- Faster updates compared to batch gradient descent. +- Can handle large datasets. +- Helps to escape local minima due to the noise in updates. + +**Disadvantages:** +- Loss function may fluctuate. +- Requires more iterations to converge. + +**Python Implementation:** +```python +def stochastic_gradient_descent(X, y, lr=0.01, epochs=1000): + m, n = X.shape + theta = np.zeros(n) + for epoch in range(epochs): + for i in range(m): + rand_index = np.random.randint(0, m) + xi = X[rand_index:rand_index+1] + yi = y[rand_index:rand_index+1] + gradient = np.dot(xi.T, (np.dot(xi, theta) - yi)) + theta -= lr * gradient + return theta +``` + +### 3. Mini-Batch Gradient Descent + +**Explanation:** +Mini-Batch Gradient Descent is a variation where instead of a single training example or the whole dataset, a mini-batch of examples is used to compute the gradient. This reduces the variance of the parameter updates, leading to more stable convergence. + +**Mathematical Formulation:** + +- theta = theta - alpha * (1/k) * sum(dJ(theta; x_i, y_i) / d(theta)) + +Where: +- \( k \) is the batch size. + +**Intuition:** +- At each iteration, a mini-batch of training examples is selected. +- The gradient is calculated for this mini-batch. +- The parameters are updated based on the average gradient of the mini-batch. + +**Advantages:** +- More stable updates compared to SGD. +- Faster convergence than batch gradient descent. +- Efficient on large datasets. + +**Disadvantages:** +- Requires tuning of batch size. +- Computationally more expensive than SGD per iteration. + +**Python Implementation:** +```python +def mini_batch_gradient_descent(X, y, lr=0.01, epochs=1000, batch_size=32): + m, n = X.shape + theta = np.zeros(n) + for epoch in range(epochs): + indices = np.random.permutation(m) + X_shuffled = X[indices] + y_shuffled = y[indices] + for i in range(0, m, batch_size): + X_i = X_shuffled[i:i+batch_size] + y_i = y_shuffled[i:i+batch_size] + gradient = np.dot(X_i.T, (np.dot(X_i, theta) - y_i)) / batch_size + theta -= lr * gradient + return theta +``` + +### 4. Momentum + +**Explanation:** +Momentum helps accelerate gradient vectors in the right directions, thus leading to faster converging. It accumulates a velocity vector in directions of persistent reduction in the objective function, which helps to smooth the path towards the minimum. + +**Mathematical Formulation:** + +- v_t = gamma * v_{t-1} + alpha * dJ(theta) / d(theta) + +- theta = theta - v_t + +where: + +- \( v_t \) is the velocity. +- \( \gamma \) is the momentum term, typically set between 0.9 and 0.99. + +**Intuition:** +- At each iteration, the gradient is calculated. +- The velocity is updated based on the current gradient and the previous velocity. +- The parameters are updated based on the velocity. + +**Advantages:** +- Faster convergence. +- Reduces oscillations in the parameter updates. + +**Disadvantages:** +- Requires tuning of the momentum term. + +**Python Implementation:** +```python +def momentum_gradient_descent(X, y, lr=0.01, epochs=1000, gamma=0.9): + m, n = X.shape + theta = np.zeros(n) + v = np.zeros(n) + for epoch in range(epochs): + gradient = np.dot(X.T, (np.dot(X, theta) - y)) / m + v = gamma * v + lr * gradient + theta -= v + return theta +``` + +### 5. Nesterov Accelerated Gradient (NAG) + +**Explanation:** +NAG is a variant of the gradient descent with momentum. It looks ahead by a step and calculates the gradient at that point, thus providing more accurate updates. This method helps to correct the overshooting problem seen in standard momentum. + +**Mathematical Formulation:** + +- v_t = gamma * v_{t-1} + alpha * dJ(theta - gamma * v_{t-1}) / d(theta) + +- theta = theta - v_t + + +**Intuition:** +- At each iteration, the parameters are temporarily updated using the previous velocity. +- The gradient is calculated at this lookahead position. +- The velocity and parameters are then updated based on this gradient. + +**Advantages:** +- More accurate updates compared to standard momentum. +- Faster convergence. + +**Disadvantages:** +- Requires tuning of the momentum term. + +**Python Implementation:** +```python +def nesterov_accelerated_gradient(X, y, lr=0.01, epochs=1000, gamma=0.9): + m, n = X.shape + theta = np.zeros(n) + v = np.zeros(n) + for epoch in range(epochs): + lookahead_theta = theta - gamma * v + gradient = np.dot(X.T, (np.dot(X, lookahead_theta) - y)) / m + v = gamma * v + lr * gradient + theta -= v + return theta +``` + +### 6. AdaGrad + +**Explanation:** +AdaGrad adapts the learning rate to the parameters, performing larger updates for infrequent and smaller updates for frequent parameters. It scales the learning rate inversely proportional to the square root of the sum of all historical squared values of the gradient. + +**Mathematical Formulation:** + +- G_t = G_{t-1} + (dJ(theta) / d(theta)) ⊙ (dJ(theta) / d(theta)) + +- theta = theta - (alpha / sqrt(G_t + epsilon)) * (dJ(theta) / d(theta)) + +Where: +- \( G_t \) is the sum of squares of the gradients up to time step \( t \). +- \( \epsilon \) is a small constant to avoid division by zero. + +**Intuition:** +- Accumulates the sum of the squares of the gradients for each parameter. +- Uses this accumulated + + sum to scale the learning rate. +- Parameters with large gradients in the past have smaller learning rates. + +**Advantages:** +- Effective for sparse data. +- Automatically adjusts learning rate. + +**Disadvantages:** +- Learning rate decreases continuously, which can lead to premature convergence. + +**Python Implementation:** +```python +def adagrad(X, y, lr=0.01, epochs=1000, epsilon=1e-8): + m, n = X.shape + theta = np.zeros(n) + G = np.zeros(n) + for epoch in range(epochs): + gradient = np.dot(X.T, (np.dot(X, theta) - y)) / m + G += gradient**2 + adjusted_lr = lr / (np.sqrt(G) + epsilon) + theta -= adjusted_lr * gradient + return theta +``` + +### 7. RMSprop + +**Explanation:** +RMSprop modifies AdaGrad to perform well in non-convex settings by using a moving average of squared gradients to scale the learning rate. It helps to keep the learning rate in check, especially in the presence of noisy gradients. + +**Mathematical Formulation:** + +E[g^2]_t = beta * E[g^2]_{t-1} + (1 - beta) * (dJ(theta) / d(theta)) ⊙ (dJ(theta) / d(theta)) + +theta = theta - (alpha / sqrt(E[g^2]_t + epsilon)) * (dJ(theta) / d(theta)) + +Where: +- \( E[g^2]_t \) is the exponentially decaying average of past squared gradients. +- \( \beta \) is the decay rate. + +**Intuition:** +- Keeps a running average of the squared gradients. +- Uses this average to scale the learning rate. +- Parameters with large gradients have their learning rates reduced. + +**Advantages:** +- Effective for non-convex problems. +- Reduces oscillations in parameter updates. + +**Disadvantages:** +- Requires tuning of the decay rate. + +**Python Implementation:** +```python +def rmsprop(X, y, lr=0.01, epochs=1000, beta=0.9, epsilon=1e-8): + m, n = X.shape + theta = np.zeros(n) + E_g = np.zeros(n) + for epoch in range(epochs): + gradient = np.dot(X.T, (np.dot(X, theta) - y)) / m + E_g = beta * E_g + (1 - beta) * gradient**2 + adjusted_lr = lr / (np.sqrt(E_g) + epsilon) + theta -= adjusted_lr * gradient + return theta +``` + +### 8. Adam + +**Explanation:** +Adam (Adaptive Moment Estimation) combines the advantages of both RMSprop and AdaGrad by keeping an exponentially decaying average of past gradients and past squared gradients. + +**Mathematical Formulation:** + +- m_t = beta1 * m_{t-1} + (1 - beta1) * (dJ(theta) / d(theta)) + +- v_t = beta2 * v_{t-1} + (1 - beta2) * ((dJ(theta) / d(theta))^2) + +- hat_m_t = m_t / (1 - beta1^t) + +- hat_v_t = v_t / (1 - beta2^t) + +- theta = theta - (alpha * hat_m_t) / (sqrt(hat_v_t) + epsilon) + +Where: +- \( m_t \) is the first moment (mean) of the gradient. +- \( v_t \) is the second moment (uncentered variance) of the gradient. +- \( \beta_1, \beta_2 \) are the decay rates for the moment estimates. + +**Intuition:** +- Keeps track of both the mean and the variance of the gradients. +- Uses these to adaptively scale the learning rate. +- Provides a balance between AdaGrad and RMSprop. + +**Advantages:** +- Efficient for large datasets. +- Well-suited for non-convex optimization. +- Handles sparse gradients well. + +**Disadvantages:** +- Requires careful tuning of hyperparameters. +- Can be computationally intensive. + +**Python Implementation:** +```python +def adam(X, y, lr=0.01, epochs=1000, beta1=0.9, beta2=0.999, epsilon=1e-8): + m, n = X.shape + theta = np.zeros(n) + m_t = np.zeros(n) + v_t = np.zeros(n) + for epoch in range(1, epochs+1): + gradient = np.dot(X.T, (np.dot(X, theta) - y)) / m + m_t = beta1 * m_t + (1 - beta1) * gradient + v_t = beta2 * v_t + (1 - beta2) * gradient**2 + m_t_hat = m_t / (1 - beta1**epoch) + v_t_hat = v_t / (1 - beta2**epoch) + theta -= lr * m_t_hat / (np.sqrt(v_t_hat) + epsilon) + return theta +``` + +These implementations are basic examples of how these optimizers can be implemented in Python using NumPy. In practice, libraries like TensorFlow and PyTorch provide highly optimized and more sophisticated implementations of these and other optimization algorithms. + +--- \ No newline at end of file diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 45235e4b..ce75c705 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -7,3 +7,4 @@ - [Support Vector Machine Algorithm](support-vector-machine.md) - [Artificial Neural Network from the Ground Up](ArtificialNeuralNetwork.md) - [TensorFlow.md](tensorFlow.md) +- [Types of optimizers](Types_of_optimizers.md) From a51621da8aabd6601923b20eab801707713b5bef Mon Sep 17 00:00:00 2001 From: manishh12 Date: Fri, 24 May 2024 23:01:05 +0530 Subject: [PATCH 197/656] updated readme issue#527 --- contrib/machine-learning/Types_of_optimizers.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/contrib/machine-learning/Types_of_optimizers.md b/contrib/machine-learning/Types_of_optimizers.md index 7d0a6172..e9415971 100644 --- a/contrib/machine-learning/Types_of_optimizers.md +++ b/contrib/machine-learning/Types_of_optimizers.md @@ -1,7 +1,5 @@ -Sure, here's a more detailed explanation for each optimizer, including the mathematical formulation, intuition, advantages, and disadvantages, along with the Python implementation. --- - # Optimizers in Machine Learning Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses. Optimization algorithms help to minimize (or maximize) an objective function (also called a loss function) which is simply a mathematical function dependent on the model's internal learnable parameters which are used in computing the target values from the set of features. From 7d1c8f449a297e0c4d2ec6ef4e902971077286b7 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Sat, 25 May 2024 00:38:57 +0530 Subject: [PATCH 198/656] Create sorting_numpy_array.md Added Introduction Added sort method Added argsort method Added lexsort method --- contrib/numpy/sorting_numpy_array.md | 104 +++++++++++++++++++++++++++ 1 file changed, 104 insertions(+) create mode 100644 contrib/numpy/sorting_numpy_array.md diff --git a/contrib/numpy/sorting_numpy_array.md b/contrib/numpy/sorting_numpy_array.md new file mode 100644 index 00000000..65e9c255 --- /dev/null +++ b/contrib/numpy/sorting_numpy_array.md @@ -0,0 +1,104 @@ +# Sorting NumPy Arrays +- Sorting arrays is a common operation in data manipulation and analysis. +- NumPy provides various functions to sort arrays efficiently. +- The primary methods are `numpy.sort`,`numpy.argsort`, and `numpy.lexsort` + +### 1. numpy.sort() + +The `numpy.sort` function returns a sorted copy of an array. + +#### Syntax : + +```python +numpy.sort(arr, axis=-1, kind=None, order=None) +``` +- **arr** : Array to be sorted. +- **axis** : Axis along which to sort. (By Default is -1) +- **kind** : Sorting algorithm. Options are 'quicksort', 'mergesort', 'heapsort', and 'stable'. (By Default 'quicksort') +- **order** : When arr is an array with fields defined, this argument specifies which fields to compare first. + +#### Example : + +```python +import numpy as np + +arr = np.array([1,7,0,4,6]) +sarr = np.sort(arr) +print(sarr) +``` + +**Output** : +```python +[0 1 4 6 7] +``` + +### 2. numpy.argsort() + +The `numpy.argsort` function returns the indices that would sort an array. Using those indices you can sort the array. + +#### Syntax : + +```python +numpy.argsort(a, axis=-1, kind=None, order=None) +``` +- **arr** : Array to be sorted. +- **axis** : Axis along which to sort. (By Default is -1) +- **kind** : Sorting algorithm. Options are 'quicksort', 'mergesort', 'heapsort', and 'stable'. (By Default 'quicksort') +- **order** : When arr is an array with fields defined, this argument specifies which fields to compare first. + +#### Example : + +```python +import numpy as np + +arr = np.array([2.1,7,4.2,4.3,6]) +indices = np.argsort(arr) +print(indices) +s_arr = arr[indices] +print(s_arr) +``` + +**Output** : +```python +[0 2 3 4 1] +[2.1 4.2 4.3 6. 7. ] +``` + +### 3. np.lexsort() + +The np.lexsort function performs an indirect stable sort using a sequence of keys. + +#### Syntax : + +```python +numpy.lexsort(keys, axis=-1) +``` +- **keys**: Sequence of arrays to sort by. The last key is the primary sort key. +- **axis**: Axis to be indirectly sorted.(By Default -1) + +#### Example : + +```python +import numpy as np + +a = np.array([5,4,3,2]) +b = np.array(['a','d','c','b']) +indices = np.lexsort((a,b)) +print(indices) + +s_arr = a[indices] +print(s_arr) + +s_arr = b[indices] +print(s_arr) +``` + +**Output** : +```python +[0 3 2 1] +[2 3 4 5] +['a' 'b' 'c' 'd'] +``` + +NumPy provides powerful and flexible functions for sorting arrays, including `np.sort`, `np.argsort`, and `np.lexsort`. +These functions support sorting along different axes, using various algorithms, and sorting by multiple keys, making them suitable for a wide range of data manipulation tasks. From 366743436cc8905d9b2163015eb27827114f14e9 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Sat, 25 May 2024 00:41:11 +0530 Subject: [PATCH 199/656] Update index.md Added sorting numpy array section --- contrib/numpy/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 18ed17af..4b4a2c80 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -7,3 +7,4 @@ - [Operations on Arrays in NumPy](operations-on-arrays.md) - [Loading Arrays from Files](loading_arrays_from_files.md) - [Saving Numpy Arrays into FIles](saving_numpy_arrays_to_files.md) +- [Sorting NumPy Arrays](sorting_numpy_array.md) From cc4fa4a47c0802f5bd50456358a888ac6212f459 Mon Sep 17 00:00:00 2001 From: Arihant Yadav <147732947+arihunter-18@users.noreply.github.com> Date: Sat, 25 May 2024 03:07:10 +0530 Subject: [PATCH 200/656] Add files via upload --- contrib/database/intro_mysql_queries.md | 371 ++++++++++++++++++++++++ 1 file changed, 371 insertions(+) create mode 100644 contrib/database/intro_mysql_queries.md diff --git a/contrib/database/intro_mysql_queries.md b/contrib/database/intro_mysql_queries.md new file mode 100644 index 00000000..b955ead6 --- /dev/null +++ b/contrib/database/intro_mysql_queries.md @@ -0,0 +1,371 @@ +# Introduction to MySQL Queries +MySQL is a widely-used open-source relational database management system (RDBMS) that utilizes SQL (Structured Query Language) for managing and querying data. In Python, the **mysql-connector-python** library allows you to connect to MySQL databases and execute SQL queries, providing a way to interact with the database from within a Python program. + +## Prerequisites +* Python and MySQL Server must be installed and configured. +* The library: **mysql-connector-python** must be installed. + +## Establishing connection with server +To establish a connection with the MySQL server, you need to import the **mysql.connector** module and create a connection object using the **connect()** function by providing the prompt server details as mentioned. + +```python +import mysql.connector + +con = mysql.connector.connect( +host ="localhost", +user ="root", +passwd ="12345" +) + +print((con.is_connected())) +``` +Having established a connection with the server, you get the following output : +``` +True +``` +## Creating a Database [CREATE] +To create a database, you need to execute the **CREATE DATABASE** query. The following code snippet demonstrates how to create a database named **GSSOC**. +```python +import mysql.connector + +# Establish the connection +conn = mysql.connector.connect( + host="localhost", + user="root", + password="12345" +) + +# Create a cursor object +cursor = conn.cursor() + +# Execute the query to show databases +cursor.execute("SHOW DATABASES") + +# Fetch and print the databases +databases = cursor.fetchall() +for database in databases: + print(database[0]) + +# Execute the query to create database GSSOC +cursor.execute("CREATE DATABASE GSSOC") + +print("\nAfter creation of the database\n") + +# Execute the query to show databases +cursor.execute("SHOW DATABASES") +# Fetch and print the databases +databases = cursor.fetchall() +for database in databases: + print(database[0]) + +cursor.close() +conn.close() +``` +You can observe in the output below, after execution of the query a new database named **GSSOC** has been created. +#### Output: +``` +information_schema +mysql +performance_schema +sakila +sys +world + +After creation of the database + +gssoc +information_schema +mysql +performance_schema +sakila +sys +world +``` +## Creating a Table in the Database [CREATE] +Now, we will create a table in the database. We will create a table named **example_table** in the database **GSSOC**. We will execute **CREATE TABLE** query and provide the fields for the table as mentioned in the code below: +```python +import mysql.connector + +# Establish the connection +conn = mysql.connector.connect( + host="localhost", + user="root", + password="12345" +) +# Create a cursor object +cursor = conn.cursor() + +# Execute the query to show tables +cursor.execute("USE GSSOC") +cursor.execute("SHOW TABLES") + +# Fetch and print the tables +tables = cursor.fetchall() +print("Before creation of table\n") +for table in tables: + print(table[0]) + +create_table_query = """ +CREATE TABLE example_table ( + name VARCHAR(255) NOT NULL, + age INT NOT NULL, + email VARCHAR(255) +) +""" +# Execute the query +cursor.execute(create_table_query) + +# Commit the changes +conn.commit() + +print("\nAfter creation of Table\n") +# Execute the query to show tables in GSSOC +cursor.execute("SHOW TABLES") + +# Fetch and print the tables +tables = cursor.fetchall() +for table in tables: + print(table[0]) + +cursor.close() +conn.close() +``` +#### Output: +``` +Before creation of table + + +After creation of Table + +example_table +``` +## Inserting Data [INSERT] +To insert data in an existing table, the **INSERT INTO** query is used, followed by the name of the table in which the data needs to be inserted. The following code demonstrates the insertion of multiple records in the table by **executemany()**. +```python +import mysql.connector + +# Establish the connection +conn = mysql.connector.connect( + host="localhost", + user="root", + password="12345" +) +# Create a cursor object +cursor = conn.cursor() +cursor.execute("USE GSSOC") +# SQL query to insert data +insert_data_query = """ +INSERT INTO example_table (name, age, email) +VALUES (%s, %s, %s) +""" + +# Data to be inserted +data_to_insert = [ + ("John Doe", 28, "john.doe@example.com"), + ("Jane Smith", 34, "jane.smith@example.com"), + ("Sam Brown", 22, "sam.brown@example.com") +] + +# Execute the query for each data entry +cursor.executemany(insert_data_query, data_to_insert) + +conn.commit() +cursor.close() +conn.close() +``` +## Displaying Data [SELECT] +To display the data from a table, the **SELECT** query is used. The following code demonstrates the display of data from the table. +```python +import mysql.connector + +# Establish the connection +conn = mysql.connector.connect( + host="localhost", + user="root", + password="12345" +) +# Create a cursor object +cursor = conn.cursor() +cursor.execute("USE GSSOC") + +# SQL query to display data +display_data_query = "SELECT * FROM example_table" + +# Execute the query for each data entry +cursor.execute(display_data_query) + +# Fetch all the rows +rows = cursor.fetchall() + +# Print the column names +column_names = [desc[0] for desc in cursor.description] +print(column_names) + +# Print the rows +for row in rows: + print(row) + +cursor.close() +conn.close() +``` +#### Output : +``` +['name', 'age', 'email'] +('John Doe', 28, 'john.doe@example.com') +('Jane Smith', 34, 'jane.smith@example.com') +('Sam Brown', 22, 'sam.brown@example.com') +``` +## Updating Data [UPDATE] +To update data in the table, **UPDATE** query is used. In the following code, we will be updating the email and age of the record where the name is John Doe. +```python +import mysql.connector + +# Establish the connection +conn = mysql.connector.connect( + host="localhost", + user="root", + password="12345" +) +# Create a cursor object +cursor = conn.cursor() +cursor.execute("USE GSSOC") + +# SQL query to display data +display_data_query = "SELECT * FROM example_table" + +# SQL Query to update data of John Doe +update_data_query = """ +UPDATE example_table +SET age = %s, email = %s +WHERE name = %s +""" + +# Data to be updated +data_to_update = (30, "new.email@example.com", "John Doe") + +# Execute the query +cursor.execute(update_data_query, data_to_update) + +# Commit the changes +conn.commit() + +# Execute the query for each data entry +cursor.execute(display_data_query) + +# Fetch all the rows +rows = cursor.fetchall() + +# Print the column names +column_names = [desc[0] for desc in cursor.description] +print(column_names) + +# Print the rows +for row in rows: + print(row) + +cursor.close() +conn.close() +``` +#### Output: +``` +['name', 'age', 'email'] +('John Doe', 30, 'new.email@example.com') +('Jane Smith', 34, 'jane.smith@example.com') +('Sam Brown', 22, 'sam.brown@example.com') +``` + +## Deleting Data [DELETE] +In this segment, we will Delete the record named "John Doe" using the **DELETE** and **WHERE** statements in the query. The following code explains the same and the observe the change in output. +```python +import mysql.connector + +# Establish the connection +conn = mysql.connector.connect( + host="localhost", + user="root", + password="12345" +) +# Create a cursor object +cursor = conn.cursor() +cursor.execute("USE GSSOC") + +# SQL query to display data +display_data_query = "SELECT * FROM example_table" + +# SQL query to delete data +delete_data_query = "DELETE FROM example_table WHERE name = %s" + +# Data to be deleted +data_to_delete = ("John Doe",) + +# Execute the query +cursor.execute(delete_data_query, data_to_delete) + +# Commit the changes +conn.commit() + +# Execute the query for each data entry +cursor.execute(display_data_query) + +# Fetch all the rows +rows = cursor.fetchall() + +# Print the column names +column_names = [desc[0] for desc in cursor.description] +print(column_names) + +# Print the rows +for row in rows: + print(row) + +cursor.close() +conn.close() +``` +#### Output: +``` +['name', 'age', 'email'] +('Jane Smith', 34, 'jane.smith@example.com') +('Sam Brown', 22, 'sam.brown@example.com') +``` +## Deleting the Table/Database [DROP] +For deleting a table, you can use the **DROP** query in the following manner: +```python +import mysql.connector + +# Establish the connection +conn = mysql.connector.connect( + host="localhost", + user="root", + password="12345" +) +# Create a cursor object +cursor = conn.cursor() +cursor.execute("USE GSSOC") + +# SQL query to delete the table +delete_table_query = "DROP TABLE IF EXISTS example_table" + +# Execute the query +cursor.execute(delete_table_query) + +# Verify the table deletion +cursor.execute("SHOW TABLES LIKE 'example_table'") +result = cursor.fetchone() + +cursor.close() +conn.close() + +if result: + print("Table deletion failed.") +else: + print("Table successfully deleted.") +``` +#### Output: +``` +Table successfully deleted. +``` +Similarly, you can delete the database also by using the **DROP** and accordingly changing the query to be executed. + + + + From fd5c4ec3345a48c46eff8ed0db3826b6b6845227 Mon Sep 17 00:00:00 2001 From: Arihant Yadav <147732947+arihunter-18@users.noreply.github.com> Date: Sat, 25 May 2024 03:09:38 +0530 Subject: [PATCH 201/656] Add files via upload --- contrib/database/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/database/index.md b/contrib/database/index.md index 82596a2f..56cd85b2 100644 --- a/contrib/database/index.md +++ b/contrib/database/index.md @@ -1,3 +1,3 @@ # List of sections -- [Section title](filename.md) +- [Introduction to MySQL and Queries](intro_mysql_queries.md) From 91c57b20b772613d779c2bbd963385d2458f6773 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 25 May 2024 04:58:13 +0530 Subject: [PATCH 202/656] Rename Introduction_to_Pandas_Library_and_DataFrames.md to introduction.md --- ...uction_to_Pandas_Library_and_DataFrames.md => introduction.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/pandas/{Introduction_to_Pandas_Library_and_DataFrames.md => introduction.md} (100%) diff --git a/contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md b/contrib/pandas/introduction.md similarity index 100% rename from contrib/pandas/Introduction_to_Pandas_Library_and_DataFrames.md rename to contrib/pandas/introduction.md From 6ec6fd6f409f96d26afb7b0b6554da02a1dfc74d Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 25 May 2024 04:58:34 +0530 Subject: [PATCH 203/656] Update index.md --- contrib/pandas/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index c578adef..28744312 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -1,7 +1,7 @@ # List of sections +- [Pandas Introduction and Dataframes in Pandas](introduction.md) - [Pandas Series Vs NumPy ndarray](pandas_series_vs_numpy_ndarray.md) -- [Pandas Introduction and Dataframes in Pandas](Introduction_to_Pandas_Library_and_DataFrames.md) - [Pandas Descriptive Statistics](Descriptive_Statistics.md) - [Group By Functions with Pandas](GroupBy_Functions_Pandas.md) - [Excel using Pandas DataFrame](excel_with_pandas.md) From 54eec70ca9001137fb9d80d442c3fd7d31f7b1ce Mon Sep 17 00:00:00 2001 From: Pranshu shah <97401387+shahpranshu27@users.noreply.github.com> Date: Sat, 25 May 2024 07:25:26 +0530 Subject: [PATCH 204/656] create map-function.md --- contrib/advanced-python/index.md | 1 + contrib/advanced-python/map-function.md | 54 +++++++++++++++++++++++++ 2 files changed, 55 insertions(+) create mode 100644 contrib/advanced-python/map-function.md diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 880b393b..e1ff216f 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -5,3 +5,4 @@ - [Regular Expressions in Python](regular_expressions.md) - [JSON module](json-module.md) - [OOPs](OOPs.md) +- [Map Function](map-function.md) diff --git a/contrib/advanced-python/map-function.md b/contrib/advanced-python/map-function.md new file mode 100644 index 00000000..be035d0c --- /dev/null +++ b/contrib/advanced-python/map-function.md @@ -0,0 +1,54 @@ +The `map()` function in Python is a built-in function used for applying a given function to each item of an iterable (like a list, tuple, or dictionary) and returning a new iterable with the results. It's a powerful tool for transforming data without the need for explicit loops. Let's break down its syntax, explore examples, and discuss various use cases. + +### Syntax: + +```python +map(function, iterable1, iterable2, ...) +``` + +- `function`: The function to apply to each item in the iterables. +- `iterable1`, `iterable2`, ...: One or more iterable objects whose items will be passed as arguments to `function`. + +### Examples: + +#### Example 1: Doubling the values in a list + +```python +# Define the function +def double(x): + return x * 2 + +# Apply the function to each item in the list using map +original_list = [1, 2, 3, 4, 5] +doubled_list = list(map(double, original_list)) +print(doubled_list) # Output: [2, 4, 6, 8, 10] +``` + +#### Example 2: Converting temperatures from Celsius to Fahrenheit + +```python +# Define the function +def celsius_to_fahrenheit(celsius): + return (celsius * 9/5) + 32 + +# Apply the function to each Celsius temperature using map +celsius_temperatures = [0, 10, 20, 30, 40] +fahrenheit_temperatures = list(map(celsius_to_fahrenheit, celsius_temperatures)) +print(fahrenheit_temperatures) # Output: [32.0, 50.0, 68.0, 86.0, 104.0] +``` + +### Use Cases: + +1. **Data Transformation**: When you need to apply a function to each item of a collection and obtain the transformed values, `map()` is very handy. + +2. **Parallel Processing**: In some cases, `map()` can be utilized in parallel processing scenarios, especially when combined with `multiprocessing` or `concurrent.futures`. + +3. **Cleaning and Formatting Data**: It's often used in data processing pipelines for tasks like converting data types, normalizing values, or applying formatting functions. + +4. **Functional Programming**: In functional programming paradigms, `map()` is frequently used along with other functional constructs like `filter()` and `reduce()` for concise and expressive code. + +5. **Generating Multiple Outputs**: You can use `map()` to generate multiple outputs simultaneously by passing multiple iterables. The function will be applied to corresponding items in the iterables. + +6. **Lazy Evaluation**: In Python 3, `map()` returns an iterator rather than a list. This means it's memory efficient and can handle large datasets without loading everything into memory at once. + +Remember, while `map()` is powerful, it's essential to balance its use with readability and clarity. Sometimes, a simple loop might be more understandable than a `map()` call. \ No newline at end of file From 9680263fa2e36061cf81665b6c1e2a6560e8e77b Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 25 May 2024 08:19:34 +0530 Subject: [PATCH 205/656] Create readme.md --- contrib/pandas/Datasets/readme.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 contrib/pandas/Datasets/readme.md diff --git a/contrib/pandas/Datasets/readme.md b/contrib/pandas/Datasets/readme.md new file mode 100644 index 00000000..ea2255c1 --- /dev/null +++ b/contrib/pandas/Datasets/readme.md @@ -0,0 +1 @@ +## This folder contains all the Datasets used in the content. From 1b654fd3abd8a6a940f99e367e5a4ca0d67e9a44 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 25 May 2024 08:19:56 +0530 Subject: [PATCH 206/656] Add files via upload --- contrib/pandas/Datasets/Titanic.csv | 1310 +++++++++++++++++++++++++++ 1 file changed, 1310 insertions(+) create mode 100644 contrib/pandas/Datasets/Titanic.csv diff --git a/contrib/pandas/Datasets/Titanic.csv b/contrib/pandas/Datasets/Titanic.csv new file mode 100644 index 00000000..f8d49dcf --- /dev/null +++ b/contrib/pandas/Datasets/Titanic.csv @@ -0,0 +1,1310 @@ +"pclass","survived","name","sex","age","sibsp","parch","ticket","fare","cabin","embarked","boat","body","home.dest" +1,1,"Allen, Miss. Elisabeth Walton","female",29,0,0,"24160",211.3375,"B5","S","2",,"St Louis, MO" +1,1,"Allison, Master. Hudson Trevor","male",0.92,1,2,"113781",151.5500,"C22 C26","S","11",,"Montreal, PQ / Chesterville, ON" +1,0,"Allison, Miss. 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Thamine","female",,1,0,"2665",14.4542,,"C",,, +3,0,"Zakarian, Mr. Mapriededer","male",26.5,0,0,"2656",7.2250,,"C",,"304", +3,0,"Zakarian, Mr. Ortin","male",27,0,0,"2670",7.2250,,"C",,, +3,0,"Zimmerman, Mr. Leo","male",29,0,0,"315082",7.8750,,"S",,, From f4b24fdfd85416c3fdceec43d5425a94a4cf3bc3 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 25 May 2024 08:24:05 +0530 Subject: [PATCH 207/656] Add files via upload --- .../Importing_and_Exporting_Data_in_Pandas.md | 103 ++++++++++++++++++ 1 file changed, 103 insertions(+) create mode 100644 contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md diff --git a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md new file mode 100644 index 00000000..bb490b9c --- /dev/null +++ b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md @@ -0,0 +1,103 @@ +# Importing_and_Exporting_Data_in_Pandas + +>Created by Krishna Kaushik + +- **Now we're able to create `Series` and `DataFrames` in pandas, but we usually do not do this , in practice we import the data which is in the form of .csv (Comma Seperated Values) , a spreadsheet file or something similar.** + +- *Good news is that pandas allows for easy importing of data like this through functions such as ``pd.read_csv()`` and ``pd.read_excel()`` for Microsoft Excel files.* + +## 1. Importing from a Google sheet to a pandas dataframe + +*Let's say that you wanted to get the information from Google Sheet document into a pandas DataFrame.*. + +*You could export it as a .csv file and then import it using ``pd.read_csv()``.* + +*In this case, the exported .csv file is called `Titanic.csv`* + + +```python +## Importing Titanic Data set +import pandas as pd + +titanic_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/Titanic.csv") +print(titanic_df) +``` + + pclass survived name \ + 0 1 1 Allen, Miss. Elisabeth Walton + 1 1 1 Allison, Master. Hudson Trevor + 2 1 0 Allison, Miss. Helen Loraine + 3 1 0 Allison, Mr. Hudson Joshua Creighton + 4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) + ... ... ... ... + 1304 3 0 Zabour, Miss. Hileni + 1305 3 0 Zabour, Miss. Thamine + 1306 3 0 Zakarian, Mr. Mapriededer + 1307 3 0 Zakarian, Mr. Ortin + 1308 3 0 Zimmerman, Mr. Leo + + sex age sibsp parch ticket fare cabin embarked boat \ + 0 female 29.00 0 0 24160 211.3375 B5 S 2 + 1 male 0.92 1 2 113781 151.5500 C22 C26 S 11 + 2 female 2.00 1 2 113781 151.5500 C22 C26 S NaN + 3 male 30.00 1 2 113781 151.5500 C22 C26 S NaN + 4 female 25.00 1 2 113781 151.5500 C22 C26 S NaN + ... ... ... ... ... ... ... ... ... ... + 1304 female 14.50 1 0 2665 14.4542 NaN C NaN + 1305 female NaN 1 0 2665 14.4542 NaN C NaN + 1306 male 26.50 0 0 2656 7.2250 NaN C NaN + 1307 male 27.00 0 0 2670 7.2250 NaN C NaN + 1308 male 29.00 0 0 315082 7.8750 NaN S NaN + + body home.dest + 0 NaN St Louis, MO + 1 NaN Montreal, PQ / Chesterville, ON + 2 NaN Montreal, PQ / Chesterville, ON + 3 135.0 Montreal, PQ / Chesterville, ON + 4 NaN Montreal, PQ / Chesterville, ON + ... ... ... + 1304 328.0 NaN + 1305 NaN NaN + 1306 304.0 NaN + 1307 NaN NaN + 1308 NaN NaN + + [1309 rows x 14 columns] + + +The dataset I am using here for your reference is taken from the same repository i.e ``learn-python`` (https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/Titanic.csv) I uploaded it in the Datasets folder,you can use it from there. + +You can also place the filename with its path in `pd.read_csv()`. + +**Now we've got the same data from the Google Spreadsheet , but now available as ``pandas DataFrame`` which means we can now apply all pandas functionality over it.** + +#### Note: The quiet important thing i am telling is that ``pd.read_csv()`` takes the location of the file (which is in your current working directory) or the hyperlink of the dataset from the other source. + +#### But if you want to import the data from Github you can't directly use its link , you have to first convert it to raw by clicking on the raw button present in the repo . + +#### Also you can't use the data directly from `Kaggle` you have to use ``kaggle API`` + +## 2. The Anatomy of DataFrame + +**Different functions use different labels for different things, and can get a little confusing.** + +- Rows are refer as ``axis=0`` +- columns are refer as ``axis=1`` + +## 3. Exporting Data + +**OK, so after you've made a few changes to your data, you might want to export it and save it so someone else can access the changes.** + +**pandas allows you to export ``DataFrame's`` to ``.csv`` format using ``.to_csv()``, or to a spreadsheet format using .to_excel().** + +### Exporting a dataframe to a CSV + +**We haven't made any changes yet to the ``titanic_df`` DataFrame but let's try to export it.** + + +```python +#Export the titanic_df DataFrame to csv +titanic_df.to_csv("exported_titanic.csv") +``` + +Running this will save a file called ``exported_titanic.csv`` to the current folder. From 485c508e4dbe68bde1a3842ca6f030f0e2c072f1 Mon Sep 17 00:00:00 2001 From: unknown Date: Sat, 25 May 2024 12:03:10 +0530 Subject: [PATCH 208/656] Created datetime_with_pandas --- contrib/pandas/datetime_with_pandas.md | 117 +++++++++++++++++++++++++ 1 file changed, 117 insertions(+) create mode 100644 contrib/pandas/datetime_with_pandas.md diff --git a/contrib/pandas/datetime_with_pandas.md b/contrib/pandas/datetime_with_pandas.md new file mode 100644 index 00000000..7b8c720d --- /dev/null +++ b/contrib/pandas/datetime_with_pandas.md @@ -0,0 +1,117 @@ +# Pandas DateTime + +Pandas is a robust Python library that is available as free source. The Pandas library is used to manipulate and analyse data. Pandas are made up of data structures and functions that allow for efficient data processing. + +While working with data, it is common to come across time series data. Pandas is a very handy tool for dealing with time series data. Pandas is a strong Python data analysis toolkit that provides a wide range of date and time data processing options. Many data science jobs require working with time series data, time zones, and date arithmetic, and pandas simplifies these processes. + +Features of Pandas `Data_Time`: + +- **Parsing dates and times**: Pandas provides a number of functions for parsing dates and times from strings, including `to_datetime()` and `parse_dates()`. These functions can handle a variety of date and time formats, Unix timestamps, and human-readable formats. + +- **Manipulating dates and times**: Pandas provides a number of functions for manipulating dates and times, including `shift()`, `resample()`, and `to_timedelta()`. These functions can be used to add or subtract time periods, change the frequency of a time series, and calculate the difference between two dates or times. + +- **Visualizing dates and times**: Pandas provides a number of functions for visualizing dates and times, including `plot()`, `hist()`, and `bar()`. These functions can be used to create line charts, histograms, and bar charts of date and time data. + + + +### Installation of libraries + +`pip install pandas` + +- **Note**: There is no need to install a seperate library for date_time operations, pandas module itself has built-in functions. + +Example for retrieving day, month and year from given date: + +```python +import pandas as pd + +ts = pd.Timestamp('2024-05-05') +y = ts.year +print('Year is: ', y) +m = ts.month +print('Month is: ', m) +d = ts.day +print('Day is: ', d) +``` + +Output: +```python +Year is: 2024 +Month is: 5 +Day is: 5 +``` + +- **Note**: The timestamp function in Pandas is used to convert a datetime object to a Unix timestamp. A Unix timestamp is a numerical representation of datetime. + + +Example for extracting time related data from given date: + +```python +import pandas as pd + +ts = pd.Timestamp('2024-10-24 12:00:00') +print('Hour is: ', ts.hour) +print('Minute is: ', ts.minute) +print('Weekday is: ', ts.weekday()) +print('Quarter is: ', ts.quarter) +``` + +Output: +```python +Hour is: 12 +Minute is: 0 +Weekday is: 1 +Quarter is: 4 +``` + +Example for getting current date and time: + +```python +import pandas as pd + +ts = pd.Timestamp.now() +print('Current date and time is: ', ts) +``` + +Output: +```python +Current date and time is: 2024-05-25 11:48:25.593213 +``` + +Example for generating dates' for next five days: + +```python +import pandas as pd + +ts = pd.date_range(start = pd.Timestamp.now(), periods = 5) +for i in ts: + print(i.date()) +``` + +Output: +```python +2024-05-25 +2024-05-26 +2024-05-27 +2024-05-28 +2024-05-29 +``` + +Example for generating dates' for previous five days: + +```python +import pandas as pd + +ts = pd.date_range(end = pd.Timestamp.now(), periods = 5) +for i in ts: + print(i.date()) +``` + +Output: +```python +2024-05-21 +2024-05-22 +2024-05-23 +2024-05-24 +2024-05-25 +``` \ No newline at end of file From e140e97690b7c06e9e3e31401c72a25b85995865 Mon Sep 17 00:00:00 2001 From: HimakarC <116370824+HimakarC@users.noreply.github.com> Date: Sat, 25 May 2024 12:06:37 +0530 Subject: [PATCH 209/656] Update index.md --- contrib/pandas/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 834d1f43..9a7a103b 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -2,3 +2,4 @@ - [Pandas Series Vs NumPy ndarray](pandas_series_vs_numpy_ndarray.md) - [Excel using Pandas DataFrame](excel_with_pandas.md) +- [Working with datetime using Pandas](datetime_with_pandas.md) From 43a1b5a652b74852b2a17cd169d1412cf5114fb0 Mon Sep 17 00:00:00 2001 From: rohit Date: Sat, 25 May 2024 12:12:28 +0530 Subject: [PATCH 210/656] Added PyTorch.md --- contrib/machine-learning/PyTorch.md | 113 ++++++++++++++++++++++++++++ contrib/machine-learning/index.md | 1 + 2 files changed, 114 insertions(+) create mode 100644 contrib/machine-learning/PyTorch.md diff --git a/contrib/machine-learning/PyTorch.md b/contrib/machine-learning/PyTorch.md new file mode 100644 index 00000000..7de853ba --- /dev/null +++ b/contrib/machine-learning/PyTorch.md @@ -0,0 +1,113 @@ +# PyTorch: A Comprehensive Overview + +## Introduction +PyTorch is an open-source deep learning framework developed by Facebook's AI Research lab. It provides a flexible and efficient platform for building and deploying machine learning models. PyTorch is known for its dynamic computational graph, ease of use, and strong support for GPU acceleration. + +## Key Features +- **Dynamic Computational Graphs**: PyTorch's dynamic computation graph (or define-by-run) allows you to change the network architecture during runtime. This feature makes debugging and experimenting with different model architectures easier. +- **GPU Acceleration**: PyTorch supports CUDA, enabling efficient computation on GPUs. +- **Extensive Libraries and Tools**: PyTorch has a rich ecosystem of libraries and tools such as torchvision for computer vision, torchtext for natural language processing, and more. +- **Community Support**: PyTorch has a large and active community, providing extensive resources, tutorials, and forums for support. + +## Installation +To install PyTorch, you can use pip: + +```sh +pip install torch torchvision +``` + +For detailed installation instructions, including GPU support, visit the [official PyTorch installation guide](https://pytorch.org/get-started/locally/). + +## Basic Usage + +### Tensors +Tensors are the fundamental building blocks in PyTorch. They are similar to NumPy arrays but can run on GPUs. + +```python +import torch + +# Creating a tensor +x = torch.tensor([1.0, 2.0, 3.0]) +print(x) + +# Performing basic operations +y = torch.tensor([4.0, 5.0, 6.0]) +z = x + y +print(z) +``` + +### Autograd +Autograd is PyTorch's automatic differentiation engine that powers neural network training. It tracks operations on tensors to automatically compute gradients. + +```python +# Requires gradient +x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True) + +# Perform operations +y = x ** 2 +z = y.sum() + +# Compute gradients +z.backward() +print(x.grad) +``` + +### Building Neural Networks +PyTorch provides the `torch.nn` module to build neural networks. + +```python +import torch +import torch.nn as nn +import torch.optim as optim + +# Define a simple neural network +class SimpleNN(nn.Module): + def __init__(self): + super(SimpleNN, self).__init__() + self.fc1 = nn.Linear(3, 1) + + def forward(self, x): + x = self.fc1(x) + return x + +# Create the network, define the criterion and optimizer +model = SimpleNN() +criterion = nn.MSELoss() +optimizer = optim.SGD(model.parameters(), lr=0.01) + +# Dummy input and target +inputs = torch.tensor([[1.0, 2.0, 3.0]]) +targets = torch.tensor([[0.5]]) + +# Forward pass +outputs = model(inputs) +loss = criterion(outputs, targets) + +# Backward pass and optimization +loss.backward() +optimizer.step() + +print(f'Loss: {loss.item()}') +``` + +## When to Use PyTorch +### Use PyTorch When: +1. **Research and Development**: PyTorch's dynamic computation graph makes it ideal for experimentation and prototyping. +2. **Computer Vision and NLP**: With extensive libraries like torchvision and torchtext, PyTorch is well-suited for these domains. +3. **Custom Operations**: If your work involves custom layers or operations, PyTorch provides the flexibility to implement and integrate them easily. +4. **Community and Ecosystem**: If you prefer a strong community support and extensive third-party resources, PyTorch is a good choice. + +### Consider Alternatives When: +1. **Production Deployment**: While PyTorch has made strides in deployment (e.g., TorchServe), TensorFlow's TensorFlow Serving is more mature for large-scale deployment. +2. **Static Graphs**: If your model architecture doesn't change frequently and you prefer static computation graphs, TensorFlow might be more suitable. +3. **Multi-Language Support**: If you need integration with languages other than Python (e.g., Java, JavaScript), TensorFlow offers better support. + +## Conclusion +PyTorch is a powerful and flexible deep learning framework that caters to both researchers and practitioners. Its ease of use, dynamic computation graph, and strong community support make it an excellent choice for many machine learning tasks. However, for certain production scenarios or specific requirements, alternatives like TensorFlow may be more appropriate. + +## Additional Resources +- [PyTorch Official Documentation](https://pytorch.org/docs/stable/index.html) +- [PyTorch Tutorials](https://pytorch.org/tutorials/) +- [PyTorch Forum](https://discuss.pytorch.org/) + +Feel free to explore and experiment with PyTorch to harness the full potential of this versatile framework! \ No newline at end of file diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 45235e4b..edb08601 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -7,3 +7,4 @@ - [Support Vector Machine Algorithm](support-vector-machine.md) - [Artificial Neural Network from the Ground Up](ArtificialNeuralNetwork.md) - [TensorFlow.md](tensorFlow.md) +- [PyTorch.md](PyTorch.md) From 1744d12d8e160aa480399af7f354b077bb8797ae Mon Sep 17 00:00:00 2001 From: HimakarC <116370824+HimakarC@users.noreply.github.com> Date: Sat, 25 May 2024 12:15:49 +0530 Subject: [PATCH 211/656] Update datetime_with_pandas.md --- contrib/pandas/datetime_with_pandas.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/contrib/pandas/datetime_with_pandas.md b/contrib/pandas/datetime_with_pandas.md index 7b8c720d..796df4a8 100644 --- a/contrib/pandas/datetime_with_pandas.md +++ b/contrib/pandas/datetime_with_pandas.md @@ -4,7 +4,7 @@ Pandas is a robust Python library that is available as free source. The Pandas l While working with data, it is common to come across time series data. Pandas is a very handy tool for dealing with time series data. Pandas is a strong Python data analysis toolkit that provides a wide range of date and time data processing options. Many data science jobs require working with time series data, time zones, and date arithmetic, and pandas simplifies these processes. -Features of Pandas `Data_Time`: +Features of Pandas `Date_Time`: - **Parsing dates and times**: Pandas provides a number of functions for parsing dates and times from strings, including `to_datetime()` and `parse_dates()`. These functions can handle a variety of date and time formats, Unix timestamps, and human-readable formats. @@ -114,4 +114,4 @@ Output: 2024-05-23 2024-05-24 2024-05-25 -``` \ No newline at end of file +``` From ce0fc1208d986f166487a35bfcbe8fcfd47c1f61 Mon Sep 17 00:00:00 2001 From: Ramya Korupolu <104009248+Ramya-korupolu@users.noreply.github.com> Date: Sat, 25 May 2024 12:34:32 +0530 Subject: [PATCH 212/656] Update sorting-algorithms.md --- contrib/ds-algorithms/sorting-algorithms.md | 159 ++++++++++++++++++++ 1 file changed, 159 insertions(+) diff --git a/contrib/ds-algorithms/sorting-algorithms.md b/contrib/ds-algorithms/sorting-algorithms.md index 6423b4b3..25bee49d 100644 --- a/contrib/ds-algorithms/sorting-algorithms.md +++ b/contrib/ds-algorithms/sorting-algorithms.md @@ -327,3 +327,162 @@ print("Sorted array:", arr) # Output: [3, 9, 10, 27, 38, 43, 82]

+ +# 5. Insertion Sort + +Insertion sort is a straightforward and efficient sorting algorithm for small datasets. It builds the final sorted array one element at a time. It is much like sorting playing cards in your hands: you take one card at a time and insert it into its correct position among the already sorted cards. + +**Algorithm Overview:** +- **Start from the Second Element:** Begin with the second element, assuming the first element is already sorted. +- **Compare with Sorted Subarray:** Take the current element and compare it with elements in the sorted subarray (the part of the array before the current element). +- **Insert in Correct Position:** Shift all elements in the sorted subarray that are greater than the current element to one position ahead. Insert the current element into its correct position. +- **Repeat Until End:** Repeat this process for all elements in the array. + +## Example with Visualization +Let's sort the list `[5, 3, 8, 1, 2]` using insertion sort. + +**Step-by-Step Visualization:** +**Initial List:** `[5, 3, 8, 1, 2]` + +1. **Pass 1:** + - Current element: 3 + - Compare 3 with 5, move 5 to the right: `[5, 5, 8, 1, 2]` + - Insert 3 in its correct position: `[3, 5, 8, 1, 2]` + +2. **Pass 2:** + - Current element: 8 + - 8 is already in the correct position: `[3, 5, 8, 1, 2]` + +3. **Pass 3:** + - Current element: 1 + - Compare 1 with 8, move 8 to the right: `[3, 5, 8, 8, 2]` + - Compare 1 with 5, move 5 to the right: `[3, 5, 5, 8, 2]` + - Compare 1 with 3, move 3 to the right: `[3, 3, 5, 8, 2]` + - Insert 1 in its correct position: `[1, 3, 5, 8, 2]` + +4. **Pass 4:** + - Current element: 2 + - Compare 2 with 8, move 8 to the right: `[1, 3, 5, 8, 8]` + - Compare 2 with 5, move 5 to the right: `[1, 3, 5, 5, 8]` + - Compare 2 with 3, move 3 to the right: `[1, 3, 3, 5, 8]` + - Insert 2 in its correct position: `[1, 2, 3, 5, 8]` + +## Insertion Sort Code in Python + + +```python + +def insertion_sort(arr): + # Traverse from 1 to len(arr) + for i in range(1, len(arr)): + key = arr[i] + # Move elements of arr[0..i-1], that are greater than key, + # to one position ahead of their current position + j = i - 1 + while j >= 0 and key < arr[j]: + arr[j + 1] = arr[j] + j -= 1 + arr[j + 1] = key + +# Example usage +arr = [5, 3, 8, 1, 2] +insertion_sort(arr) +print("Sorted array:", arr) # Output: [1, 2, 3, 5, 8] +``` +## Complexity Analysis + - **Worst Case:** `𝑂(𝑛^2)` comparisons and swaps. This occurs when the array is in reverse order. + - **Best Case:** `𝑂(𝑛)` comparisons and `𝑂(1)` swaps. This happens when the array is already sorted. + - **Average Case:** `𝑂(𝑛^2)` comparisons and swaps. This is the expected number of comparisons and swaps over all possible input sequences. + + +
+
+
+ +# 6. Heap Sort + +Heap Sort is an efficient comparison-based sorting algorithm that uses a binary heap data structure. It divides its input into a sorted and an unsorted region and iteratively shrinks the unsorted region by extracting the largest (or smallest) element and moving it to the sorted region. + +**Algorithm Overview:** +- **Build a Max Heap:** Convert the array into a max heap, a complete binary tree where the value of each node is greater than or equal to the values of its children. +- **Heapify:** Ensure that the subtree rooted at each node satisfies the max heap property. This process is called heapify. +- **Extract Maximum:** Swap the root (the maximum element) with the last element of the heap and reduce the heap size by one. Restore the max heap property by heapifying the root. +- **Repeat:** Continue extracting the maximum element and heapifying until the entire array is sorted. + +## Example with Visualization + +Let's sort the list `[5, 3, 8, 1, 2]` using heap sort. + +1. **Build Max Heap:** + - Initial array: `[5, 3, 8, 1, 2]` + - Start heapifying from the last non-leaf node. + - Heapify at index 1: `[5, 3, 8, 1, 2]` (no change, children are already less than the parent) + - Heapify at index 0: `[8, 3, 5, 1, 2]` (swap 5 and 8 to make 8 the root) + +2. **Heapify Process:** + - Heapify at index 0: `[8, 3, 5, 1, 2]` (no change needed, already a max heap) + +3. **Extract Maximum:** + - Swap root with the last element: `[2, 3, 5, 1, 8]` + - Heapify at index 0: `[5, 3, 2, 1, 8]` (swap 2 and 5) + +4. **Repeat Extraction:** + - Swap root with the second last element: `[1, 3, 2, 5, 8]` + - Heapify at index 0: `[3, 1, 2, 5, 8]` (swap 1 and 3) + - Swap root with the third last element: `[2, 1, 3, 5, 8]` + - Heapify at index 0: `[2, 1, 3, 5, 8]` (no change needed) + - Swap root with the fourth last element: `[1, 2, 3, 5, 8]` + +After all extractions, the array is sorted: `[1, 2, 3, 5, 8]`. + +## Heap Sort Code in Python + +```python +def heapify(arr, n, i): + largest = i # Initialize largest as root + left = 2 * i + 1 # left child index + right = 2 * i + 2 # right child index + + # See if left child of root exists and is greater than root + if left < n and arr[largest] < arr[left]: + largest = left + + # See if right child of root exists and is greater than root + if right < n and arr[largest] < arr[right]: + largest = right + + # Change root, if needed + if largest != i: + arr[i], arr[largest] = arr[largest], arr[i] # swap + + # Heapify the root. + heapify(arr, n, largest) + +def heap_sort(arr): + n = len(arr) + + # Build a maxheap. + for i in range(n // 2 - 1, -1, -1): + heapify(arr, n, i) + + # One by one extract elements + for i in range(n - 1, 0, -1): + arr[i], arr[0] = arr[0], arr[i] # swap + heapify(arr, i, 0) + +# Example usage +arr = [5, 3, 8, 1, 2] +heap_sort(arr) +print("Sorted array:", arr) # Output: [1, 2, 3, 5, 8] +``` + +## Complexity Analysis + - **Worst Case:** `𝑂(𝑛log𝑛)`. Building the heap takes `𝑂(𝑛)` time, and each of the 𝑛 element extractions takes `𝑂(log𝑛)` time. + - **Best Case:** `𝑂(𝑛log𝑛)`. Even if the array is already sorted, heap sort will still build the heap and perform the extractions. + - **Average Case:** `𝑂(𝑛log𝑛)`. Similar to the worst-case, the overall complexity remains `𝑂(𝑛log𝑛)` because each insertion and deletion in a heap takes `𝑂(log𝑛)` time, and these operations are performed 𝑛 times. + +
+
+
+ + From 7db982f3a3b94320c5638f7925cfa64054d1bd51 Mon Sep 17 00:00:00 2001 From: Labqari Date: Sat, 25 May 2024 12:50:18 +0530 Subject: [PATCH 213/656] Adding Flask --- contrib/web-scrapping/index.md | 1 + ...duction-to-flask-a-python-web-framework.md | 440 ++++++++++++++++++ 2 files changed, 441 insertions(+) create mode 100644 contrib/web-scrapping/introduction-to-flask-a-python-web-framework.md diff --git a/contrib/web-scrapping/index.md b/contrib/web-scrapping/index.md index 82596a2f..a78be3d7 100644 --- a/contrib/web-scrapping/index.md +++ b/contrib/web-scrapping/index.md @@ -1,3 +1,4 @@ # List of sections - [Section title](filename.md) +- [Introduction-to-flask-a-python-web-framework](introduction-to-flask-a-python-web-framework.md) diff --git a/contrib/web-scrapping/introduction-to-flask-a-python-web-framework.md b/contrib/web-scrapping/introduction-to-flask-a-python-web-framework.md new file mode 100644 index 00000000..957f39b4 --- /dev/null +++ b/contrib/web-scrapping/introduction-to-flask-a-python-web-framework.md @@ -0,0 +1,440 @@ +Sure, here's the guide without Markdown formatting: + +--- + +# Introduction to Flask: A Python Web Framework + +## Table of Contents +1. Introduction +2. Prerequisites +3. Setting Up Your Environment +4. Creating Your First Flask Application + - Project Structure + - Hello World Application +5. Routing +6. Templates and Static Files + - Jinja2 Templating Engine + - Serving Static Files +7. Working with Forms + - Handling Form Data +8. Database Integration + - Setting Up SQLAlchemy + - Performing CRUD Operations +9. Error Handling +10. Testing Your Application +11. Deploying Your Flask Application + - Using Gunicorn + - Deploying to Render +12. Conclusion +13. Further Reading and Resources + +--- + +## 1. Introduction +Flask is a lightweight WSGI web application framework in Python. It is designed with simplicity and flexibility in mind, allowing developers to create web applications with minimal setup. Flask was created by Armin Ronacher as part of the Pocoo project and has gained popularity for its ease of use and extensive documentation. + +## 2. Prerequisites +Before starting with Flask, ensure you have the following: +- Basic knowledge of Python. +- Understanding of web development concepts (HTML, CSS, JavaScript). +- Python installed on your machine (version 3.6 or higher). +- pip (Python package installer) installed. + +## 3. Setting Up Your Environment +1. **Install Python**: Download and install Python from python.org. +2. **Create a Virtual Environment**: + ``` + python -m venv venv + ``` +3. **Activate the Virtual Environment**: + - On Windows: + ``` + venv\Scripts\activate + ``` + - On macOS/Linux: + ``` + source venv/bin/activate + ``` +4. **Install Flask**: + ``` + pip install Flask + ``` + +## 4. Creating Your First Flask Application +### Project Structure +A typical Flask project structure might look like this: +``` +my_flask_app/ + app/ + __init__.py + routes.py + templates/ + static/ + venv/ + run.py +``` + +### Hello World Application +1. **Create a Directory for Your Project**: + ``` + mkdir my_flask_app + cd my_flask_app + ``` +2. **Initialize the Application**: + - Create `app/__init__.py`: + ```python + from flask import Flask + + def create_app(): + app = Flask(__name__) + + with app.app_context(): + from . import routes + return app + ``` + - Create `run.py`: + ```python + from app import create_app + + app = create_app() + + if __name__ == "__main__": + app.run(debug=True) + ``` + - Create `app/routes.py`: + ```python + from flask import current_app as app + + @app.route('/') + def hello_world(): + return 'Hello, World!' + ``` + +3. **Run the Application**: + ``` + python run.py + ``` + Navigate to `http://127.0.0.1:5000` in your browser to see "Hello, World!". + +## 5. Routing +In Flask, routes are defined using the `@app.route` decorator. Here's an example of different routes: + +```python +from flask import Flask + +app = Flask(__name__) + +@app.route('/') +def home(): + return 'Home Page' + +@app.route('/about') +def about(): + return 'About Page' + +@app.route('/user/') +def show_user_profile(username): + return f'User: {username}' +``` + +- **Explanation**: + - The `@app.route('/')` decorator binds the URL `'/'` to the `home` function, which returns 'Home Page'. + - The `@app.route('/about')` decorator binds the URL `/about` to the `about` function. + - The `@app.route('/user/')` decorator binds the URL `/user/` to the `show_user_profile` function, capturing the part of the URL as the `username` variable. + +## 6. Templates and Static Files +### Jinja2 Templating Engine +Jinja2 is Flask's templating engine. Templates are HTML files that can include dynamic content. + +- **Create a Template**: + - `app/templates/index.html`: + ```html + + + + {{ title }} + + +

{{ heading }}

+

{{ content }}

+ + + ``` + +- **Render the Template**: + ```python + from flask import Flask, render_template + + app = Flask(__name__) + + @app.route('/') + def home(): + return render_template('index.html', title='Home', heading='Welcome to Flask', content='This is a Flask application.') + ``` + +### Serving Static Files +Static files like CSS, JavaScript, and images are placed in the `static` directory. + +- **Create Static Files**: + - `app/static/style.css`: + ```css + body { + font-family: Arial, sans-serif; + } + ``` + +- **Include Static Files in Templates**: + ```html + + + + {{ title }} + + + +

{{ heading }}

+

{{ content }}

+ + + ``` + +## 7. Working with Forms +### Handling Form Data +Forms are used to collect user input. Flask provides utilities to handle form submissions. + +- **Create a Form**: + - `app/templates/form.html`: + ```html + + + + Form + + +
+ + + +
+ + + ``` + +- **Handle Form Submission**: + ```python + from flask import Flask, request, render_template + + app = Flask(__name__) + + @app.route('/form') + def form(): + return render_template('form.html') + + @app.route('/submit', methods=['POST']) + def submit(): + name = request.form['name'] + return f'Hello, {name}!' + ``` + +- **Explanation**: + - The `@app.route('/form')` route renders the form. + - The `@app.route('/submit', methods=['POST'])` route handles the form submission and displays the input name. + +## 8. Database Integration +### Setting Up SQLAlchemy +SQLAlchemy is an ORM that allows you to interact with databases using Python objects. + +- **Install SQLAlchemy**: + ``` + pip install flask_sqlalchemy + ``` + +- **Configure SQLAlchemy**: + - `app/__init__.py`: + ```python + from flask import Flask + from flask_sqlalchemy import SQLAlchemy + + db = SQLAlchemy() + + def create_app(): + app = Flask(__name__) + app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///site.db' + db.init_app(app) + return app + ``` + +### Performing CRUD Operations +Define models and perform CRUD operations. + +- **Define a Model**: + - `app/models.py`: + ```python + from app import db + + class User(db.Model): + id = db.Column(db.Integer, primary key=True) + username = db.Column(db.String(80), unique=True, nullable=False) + + def __repr__(self): + return f'' + ``` + +- **Create the Database**: + ```python + from app import create_app, db + from app.models import User + + app = create_app() + with app.app_context(): + db.create_all() + ``` + +- **Perform CRUD Operations**: + ```python + from app import db + from app.models import User + + # Create + new_user = User(username='new_user') + db.session.add(new_user) + db.session.commit() + + # Read + user = User.query.first() + + # Update + user.username = 'updated_user' + db.session.commit() + + # Delete + db.session.delete(user) + db.session.commit() + ``` + +## 9. Error Handling +Error handling in Flask can be managed by defining error handlers for different HTTP status codes. + +- **Define an Error Handler**: + ```python + from flask import Flask, render_template + + app = Flask(__name__) + + @app.errorhandler(404) + def page_not_found(e): + return render_template('404.html'), 404 + + @app.errorhandler(500) + def internal_server_error(e): + return render_template('500.html'), 500 + ``` + + - **Create Error Pages**: + `app/templates/404.html`: + + + + + Page Not Found + + +

404 - Page Not Found

+

The page you are looking for does not exist.

+ + + + + + - **app/templates/500.html:** + + + + + Internal Server Error + + +

500 - Internal Server Error

+

Something went wrong on our end. Please try again later.

+ + + + +## 10. Testing Your Application +Flask applications can be tested using Python's built-in `unittest` framework. + +- **Write a Test Case**: + - `tests/test_app.py`: + ```python + import unittest + from app import create_app + + class BasicTestCase(unittest.TestCase): + def setUp(self): + self.app = create_app() + self.app.config['TESTING'] = True + self.client = self.app.test_client() + + def test_home(self): + response = self.client.get('/') + self.assertEqual(response.status_code, 200) + self.assertIn(b'Hello, World!', response.data) + + if __name__ == '__main__': + unittest.main() + ``` + + - **Run the Tests**: + ``` + python -m unittest discover -s tests + ``` + +## 11. Deploying Your Flask Application +### Using Gunicorn +Gunicorn is a Python WSGI HTTP Server for UNIX. It’s a pre-fork worker model, meaning that it forks multiple worker processes to handle requests. + +- **Install Gunicorn**: + ``` + pip install gunicorn + ``` + +- **Run Your Application with Gunicorn**: + ``` + gunicorn -w 4 run:app + ``` + +### Deploying to Render +Render is a cloud platform for deploying web applications. + +- **Create a `requirements.txt` File**: + ``` + Flask + gunicorn + flask_sqlalchemy + ``` + +- **Create a `render.yaml` File**: + ```yaml + services: + - type: web + name: my-flask-app + env: python + plan: free + buildCommand: pip install -r requirements.txt + startCommand: gunicorn -w 4 run:app + ``` + +- **Deploy Your Application**: + 1. Push your code to a Git repository. + 2. Sign in to Render and create a new Web Service. + 3. Connect your repository and select the branch to deploy. + 4. Render will automatically use the `render.yaml` file to configure and deploy your application. + +## 12. Conclusion +Flask is a powerful and flexible framework for building web applications in Python. It offers simplicity and ease of use, making it a great choice for both beginners and experienced developers. This guide covered the basics of setting up a Flask application, routing, templating, working with forms, integrating databases, error handling, testing, and deployment. + +## 13. Further Reading and Resources +- Flask Documentation: https://flask.palletsprojects.com/en/latest/ +- Jinja2 Documentation: https://jinja.palletsprojects.com/en/latest/ +- SQLAlchemy Documentation: https://docs.sqlalchemy.org/en/latest/ +- Render Documentation: https://render.com/docs \ No newline at end of file From 365cc3a62f07d3b013529bca18590f0041524fee Mon Sep 17 00:00:00 2001 From: Labqari Date: Sat, 25 May 2024 13:04:32 +0530 Subject: [PATCH 214/656] flask commit --- contrib/advanced-python/index.md | 1 - ...duction-to-flask-a-python-web-framework.md | 440 ------------------ 2 files changed, 441 deletions(-) delete mode 100644 contrib/advanced-python/introduction-to-flask-a-python-web-framework.md diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 027f9892..5ea5081c 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -1,4 +1,3 @@ # List of sections - [Decorators/\*args/**kwargs](decorator-kwargs-args.md) -- [Decorators/\*args/**kwargs](introduction-to-flask-a-python-web-framework.md) diff --git a/contrib/advanced-python/introduction-to-flask-a-python-web-framework.md b/contrib/advanced-python/introduction-to-flask-a-python-web-framework.md deleted file mode 100644 index 957f39b4..00000000 --- a/contrib/advanced-python/introduction-to-flask-a-python-web-framework.md +++ /dev/null @@ -1,440 +0,0 @@ -Sure, here's the guide without Markdown formatting: - ---- - -# Introduction to Flask: A Python Web Framework - -## Table of Contents -1. Introduction -2. Prerequisites -3. Setting Up Your Environment -4. Creating Your First Flask Application - - Project Structure - - Hello World Application -5. Routing -6. Templates and Static Files - - Jinja2 Templating Engine - - Serving Static Files -7. Working with Forms - - Handling Form Data -8. Database Integration - - Setting Up SQLAlchemy - - Performing CRUD Operations -9. Error Handling -10. Testing Your Application -11. Deploying Your Flask Application - - Using Gunicorn - - Deploying to Render -12. Conclusion -13. Further Reading and Resources - ---- - -## 1. Introduction -Flask is a lightweight WSGI web application framework in Python. It is designed with simplicity and flexibility in mind, allowing developers to create web applications with minimal setup. Flask was created by Armin Ronacher as part of the Pocoo project and has gained popularity for its ease of use and extensive documentation. - -## 2. Prerequisites -Before starting with Flask, ensure you have the following: -- Basic knowledge of Python. -- Understanding of web development concepts (HTML, CSS, JavaScript). -- Python installed on your machine (version 3.6 or higher). -- pip (Python package installer) installed. - -## 3. Setting Up Your Environment -1. **Install Python**: Download and install Python from python.org. -2. **Create a Virtual Environment**: - ``` - python -m venv venv - ``` -3. **Activate the Virtual Environment**: - - On Windows: - ``` - venv\Scripts\activate - ``` - - On macOS/Linux: - ``` - source venv/bin/activate - ``` -4. **Install Flask**: - ``` - pip install Flask - ``` - -## 4. Creating Your First Flask Application -### Project Structure -A typical Flask project structure might look like this: -``` -my_flask_app/ - app/ - __init__.py - routes.py - templates/ - static/ - venv/ - run.py -``` - -### Hello World Application -1. **Create a Directory for Your Project**: - ``` - mkdir my_flask_app - cd my_flask_app - ``` -2. **Initialize the Application**: - - Create `app/__init__.py`: - ```python - from flask import Flask - - def create_app(): - app = Flask(__name__) - - with app.app_context(): - from . import routes - return app - ``` - - Create `run.py`: - ```python - from app import create_app - - app = create_app() - - if __name__ == "__main__": - app.run(debug=True) - ``` - - Create `app/routes.py`: - ```python - from flask import current_app as app - - @app.route('/') - def hello_world(): - return 'Hello, World!' - ``` - -3. **Run the Application**: - ``` - python run.py - ``` - Navigate to `http://127.0.0.1:5000` in your browser to see "Hello, World!". - -## 5. Routing -In Flask, routes are defined using the `@app.route` decorator. Here's an example of different routes: - -```python -from flask import Flask - -app = Flask(__name__) - -@app.route('/') -def home(): - return 'Home Page' - -@app.route('/about') -def about(): - return 'About Page' - -@app.route('/user/') -def show_user_profile(username): - return f'User: {username}' -``` - -- **Explanation**: - - The `@app.route('/')` decorator binds the URL `'/'` to the `home` function, which returns 'Home Page'. - - The `@app.route('/about')` decorator binds the URL `/about` to the `about` function. - - The `@app.route('/user/')` decorator binds the URL `/user/` to the `show_user_profile` function, capturing the part of the URL as the `username` variable. - -## 6. Templates and Static Files -### Jinja2 Templating Engine -Jinja2 is Flask's templating engine. Templates are HTML files that can include dynamic content. - -- **Create a Template**: - - `app/templates/index.html`: - ```html - - - - {{ title }} - - -

{{ heading }}

-

{{ content }}

- - - ``` - -- **Render the Template**: - ```python - from flask import Flask, render_template - - app = Flask(__name__) - - @app.route('/') - def home(): - return render_template('index.html', title='Home', heading='Welcome to Flask', content='This is a Flask application.') - ``` - -### Serving Static Files -Static files like CSS, JavaScript, and images are placed in the `static` directory. - -- **Create Static Files**: - - `app/static/style.css`: - ```css - body { - font-family: Arial, sans-serif; - } - ``` - -- **Include Static Files in Templates**: - ```html - - - - {{ title }} - - - -

{{ heading }}

-

{{ content }}

- - - ``` - -## 7. Working with Forms -### Handling Form Data -Forms are used to collect user input. Flask provides utilities to handle form submissions. - -- **Create a Form**: - - `app/templates/form.html`: - ```html - - - - Form - - -
- - - -
- - - ``` - -- **Handle Form Submission**: - ```python - from flask import Flask, request, render_template - - app = Flask(__name__) - - @app.route('/form') - def form(): - return render_template('form.html') - - @app.route('/submit', methods=['POST']) - def submit(): - name = request.form['name'] - return f'Hello, {name}!' - ``` - -- **Explanation**: - - The `@app.route('/form')` route renders the form. - - The `@app.route('/submit', methods=['POST'])` route handles the form submission and displays the input name. - -## 8. Database Integration -### Setting Up SQLAlchemy -SQLAlchemy is an ORM that allows you to interact with databases using Python objects. - -- **Install SQLAlchemy**: - ``` - pip install flask_sqlalchemy - ``` - -- **Configure SQLAlchemy**: - - `app/__init__.py`: - ```python - from flask import Flask - from flask_sqlalchemy import SQLAlchemy - - db = SQLAlchemy() - - def create_app(): - app = Flask(__name__) - app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///site.db' - db.init_app(app) - return app - ``` - -### Performing CRUD Operations -Define models and perform CRUD operations. - -- **Define a Model**: - - `app/models.py`: - ```python - from app import db - - class User(db.Model): - id = db.Column(db.Integer, primary key=True) - username = db.Column(db.String(80), unique=True, nullable=False) - - def __repr__(self): - return f'' - ``` - -- **Create the Database**: - ```python - from app import create_app, db - from app.models import User - - app = create_app() - with app.app_context(): - db.create_all() - ``` - -- **Perform CRUD Operations**: - ```python - from app import db - from app.models import User - - # Create - new_user = User(username='new_user') - db.session.add(new_user) - db.session.commit() - - # Read - user = User.query.first() - - # Update - user.username = 'updated_user' - db.session.commit() - - # Delete - db.session.delete(user) - db.session.commit() - ``` - -## 9. Error Handling -Error handling in Flask can be managed by defining error handlers for different HTTP status codes. - -- **Define an Error Handler**: - ```python - from flask import Flask, render_template - - app = Flask(__name__) - - @app.errorhandler(404) - def page_not_found(e): - return render_template('404.html'), 404 - - @app.errorhandler(500) - def internal_server_error(e): - return render_template('500.html'), 500 - ``` - - - **Create Error Pages**: - `app/templates/404.html`: - - - - - Page Not Found - - -

404 - Page Not Found

-

The page you are looking for does not exist.

- - - - - - - **app/templates/500.html:** - - - - - Internal Server Error - - -

500 - Internal Server Error

-

Something went wrong on our end. Please try again later.

- - - - -## 10. Testing Your Application -Flask applications can be tested using Python's built-in `unittest` framework. - -- **Write a Test Case**: - - `tests/test_app.py`: - ```python - import unittest - from app import create_app - - class BasicTestCase(unittest.TestCase): - def setUp(self): - self.app = create_app() - self.app.config['TESTING'] = True - self.client = self.app.test_client() - - def test_home(self): - response = self.client.get('/') - self.assertEqual(response.status_code, 200) - self.assertIn(b'Hello, World!', response.data) - - if __name__ == '__main__': - unittest.main() - ``` - - - **Run the Tests**: - ``` - python -m unittest discover -s tests - ``` - -## 11. Deploying Your Flask Application -### Using Gunicorn -Gunicorn is a Python WSGI HTTP Server for UNIX. It’s a pre-fork worker model, meaning that it forks multiple worker processes to handle requests. - -- **Install Gunicorn**: - ``` - pip install gunicorn - ``` - -- **Run Your Application with Gunicorn**: - ``` - gunicorn -w 4 run:app - ``` - -### Deploying to Render -Render is a cloud platform for deploying web applications. - -- **Create a `requirements.txt` File**: - ``` - Flask - gunicorn - flask_sqlalchemy - ``` - -- **Create a `render.yaml` File**: - ```yaml - services: - - type: web - name: my-flask-app - env: python - plan: free - buildCommand: pip install -r requirements.txt - startCommand: gunicorn -w 4 run:app - ``` - -- **Deploy Your Application**: - 1. Push your code to a Git repository. - 2. Sign in to Render and create a new Web Service. - 3. Connect your repository and select the branch to deploy. - 4. Render will automatically use the `render.yaml` file to configure and deploy your application. - -## 12. Conclusion -Flask is a powerful and flexible framework for building web applications in Python. It offers simplicity and ease of use, making it a great choice for both beginners and experienced developers. This guide covered the basics of setting up a Flask application, routing, templating, working with forms, integrating databases, error handling, testing, and deployment. - -## 13. Further Reading and Resources -- Flask Documentation: https://flask.palletsprojects.com/en/latest/ -- Jinja2 Documentation: https://jinja.palletsprojects.com/en/latest/ -- SQLAlchemy Documentation: https://docs.sqlalchemy.org/en/latest/ -- Render Documentation: https://render.com/docs \ No newline at end of file From 1496dac821b26a46cd5bd2432af95b052cc43494 Mon Sep 17 00:00:00 2001 From: Manish kumar gupta <97523900+manishh12@users.noreply.github.com> Date: Sat, 25 May 2024 16:10:18 +0530 Subject: [PATCH 215/656] Updated maths formulas --- .../machine-learning/Types_of_optimizers.md | 74 ++++++++++--------- 1 file changed, 38 insertions(+), 36 deletions(-) diff --git a/contrib/machine-learning/Types_of_optimizers.md b/contrib/machine-learning/Types_of_optimizers.md index e9415971..ae2759d1 100644 --- a/contrib/machine-learning/Types_of_optimizers.md +++ b/contrib/machine-learning/Types_of_optimizers.md @@ -6,6 +6,8 @@ Optimizers are algorithms or methods used to change the attributes of your neura ## Types of Optimizers + + ### 1. Gradient Descent **Explanation:** @@ -15,19 +17,20 @@ Gradient Descent is the simplest and most commonly used optimization algorithm. The update rule for the parameter vector θ in gradient descent is represented by the equation: -- \(theta_new = theta_old - alpha * gradient/) +- $$\theta_{\text{new}} = \theta_{\text{old}} - \alpha \cdot \nabla J(\theta)$$ Where: -- theta_old is the old parameter vector. -- theta_new is the updated parameter vector. -- alpha is the learning rate. -- gradient is the gradient of the objective function with respect to the parameters. +- θold is the old parameter vector. +- θnew is the updated parameter vector. +- alpha(α) is the learning rate. +- ∇J(θ) is the gradient of the objective function with respect to the parameters. + **Intuition:** - At each iteration, we calculate the gradient of the cost function. - The parameters are updated in the opposite direction of the gradient. -- The size of the step is controlled by the learning rate \( \alpha \). +- The size of the step is controlled by the learning rate α. **Advantages:** - Simple to implement. @@ -58,9 +61,10 @@ SGD is a variation of gradient descent where we use only one training example to **Mathematical Formulation:** -- \(theta = theta - alpha * dJ(theta; x_i, y_i) / d(theta)/) +- $$θ = θ - α \cdot \frac{∂J (θ; xᵢ, yᵢ)}{∂θ}$$ + -\( x_i, y_i \) are a single training example and its target. +- xᵢ, yᵢ are a single training example and its target. **Intuition:** - At each iteration, a random training example is selected. @@ -98,7 +102,8 @@ Mini-Batch Gradient Descent is a variation where instead of a single training ex **Mathematical Formulation:** -- theta = theta - alpha * (1/k) * sum(dJ(theta; x_i, y_i) / d(theta)) +- $$θ = θ - α \cdot \frac{1}{k} \sum_{i=1}^{k} \frac{∂J (θ; xᵢ, yᵢ)}{∂θ}$$ + Where: - \( k \) is the batch size. @@ -141,14 +146,13 @@ Momentum helps accelerate gradient vectors in the right directions, thus leading **Mathematical Formulation:** -- v_t = gamma * v_{t-1} + alpha * dJ(theta) / d(theta) - -- theta = theta - v_t +- $$v_t = γ \cdot v_{t-1} + α \cdot ∇J(θ)$$ +- $$θ = θ - v_t$$ where: - \( v_t \) is the velocity. -- \( \gamma \) is the momentum term, typically set between 0.9 and 0.99. +- γ is the momentum term, typically set between 0.9 and 0.99. **Intuition:** - At each iteration, the gradient is calculated. @@ -182,9 +186,11 @@ NAG is a variant of the gradient descent with momentum. It looks ahead by a step **Mathematical Formulation:** -- v_t = gamma * v_{t-1} + alpha * dJ(theta - gamma * v_{t-1}) / d(theta) +- $$v_t = γv_{t-1} + α \cdot ∇J(θ - γ \cdot v_{t-1})$$ + +- $$θ = θ - v_t$$ + -- theta = theta - v_t **Intuition:** @@ -220,13 +226,13 @@ AdaGrad adapts the learning rate to the parameters, performing larger updates fo **Mathematical Formulation:** -- G_t = G_{t-1} + (dJ(theta) / d(theta)) ⊙ (dJ(theta) / d(theta)) +- $$G_t = G_{t-1} + (∂J(θ)/∂θ)^2$$ -- theta = theta - (alpha / sqrt(G_t + epsilon)) * (dJ(theta) / d(theta)) +- $$θ = θ - \frac{α}{\sqrt{G_t + ε}} \cdot ∇J(θ)$$ Where: -- \( G_t \) is the sum of squares of the gradients up to time step \( t \). -- \( \epsilon \) is a small constant to avoid division by zero. +- \(G_t\) is the sum of squares of the gradients up to time step \( t \). +- ε is a small constant to avoid division by zero. **Intuition:** - Accumulates the sum of the squares of the gradients for each parameter. @@ -263,13 +269,13 @@ RMSprop modifies AdaGrad to perform well in non-convex settings by using a movin **Mathematical Formulation:** -E[g^2]_t = beta * E[g^2]_{t-1} + (1 - beta) * (dJ(theta) / d(theta)) ⊙ (dJ(theta) / d(theta)) +- E[g²]ₜ = βE[g²]ₜ₋₁ + (1 - β)(∂J(θ) / ∂θ)² -theta = theta - (alpha / sqrt(E[g^2]_t + epsilon)) * (dJ(theta) / d(theta)) +- $$θ = θ - \frac{α}{\sqrt{E[g^2]_t + ε}} \cdot ∇J(θ)$$ Where: -- \( E[g^2]_t \) is the exponentially decaying average of past squared gradients. -- \( \beta \) is the decay rate. +- \( E[g^2]_t \) is the exponentially decaying average of past squared gradients. +- β is the decay rate. **Intuition:** - Keeps a running average of the squared gradients. @@ -304,20 +310,16 @@ Adam (Adaptive Moment Estimation) combines the advantages of both RMSprop and Ad **Mathematical Formulation:** -- m_t = beta1 * m_{t-1} + (1 - beta1) * (dJ(theta) / d(theta)) - -- v_t = beta2 * v_{t-1} + (1 - beta2) * ((dJ(theta) / d(theta))^2) - -- hat_m_t = m_t / (1 - beta1^t) - -- hat_v_t = v_t / (1 - beta2^t) - -- theta = theta - (alpha * hat_m_t) / (sqrt(hat_v_t) + epsilon) +- $$m_t = β_1m_{t-1} + (1 - β_1)(∂J(θ)/∂θ)$$ +- $$v_t = β_2v_{t-1} + (1 - β_2)(∂J(θ)/∂θ)^2$$ +- $$\hat{m}_t = \frac{m_t}{1 - β_1^t}$$ +- $$\hat{v}_t = \frac{v_t}{1 - β_2^t}$$ +- $$θ = θ - \frac{α\hat{m}_t}{\sqrt{\hat{v}_t} + ε}$$ Where: -- \( m_t \) is the first moment (mean) of the gradient. -- \( v_t \) is the second moment (uncentered variance) of the gradient. -- \( \beta_1, \beta_2 \) are the decay rates for the moment estimates. +- \( mt \) is the first moment (mean) of the gradient. +- \( vt \) is the second moment (uncentered variance) of the gradient. +- β_1.β_2 are the decay rates for the moment estimates. **Intuition:** - Keeps track of both the mean and the variance of the gradients. @@ -352,4 +354,4 @@ def adam(X, y, lr=0.01, epochs=1000, beta1=0.9, beta2=0.999, epsilon=1e-8): These implementations are basic examples of how these optimizers can be implemented in Python using NumPy. In practice, libraries like TensorFlow and PyTorch provide highly optimized and more sophisticated implementations of these and other optimization algorithms. ---- \ No newline at end of file +--- From 55cad9a70d626ac497826ec15350e8d00f065578 Mon Sep 17 00:00:00 2001 From: Chethana Potukanam Date: Sat, 25 May 2024 17:50:30 +0530 Subject: [PATCH 216/656] Added SeabornPlotting Functions --- .../SeabornPlotting .md | 238 ++++++++++++++++++ contrib/plotting-visualization/index.md | 1 + images/plot_images/image1.png | Bin 0 -> 53217 bytes images/plot_images/image10.png | Bin 0 -> 38312 bytes images/plot_images/image11.png | Bin 0 -> 108739 bytes images/plot_images/image12.png | Bin 0 -> 121680 bytes images/plot_images/image13.png | Bin 0 -> 28883 bytes images/plot_images/image14.png | Bin 0 -> 26863 bytes images/plot_images/image15.png | Bin 0 -> 16226 bytes images/plot_images/image16.png | Bin 0 -> 25392 bytes images/plot_images/image2.png | Bin 0 -> 44101 bytes images/plot_images/image3.png | Bin 0 -> 22115 bytes images/plot_images/image4.png | Bin 0 -> 34843 bytes images/plot_images/image5.png | Bin 0 -> 20812 bytes images/plot_images/image6.png | Bin 0 -> 13023 bytes images/plot_images/image7.png | Bin 0 -> 15950 bytes images/plot_images/image8.png | Bin 0 -> 25305 bytes images/plot_images/image9.png | Bin 0 -> 18847 bytes 18 files changed, 239 insertions(+) create mode 100644 contrib/plotting-visualization/SeabornPlotting .md create mode 100644 images/plot_images/image1.png create mode 100644 images/plot_images/image10.png create mode 100644 images/plot_images/image11.png create mode 100644 images/plot_images/image12.png create mode 100644 images/plot_images/image13.png create mode 100644 images/plot_images/image14.png create mode 100644 images/plot_images/image15.png create mode 100644 images/plot_images/image16.png create mode 100644 images/plot_images/image2.png create mode 100644 images/plot_images/image3.png create mode 100644 images/plot_images/image4.png create mode 100644 images/plot_images/image5.png create mode 100644 images/plot_images/image6.png create mode 100644 images/plot_images/image7.png create mode 100644 images/plot_images/image8.png create mode 100644 images/plot_images/image9.png diff --git a/contrib/plotting-visualization/SeabornPlotting .md b/contrib/plotting-visualization/SeabornPlotting .md new file mode 100644 index 00000000..0e219c49 --- /dev/null +++ b/contrib/plotting-visualization/SeabornPlotting .md @@ -0,0 +1,238 @@ +### Seaborn Plotting Functions: +Seaborn is a powerful and easy-to-use data visualization library in Python built on top of Matplotlib.It provides a high-level interface for drawing attractive and informative statistical graphics.Now we will cover various functions covered by Seaborn, along with examples to illustrate their usage. +Seaborn simplifies the process of creating complex visualizations with a few lines of code and it integrates closely with pandas data structure , making it an excellent choice for data analysis and exploration. +### Setting up Seaborn: +Make sure seaborn library is installed in your system. If not use command +`pip install seaborn` +After installing you are all set to experiment with plotting functions. + +``` +#import necessary libraries + +import seaborn as sns +import matplotlib.pyplot as plt +import pandas as pd +``` + +Seaborn includes several built-in datasets that you can use for practice +You can list all available datasets using below command +`sns.get_dataset_names()` +Here we are using 'tips' dataset + +``` +# loading an example dataset +tips=sns.load_dataset('tips') +``` + +Before delving into plotting, make yourself comfortable with the dataset. To do that, use the pandas library to understand what information the dataset contains and preprocess the data. If you get stuck, feel free to refer to the pandas documentation. + +### Relational Plots: +Relational plots are used to visualize the relationship between two or more variables +##### Scatter Plot: +A scatter plot displays data points based on two numerical variables.Seaborn `scatterplot` function allows you to create scatter plots with ease +##### Line Plot: +A line plot connects data points in the order they appear in the dataset.This is useful for time series data.`lineplot` function allows you to create lineplots. + +``` +# scatter plot using Seaborn + +plt.figure(figsize=(5,5)) +sns.scatterplot(data=tips,x='total_bill',y='tip',hue='day',style='time') +plt.title('Scatter Plot of Total Bill vs Tip') +plt.show() +``` +![Alt text](/images/plot_images/image1.png) + +``` +# lineplot using seaborn + +plt.figure(figsize=(5,5)) +sns.lineplot(data=tips,x='size',y='total_bill',hue='day') +plt.title('Line Plot of Total Bill by Size and Day') +plt.show() +``` +![Alt text](images/plot_images/image2.png) + +### Distribution Plots: +Distribution Plots visualize the distribution of a single numerical variable +##### HistPlot: +A histplot displays the distribution of a numerical variable by dividing the data into bins. +##### KDE Plot: +A Kernel Density Estimate (KDE) plot represents the distribution of a variable as a smooth curve. +##### ECDF Plot: +An Empirical Cumulative Distribution Function (ECDF) plot shows the proportion of data points below each value. +##### Rug Plot: +A rug plot in Seaborn is a simple way to show the distribution of a variable by drawing small vertical lines (or "rugs") at each data point along the x-axis. + +``` +# Histplot using Seaborn + +plt.figure(figsize=(5,5)) +sns.histplot(data=tips, x='total_bill', kde=True) +plt.title('Histplot of Total Bill') +plt.show() +``` +![Alt text](/images/plot_images/image3.png) + +``` +# KDE Plot using Seaborn + +plt.figure(figsize=(5,5)) +sns.kdeplot(data=tips, x='total_bill', hue='sex', fill=True) +plt.title('KDE Plot of Total Bill by Sex') +plt.show() +``` +![Alt text](/images/plot_images/image4.png) + +``` +# ECDF Plot using Seaborn + +plt.figure(figsize=(5,5)) +sns.ecdfplot(data=tips, x='total_bill', hue='sex') +plt.title('ECDF Plot of Total Bill by Sex') +plt.show() +``` +![Alt text](/images/plot_images/image5.png) + +``` +# Rug Plot using Seaborn + +plt.figure(figsize=(3,3)) +sns.rugplot(x='total_bill', data=tips) +plt.title('Rug Plot of Total Bill Amounts') +plt.show() +``` +![Alt text](/images/plot_images/image6.png) + + ### Categorical Plots: + Categorical plots are used to visualize data where one or more variables are categorical. + ##### Bar Plot: + A bar plot shows the relationship between a categorical variable and a numerical variable. + ##### Point Plot: + A point plot in Seaborn is used to show the relationship between two categorical variables, with the size of the points representing the values of third variable. + ##### Box Plot: + A box plot displays the distribution of a numerical variable across different categories. + ##### Violin Plot: + A violin plot combines aspects of a box plot and a KDE plot to show the distribution of data + + ``` + # Bar Plot using Seaborn + +plt.figure(figsize=(5,5)) +sns.barplot(data=tips,x='day',y='total_bill',hue='sex') +plt.title('Bar Plot of Total Bill by Day and Sex') +plt.show() + ``` +![Alt text](/images/plot_images/image7.png) + +``` +# Point Plot using Seaborn + +plt.figure(figsize=(5,5)) +sns.pointplot(x='day',y='total_bill',hue='sex',data=tips) +plt.title('Average Total Bill by Day and Sex') +plt.show() +``` +![Alt text](/images/plot_images/image8.png) + +``` +# Box Plot using Seaborn + +plt.figure(figsize=(5,5)) +sns.boxplot(data=tips, x='day', y='total_bill', hue='sex') +plt.title('Box Plot of Total Bill by Day and Sex') +plt.show() +``` +![Alt text](/images/plot_images/image9.png) + +``` +# Violin Plot using Seaborn + +plt.figure(figsize=(5,5)) +sns.violinplot(data=tips,x='day',y='total_bill',hue='sex',split=True) +plt.title('Violin Plot of Total Bill by Day and Sex') +plt.show() +``` +![Alt text](/images/plot_images/image10.png) + +### Matrix Plots: +Matrix plots are useful for visualizing data in a matrix format. +##### Heatmap: +A heatmap displays data in a matrix where values are represented by color. + +``` +# Heatmap using Seaborn + +plt.figure(figsize=(10,8)) +flights = sns.load_dataset('flights') +flights_pivot = flights.pivot(index='month', columns='year', values='passengers') +sns.heatmap(flights_pivot, annot=True, fmt='d', cmap='YlGnBu') +plt.title('Heatmap of Flight Passengers') +plt.show() +``` +![Alt text](/images/plot_images/image11.png) + +### Pair Plot: +A pair plot shows the pairwise relationships between multiple variables in a dataset. + +``` +#Pairplot using Seaborn + +plt.figure(figsize=(5,5)) +sns.pairplot(tips, hue='sex') +plt.title('Pair Plot of Tips Dataset') +plt.show() +``` +![Alt text](/images/plot_images/image12.png) + +### FacetGrid: +FacetGrid allows you to create a grid of plots based on the values of one or more categorical variables. + +``` +#Facetgrid using Seaborn + +plt.figure(figsize=(5,5)) +g = sns.FacetGrid(tips, col='sex', row='time', margin_titles=True) +g.map(sns.scatterplot, 'total_bill', 'tip') +plt.show() +``` +![Alt text](/images/plot_images/image13.png) + +### Customizing Seaborn Plots: +Seaborn plots can be customized to improve their appearance and convey more information. + +##### Changing the Aesthetic Style: +Seaborn comes with several built-in themes. + +``` +sns.set_style('whitegrid') +sns.scatterplot(data=tips, x='total_bill', y='tip') +plt.title('Scatter Plot with Whitegrid Style') +plt.show() +``` +![Alt text](/images/plot_images/image14.png) + +##### Customizing Colors: +You can use color palettes to customize the colors in your plots. + +``` +sns.set_palette('pastel') +sns.barplot(data=tips, x='day', y='total_bill', hue='sex') +plt.title('Bar Plot with Pastel Palette') +plt.show() +``` +![Alt text](/images/plot_images/image15.png) + +##### Adding Titles and Labels: +Titles and labels can be added to make plots more informative. + +``` +plot = sns.scatterplot(data=tips, x='total_bill', y='tip') +plot.set_title('Scatter Plot of Total Bill vs Tip') +plot.set_xlabel('Total Bill ($)') +plot.set_ylabel('Tip ($)') +plt.show() +``` +![Alt text](/images/plot_images/image16.png) + +Seaborn is a versatile library that simplifies the creation of complex visualizations. By using Seaborn's plotting functions, you can create a wide range of statistical graphics with minimal effort. 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insertions(+) create mode 100644 contrib/pandas/Datasets/car-sales.csv diff --git a/contrib/pandas/Datasets/car-sales.csv b/contrib/pandas/Datasets/car-sales.csv new file mode 100644 index 00000000..63a63bf9 --- /dev/null +++ b/contrib/pandas/Datasets/car-sales.csv @@ -0,0 +1,11 @@ +Make,Colour,Odometer (KM),Doors,Price +Toyota,White,150043,4,"$4,000.00" +Honda,Red,87899,4,"$5,000.00" +Toyota,Blue,32549,3,"$7,000.00" +BMW,Black,11179,5,"$22,000.00" +Nissan,White,213095,4,"$3,500.00" +Toyota,Green,99213,4,"$4,500.00" +Honda,Blue,45698,4,"$7,500.00" +Honda,Blue,54738,4,"$7,000.00" +Toyota,White,60000,4,"$6,250.00" +Nissan,White,31600,4,"$9,700.00" \ No newline at end of file From 5fd2169b54ec03e88b3d4d645cef209c8feab85e Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 25 May 2024 19:40:22 +0530 Subject: [PATCH 218/656] Delete contrib/pandas/Datasets/Titanic.csv --- contrib/pandas/Datasets/Titanic.csv | 1310 --------------------------- 1 file changed, 1310 deletions(-) delete mode 100644 contrib/pandas/Datasets/Titanic.csv diff --git a/contrib/pandas/Datasets/Titanic.csv b/contrib/pandas/Datasets/Titanic.csv deleted file mode 100644 index f8d49dcf..00000000 --- a/contrib/pandas/Datasets/Titanic.csv +++ /dev/null @@ -1,1310 +0,0 @@ -"pclass","survived","name","sex","age","sibsp","parch","ticket","fare","cabin","embarked","boat","body","home.dest" -1,1,"Allen, Miss. Elisabeth Walton","female",29,0,0,"24160",211.3375,"B5","S","2",,"St Louis, MO" -1,1,"Allison, Master. Hudson Trevor","male",0.92,1,2,"113781",151.5500,"C22 C26","S","11",,"Montreal, PQ / Chesterville, ON" -1,0,"Allison, Miss. Helen Loraine","female",2,1,2,"113781",151.5500,"C22 C26","S",,,"Montreal, PQ / Chesterville, ON" -1,0,"Allison, Mr. Hudson Joshua Creighton","male",30,1,2,"113781",151.5500,"C22 C26","S",,"135","Montreal, PQ / Chesterville, ON" -1,0,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)","female",25,1,2,"113781",151.5500,"C22 C26","S",,,"Montreal, PQ / Chesterville, ON" -1,1,"Anderson, Mr. Harry","male",48,0,0,"19952",26.5500,"E12","S","3",,"New York, NY" -1,1,"Andrews, Miss. Kornelia Theodosia","female",63,1,0,"13502",77.9583,"D7","S","10",,"Hudson, NY" -1,0,"Andrews, Mr. Thomas Jr","male",39,0,0,"112050",0.0000,"A36","S",,,"Belfast, NI" -1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)","female",53,2,0,"11769",51.4792,"C101","S","D",,"Bayside, Queens, NY" -1,0,"Artagaveytia, Mr. Ramon","male",71,0,0,"PC 17609",49.5042,,"C",,"22","Montevideo, Uruguay" -1,0,"Astor, Col. John Jacob","male",47,1,0,"PC 17757",227.5250,"C62 C64","C",,"124","New York, NY" -1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)","female",18,1,0,"PC 17757",227.5250,"C62 C64","C","4",,"New York, NY" -1,1,"Aubart, Mme. Leontine Pauline","female",24,0,0,"PC 17477",69.3000,"B35","C","9",,"Paris, France" -1,1,"Barber, Miss. Ellen ""Nellie""","female",26,0,0,"19877",78.8500,,"S","6",, -1,1,"Barkworth, Mr. Algernon Henry Wilson","male",80,0,0,"27042",30.0000,"A23","S","B",,"Hessle, Yorks" -1,0,"Baumann, Mr. John D","male",,0,0,"PC 17318",25.9250,,"S",,,"New York, NY" -1,0,"Baxter, Mr. Quigg Edmond","male",24,0,1,"PC 17558",247.5208,"B58 B60","C",,,"Montreal, PQ" -1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)","female",50,0,1,"PC 17558",247.5208,"B58 B60","C","6",,"Montreal, PQ" -1,1,"Bazzani, Miss. Albina","female",32,0,0,"11813",76.2917,"D15","C","8",, -1,0,"Beattie, Mr. Thomson","male",36,0,0,"13050",75.2417,"C6","C","A",,"Winnipeg, MN" -1,1,"Beckwith, Mr. Richard Leonard","male",37,1,1,"11751",52.5542,"D35","S","5",,"New York, NY" -1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)","female",47,1,1,"11751",52.5542,"D35","S","5",,"New York, NY" -1,1,"Behr, Mr. Karl Howell","male",26,0,0,"111369",30.0000,"C148","C","5",,"New York, NY" -1,1,"Bidois, Miss. Rosalie","female",42,0,0,"PC 17757",227.5250,,"C","4",, -1,1,"Bird, Miss. Ellen","female",29,0,0,"PC 17483",221.7792,"C97","S","8",, -1,0,"Birnbaum, Mr. Jakob","male",25,0,0,"13905",26.0000,,"C",,"148","San Francisco, CA" -1,1,"Bishop, Mr. Dickinson H","male",25,1,0,"11967",91.0792,"B49","C","7",,"Dowagiac, MI" -1,1,"Bishop, Mrs. Dickinson H (Helen Walton)","female",19,1,0,"11967",91.0792,"B49","C","7",,"Dowagiac, MI" -1,1,"Bissette, Miss. Amelia","female",35,0,0,"PC 17760",135.6333,"C99","S","8",, -1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan","male",28,0,0,"110564",26.5500,"C52","S","D",,"Stockholm, Sweden / Washington, DC" -1,0,"Blackwell, Mr. Stephen Weart","male",45,0,0,"113784",35.5000,"T","S",,,"Trenton, NJ" -1,1,"Blank, Mr. Henry","male",40,0,0,"112277",31.0000,"A31","C","7",,"Glen Ridge, NJ" -1,1,"Bonnell, Miss. Caroline","female",30,0,0,"36928",164.8667,"C7","S","8",,"Youngstown, OH" -1,1,"Bonnell, Miss. 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Hileni","female",14.5,1,0,"2665",14.4542,,"C",,"328", -3,0,"Zabour, Miss. Thamine","female",,1,0,"2665",14.4542,,"C",,, -3,0,"Zakarian, Mr. Mapriededer","male",26.5,0,0,"2656",7.2250,,"C",,"304", -3,0,"Zakarian, Mr. Ortin","male",27,0,0,"2670",7.2250,,"C",,, -3,0,"Zimmerman, Mr. Leo","male",29,0,0,"315082",7.8750,,"S",,, From 255d6092bc304c46e471f324b28e6e6d1ab49790 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 25 May 2024 19:44:31 +0530 Subject: [PATCH 219/656] Update Importing_and_Exporting_Data_in_Pandas.md --- .../Importing_and_Exporting_Data_in_Pandas.md | 63 +++++-------------- 1 file changed, 17 insertions(+), 46 deletions(-) diff --git a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md index bb490b9c..a4bc1bea 100644 --- a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md +++ b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md @@ -12,60 +12,31 @@ *You could export it as a .csv file and then import it using ``pd.read_csv()``.* -*In this case, the exported .csv file is called `Titanic.csv`* +*In this case, the exported .csv file is called `car-sales.csv`* ```python ## Importing Titanic Data set import pandas as pd -titanic_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/Titanic.csv") -print(titanic_df) +car_sales_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/car-sales.csv") +print(car_sales_df) ``` - pclass survived name \ - 0 1 1 Allen, Miss. Elisabeth Walton - 1 1 1 Allison, Master. Hudson Trevor - 2 1 0 Allison, Miss. Helen Loraine - 3 1 0 Allison, Mr. Hudson Joshua Creighton - 4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) - ... ... ... ... - 1304 3 0 Zabour, Miss. Hileni - 1305 3 0 Zabour, Miss. Thamine - 1306 3 0 Zakarian, Mr. Mapriededer - 1307 3 0 Zakarian, Mr. Ortin - 1308 3 0 Zimmerman, Mr. Leo - - sex age sibsp parch ticket fare cabin embarked boat \ - 0 female 29.00 0 0 24160 211.3375 B5 S 2 - 1 male 0.92 1 2 113781 151.5500 C22 C26 S 11 - 2 female 2.00 1 2 113781 151.5500 C22 C26 S NaN - 3 male 30.00 1 2 113781 151.5500 C22 C26 S NaN - 4 female 25.00 1 2 113781 151.5500 C22 C26 S NaN - ... ... ... ... ... ... ... ... ... ... - 1304 female 14.50 1 0 2665 14.4542 NaN C NaN - 1305 female NaN 1 0 2665 14.4542 NaN C NaN - 1306 male 26.50 0 0 2656 7.2250 NaN C NaN - 1307 male 27.00 0 0 2670 7.2250 NaN C NaN - 1308 male 29.00 0 0 315082 7.8750 NaN S NaN - - body home.dest - 0 NaN St Louis, MO - 1 NaN Montreal, PQ / Chesterville, ON - 2 NaN Montreal, PQ / Chesterville, ON - 3 135.0 Montreal, PQ / Chesterville, ON - 4 NaN Montreal, PQ / Chesterville, ON - ... ... ... - 1304 328.0 NaN - 1305 NaN NaN - 1306 304.0 NaN - 1307 NaN NaN - 1308 NaN NaN - - [1309 rows x 14 columns] + Make Colour Odometer (KM) Doors Price + 0 Toyota White 150043 4 $4,000.00 + 1 Honda Red 87899 4 $5,000.00 + 2 Toyota Blue 32549 3 $7,000.00 + 3 BMW Black 11179 5 $22,000.00 + 4 Nissan White 213095 4 $3,500.00 + 5 Toyota Green 99213 4 $4,500.00 + 6 Honda Blue 45698 4 $7,500.00 + 7 Honda Blue 54738 4 $7,000.00 + 8 Toyota White 60000 4 $6,250.00 + 9 Nissan White 31600 4 $9,700.00 -The dataset I am using here for your reference is taken from the same repository i.e ``learn-python`` (https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/Titanic.csv) I uploaded it in the Datasets folder,you can use it from there. +The dataset I am using here for your reference is taken from the same repository i.e ``learn-python`` (https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/car-sales.csv) I uploaded it in the Datasets folder,you can use it from there. You can also place the filename with its path in `pd.read_csv()`. @@ -92,12 +63,12 @@ You can also place the filename with its path in `pd.read_csv()`. ### Exporting a dataframe to a CSV -**We haven't made any changes yet to the ``titanic_df`` DataFrame but let's try to export it.** +**We haven't made any changes yet to the ``car_sales_df`` DataFrame but let's try to export it.** ```python #Export the titanic_df DataFrame to csv -titanic_df.to_csv("exported_titanic.csv") +car_sales_df.to_csv("exported_car_sales_df.csv") ``` Running this will save a file called ``exported_titanic.csv`` to the current folder. From cb52a821a21faa902dc53ee012732a8232ffd7f0 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 25 May 2024 19:45:29 +0530 Subject: [PATCH 220/656] Update index.md --- contrib/pandas/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 28744312..a2d826c8 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -5,3 +5,4 @@ - [Pandas Descriptive Statistics](Descriptive_Statistics.md) - [Group By Functions with Pandas](GroupBy_Functions_Pandas.md) - [Excel using Pandas DataFrame](excel_with_pandas.md) +- [Importing and Exporting Data in Pandas](Importing_and_Exporting_Data_in_Pandas.md) From 577df2c86e89ea943be4d9ac7415b88006181c2a Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 25 May 2024 20:46:11 +0530 Subject: [PATCH 221/656] Update car-sales.csv --- contrib/pandas/Datasets/car-sales.csv | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/pandas/Datasets/car-sales.csv b/contrib/pandas/Datasets/car-sales.csv index 63a63bf9..81e534a5 100644 --- a/contrib/pandas/Datasets/car-sales.csv +++ b/contrib/pandas/Datasets/car-sales.csv @@ -8,4 +8,4 @@ Toyota,Green,99213,4,"$4,500.00" Honda,Blue,45698,4,"$7,500.00" Honda,Blue,54738,4,"$7,000.00" Toyota,White,60000,4,"$6,250.00" -Nissan,White,31600,4,"$9,700.00" \ No newline at end of file +Nissan,White,31600,4,"$9,700.00" From 14d806ff7052ac1eace6a200abde29c4aaf06562 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 25 May 2024 21:01:18 +0530 Subject: [PATCH 222/656] Update and rename Importing_and_Exporting_Data_in_Pandas.md to import-export.md --- .../Importing_and_Exporting_Data_in_Pandas.md | 74 ------------------- contrib/pandas/import-export.md | 46 ++++++++++++ 2 files changed, 46 insertions(+), 74 deletions(-) delete mode 100644 contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md create mode 100644 contrib/pandas/import-export.md diff --git a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md b/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md deleted file mode 100644 index a4bc1bea..00000000 --- a/contrib/pandas/Importing_and_Exporting_Data_in_Pandas.md +++ /dev/null @@ -1,74 +0,0 @@ -# Importing_and_Exporting_Data_in_Pandas - ->Created by Krishna Kaushik - -- **Now we're able to create `Series` and `DataFrames` in pandas, but we usually do not do this , in practice we import the data which is in the form of .csv (Comma Seperated Values) , a spreadsheet file or something similar.** - -- *Good news is that pandas allows for easy importing of data like this through functions such as ``pd.read_csv()`` and ``pd.read_excel()`` for Microsoft Excel files.* - -## 1. Importing from a Google sheet to a pandas dataframe - -*Let's say that you wanted to get the information from Google Sheet document into a pandas DataFrame.*. - -*You could export it as a .csv file and then import it using ``pd.read_csv()``.* - -*In this case, the exported .csv file is called `car-sales.csv`* - - -```python -## Importing Titanic Data set -import pandas as pd - -car_sales_df= pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/car-sales.csv") -print(car_sales_df) -``` - - Make Colour Odometer (KM) Doors Price - 0 Toyota White 150043 4 $4,000.00 - 1 Honda Red 87899 4 $5,000.00 - 2 Toyota Blue 32549 3 $7,000.00 - 3 BMW Black 11179 5 $22,000.00 - 4 Nissan White 213095 4 $3,500.00 - 5 Toyota Green 99213 4 $4,500.00 - 6 Honda Blue 45698 4 $7,500.00 - 7 Honda Blue 54738 4 $7,000.00 - 8 Toyota White 60000 4 $6,250.00 - 9 Nissan White 31600 4 $9,700.00 - - -The dataset I am using here for your reference is taken from the same repository i.e ``learn-python`` (https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/car-sales.csv) I uploaded it in the Datasets folder,you can use it from there. - -You can also place the filename with its path in `pd.read_csv()`. - -**Now we've got the same data from the Google Spreadsheet , but now available as ``pandas DataFrame`` which means we can now apply all pandas functionality over it.** - -#### Note: The quiet important thing i am telling is that ``pd.read_csv()`` takes the location of the file (which is in your current working directory) or the hyperlink of the dataset from the other source. - -#### But if you want to import the data from Github you can't directly use its link , you have to first convert it to raw by clicking on the raw button present in the repo . - -#### Also you can't use the data directly from `Kaggle` you have to use ``kaggle API`` - -## 2. The Anatomy of DataFrame - -**Different functions use different labels for different things, and can get a little confusing.** - -- Rows are refer as ``axis=0`` -- columns are refer as ``axis=1`` - -## 3. Exporting Data - -**OK, so after you've made a few changes to your data, you might want to export it and save it so someone else can access the changes.** - -**pandas allows you to export ``DataFrame's`` to ``.csv`` format using ``.to_csv()``, or to a spreadsheet format using .to_excel().** - -### Exporting a dataframe to a CSV - -**We haven't made any changes yet to the ``car_sales_df`` DataFrame but let's try to export it.** - - -```python -#Export the titanic_df DataFrame to csv -car_sales_df.to_csv("exported_car_sales_df.csv") -``` - -Running this will save a file called ``exported_titanic.csv`` to the current folder. diff --git a/contrib/pandas/import-export.md b/contrib/pandas/import-export.md new file mode 100644 index 00000000..23d1ad82 --- /dev/null +++ b/contrib/pandas/import-export.md @@ -0,0 +1,46 @@ +# Importing and Exporting Data in Pandas + +## Importing Data from a CSV + +We can create `Series` and `DataFrame` in pandas, but often we have to import the data which is in the form of `.csv` (Comma Separated Values), a spreadsheet file or similar tabular data file format. + +`pandas` allows for easy importing of this data using functions such as `read_csv()` and `read_excel()` for Microsoft Excel files. + +*Note: In case you want to get the information from a **Google Sheet** you can export it as a .csv file.* + +The `read_csv()` function can be used to import a CSV file into a pandas DataFrame. The path can be a file system path or a URL where the CSV is available. + +```python +import pandas as pd + +car_sales_df= pd.read_csv("Datasets/car-sales.csv") +print(car_sales_df) +``` + +``` + Make Colour Odometer (KM) Doors Price + 0 Toyota White 150043 4 $4,000.00 + 1 Honda Red 87899 4 $5,000.00 + 2 Toyota Blue 32549 3 $7,000.00 + 3 BMW Black 11179 5 $22,000.00 + 4 Nissan White 213095 4 $3,500.00 + 5 Toyota Green 99213 4 $4,500.00 + 6 Honda Blue 45698 4 $7,500.00 + 7 Honda Blue 54738 4 $7,000.00 + 8 Toyota White 60000 4 $6,250.00 + 9 Nissan White 31600 4 $9,700.00 +``` + +You can find the dataset used above in the `Datasets` folder. + +*Note: If you want to import the data from Github you can't directly use its link, you have to first obtain the raw file URL by clicking on the raw button present in the repo* + +## Exporting Data to a CSV + +`pandas` allows you to export `DataFrame` to `.csv` format using `.to_csv()`, or to a Excel spreadsheet using `.to_excel()`. + +```python +car_sales_df.to_csv("exported_car_sales.csv") +``` + +Running this will save a file called ``exported_car_sales.csv`` to the current folder. From 0db944c7900aa2e72c0df64af1049864dbcb5541 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 25 May 2024 21:01:48 +0530 Subject: [PATCH 223/656] Update index.md --- contrib/pandas/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index a2d826c8..bf677cfa 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -5,4 +5,4 @@ - [Pandas Descriptive Statistics](Descriptive_Statistics.md) - [Group By Functions with Pandas](GroupBy_Functions_Pandas.md) - [Excel using Pandas DataFrame](excel_with_pandas.md) -- [Importing and Exporting Data in Pandas](Importing_and_Exporting_Data_in_Pandas.md) +- [Importing and Exporting Data in Pandas](import-export.md) From d0e9b353f7aaaeecf87096aae77c6a560755ddd0 Mon Sep 17 00:00:00 2001 From: srilekha279 <158455553+srilekha279@users.noreply.github.com> Date: Sat, 25 May 2024 21:17:23 +0530 Subject: [PATCH 224/656] Updated recursion.md with example to explain the working of recursion with simple python program This commit updates the recursion explanation in the recursion.md file to be more beginner-friendly and comprehensive. The factorial example has been revised with n = 4 and stack variables have been included for each recursive call to demonstrate how functions are called and how values are returned. These changes aim to provide a clearer understanding of recursion, especially for beginners, by illustrating the step-by-step process of function calls and return values. --- contrib/ds-algorithms/recursion.md | 43 ++++++++++++++++++++++++++++++ 1 file changed, 43 insertions(+) diff --git a/contrib/ds-algorithms/recursion.md b/contrib/ds-algorithms/recursion.md index 7ab31364..67c630b3 100644 --- a/contrib/ds-algorithms/recursion.md +++ b/contrib/ds-algorithms/recursion.md @@ -17,6 +17,49 @@ When a recursive function is called, the following sequence of events occurs: - Stack Management: Each recursive call is placed on the call stack. The stack keeps track of each function call, its argument, and the point to return to once the call completes. - Unwinding the Stack: When the base case is eventually met, the function returns a value, and the stack starts unwinding, returning values to previous function calls until the initial call is resolved. +# Python Code: Factorial using Recursion + +```python +def fact(n): + if n == 0 or n == 1: + return 1 + return n * fact(n - 1) + +if __name__ == "__main__": + n = int(input("Enter a positive number: ")) + print("Factorial of", n, "is", fact(n)) +``` + +## Explanation + +This Python script calculates the factorial of a given number using recursion. + +- **Function `fact(n)`:** + - The function takes an integer `n` as input and calculates its factorial. + - It checks if `n` is 0 or 1. If so, it returns 1 (since the factorial of 0 and 1 is 1). + - Otherwise, it returns `n * fact(n - 1)`, which means it recursively calls itself with `n - 1` until it reaches either 0 or 1. + +- **Main Section:** + - The main section prompts the user to enter a positive number. + - It then calls the `fact` function with the input number and prints the result. + +### Example : Let n = 4 + + +The recursion unfolds as follows: +1. When `fact(4)` is called, it computes `4 * fact(3)`. +2. Inside `fact(3)`, it computes `3 * fact(2)`. +3. Inside `fact(2)`, it computes `2 * fact(1)`. +4. `fact(1)` returns 1 (`if` statement executes), which is received by `fact(2)`, resulting in `2 * 1` i.e. `2`. +5. Back to `fact(3)`, it receives the value from `fact(2)`, giving `3 * 2` i.e. `6`. +6. `fact(4)` receives the value from `fact(3)`, resulting in `4 * 6` i.e. `24`. +7. Finally, `fact(4)` returns 24 to the main function. + + +#### So, the result is 24. + + + # What is Stack Overflow in Recursion Stack overflow is an error that occurs when the call stack memory limit is exceeded. During execution of recursion calls they are simultaneously stored in a recursion stack waiting for the recursive function to be completed. Without a base case, the function would call itself indefinitely, leading to a stack overflow. From 18b4c52c96d8d74a79816fb96e0b7789f1b3ba11 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sun, 26 May 2024 02:23:42 +0530 Subject: [PATCH 225/656] Add files via upload --- contrib/pandas/Datasets/car-sales-missing-data.csv | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 contrib/pandas/Datasets/car-sales-missing-data.csv diff --git a/contrib/pandas/Datasets/car-sales-missing-data.csv b/contrib/pandas/Datasets/car-sales-missing-data.csv new file mode 100644 index 00000000..e34cd5f6 --- /dev/null +++ b/contrib/pandas/Datasets/car-sales-missing-data.csv @@ -0,0 +1,11 @@ +Make,Colour,Odometer,Doors,Price +Toyota,White,150043,4,"$4,000" +Honda,Red,87899,4,"$5,000" +Toyota,Blue,,3,"$7,000" +BMW,Black,11179,5,"$22,000" +Nissan,White,213095,4,"$3,500" +Toyota,Green,,4,"$4,500" +Honda,,,4,"$7,500" +Honda,Blue,,4, +Toyota,White,60000,, +,White,31600,4,"$9,700" \ No newline at end of file From 76fadaa2fd3edf6d17dd96e3d9d1aa13dd88de43 Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 12:09:47 +0530 Subject: [PATCH 226/656] this is the new change --- contrib/Linklist/linklist.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 contrib/Linklist/linklist.md diff --git a/contrib/Linklist/linklist.md b/contrib/Linklist/linklist.md new file mode 100644 index 00000000..e69de29b From 05e14ba89825fe857c9b3ec1333e0f288161d83e Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 13:26:15 +0530 Subject: [PATCH 227/656] New change --- contrib/{Linklist/linklist.md => ds-algorithms/Linked-list.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/{Linklist/linklist.md => ds-algorithms/Linked-list.md} (100%) diff --git a/contrib/Linklist/linklist.md b/contrib/ds-algorithms/Linked-list.md similarity index 100% rename from contrib/Linklist/linklist.md rename to contrib/ds-algorithms/Linked-list.md From b2a16691059d9b0cff7b544a779299b1722708b1 Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:09:10 +0530 Subject: [PATCH 228/656] this is commit --- contrib/ds-algorithms/Linked-list.md | 181 +++++++++++++++++++++++++++ contrib/ds-algorithms/index.md | 1 + 2 files changed, 182 insertions(+) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index e69de29b..d3d7ec1a 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -0,0 +1,181 @@ +# Linked List Data Structure + +Link list is a linear data Structure which can be defined as collection of objects called nodes that are randomly stored in the memory. +A node contains two types of metadata i.e. data stored at that particular address and the pointer which contains the address of the next node in the memory. +The last node of the list contains pointer to the null. + +## Why use linked list over array? + +From the beginning, we are using array data structure to organize the group of elements that are stored individually in the memory. +However, there are some advantage and disadvantage of array which should be known to decide which data structure will used throughout the program. + +limitations + +1. The size of array must be known in advance before using it in the program. +2. Increasing size of the array is a time taking process. It is almost impossible to expand the size of the array at run time. +3. All the elements in the array need to be contiguously stored in the memory. Inserting any element in the array needs shifting of all its predecessors. + +So we introduce a new data structure to overcome these limitations. + +Linked list is used because, +1. It allocates the memory dynamically. All the nodes of linked list are non-contiguously stored in the memory and linked together with the help of pointers. +2. Sizing is no longer a problem since we do not need to define its size at the time of declaration. List grows as per the program's demand and limited to the available memory space. + +Let's code something + +The smallest Unit: Node + +class Node: + def __init__(self, data): + self.data = data # Assigns the given data to the node + self.next = None # Initialize the next attribute to null + +Now, we will see the types of linked list. + +There are mainly four types of linked list, +1. Singly Link list +2. Doubly link list +3. Circular link list +4. Doubly circular link list + + +## 1. Singly linked list. + +Simply think it is a chain of nodes in which each node remember(contains) the addresses of it next node. + +### Creating a linked list class + +class LinkedList: + def __init__(self): + self.head = None # Initialize head as None + +### Inserting a new node at the beginning of a linked list + + def insertAtBeginning(self, new_data): + new_node = Node(new_data) # Create a new node + new_node.next = self.head # Next for new node becomes the current head + self.head = new_node # Head now points to the new node + +### Inserting a new node at the end of a linked list + + def insertAtEnd(self, new_data): + new_node = Node(new_data) # Create a new node + if self.head is None: + self.head = new_node # If the list is empty, make the new node the head + return + last = self.head + while last.next: # Otherwise, traverse the list to find the last node + last = last.next + last.next = new_node # Make the new node the next node of the last node + +### Inserting a new node at the middle of a linked list + + def insertAtPosition(self, data, position): + new_node = Node(data) + if position <= 0: #check if position is valid or not + print("Position should be greater than 0") + return + if position == 1: + new_node.next = self.head + self.head = new_node + return + current_node = self.head + current_position = 1 + while current_node and current_position < position - 1: #Iterating to behind of the postion. + current_node = current_node.next + current_position += 1 + if not current_node: #Check if Position is out of bound or not + print("Position is out of bounds") + return + new_node.next = current_node.next #connect the intermediate node + current_node.next = new_node + +### Printing the Linked list + + def printList(self): + temp = self.head # Start from the head of the list + while temp: + print(temp.data,end=' ') # Print the data in the current node + temp = temp.next # Move to the next node + print() # Ensures the output is followed by a new line + + +Lets complete the code and create a linked list. + +Connect all the code. + +if __name__ == '__main__': + llist = LinkedList() + + # Insert words at the beginning + llist.insertAtBeginning(4) # <4> + llist.insertAtBeginning(3) # <3> 4 + llist.insertAtBeginning(2) # <2> 3 4 + llist.insertAtBeginning(1) # <1> 2 3 4 + + # Insert a word at the end + llist.insertAtEnd(10) # 1 2 3 4 <10> + llist.insertAtEnd(7) # 1 2 3 4 10 <7> + + #Insert at a random position + llist.insertAtPosition(9,4) ## 1 2 3 <9> 4 10 7 + # Print the list + llist.printList() + + + + output: + 1 2 3 9 4 10 7 + + +### Deleting a node from the beginning of a linked list +check the list is empty otherwise shift the head to next node. + +def deleteFromBeginning(self): + if self.head is None: + return "The list is empty" # If the list is empty, return this string + self.head = self.head.next # Otherwise, remove the head by making the next node the new head + +### Deleting a node from the end of a linked list + +def deleteFromEnd(self): + if self.head is None: + return "The list is empty" + if self.head.next is None: + self.head = None # If there's only one node, remove the head by making it None + return + temp = self.head + while temp.next.next: # Otherwise, go to the second-last node + temp = temp.next + temp.next = None # Remove the last node by setting the next pointer of the second-last node to None + + +### Search in a linked list + +def search(self, value): + current = self.head # Start with the head of the list + position = 0 # Counter to keep track of the position + while current: # Traverse the list + if current.data == value: # Compare the list's data to the search value + return f"Value '{value}' found at position {position}" # Print the value if a match is found + current = current.next + position += 1 + return f"Value '{value}' not found in the list" + + + + + + + + + + + + + + + + + + diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 666bcfd3..4b6e20dd 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -8,3 +8,4 @@ - [Searching Algorithms](searching-algorithms.md) - [Greedy Algorithms](greedy-algorithms.md) - [Dynamic Programming](dynamic-programming.md) +- [Linked list](Linked-list.md) From d2a25f16cca049ed1f1b06cd5d25a677144f4ea4 Mon Sep 17 00:00:00 2001 From: Vinay Sagar <64737008+vinay-sagar123@users.noreply.github.com> Date: Sun, 26 May 2024 14:13:10 +0530 Subject: [PATCH 229/656] Update Linked-list.md new change --- contrib/ds-algorithms/Linked-list.md | 39 ++++++++++++++-------------- 1 file changed, 19 insertions(+), 20 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index d3d7ec1a..a660b1cd 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -104,28 +104,27 @@ Lets complete the code and create a linked list. Connect all the code. -if __name__ == '__main__': - llist = LinkedList() - - # Insert words at the beginning - llist.insertAtBeginning(4) # <4> - llist.insertAtBeginning(3) # <3> 4 - llist.insertAtBeginning(2) # <2> 3 4 - llist.insertAtBeginning(1) # <1> 2 3 4 - - # Insert a word at the end - llist.insertAtEnd(10) # 1 2 3 4 <10> - llist.insertAtEnd(7) # 1 2 3 4 10 <7> - - #Insert at a random position - llist.insertAtPosition(9,4) ## 1 2 3 <9> 4 10 7 - # Print the list - llist.printList() + if __name__ == '__main__': + llist = LinkedList() + + # Insert words at the beginning + llist.insertAtBeginning(4) # <4> + llist.insertAtBeginning(3) # <3> 4 + llist.insertAtBeginning(2) # <2> 3 4 + llist.insertAtBeginning(1) # <1> 2 3 4 + # Insert a word at the end + llist.insertAtEnd(10) # 1 2 3 4 <10> + llist.insertAtEnd(7) # 1 2 3 4 10 <7> - - output: - 1 2 3 9 4 10 7 + #Insert at a random position + llist.insertAtPosition(9,4) ## 1 2 3 <9> 4 10 7 + # Print the list + llist.printList() + + +## output: +1 2 3 9 4 10 7 ### Deleting a node from the beginning of a linked list From 4a0576633fc3e06900b2c34e134c0ac5a77de8d1 Mon Sep 17 00:00:00 2001 From: Vinay Sagar <64737008+vinay-sagar123@users.noreply.github.com> Date: Sun, 26 May 2024 14:14:13 +0530 Subject: [PATCH 230/656] Update Linked-list.md change2 --- contrib/ds-algorithms/Linked-list.md | 62 ++++++++++++++-------------- 1 file changed, 31 insertions(+), 31 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index a660b1cd..e3abc9ad 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -25,10 +25,10 @@ Let's code something The smallest Unit: Node -class Node: - def __init__(self, data): - self.data = data # Assigns the given data to the node - self.next = None # Initialize the next attribute to null + class Node: + def __init__(self, data): + self.data = data # Assigns the given data to the node + self.next = None # Initialize the next attribute to null Now, we will see the types of linked list. @@ -45,9 +45,9 @@ Simply think it is a chain of nodes in which each node remember(contains) the ad ### Creating a linked list class -class LinkedList: - def __init__(self): - self.head = None # Initialize head as None + class LinkedList: + def __init__(self): + self.head = None # Initialize head as None ### Inserting a new node at the beginning of a linked list @@ -130,37 +130,37 @@ Connect all the code. ### Deleting a node from the beginning of a linked list check the list is empty otherwise shift the head to next node. -def deleteFromBeginning(self): - if self.head is None: - return "The list is empty" # If the list is empty, return this string - self.head = self.head.next # Otherwise, remove the head by making the next node the new head + def deleteFromBeginning(self): + if self.head is None: + return "The list is empty" # If the list is empty, return this string + self.head = self.head.next # Otherwise, remove the head by making the next node the new head ### Deleting a node from the end of a linked list -def deleteFromEnd(self): - if self.head is None: - return "The list is empty" - if self.head.next is None: - self.head = None # If there's only one node, remove the head by making it None - return - temp = self.head - while temp.next.next: # Otherwise, go to the second-last node - temp = temp.next - temp.next = None # Remove the last node by setting the next pointer of the second-last node to None + def deleteFromEnd(self): + if self.head is None: + return "The list is empty" + if self.head.next is None: + self.head = None # If there's only one node, remove the head by making it None + return + temp = self.head + while temp.next.next: # Otherwise, go to the second-last node + temp = temp.next + temp.next = None # Remove the last node by setting the next pointer of the second-last node to None ### Search in a linked list -def search(self, value): - current = self.head # Start with the head of the list - position = 0 # Counter to keep track of the position - while current: # Traverse the list - if current.data == value: # Compare the list's data to the search value - return f"Value '{value}' found at position {position}" # Print the value if a match is found - current = current.next - position += 1 - return f"Value '{value}' not found in the list" - + def search(self, value): + current = self.head # Start with the head of the list + position = 0 # Counter to keep track of the position + while current: # Traverse the list + if current.data == value: # Compare the list's data to the search value + return f"Value '{value}' found at position {position}" # Print the value if a match is found + current = current.next + position += 1 + return f"Value '{value}' not found in the list" + From b2264d4e6fe3dfe738bc321fe3e6edd946fb9f7e Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:22:13 +0530 Subject: [PATCH 231/656] COMM3 --- contrib/ds-algorithms/Linked-list.md | 25 ++++++++++++++++++------- 1 file changed, 18 insertions(+), 7 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index e3abc9ad..147527bb 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -25,10 +25,12 @@ Let's code something The smallest Unit: Node + ```python class Node: def __init__(self, data): self.data = data # Assigns the given data to the node self.next = None # Initialize the next attribute to null + ``` Now, we will see the types of linked list. @@ -44,20 +46,24 @@ There are mainly four types of linked list, Simply think it is a chain of nodes in which each node remember(contains) the addresses of it next node. ### Creating a linked list class - + ```python class LinkedList: def __init__(self): self.head = None # Initialize head as None + ``` ### Inserting a new node at the beginning of a linked list + ```python def insertAtBeginning(self, new_data): new_node = Node(new_data) # Create a new node new_node.next = self.head # Next for new node becomes the current head self.head = new_node # Head now points to the new node + ``` ### Inserting a new node at the end of a linked list +```python def insertAtEnd(self, new_data): new_node = Node(new_data) # Create a new node if self.head is None: @@ -67,9 +73,10 @@ Simply think it is a chain of nodes in which each node remember(contains) the ad while last.next: # Otherwise, traverse the list to find the last node last = last.next last.next = new_node # Make the new node the next node of the last node - +``` ### Inserting a new node at the middle of a linked list +```python def insertAtPosition(self, data, position): new_node = Node(data) if position <= 0: #check if position is valid or not @@ -89,21 +96,23 @@ Simply think it is a chain of nodes in which each node remember(contains) the ad return new_node.next = current_node.next #connect the intermediate node current_node.next = new_node - +``` ### Printing the Linked list +```python def printList(self): temp = self.head # Start from the head of the list while temp: print(temp.data,end=' ') # Print the data in the current node temp = temp.next # Move to the next node print() # Ensures the output is followed by a new line - +``` Lets complete the code and create a linked list. Connect all the code. +```python if __name__ == '__main__': llist = LinkedList() @@ -121,7 +130,7 @@ Connect all the code. llist.insertAtPosition(9,4) ## 1 2 3 <9> 4 10 7 # Print the list llist.printList() - +``` ## output: 1 2 3 9 4 10 7 @@ -129,14 +138,15 @@ Connect all the code. ### Deleting a node from the beginning of a linked list check the list is empty otherwise shift the head to next node. - +```python def deleteFromBeginning(self): if self.head is None: return "The list is empty" # If the list is empty, return this string self.head = self.head.next # Otherwise, remove the head by making the next node the new head - +``` ### Deleting a node from the end of a linked list +```python def deleteFromEnd(self): if self.head is None: return "The list is empty" @@ -147,6 +157,7 @@ check the list is empty otherwise shift the head to next node. while temp.next.next: # Otherwise, go to the second-last node temp = temp.next temp.next = None # Remove the last node by setting the next pointer of the second-last node to None + ``` ### Search in a linked list From a1dd770b20179f859dc669fdda163bc7c01a96a2 Mon Sep 17 00:00:00 2001 From: Vinay Sagar <64737008+vinay-sagar123@users.noreply.github.com> Date: Sun, 26 May 2024 14:24:19 +0530 Subject: [PATCH 232/656] Update Linked-list.md chnage3 --- contrib/ds-algorithms/Linked-list.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 147527bb..541410c7 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -25,12 +25,12 @@ Let's code something The smallest Unit: Node - ```python +```python class Node: def __init__(self, data): self.data = data # Assigns the given data to the node self.next = None # Initialize the next attribute to null - ``` +``` Now, we will see the types of linked list. @@ -46,20 +46,20 @@ There are mainly four types of linked list, Simply think it is a chain of nodes in which each node remember(contains) the addresses of it next node. ### Creating a linked list class - ```python +```python class LinkedList: def __init__(self): self.head = None # Initialize head as None - ``` +``` ### Inserting a new node at the beginning of a linked list - ```python +```python def insertAtBeginning(self, new_data): new_node = Node(new_data) # Create a new node new_node.next = self.head # Next for new node becomes the current head self.head = new_node # Head now points to the new node - ``` +``` ### Inserting a new node at the end of a linked list From beba33d28736209440c9d9cb4784426a9d38ce9b Mon Sep 17 00:00:00 2001 From: Vinay Sagar <64737008+vinay-sagar123@users.noreply.github.com> Date: Sun, 26 May 2024 14:24:51 +0530 Subject: [PATCH 233/656] Update Linked-list.md change4 --- contrib/ds-algorithms/Linked-list.md | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 541410c7..9e51ab54 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -171,21 +171,3 @@ check the list is empty otherwise shift the head to next node. current = current.next position += 1 return f"Value '{value}' not found in the list" - - - - - - - - - - - - - - - - - - From c3b01f340fc1948f8d6f03d4115d822bb07221f7 Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:32:08 +0530 Subject: [PATCH 234/656] commit4 --- contrib/ds-algorithms/Linked-list.md | 37 +++++++++++++++++++++++++--- 1 file changed, 33 insertions(+), 4 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 147527bb..194cd926 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -157,11 +157,11 @@ check the list is empty otherwise shift the head to next node. while temp.next.next: # Otherwise, go to the second-last node temp = temp.next temp.next = None # Remove the last node by setting the next pointer of the second-last node to None - ``` +``` ### Search in a linked list - +```python def search(self, value): current = self.head # Start with the head of the list position = 0 # Counter to keep track of the position @@ -171,14 +171,43 @@ check the list is empty otherwise shift the head to next node. current = current.next position += 1 return f"Value '{value}' not found in the list" - - +``` +```python + if __name__ == '__main__': + llist = LinkedList() + + # Insert words at the beginning + llist.insertAtBeginning(4) # <4> + llist.insertAtBeginning(3) # <3> 4 + llist.insertAtBeginning(2) # <2> 3 4 + llist.insertAtBeginning(1) # <1> 2 3 4 + + # Insert a word at the end + llist.insertAtEnd(10) # 1 2 3 4 <10> + llist.insertAtEnd(7) # 1 2 3 4 10 <7> + + #Insert at a random position + llist.insertAtPosition(9,4) # 1 2 3 <9> 4 10 7 + llist.insertAtPositon(56,4) # 1 2 3 <56> 9 4 10 7 + #delete at the beginning + llist.deleteFromBeginning() # 2 3 56 9 4 10 7 + #delete at the end + llist.deleteFromEnd() # 2 3 56 9 4 10 + # Print the list + llist.printList() +``` +## Real Life uses of Linked List +Music Player – Songs in the music player are linked to the previous and next songs. So you can play songs either from starting or ending of the list. +GPS navigation systems- Linked lists can be used to store and manage a list of locations and routes, allowing users to easily navigate to their desired destination. +Task Scheduling- Operating systems use linked lists to manage task scheduling, where each process waiting to be executed is represented as a node in the list. +Speech Recognition- Speech recognition software uses linked lists to represent the possible phonetic pronunciations of a word, where each possible pronunciation is represented as a node in the list. +and more.... From c91b657e2c186a509cd0e32a3cf398d30c79ab6b Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:36:28 +0530 Subject: [PATCH 235/656] hello --- contrib/ds-algorithms/Linked-list.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 5febd224..0ec285d6 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -171,7 +171,6 @@ check the list is empty otherwise shift the head to next node. current = current.next position += 1 return f"Value '{value}' not found in the list" -<<<<<<< HEAD ``` ```python @@ -219,5 +218,3 @@ and more.... -======= ->>>>>>> beba33d28736209440c9d9cb4784426a9d38ce9b From 297d68fadbc26f0f752c3272c9bc04094882b7fe Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:38:31 +0530 Subject: [PATCH 236/656] comm6 --- contrib/ds-algorithms/Linked-list.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 0ec285d6..007674fd 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -204,9 +204,13 @@ check the list is empty otherwise shift the head to next node. ## Real Life uses of Linked List Music Player – Songs in the music player are linked to the previous and next songs. So you can play songs either from starting or ending of the list. +
GPS navigation systems- Linked lists can be used to store and manage a list of locations and routes, allowing users to easily navigate to their desired destination. +
Task Scheduling- Operating systems use linked lists to manage task scheduling, where each process waiting to be executed is represented as a node in the list. +
Speech Recognition- Speech recognition software uses linked lists to represent the possible phonetic pronunciations of a word, where each possible pronunciation is represented as a node in the list. +
and more.... From e8a1d62febf2195d49d0207f76cd82591fa24c31 Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:40:43 +0530 Subject: [PATCH 237/656] commit7 --- contrib/ds-algorithms/Linked-list.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 007674fd..95350fd3 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -199,17 +199,19 @@ check the list is empty otherwise shift the head to next node. # Print the list llist.printList() ``` +##Output +2 3 56 9 4 10 ## Real Life uses of Linked List -Music Player – Songs in the music player are linked to the previous and next songs. So you can play songs either from starting or ending of the list. +1. Music Player – Songs in the music player are linked to the previous and next songs. So you can play songs either from starting or ending of the list.
-GPS navigation systems- Linked lists can be used to store and manage a list of locations and routes, allowing users to easily navigate to their desired destination. +2. GPS navigation systems- Linked lists can be used to store and manage a list of locations and routes, allowing users to easily navigate to their desired destination.
-Task Scheduling- Operating systems use linked lists to manage task scheduling, where each process waiting to be executed is represented as a node in the list. +3. Task Scheduling- Operating systems use linked lists to manage task scheduling, where each process waiting to be executed is represented as a node in the list.
-Speech Recognition- Speech recognition software uses linked lists to represent the possible phonetic pronunciations of a word, where each possible pronunciation is represented as a node in the list. +4. Speech Recognition- Speech recognition software uses linked lists to represent the possible phonetic pronunciations of a word, where each possible pronunciation is represented as a node in the list.
and more.... From 7e979edff8636325b197246275db488e13952347 Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:41:38 +0530 Subject: [PATCH 238/656] commit8 --- contrib/ds-algorithms/Linked-list.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 95350fd3..424a8fb3 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -199,7 +199,8 @@ check the list is empty otherwise shift the head to next node. # Print the list llist.printList() ``` -##Output +## Output: + 2 3 56 9 4 10 From 4c51b5916e64e1e6f64fa2987905e9e98d9a0efd Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:42:37 +0530 Subject: [PATCH 239/656] c10 --- contrib/ds-algorithms/Linked-list.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 424a8fb3..1c4511e7 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -207,13 +207,9 @@ check the list is empty otherwise shift the head to next node. ## Real Life uses of Linked List 1. Music Player – Songs in the music player are linked to the previous and next songs. So you can play songs either from starting or ending of the list. -
2. GPS navigation systems- Linked lists can be used to store and manage a list of locations and routes, allowing users to easily navigate to their desired destination. -
3. Task Scheduling- Operating systems use linked lists to manage task scheduling, where each process waiting to be executed is represented as a node in the list. -
4. Speech Recognition- Speech recognition software uses linked lists to represent the possible phonetic pronunciations of a word, where each possible pronunciation is represented as a node in the list. -
and more.... From 45e8763b5a25403f8a3eba79cf0e8a7c0fa46879 Mon Sep 17 00:00:00 2001 From: Vinay Date: Sun, 26 May 2024 14:53:04 +0530 Subject: [PATCH 240/656] com11 --- contrib/ds-algorithms/Linked-list.md | 31 ++++++++++++++++++---------- 1 file changed, 20 insertions(+), 11 deletions(-) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/Linked-list.md index 1c4511e7..11f4016c 100644 --- a/contrib/ds-algorithms/Linked-list.md +++ b/contrib/ds-algorithms/Linked-list.md @@ -2,7 +2,8 @@ Link list is a linear data Structure which can be defined as collection of objects called nodes that are randomly stored in the memory. A node contains two types of metadata i.e. data stored at that particular address and the pointer which contains the address of the next node in the memory. -The last node of the list contains pointer to the null. + +The last element in a linked list features a null pointer. ## Why use linked list over array? @@ -11,15 +12,15 @@ However, there are some advantage and disadvantage of array which should be know limitations -1. The size of array must be known in advance before using it in the program. -2. Increasing size of the array is a time taking process. It is almost impossible to expand the size of the array at run time. -3. All the elements in the array need to be contiguously stored in the memory. Inserting any element in the array needs shifting of all its predecessors. +1. Before an array can be utilized in a program, its size must be established in advance. +2. Expanding an array's size is a lengthy process and is almost impossible to achieve during runtime. +3. Array elements must be stored in contiguous memory locations. To insert an element, all subsequent elements must be shifted So we introduce a new data structure to overcome these limitations. Linked list is used because, -1. It allocates the memory dynamically. All the nodes of linked list are non-contiguously stored in the memory and linked together with the help of pointers. -2. Sizing is no longer a problem since we do not need to define its size at the time of declaration. List grows as per the program's demand and limited to the available memory space. +1. Dynamic Memory Management: Linked lists allocate memory dynamically, meaning nodes can be located anywhere in memory and are connected through pointers, rather than being stored contiguously. +2. Adaptive Sizing: There is no need to predefine the size of a linked list. It can expand or contract during runtime, adapting to the program's requirements within the constraints of the available memory. Let's code something @@ -206,11 +207,19 @@ check the list is empty otherwise shift the head to next node. ## Real Life uses of Linked List -1. Music Player – Songs in the music player are linked to the previous and next songs. So you can play songs either from starting or ending of the list. -2. GPS navigation systems- Linked lists can be used to store and manage a list of locations and routes, allowing users to easily navigate to their desired destination. -3. Task Scheduling- Operating systems use linked lists to manage task scheduling, where each process waiting to be executed is represented as a node in the list. -4. Speech Recognition- Speech recognition software uses linked lists to represent the possible phonetic pronunciations of a word, where each possible pronunciation is represented as a node in the list. -and more.... + + +Here are a few practical applications of linked lists in various fields: + +1. **Music Player**: In a music player, songs are often linked to the previous and next tracks. This allows for seamless navigation between songs, enabling you to play tracks either from the beginning or the end of the playlist. This is akin to a doubly linked list where each song node points to both the previous and the next song, enhancing the flexibility of song selection. + +2. **GPS Navigation Systems**: Linked lists can be highly effective for managing lists of locations and routes in GPS navigation systems. Each location or waypoint can be represented as a node, making it easy to add or remove destinations and to navigate smoothly from one location to another. This is similar to how you might plan a road trip, plotting stops along the way in a flexible, dynamic manner. + +3. **Task Scheduling**: Operating systems utilize linked lists to manage task scheduling. Each process waiting to be executed is represented as a node in a linked list. This organization allows the system to efficiently keep track of which processes need to be run, enabling fair and systematic scheduling of tasks. Think of it like a to-do list where each task is a node, and the system executes tasks in a structured order. + +4. **Speech Recognition**: Speech recognition software uses linked lists to represent possible phonetic pronunciations of words. Each potential pronunciation is a node, allowing the software to dynamically explore different pronunciation paths as it processes spoken input. This method helps in accurately recognizing and understanding speech by considering multiple possibilities in a flexible manner, much like evaluating various potential meanings in a conversation. + +These examples illustrate how linked lists provide a flexible, dynamic data structure that can be adapted to a wide range of practical applications, making them a valuable tool in both software development and real-world problem-solving. 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It's designed for high performance and handles thousands of requests per second . +2. **Easy to use 😃:** FastAPI is designed to be intuitive and easy to use, especially for developers familiar with Python. It uses standard Python type hints for request and response validation, making it easy to understand and write code. +3. **Automatic Interactive API Documentation generation 🤩:** FastAPI automatically generates interactive API documentation (Swagger UI or ReDoc) based on your code and type annotations. Swagger UI also allows you to test API endpoints. +4. **Asynchronous Support 🔁:** FastAPI fully supports asynchronous programming, allowing you to write asynchronous code with async/await syntax. This enables handling high-concurrency scenarios and improves overall performance. + +Now, lets get hands-on with FastAPI. + + +## Installation + +Make sure that you have python version 3.7 or greater. + +Then, simply open your command shell and give the following command. + +```bash + pip install fastapi +``` +After this, you need to install uvicorn. uvicorn is an ASGI server on which we will be running our API. + +```bash + pip install uvicorn +``` + + + +## Making First API + +After successful installation we will be moving towards making an API and seeing how to use it. + +Firstly, the first thing in an API is its root/index page which is sent as response when API is called. + +Follow the given steps to make your first FastAPI🫨 + +First, lets import FastAPI to get things started. + +```python +from fastapi import FastAPI +app = FastAPI() +``` +Now, we will write the ``GET`` method for the root of the API. As you have already seen, the GET method is ``HTTP request`` method used to fetch data from a source. In web development, it is primarily used to *retrieve data* from server. + +The root of the app is ``"/"`` When the API will be called, response will be generated by on this url: ```localhost:8000``` + +### GET method +Following is the code to write GET method which will be calling API. + +When the API is called, the ``read_root()`` function will be hit and the JSON response will be returned which will be shown on your web browser. + +```python +@app.get("/") +def read_root(): + return {"Hello": "World"} + +``` + +Tadaaa! you have made your first FastAPI! Now lets run it! + +### Running Server and calling API + +Open your terminal and give following command: +```bash +uvicorn myapi:app --reload +``` +Here, ``myapi`` is the name of your API which is name of your python file. ``app`` is the name you have given to your API in assignment ``app = FastAPI()`` + +After running this command, uvicorn server will be live and you can access your API. + +As right now we have only written root ``GET`` method, only its corresponding response will be displayed. + +On running this API, we get the response in JSON form: + +```json +{ + "Hello": "World" +} +``` +## Path Parameters +Path parameters are a way to send variables to an API endpoint so that an operation may be perfomed on it. + +This feature is particularly useful for defining routes that need to operate on resources identified by unique identifiers, such as user IDs, product IDs, or any other unique value. + +### Example +Lets take an example to make it understandable. + + +Assume that we have some Students 🧑‍🎓 in our class and we have saved their data in form of dictionary in our API (in practical scenarios they will be saved in a database and API will query database). +So we have a student dictionary that looks something like this: + +```python +students = { + 1: { + "name": "John", + "age": 17, + "class": "year 12" + }, + 2: { + "name": "Jane", + "age": 16, + "class": "year 11" + }, + 3: { + "name": "Alice", + "age": 17, + "class": "year 12" + } +} +``` +Here, keys are ``student_id``. + +Let's say user wants the data of the student whose ID is 2. Here, we will take ID as **path parameter** from the user and return the data of that ID. + + +Lets see how it will be done! + +```python +@app.get("/students/{student_id}") +def read_student(student_id: int): + return students[student_id] +``` +Here is the explanatory breakdown of the method: + +- ``/students`` is the URL of students endpoint in API. +- ``{student_id}`` is the path parameter, which is a dynamic variable the user will give to fetch the record of a particular student. +- ``def read_student(student_id: int)`` is the signature of function which takes the student_id we got from path parameter. Its type is defined as ``int`` as our ID will be an integer. +**Note that there will be automatic type checking of the parameter. If it is not same as type defined in method, an Error response ⛔ will be generated.** + +- ``return students[student_id]`` will return the data of required student from dictionary. + +When the user passes the URL ``http://127.0.0.1:8000/students/1`` the data of student with student_id=1 is fetched and displayed. +In this case following output will be displayed: + +```json +{ + "name": "John", + "age": 17, + "class": "year 12" +} +``` + +## Query Parameters +Query parameters in FastAPI allow you to pass data to your API endpoints via the URL's query string. This is useful for filtering, searching, and other operations that do not fit well with the path parameters. + +Query parameters are specified after the ``?`` symbol in the URL and are typically used for optional parameters. + +### Example +Lets continue the example of students to understand the query parameters. + +Assume that we want to search students by name. In this case, we will be sending datat in query parameter which will be read by our method and respective result will be returned. + +Lets see the method: + +```python +@app.get("/get-by-name") +def read_student(name: str): + for student_id in students: + if students[student_id]["name"] == name: + return students[student_id] + return {"Error": "Student not found"} +``` +Here is the explanatory breakdown of this process: + +- ``/get-by-name`` is the URL of the endpoint. After this URL, client will enter the query parameter(s). +- ``http://127.0.0.1:8000/get-by-name?name=Jane`` In this URL, ``name=Jane`` is the query parameter. It means that user needs to search the student whose name is Jane. When you hit this URL, ``read_student(name:str)`` method is called and respective response is returned. + +In this case, the output will be: +```json +{ + "name": "Jane", + "age": 16, + "class": "year 11" +} +``` +If we pass a name that doesn't exist in dictionary, Error response will be returned. + +## POST Method +The ``POST`` method in FastAPI is used to **create resources** or submit data to an API endpoint. This method typically involves sending data in the request body, which the server processes to create or modify resources. + +**⛔ In case of ``GET`` method, sent data is part of URL, but in case of ``POST`` metohod, sent data is part of request body.** + +### Example +Again continuing with the example of student. Now, lets assume we need to add student. Following is the ``POST`` method to do this: + +```python +@app.post("/create-student/{student_id}") +def create_student(student_id: int, student: dict): + if student_id in students: + return {"Error": "Student exists"} + students[student_id] = student + return students +``` +Here is the explanation of process: + +- ``/create-student/{student_id}`` shows that only student_id will be part of URL, rest of the data will be sent in request body. +- Data in the request body will be in JSON format and will be received in ``student: dict`` +- Data sent in JSON format is given as: +```json +{ +"name":"Seerat", +"age":22, +"class":"8 sem" + +} +``` +*Note:* I have used Swagger UI to send data in request body to test my ``POST`` method but you may use any other API tesing tool like Postman etc. + +- This new student will be added in the dictionary, and if operation is successful, new dictionary will be returned as response. + +Following is the output of this ``POST`` method call: + +```json +{ + "1": { + "name": "John", + "age": 17, + "class": "year 12" + }, + "2": { + "name": "Jane", + "age": 16, + "class": "year 11" + }, + "3": { + "name": "Alice", + "age": 17, + "class": "year 12" + }, + "4": { + "name": "Seerat", + "age": 22, + "class": "8 sem" + } +} +``` + +## PUT Method +The ``PUT`` method in FastAPI is used to **update** existing resources or create resources if they do not already exist. It is one of the standard HTTP methods and is idempotent, meaning that multiple identical requests should have the same effect as a single request. + +### Example +Let's update the record of a student. + +```python +@app.put("/update-student/{student_id}") +def update_student(student_id: int, student: dict): + if student_id not in students: + return {"Error": "Student does not exist"} + students[student_id] = student + return students +``` +``PUT`` method is nearly same as ``POST`` method but ``PUT`` is indempotent while ``POST`` is not. + +The given method will update an existing student record and if student doesnt exist, it'll send error response. + +## Additional Content + +### Swagger UI + +Swagger UI automatically generates UI for API tesing. Just write ``/docs`` with the URL and UI mode of Swagger UI will be launched. + +Following Screenshot shows the Swagger UI +![App Screenshot](/assets/image.png) + +Here is how I tested ``POST`` method in UI: +![Screenshot](/assets/image2.png) + +That's all for FastAPI for now.... Happy Learning! \ No newline at end of file diff --git a/contrib/api-development/index.md b/contrib/api-development/index.md index 75c702ac..72789070 100644 --- a/contrib/api-development/index.md +++ b/contrib/api-development/index.md @@ -1,3 +1,4 @@ # List of sections - [API Methods](api-methods.md) +- [FastAPI](fast-api.md) \ No newline at end of file From 8dc01e263549680c0cda00b53d49f9d33133a603 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sun, 26 May 2024 21:48:11 +0530 Subject: [PATCH 242/656] Add files via upload --- contrib/pandas/Handling_Missing_Values.md | 273 ++++++++++++++++++++++ 1 file changed, 273 insertions(+) create mode 100644 contrib/pandas/Handling_Missing_Values.md diff --git a/contrib/pandas/Handling_Missing_Values.md b/contrib/pandas/Handling_Missing_Values.md new file mode 100644 index 00000000..7207d818 --- /dev/null +++ b/contrib/pandas/Handling_Missing_Values.md @@ -0,0 +1,273 @@ +# Handling Missing Values in Pandas + +**Upuntil now we're working on complete data i.e not having any missing values. But in real life it is the one of the main problem.** + +*Many datasets arrive with missing data either because it exists and was not collected or it never existed.* + +In Pandas missing data is represented by two values: + +* `None` : None is simply is `keyword` refer as empty or none. +* `NaN` : Acronym for `Not a Number`. + +**There are several useful functions for detecting, removing, and replacing null values in Pandas DataFrame :** + +1. isnull() +2. notnull() +3. dropna() +4. fillna() +5. replace() + +## 2. Checking for missing values using `isnull()` and `notnull()` + +Let's import pandas and our fancy car-sales dataset having some missing values. + + +```python +import pandas as pd +``` + + +```python +car_sales_missing_df = pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/car-sales-missing-data.csv") +print(car_sales_missing_df) +``` + + Make Colour Odometer Doors Price + 0 Toyota White 150043.0 4.0 $4,000 + 1 Honda Red 87899.0 4.0 $5,000 + 2 Toyota Blue NaN 3.0 $7,000 + 3 BMW Black 11179.0 5.0 $22,000 + 4 Nissan White 213095.0 4.0 $3,500 + 5 Toyota Green NaN 4.0 $4,500 + 6 Honda NaN NaN 4.0 $7,500 + 7 Honda Blue NaN 4.0 NaN + 8 Toyota White 60000.0 NaN NaN + 9 NaN White 31600.0 4.0 $9,700 + + + +```python +## Using isnull() + +print(car_sales_missing_df.isnull()) +``` + + Make Colour Odometer Doors Price + 0 False False False False False + 1 False False False False False + 2 False False True False False + 3 False False False False False + 4 False False False False False + 5 False False True False False + 6 False True True False False + 7 False False True False True + 8 False False False True True + 9 True False False False False + + +Note here: +* `True` means for `NaN` values +* `False` means for no `Nan` values + +If we want to find the number of missing values in each column use `isnull().sum()`. + + +```python +print(car_sales_missing_df.isnull().sum()) +``` + + Make 1 + Colour 1 + Odometer 4 + Doors 1 + Price 2 + dtype: int64 + + +You can also check presense of null values in a single column. + + +```python +print(car_sales_missing_df["Odometer"].isnull()) +``` + + 0 False + 1 False + 2 True + 3 False + 4 False + 5 True + 6 True + 7 True + 8 False + 9 False + Name: Odometer, dtype: bool + + + +```python +## using notnull() + +print(car_sales_missing_df.notnull()) +``` + + Make Colour Odometer Doors Price + 0 True True True True True + 1 True True True True True + 2 True True False True True + 3 True True True True True + 4 True True True True True + 5 True True False True True + 6 True False False True True + 7 True True False True False + 8 True True True False False + 9 False True True True True + + +Note here: +* `True` means no `NaN` values +* `False` means for `NaN` values + +#### A little note here : `isnull()` means having null values so it gives boolean `True` for NaN values. And `notnull()` means having no null values so it gives `True` for no NaN value. + +## 2. Filling missing values using `fillna()`, `replace()`. + + +```python +## Filling missing values with a single value using `fillna` +print(car_sales_missing_df.fillna(0)) +``` + + Make Colour Odometer Doors Price + 0 Toyota White 150043.0 4.0 $4,000 + 1 Honda Red 87899.0 4.0 $5,000 + 2 Toyota Blue 0.0 3.0 $7,000 + 3 BMW Black 11179.0 5.0 $22,000 + 4 Nissan White 213095.0 4.0 $3,500 + 5 Toyota Green 0.0 4.0 $4,500 + 6 Honda 0 0.0 4.0 $7,500 + 7 Honda Blue 0.0 4.0 0 + 8 Toyota White 60000.0 0.0 0 + 9 0 White 31600.0 4.0 $9,700 + + + +```python +## Filling missing values with the previous value using `ffill()` +print(car_sales_missing_df.ffill()) +``` + + Make Colour Odometer Doors Price + 0 Toyota White 150043.0 4.0 $4,000 + 1 Honda Red 87899.0 4.0 $5,000 + 2 Toyota Blue 87899.0 3.0 $7,000 + 3 BMW Black 11179.0 5.0 $22,000 + 4 Nissan White 213095.0 4.0 $3,500 + 5 Toyota Green 213095.0 4.0 $4,500 + 6 Honda Green 213095.0 4.0 $7,500 + 7 Honda Blue 213095.0 4.0 $7,500 + 8 Toyota White 60000.0 4.0 $7,500 + 9 Toyota White 31600.0 4.0 $9,700 + + + +```python +## illing null value with the next ones using 'bfill()' +print(car_sales_missing_df.bfill()) +``` + + Make Colour Odometer Doors Price + 0 Toyota White 150043.0 4.0 $4,000 + 1 Honda Red 87899.0 4.0 $5,000 + 2 Toyota Blue 11179.0 3.0 $7,000 + 3 BMW Black 11179.0 5.0 $22,000 + 4 Nissan White 213095.0 4.0 $3,500 + 5 Toyota Green 60000.0 4.0 $4,500 + 6 Honda Blue 60000.0 4.0 $7,500 + 7 Honda Blue 60000.0 4.0 $9,700 + 8 Toyota White 60000.0 4.0 $9,700 + 9 NaN White 31600.0 4.0 $9,700 + + +#### Filling a null values using `replace()` method + +**Now we are going to replace the all Nan value in the data frame with -125 value** + +*For this we will need numpy also* + + +```python +import numpy as np +``` + + +```python +print(car_sales_missing_df.replace(to_replace = np.nan, value = -125) ) +``` + + Make Colour Odometer Doors Price + 0 Toyota White 150043.0 4.0 $4,000 + 1 Honda Red 87899.0 4.0 $5,000 + 2 Toyota Blue -125.0 3.0 $7,000 + 3 BMW Black 11179.0 5.0 $22,000 + 4 Nissan White 213095.0 4.0 $3,500 + 5 Toyota Green -125.0 4.0 $4,500 + 6 Honda -125 -125.0 4.0 $7,500 + 7 Honda Blue -125.0 4.0 -125 + 8 Toyota White 60000.0 -125.0 -125 + 9 -125 White 31600.0 4.0 $9,700 + + +## 3. Dropping missing values using `dropna()` + +**In order to drop a null values from a dataframe, we used `dropna()` function this function drop Rows/Columns of datasets with Null values in different ways.** + +#### Dropping rows with at least 1 null value. + + +```python +print(car_sales_missing_df.dropna(axis = 0)) ##Now we drop rows with at least one Nan value (Null value) +``` + + Make Colour Odometer Doors Price + 0 Toyota White 150043.0 4.0 $4,000 + 1 Honda Red 87899.0 4.0 $5,000 + 3 BMW Black 11179.0 5.0 $22,000 + 4 Nissan White 213095.0 4.0 $3,500 + + +#### Dropping rows if all values in that row are missing. + + +```python +print(car_sales_missing_df.dropna(how = 'all',axis = 0)) ## If not have leave the row as it is +``` + + Make Colour Odometer Doors Price + 0 Toyota White 150043.0 4.0 $4,000 + 1 Honda Red 87899.0 4.0 $5,000 + 2 Toyota Blue NaN 3.0 $7,000 + 3 BMW Black 11179.0 5.0 $22,000 + 4 Nissan White 213095.0 4.0 $3,500 + 5 Toyota Green NaN 4.0 $4,500 + 6 Honda NaN NaN 4.0 $7,500 + 7 Honda Blue NaN 4.0 NaN + 8 Toyota White 60000.0 NaN NaN + 9 NaN White 31600.0 4.0 $9,700 + + +#### Dropping columns with at least 1 null value + + +```python +print(car_sales_missing_df.dropna(axis = 1)) +``` + + Empty DataFrame + Columns: [] + Index: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] + + +Now we drop a columns which have at least 1 missing values. + +**Here the dataset becomes empty after dropna() because each column as atleast 1 null value so it remove that columns resulting in an empty dataframe.** From eeef1dc21c19e1275c92fbbb6923b81d0a0635d0 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sun, 26 May 2024 21:50:20 +0530 Subject: [PATCH 243/656] Update index.md --- contrib/pandas/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index bf677cfa..894be520 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -6,3 +6,4 @@ - [Group By Functions with Pandas](GroupBy_Functions_Pandas.md) - [Excel using Pandas DataFrame](excel_with_pandas.md) - [Importing and Exporting Data in Pandas](import-export.md) +- [Handling Missing Values in Pandas](Handling_Missing_Values.md) From be7781b1e35a22cfcbc853bf8cb2c31304308e15 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:16:52 +0530 Subject: [PATCH 244/656] Rename Handling_Missing_Values.md to handling-missing-values.md --- .../{Handling_Missing_Values.md => handling-missing-values.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/pandas/{Handling_Missing_Values.md => handling-missing-values.md} (100%) diff --git a/contrib/pandas/Handling_Missing_Values.md b/contrib/pandas/handling-missing-values.md similarity index 100% rename from contrib/pandas/Handling_Missing_Values.md rename to contrib/pandas/handling-missing-values.md From ad6fd6b6233140c0e128cdd424409247921d1b6f Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:17:16 +0530 Subject: [PATCH 245/656] Update index.md --- contrib/pandas/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 894be520..c71c3243 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -6,4 +6,4 @@ - [Group By Functions with Pandas](GroupBy_Functions_Pandas.md) - [Excel using Pandas DataFrame](excel_with_pandas.md) - [Importing and Exporting Data in Pandas](import-export.md) -- [Handling Missing Values in Pandas](Handling_Missing_Values.md) +- [Handling Missing Values in Pandas](handling-missing-values.md) From 5d15c73a87290afdf5cf8928f92e4f315b6e4c74 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:17:31 +0530 Subject: [PATCH 246/656] Update car-sales-missing-data.csv --- contrib/pandas/Datasets/car-sales-missing-data.csv | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/pandas/Datasets/car-sales-missing-data.csv b/contrib/pandas/Datasets/car-sales-missing-data.csv index e34cd5f6..21a3157f 100644 --- a/contrib/pandas/Datasets/car-sales-missing-data.csv +++ b/contrib/pandas/Datasets/car-sales-missing-data.csv @@ -8,4 +8,4 @@ Toyota,Green,,4,"$4,500" Honda,,,4,"$7,500" Honda,Blue,,4, Toyota,White,60000,, -,White,31600,4,"$9,700" \ No newline at end of file +,White,31600,4,"$9,700" From 8c95bb1de779e455c53c52bd22622b8a533d63c4 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:21:10 +0530 Subject: [PATCH 247/656] Update handling-missing-values.md --- contrib/pandas/handling-missing-values.md | 37 +++++++++-------------- 1 file changed, 14 insertions(+), 23 deletions(-) diff --git a/contrib/pandas/handling-missing-values.md b/contrib/pandas/handling-missing-values.md index 7207d818..da6c377a 100644 --- a/contrib/pandas/handling-missing-values.md +++ b/contrib/pandas/handling-missing-values.md @@ -1,34 +1,28 @@ # Handling Missing Values in Pandas -**Upuntil now we're working on complete data i.e not having any missing values. But in real life it is the one of the main problem.** - -*Many datasets arrive with missing data either because it exists and was not collected or it never existed.* +In real life, many datasets arrive with missing data either because it exists and was not collected or it never existed. In Pandas missing data is represented by two values: * `None` : None is simply is `keyword` refer as empty or none. * `NaN` : Acronym for `Not a Number`. -**There are several useful functions for detecting, removing, and replacing null values in Pandas DataFrame :** +There are several useful functions for detecting, removing, and replacing null values in Pandas DataFrame: -1. isnull() -2. notnull() -3. dropna() -4. fillna() -5. replace() +1. `isnull()` +2. `notnull()` +3. `dropna()` +4. `fillna()` +5. `replace()` ## 2. Checking for missing values using `isnull()` and `notnull()` Let's import pandas and our fancy car-sales dataset having some missing values. - ```python import pandas as pd -``` - -```python -car_sales_missing_df = pd.read_csv("https://raw.githubusercontent.com/kRiShNa-429407/learn-python/main/contrib/pandas/Datasets/car-sales-missing-data.csv") +car_sales_missing_df = pd.read_csv("Datasets/car-sales-missing-data.csv") print(car_sales_missing_df) ``` @@ -128,7 +122,7 @@ Note here: * `True` means no `NaN` values * `False` means for `NaN` values -#### A little note here : `isnull()` means having null values so it gives boolean `True` for NaN values. And `notnull()` means having no null values so it gives `True` for no NaN value. +`isnull()` means having null values so it gives boolean `True` for NaN values. And `notnull()` means having no null values so it gives `True` for no NaN value. ## 2. Filling missing values using `fillna()`, `replace()`. @@ -191,18 +185,15 @@ print(car_sales_missing_df.bfill()) #### Filling a null values using `replace()` method -**Now we are going to replace the all Nan value in the data frame with -125 value** +Now we are going to replace the all `NaN` value in the data frame with -125 value -*For this we will need numpy also* +For this we will also need numpy ```python import numpy as np -``` - -```python -print(car_sales_missing_df.replace(to_replace = np.nan, value = -125) ) +print(car_sales_missing_df.replace(to_replace = np.nan, value = -125)) ``` Make Colour Odometer Doors Price @@ -220,7 +211,7 @@ print(car_sales_missing_df.replace(to_replace = np.nan, value = -125) ) ## 3. Dropping missing values using `dropna()` -**In order to drop a null values from a dataframe, we used `dropna()` function this function drop Rows/Columns of datasets with Null values in different ways.** +In order to drop a null values from a dataframe, we used `dropna()` function this function drop Rows/Columns of datasets with Null values in different ways. #### Dropping rows with at least 1 null value. @@ -270,4 +261,4 @@ print(car_sales_missing_df.dropna(axis = 1)) Now we drop a columns which have at least 1 missing values. -**Here the dataset becomes empty after dropna() because each column as atleast 1 null value so it remove that columns resulting in an empty dataframe.** +Here the dataset becomes empty after `dropna()` because each column as atleast 1 null value so it remove that columns resulting in an empty dataframe. From 02081e65791893aa1075b0d2a12a1b731e40a5d6 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:23:06 +0530 Subject: [PATCH 248/656] Update fast-api.md --- contrib/api-development/fast-api.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/contrib/api-development/fast-api.md b/contrib/api-development/fast-api.md index 52966d65..b67c56ef 100644 --- a/contrib/api-development/fast-api.md +++ b/contrib/api-development/fast-api.md @@ -281,9 +281,9 @@ The given method will update an existing student record and if student doesnt ex Swagger UI automatically generates UI for API tesing. Just write ``/docs`` with the URL and UI mode of Swagger UI will be launched. Following Screenshot shows the Swagger UI -![App Screenshot](/assets/image.png) +![App Screenshot](assets/image.png) Here is how I tested ``POST`` method in UI: -![Screenshot](/assets/image2.png) +![Screenshot](assets/image2.png) -That's all for FastAPI for now.... Happy Learning! \ No newline at end of file +That's all for FastAPI for now.... Happy Learning! From 1c6166e03c00415a836f784659b9016f91695dc3 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:38:32 +0530 Subject: [PATCH 249/656] Rename Linked-list.md to linked-list.md --- contrib/ds-algorithms/{Linked-list.md => linked-list.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/ds-algorithms/{Linked-list.md => linked-list.md} (100%) diff --git a/contrib/ds-algorithms/Linked-list.md b/contrib/ds-algorithms/linked-list.md similarity index 100% rename from contrib/ds-algorithms/Linked-list.md rename to contrib/ds-algorithms/linked-list.md From fd0c10df6bb1e5c3d11e42861db95dc212aa9777 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:39:03 +0530 Subject: [PATCH 250/656] Update linked-list.md --- contrib/ds-algorithms/linked-list.md | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/contrib/ds-algorithms/linked-list.md b/contrib/ds-algorithms/linked-list.md index 11f4016c..ddbc6d56 100644 --- a/contrib/ds-algorithms/linked-list.md +++ b/contrib/ds-algorithms/linked-list.md @@ -220,13 +220,3 @@ Here are a few practical applications of linked lists in various fields: 4. **Speech Recognition**: Speech recognition software uses linked lists to represent possible phonetic pronunciations of words. Each potential pronunciation is a node, allowing the software to dynamically explore different pronunciation paths as it processes spoken input. This method helps in accurately recognizing and understanding speech by considering multiple possibilities in a flexible manner, much like evaluating various potential meanings in a conversation. These examples illustrate how linked lists provide a flexible, dynamic data structure that can be adapted to a wide range of practical applications, making them a valuable tool in both software development and real-world problem-solving. - - - - - - - - - - From 76df5a6a9fb679418444caed6e233c456f19991d Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:39:22 +0530 Subject: [PATCH 251/656] Update index.md --- contrib/ds-algorithms/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 4b6e20dd..c61ca0b5 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -8,4 +8,4 @@ - [Searching Algorithms](searching-algorithms.md) - [Greedy Algorithms](greedy-algorithms.md) - [Dynamic Programming](dynamic-programming.md) -- [Linked list](Linked-list.md) +- [Linked list](linked-list.md) From 8f0a0865d944d30d121fb4c14122cd8c431e4bf9 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:40:56 +0530 Subject: [PATCH 252/656] Update index.md --- contrib/machine-learning/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index edb08601..ce425388 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -7,4 +7,4 @@ - [Support Vector Machine Algorithm](support-vector-machine.md) - [Artificial Neural Network from the Ground Up](ArtificialNeuralNetwork.md) - [TensorFlow.md](tensorFlow.md) -- [PyTorch.md](PyTorch.md) +- [PyTorch.md](pytorch.md) From d9129ccde313a1b3ef56d102da5abb5ba09da076 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:41:13 +0530 Subject: [PATCH 253/656] Rename PyTorch.md to pytorch.md --- contrib/machine-learning/{PyTorch.md => pytorch.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/machine-learning/{PyTorch.md => pytorch.md} (99%) diff --git a/contrib/machine-learning/PyTorch.md b/contrib/machine-learning/pytorch.md similarity index 99% rename from contrib/machine-learning/PyTorch.md rename to contrib/machine-learning/pytorch.md index 7de853ba..ccfee216 100644 --- a/contrib/machine-learning/PyTorch.md +++ b/contrib/machine-learning/pytorch.md @@ -110,4 +110,4 @@ PyTorch is a powerful and flexible deep learning framework that caters to both r - [PyTorch Tutorials](https://pytorch.org/tutorials/) - [PyTorch Forum](https://discuss.pytorch.org/) -Feel free to explore and experiment with PyTorch to harness the full potential of this versatile framework! \ No newline at end of file +Feel free to explore and experiment with PyTorch to harness the full potential of this versatile framework! From 8761dcec7a3b333b8562724b77c740a25e7b4012 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:49:39 +0530 Subject: [PATCH 254/656] Rename introduction-to-flask-a-python-web-framework.md to flask.md --- ...introduction-to-flask-a-python-web-framework.md => flask.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/web-scrapping/{introduction-to-flask-a-python-web-framework.md => flask.md} (99%) diff --git a/contrib/web-scrapping/introduction-to-flask-a-python-web-framework.md b/contrib/web-scrapping/flask.md similarity index 99% rename from contrib/web-scrapping/introduction-to-flask-a-python-web-framework.md rename to contrib/web-scrapping/flask.md index 957f39b4..fbd1829e 100644 --- a/contrib/web-scrapping/introduction-to-flask-a-python-web-framework.md +++ b/contrib/web-scrapping/flask.md @@ -437,4 +437,4 @@ Flask is a powerful and flexible framework for building web applications in Pyth - Flask Documentation: https://flask.palletsprojects.com/en/latest/ - Jinja2 Documentation: https://jinja.palletsprojects.com/en/latest/ - SQLAlchemy Documentation: https://docs.sqlalchemy.org/en/latest/ -- Render Documentation: https://render.com/docs \ No newline at end of file +- Render Documentation: https://render.com/docs From 2acbfeab38ba07eccb00c4e3281f9ff60a8f53f9 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:50:06 +0530 Subject: [PATCH 255/656] Update flask.md --- contrib/web-scrapping/flask.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/contrib/web-scrapping/flask.md b/contrib/web-scrapping/flask.md index fbd1829e..d65da024 100644 --- a/contrib/web-scrapping/flask.md +++ b/contrib/web-scrapping/flask.md @@ -1,7 +1,3 @@ -Sure, here's the guide without Markdown formatting: - ---- - # Introduction to Flask: A Python Web Framework ## Table of Contents From b0e24b769f157a3776512305ac10ffab0d3f8ae7 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 08:50:47 +0530 Subject: [PATCH 256/656] Update index.md --- contrib/web-scrapping/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/web-scrapping/index.md b/contrib/web-scrapping/index.md index a78be3d7..276014ea 100644 --- a/contrib/web-scrapping/index.md +++ b/contrib/web-scrapping/index.md @@ -1,4 +1,4 @@ # List of sections - [Section title](filename.md) -- [Introduction-to-flask-a-python-web-framework](introduction-to-flask-a-python-web-framework.md) +- [Introduction to Flask](flask.md) From dcf0065e1216c106657115354c0d90c5b242c079 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 09:04:27 +0530 Subject: [PATCH 257/656] Update sorting-algorithms.md --- contrib/ds-algorithms/sorting-algorithms.md | 69 +++++++++------------ 1 file changed, 29 insertions(+), 40 deletions(-) diff --git a/contrib/ds-algorithms/sorting-algorithms.md b/contrib/ds-algorithms/sorting-algorithms.md index 6423b4b3..09eccd74 100644 --- a/contrib/ds-algorithms/sorting-algorithms.md +++ b/contrib/ds-algorithms/sorting-algorithms.md @@ -2,14 +2,15 @@ In computer science, a sorting algorithm takes a collection of items and arranges them in a specific order. This order is usually determined by comparing the items using a defined rule. -## Real Life Example of Sorting +Real Life Example of Sorting + - Sorting a deck of cards - Sorting names in alphabetical order - Sorting a list of items, etc. -# Some common sorting techniques +Some common sorting techniques: -# 1. Bubble Sort +## 1. Bubble Sort Bubble sort is a basic sorting technique that iteratively steps through a list, comparing neighboring elements. If elements are out of order, it swaps them. While easy to understand, bubble sort becomes inefficient for large datasets due to its slow execution time. @@ -21,7 +22,7 @@ Bubble sort is a basic sorting technique that iteratively steps through a list, - **Repeating Until Sorted:** The algorithm continues making passes through the list until no more swaps are needed. This indicates the entire list is sorted. -## Bubble Sort Code in Python +### Bubble Sort Code in Python ```python def bubble_sort(arr): @@ -35,7 +36,8 @@ arr = [5, 3, 8, 1, 2] bubble_sort(arr) print("Sorted array:", arr) # Output: [1, 2, 3, 5, 8] ``` -## Example with Visualization + +### Example with Visualization Let's sort the list `[5, 3, 8, 1, 2]` using bubble sort. @@ -53,17 +55,13 @@ Let's sort the list `[5, 3, 8, 1, 2]` using bubble sort. - Comparing neighbors: `[1, 2, 3, 5, 8]` - No swapping needed, the list is already sorted. -## Complexity Analysis +### Complexity Analysis - **Worst Case:** `O(n^2)` comparisons and swaps. This happens when the list is in reverse order, and we need to make maximum swaps. - **Best Case:** `O(n)` comparisons. This occurs when the list is already sorted, but we still need O(n^2) swaps because of the nested loops. - **Average Case:** `O(n^2)` comparisons and swaps. This is the expected number of comparisons and swaps over all possible input sequences. -
-

-
- -# 2. Selection Sort +## 2. Selection Sort Selection sort is a simple sorting algorithm that divides the input list into two parts: a sorted sublist and an unsorted sublist. The algorithm repeatedly finds the smallest (or largest, depending on sorting order) element from the unsorted sublist and moves it to the sorted sublist. It's not efficient for large datasets but performs better than bubble sort due to fewer swaps. @@ -73,7 +71,7 @@ Selection sort is a simple sorting algorithm that divides the input list into tw - **Expanding the Sorted Sublist:** As elements are moved to the sorted sublist, it expands until all elements are sorted. - **Repeating Until Sorted:** The process continues until the entire list is sorted. -## Example with Visualization +### Example with Visualization Let's sort the list `[5, 3, 8, 1, 2]` using selection sort. @@ -97,7 +95,7 @@ Let's sort the list `[5, 3, 8, 1, 2]` using selection sort. - Find the minimum: `5` - No swapping needed, the list is already sorted. -## Selection Sort Code in Python +### Selection Sort Code in Python ```python def selection_sort(arr): @@ -114,15 +112,13 @@ selection_sort(arr) print("Sorted array:", arr) # Output: [1, 2, 3, 5, 8] ``` -## Complexity Analysis +### Complexity Analysis + - **Worst Case**: `O(n^2)` comparisons and O(n) swaps. This occurs when the list is in reverse order, and we need to make maximum comparisons and swaps. - **Best Case**: `O(n^2)` comparisons and O(n) swaps. This happens when the list is in sorted order, but the algorithm still needs to iterate through all elements for comparisons. - **Average Case**: `O(n^2)` comparisons and O(n) swaps. This is the expected number of comparisons and swaps over all possible input sequences. -
-
-
-# 3. Quick Sort +## 3. Quick Sort Quick sort is a popular divide-and-conquer sorting algorithm known for its efficiency on average. It works by selecting a 'pivot' element from the array and partitioning the other elements into two sub-arrays according to whether they are less than or greater than the pivot. The sub-arrays are then recursively sorted. **Algorithm Overview:** @@ -131,29 +127,24 @@ Quick sort is a popular divide-and-conquer sorting algorithm known for its effic - **Recursion:** Apply the above steps recursively to the sub-arrays formed by partitioning until the base case is reached. The base case is usually when the size of the sub-array becomes 0 or 1, indicating it is already sorted. - **Base Case:** If the sub-array size becomes 0 or 1, it is already sorted. -## Example with Visualization +### Example with Visualization Let's sort the list `[5, 3, 8, 1, 2]` using quick sort. 1. **Initial Array:** `[5, 3, 8, 1, 2]` - 2. **Choose Pivot:** Let's choose the last element, `2`, as the pivot. - 3. **Partitioning:** - We'll partition the array around the pivot `2`. All elements less than `2` will be placed to its left, and all elements greater than `2` will be placed to its right. - - After partitioning, the array becomes `[1, 2, 5, 3, 8]`. The pivot element, `2`, is now in its correct sorted position. - 4. **Recursion:** - Now, we recursively sort the sub-arrays `[1]` and `[5, 3, 8]`. - For the sub-array `[5, 3, 8]`, we choose `8` as the pivot and partition it. - After partitioning, the sub-array becomes `[3, 5, 8]`. The pivot element, `8`, is now in its correct sorted position. - - 5. **Concatenation:** - Concatenating the sorted sub-arrays `[1]`, `[2]`, `[3, 5, 8]`, we get the final sorted array `[1, 2, 3, 5, 8]`. -## Quick Sort Code in Python (Iterative) +### Quick Sort Code in Python (Iterative) + ```python def partition(arr, low, high): pivot = arr[high] @@ -180,7 +171,8 @@ quick_sort_iterative(arr) print("Sorted array:", arr) # Output: [3, 9, 10, 27, 38, 43, 82] ``` -## Quick Sort Code in Python (Recursive) + +### Quick Sort Code in Python (Recursive) ```python def quick_sort(arr): @@ -196,17 +188,15 @@ arr = [5, 3, 8, 1, 2] sorted_arr = quick_sort(arr) print("Sorted array:", sorted_arr) # Output: [1, 2, 3, 5, 8] ``` -## Complexity Analysis + +### Complexity Analysis - **Worst Case**: The worst-case time complexity of quick sort is `O(n^2)`. This occurs when the pivot selection consistently results in unbalanced partitioning, such as choosing the smallest or largest element as the pivot. -**Best Case**: The best-case time complexity is `O(n log n)`. This happens when the pivot selection leads to well-balanced partitioning, halving the array size in each recursive call. - **Average Case**: The average-case time complexity is `O(n log n)`. This is the expected time complexity when the pivot selection results in reasonably balanced partitioning across recursive calls. - **Space Complexity**: Quick sort has an `O(log n)` space complexity for the recursion stack, as it recursively sorts sub-arrays. -
-
-
-# 4. Merge Sort +## 4. Merge Sort Merge sort is a divide-and-conquer algorithm that recursively divides the input list into smaller sublists until each sublist contains only one element. Then, it repeatedly merges adjacent sublists while maintaining the sorted order until there is only one sublist remaining, which represents the sorted list. @@ -214,7 +204,7 @@ Merge sort is a divide-and-conquer algorithm that recursively divides the input - **Divide:** Split the input list into smaller sublists recursively until each sublist contains only one element. - **Merge:** Repeatedly merge adjacent sublists while maintaining the sorted order until there is only one sublist remaining, which represents the sorted list. -## Example with Visualization +### Example with Visualization Let's sort the list `[38, 27, 43, 3, 9, 82, 10]` using merge sort. @@ -233,7 +223,7 @@ Let's sort the list `[38, 27, 43, 3, 9, 82, 10]` using merge sort. 3. **Final Sorted List:** - `[3, 9, 10, 27, 38, 43, 82]` -## Merge Sort Code in Python (Iterative) +### Merge Sort Code in Python (Iterative) ```python def merge_sort_iterative(arr): @@ -280,7 +270,9 @@ arr = [38, 27, 43, 3, 9, 82, 10] merge_sort_iterative(arr) print("Sorted array:", arr) # Output: [3, 9, 10, 27, 38, 43, 82] ``` -## Merge Sort Code in Python (Recursive) + +### Merge Sort Code in Python (Recursive) + ```python def merge_sort(arr): if len(arr) > 1: @@ -320,10 +312,7 @@ arr = [38, 27, 43, 3, 9, 82, 10] merge_sort(arr) print("Sorted array:", arr) # Output: [3, 9, 10, 27, 38, 43, 82] ``` -## Complexity Analysis + +### Complexity Analysis - **Time Complexity**: `O(n log n)` for all cases. Merge sort always divides the list into halves until each sublist contains only one element, and then merges them back together, resulting in O(n log n) time complexity. - **Space Complexity**: `O(n)` auxiliary space. In the iterative version, merge sort uses additional space for creating temporary sublists during merging operations. - -
-
-
From 9d35a7fd1827eebce44a08660ed7f9e5cc4496b4 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 09:36:18 +0530 Subject: [PATCH 258/656] Rename sorting_numpy_array.md to sorting-array.md --- contrib/numpy/{sorting_numpy_array.md => sorting-array.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/numpy/{sorting_numpy_array.md => sorting-array.md} (100%) diff --git a/contrib/numpy/sorting_numpy_array.md b/contrib/numpy/sorting-array.md similarity index 100% rename from contrib/numpy/sorting_numpy_array.md rename to contrib/numpy/sorting-array.md From 8c756d0534fda5ba190575b64df40290058eb239 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Mon, 27 May 2024 09:36:37 +0530 Subject: [PATCH 259/656] Update index.md --- contrib/numpy/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 4b4a2c80..b2d459ad 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -7,4 +7,4 @@ - [Operations on Arrays in NumPy](operations-on-arrays.md) - [Loading Arrays from Files](loading_arrays_from_files.md) - [Saving Numpy Arrays into FIles](saving_numpy_arrays_to_files.md) -- [Sorting NumPy Arrays](sorting_numpy_array.md) +- [Sorting NumPy Arrays](sorting-array.md) From 5dbf1f07cae3eb5c0694f1a9019a23693f423414 Mon Sep 17 00:00:00 2001 From: HimakarC <116370824+HimakarC@users.noreply.github.com> Date: Mon, 27 May 2024 10:49:15 +0530 Subject: [PATCH 260/656] Update datetime_with_pandas.md --- contrib/pandas/datetime_with_pandas.md | 49 ++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) diff --git a/contrib/pandas/datetime_with_pandas.md b/contrib/pandas/datetime_with_pandas.md index 796df4a8..d87d9ec4 100644 --- a/contrib/pandas/datetime_with_pandas.md +++ b/contrib/pandas/datetime_with_pandas.md @@ -115,3 +115,52 @@ Output: 2024-05-24 2024-05-25 ``` + +### Pandas `DateTime` is Efficient than Built-in `DateTime` library in various aspects like: + +- **In pandas, you may add a time delta to a full column of dates in a single action, but Python's datetime requires a loop.** + +Example using Pandas DateTime: + +```python +import pandas as pd + +dates = pd.DataFrame(pd.date_range('2023-01-01', periods=100000, freq='T')) +dates += pd.Timedelta(days=1) +print(dates) +``` + +Output: +```python + 0 +0 2023-01-02 00:00:00 +1 2023-01-02 00:01:00 +2 2023-01-02 00:02:00 +3 2023-01-02 00:03:00 +4 2023-01-02 00:04:00 +... ... +99995 2023-03-12 10:35:00 +99996 2023-03-12 10:36:00 +99997 2023-03-12 10:37:00 +99998 2023-03-12 10:38:00 +99999 2023-03-12 10:39:00 +``` + +Example using Built-In datetime library: + +```python +from datetime import datetime, timedelta + +dates = [datetime(2023, 1, 1) + timedelta(minutes=i) for i in range(100000)] +dates = [date + timedelta(days=1) for date in dates] +``` + +Output: +```The output is very large to display and taking more time to display``` + + +- **Pandas employs NumPy's datetime64 dtype, which takes up a set amount of bytes (usually 8 bytes per date), to store datetime data more compactly and efficiently.** +- **Each datetime object in Python takes up extra memory since it contains not only the date and time but also the additional metadata and overhead associated with Python objects.** + +- **Pandas Offers a wide range of convenient functions and methods for date manipulation, extraction, and conversion, such as `pd.to_datetime()`, `date_range()`, `timedelta_range()`, and more.** +- **datetime library requires manual implementation for many of these operations, leading to longer and less efficient code.** From b819dfeaf7af4ca999fce1fa74da124b98bbad08 Mon Sep 17 00:00:00 2001 From: Yatharth Date: Mon, 27 May 2024 13:23:28 +0530 Subject: [PATCH 261/656] figma drawn images --- .../Time-And-Space-Complexity-BigOh.png | Bin 17286 -> 12856 bytes .../Time-And-Space-Complexity-BigOmega.png | Bin 15822 -> 9438 bytes .../Time-And-Space-Complexity-BigTheta.png | Bin 21022 -> 13438 bytes 3 files 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first --- contrib/machine-learning/Random_Forest.md | 175 ++++++++++++++++++++++ 1 file changed, 175 insertions(+) create mode 100644 contrib/machine-learning/Random_Forest.md diff --git a/contrib/machine-learning/Random_Forest.md b/contrib/machine-learning/Random_Forest.md new file mode 100644 index 00000000..3506a34e --- /dev/null +++ b/contrib/machine-learning/Random_Forest.md @@ -0,0 +1,175 @@ +Random Forest +Random Forest is a versatile machine learning algorithm capable of performing both regression and classification tasks. It is an ensemble method that operates by constructing a multitude of decision trees during training and outputting the average prediction of the individual trees (for regression) or the mode of the classes (for classification). + +Table of Contents +Introduction +How Random Forest Works +Advantages and Disadvantages +Hyperparameters +Code Examples +Classification Example +Feature Importance +Hyperparameter Tuning +Regression Example +Conclusion +References +Introduction +Random Forest is an ensemble learning method used for classification and regression tasks. It is built from multiple decision trees and combines their outputs to improve the model's accuracy and control over-fitting. + +How Random Forest Works +Bootstrap Sampling: +Random subsets of the training dataset are created with replacement. Each subset is used to train an individual tree. +Decision Trees: +Multiple decision trees are trained on these subsets. +Feature Selection: +At each split in the decision tree, a random selection of features is chosen. This randomness helps create diverse trees. +Voting/Averaging: +For classification, the mode of the classes predicted by individual trees is taken (majority vote). +For regression, the average of the outputs of the individual trees is taken. +Detailed Working Mechanism +Step 1: Bootstrap Sampling: Each tree is trained on a random sample of the original data, drawn with replacement (bootstrap sample). This means some data points may appear multiple times in a sample while others may not appear at all. +Step 2: Tree Construction: Each node in the tree is split using the best split among a random subset of the features. This process adds an additional layer of randomness, contributing to the robustness of the model. +Step 3: Aggregation: For classification tasks, the final prediction is based on the majority vote from all the trees. For regression tasks, the final prediction is the average of all the tree predictions. +Advantages and Disadvantages +Advantages +Robustness: Reduces overfitting and generalizes well due to the law of large numbers. +Accuracy: Often provides high accuracy because of the ensemble method. +Versatility: Can be used for both classification and regression tasks. +Handles Missing Values: Can handle missing data better than many other algorithms. +Feature Importance: Provides estimates of feature importance, which can be valuable for understanding the model. +Disadvantages +Complexity: More complex than individual decision trees, making interpretation difficult. +Computational Cost: Requires more computational resources due to multiple trees. +Training Time: Can be slow to train compared to simpler models, especially with large datasets. +Hyperparameters +Key Hyperparameters +n_estimators: The number of trees in the forest. +max_features: The number of features to consider when looking for the best split. +max_depth: The maximum depth of the tree. +min_samples_split: The minimum number of samples required to split an internal node. +min_samples_leaf: The minimum number of samples required to be at a leaf node. +bootstrap: Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree. +Tuning Hyperparameters +Hyperparameter tuning can significantly improve the performance of a Random Forest model. Common techniques include Grid Search and Random Search. + +Code Examples +Classification Example +Below is a simple example of using Random Forest for a classification task with the Iris dataset. + +python +Copy code +import numpy as np +import pandas as pd +from sklearn.datasets import load_iris +from sklearn.ensemble import RandomForestClassifier +from sklearn.model_selection import train_test_split +from sklearn.metrics import accuracy_score, classification_report + +# Load dataset +iris = load_iris() +X, y = iris.data, iris.target + +# Split dataset +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) + +# Initialize Random Forest model +clf = RandomForestClassifier(n_estimators=100, random_state=42) + +# Train the model +clf.fit(X_train, y_train) + +# Make predictions +y_pred = clf.predict(X_test) + +# Evaluate the model +accuracy = accuracy_score(y_test, y_pred) +print(f"Accuracy: {accuracy * 100:.2f}%") +print("Classification Report:\n", classification_report(y_test, y_pred)) +Feature Importance +Random Forest provides a way to measure the importance of each feature in making predictions. + +python +Copy code +import matplotlib.pyplot as plt + +# Get feature importances +importances = clf.feature_importances_ +indices = np.argsort(importances)[::-1] + +# Print feature ranking +print("Feature ranking:") +for f in range(X.shape[1]): + print(f"{f + 1}. Feature {indices[f]} ({importances[indices[f]]})") + +# Plot the feature importances +plt.figure() +plt.title("Feature importances") +plt.bar(range(X.shape[1]), importances[indices], align='center') +plt.xticks(range(X.shape[1]), indices) +plt.xlim([-1, X.shape[1]]) +plt.show() +Hyperparameter Tuning +Using Grid Search for hyperparameter tuning. + +python +Copy code +from sklearn.model_selection import GridSearchCV + +# Define the parameter grid +param_grid = { + 'n_estimators': [100, 200, 300], + 'max_features': ['auto', 'sqrt', 'log2'], + 'max_depth': [4, 6, 8, 10, 12], + 'criterion': ['gini', 'entropy'] +} + +# Initialize the Grid Search model +grid_search = GridSearchCV(estimator=clf, param_grid=param_grid, cv=3, n_jobs=-1, verbose=2) + +# Fit the model +grid_search.fit(X_train, y_train) + +# Print the best parameters +print("Best parameters found: ", grid_search.best_params_) +Regression Example +Below is a simple example of using Random Forest for a regression task with the Boston housing dataset. + +python +Copy code +import numpy as np +import pandas as pd +from sklearn.datasets import load_boston +from sklearn.ensemble import RandomForestRegressor +from sklearn.model_selection import train_test_split +from sklearn.metrics import mean_squared_error, r2_score + +# Load dataset +boston = load_boston() +X, y = boston.data, boston.target + +# Split dataset +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) + +# Initialize Random Forest model +regr = RandomForestRegressor(n_estimators=100, random_state=42) + +# Train the model +regr.fit(X_train, y_train) + +# Make predictions +y_pred = regr.predict(X_test) + +# Evaluate the model +mse = mean_squared_error(y_test, y_pred) +r2 = r2_score(y_test, y_pred) +print(f"Mean Squared Error: {mse:.2f}") +print(f"R^2 Score: {r2:.2f}") +Conclusion +Random Forest is a powerful and flexible machine learning algorithm that can handle both classification and regression tasks. Its ability to create an ensemble of decision trees leads to robust and accurate models. However, it is important to be mindful of the computational cost associated with training multiple trees. + +References +Scikit-learn Random Forest Documentation +Wikipedia: Random Forest +Machine Learning Mastery: Introduction to Random Forest +Kaggle: Random Forest Guide +Towards Data Science: Understanding Random Forests \ No newline at end of file From 9e1a20de440dc20bd465e20fe2b0709241ac3595 Mon Sep 17 00:00:00 2001 From: Soubeer Koley Date: Mon, 27 May 2024 19:19:52 +0530 Subject: [PATCH 263/656] second --- contrib/machine-learning/Random_Forest.md | 97 ++++++++++++----------- 1 file changed, 52 insertions(+), 45 deletions(-) diff --git a/contrib/machine-learning/Random_Forest.md b/contrib/machine-learning/Random_Forest.md index 3506a34e..8c62255b 100644 --- a/contrib/machine-learning/Random_Forest.md +++ b/contrib/machine-learning/Random_Forest.md @@ -1,7 +1,8 @@ -Random Forest +# Random Forest + Random Forest is a versatile machine learning algorithm capable of performing both regression and classification tasks. It is an ensemble method that operates by constructing a multitude of decision trees during training and outputting the average prediction of the individual trees (for regression) or the mode of the classes (for classification). -Table of Contents +## Table of Contents Introduction How Random Forest Works Advantages and Disadvantages @@ -13,51 +14,54 @@ Hyperparameter Tuning Regression Example Conclusion References -Introduction + +## Introduction Random Forest is an ensemble learning method used for classification and regression tasks. It is built from multiple decision trees and combines their outputs to improve the model's accuracy and control over-fitting. -How Random Forest Works -Bootstrap Sampling: -Random subsets of the training dataset are created with replacement. Each subset is used to train an individual tree. -Decision Trees: -Multiple decision trees are trained on these subsets. -Feature Selection: -At each split in the decision tree, a random selection of features is chosen. This randomness helps create diverse trees. -Voting/Averaging: +## How Random Forest Works +### 1. Bootstrap Sampling: +* Random subsets of the training dataset are created with replacement. Each subset is used to train an individual tree. +### 2. Decision Trees: +* Multiple decision trees are trained on these subsets. +### 3. Feature Selection: +* At each split in the decision tree, a random selection of features is chosen. This randomness helps create diverse trees. +### 4. Voting/Averaging: For classification, the mode of the classes predicted by individual trees is taken (majority vote). For regression, the average of the outputs of the individual trees is taken. -Detailed Working Mechanism -Step 1: Bootstrap Sampling: Each tree is trained on a random sample of the original data, drawn with replacement (bootstrap sample). This means some data points may appear multiple times in a sample while others may not appear at all. -Step 2: Tree Construction: Each node in the tree is split using the best split among a random subset of the features. This process adds an additional layer of randomness, contributing to the robustness of the model. -Step 3: Aggregation: For classification tasks, the final prediction is based on the majority vote from all the trees. For regression tasks, the final prediction is the average of all the tree predictions. -Advantages and Disadvantages -Advantages -Robustness: Reduces overfitting and generalizes well due to the law of large numbers. -Accuracy: Often provides high accuracy because of the ensemble method. -Versatility: Can be used for both classification and regression tasks. -Handles Missing Values: Can handle missing data better than many other algorithms. -Feature Importance: Provides estimates of feature importance, which can be valuable for understanding the model. -Disadvantages -Complexity: More complex than individual decision trees, making interpretation difficult. -Computational Cost: Requires more computational resources due to multiple trees. -Training Time: Can be slow to train compared to simpler models, especially with large datasets. -Hyperparameters -Key Hyperparameters -n_estimators: The number of trees in the forest. -max_features: The number of features to consider when looking for the best split. -max_depth: The maximum depth of the tree. -min_samples_split: The minimum number of samples required to split an internal node. -min_samples_leaf: The minimum number of samples required to be at a leaf node. -bootstrap: Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree. -Tuning Hyperparameters +### Detailed Working Mechanism +* #### Step 1: Bootstrap Sampling: + Each tree is trained on a random sample of the original data, drawn with replacement (bootstrap sample). This means some data points may appear multiple times in a sample while others may not appear at all. +* #### Step 2: Tree Construction: + Each node in the tree is split using the best split among a random subset of the features. This process adds an additional layer of randomness, contributing to the robustness of the model. +#### Step 3: Aggregation: + For classification tasks, the final prediction is based on the majority vote from all the trees. For regression tasks, the final prediction is the average of all the tree predictions. +### Advantages and Disadvantages +#### Advantages +* Robustness: Reduces overfitting and generalizes well due to the law of large numbers. +* Accuracy: Often provides high accuracy because of the ensemble method. +* Versatility: Can be used for both classification and regression tasks. +* Handles Missing Values: Can handle missing data better than many other algorithms. +* Feature Importance: Provides estimates of feature importance, which can be valuable for understanding the model. +#### Disadvantages +* Complexity: More complex than individual decision trees, making interpretation difficult. +* Computational Cost: Requires more computational resources due to multiple trees. +* Training Time: Can be slow to train compared to simpler models, especially with large datasets. +### Hyperparameters +#### Key Hyperparameters +* n_estimators: The number of trees in the forest. +* max_features: The number of features to consider when looking for the best split. +* max_depth: The maximum depth of the tree. +* min_samples_split: The minimum number of samples required to split an internal node. +* min_samples_leaf: The minimum number of samples required to be at a leaf node. +* bootstrap: Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree. +##### Tuning Hyperparameters Hyperparameter tuning can significantly improve the performance of a Random Forest model. Common techniques include Grid Search and Random Search. -Code Examples -Classification Example +### Code Examples +#### Classification Example Below is a simple example of using Random Forest for a classification task with the Iris dataset. -python -Copy code +''' import numpy as np import pandas as pd from sklearn.datasets import load_iris @@ -85,11 +89,14 @@ y_pred = clf.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print(f"Accuracy: {accuracy * 100:.2f}%") print("Classification Report:\n", classification_report(y_test, y_pred)) -Feature Importance + +''' + +#### Feature Importance Random Forest provides a way to measure the importance of each feature in making predictions. -python -Copy code + +''' import matplotlib.pyplot as plt # Get feature importances @@ -108,11 +115,11 @@ plt.bar(range(X.shape[1]), importances[indices], align='center') plt.xticks(range(X.shape[1]), indices) plt.xlim([-1, X.shape[1]]) plt.show() -Hyperparameter Tuning +''' +#### Hyperparameter Tuning Using Grid Search for hyperparameter tuning. -python -Copy code +''' from sklearn.model_selection import GridSearchCV # Define the parameter grid From d138b0e6252f0cf28be7e014f8625352c4e90dd1 Mon Sep 17 00:00:00 2001 From: Soubeer Koley Date: Mon, 27 May 2024 19:23:51 +0530 Subject: [PATCH 264/656] third --- contrib/machine-learning/Random_Forest.md | 22 ++++++++++++---------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/contrib/machine-learning/Random_Forest.md b/contrib/machine-learning/Random_Forest.md index 8c62255b..a3672d0a 100644 --- a/contrib/machine-learning/Random_Forest.md +++ b/contrib/machine-learning/Random_Forest.md @@ -61,7 +61,7 @@ Hyperparameter tuning can significantly improve the performance of a Random Fore #### Classification Example Below is a simple example of using Random Forest for a classification task with the Iris dataset. -''' +``` import numpy as np import pandas as pd from sklearn.datasets import load_iris @@ -69,6 +69,7 @@ from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, classification_report + # Load dataset iris = load_iris() X, y = iris.data, iris.target @@ -90,13 +91,13 @@ accuracy = accuracy_score(y_test, y_pred) print(f"Accuracy: {accuracy * 100:.2f}%") print("Classification Report:\n", classification_report(y_test, y_pred)) -''' +``` #### Feature Importance Random Forest provides a way to measure the importance of each feature in making predictions. -''' +``` import matplotlib.pyplot as plt # Get feature importances @@ -115,11 +116,11 @@ plt.bar(range(X.shape[1]), importances[indices], align='center') plt.xticks(range(X.shape[1]), indices) plt.xlim([-1, X.shape[1]]) plt.show() -''' +``` #### Hyperparameter Tuning Using Grid Search for hyperparameter tuning. -''' +``` from sklearn.model_selection import GridSearchCV # Define the parameter grid @@ -138,11 +139,11 @@ grid_search.fit(X_train, y_train) # Print the best parameters print("Best parameters found: ", grid_search.best_params_) -Regression Example +``` +#### Regression Example Below is a simple example of using Random Forest for a regression task with the Boston housing dataset. -python -Copy code +``` import numpy as np import pandas as pd from sklearn.datasets import load_boston @@ -171,10 +172,11 @@ mse = mean_squared_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) print(f"Mean Squared Error: {mse:.2f}") print(f"R^2 Score: {r2:.2f}") -Conclusion +``` +### Conclusion Random Forest is a powerful and flexible machine learning algorithm that can handle both classification and regression tasks. Its ability to create an ensemble of decision trees leads to robust and accurate models. However, it is important to be mindful of the computational cost associated with training multiple trees. -References +### References Scikit-learn Random Forest Documentation Wikipedia: Random Forest Machine Learning Mastery: Introduction to Random Forest From 834aade79acb60eb6cfc8dae1731ac93f794c2b6 Mon Sep 17 00:00:00 2001 From: Soubeer Koley Date: Mon, 27 May 2024 19:41:22 +0530 Subject: [PATCH 265/656] third --- contrib/machine-learning/Random_Forest.md | 36 +++++++++++++++-------- 1 file changed, 24 insertions(+), 12 deletions(-) diff --git a/contrib/machine-learning/Random_Forest.md b/contrib/machine-learning/Random_Forest.md index a3672d0a..d2d7f5c3 100644 --- a/contrib/machine-learning/Random_Forest.md +++ b/contrib/machine-learning/Random_Forest.md @@ -2,18 +2,30 @@ Random Forest is a versatile machine learning algorithm capable of performing both regression and classification tasks. It is an ensemble method that operates by constructing a multitude of decision trees during training and outputting the average prediction of the individual trees (for regression) or the mode of the classes (for classification). -## Table of Contents -Introduction -How Random Forest Works -Advantages and Disadvantages -Hyperparameters -Code Examples -Classification Example -Feature Importance -Hyperparameter Tuning -Regression Example -Conclusion -References + +- [Random Forest](#random-forest) + - [Introduction](#introduction) + - [How Random Forest Works](#how-random-forest-works) + - [1. Bootstrap Sampling:](#1-bootstrap-sampling) + - [2. Decision Trees:](#2-decision-trees) + - [3. Feature Selection:](#3-feature-selection) + - [4. Voting/Averaging:](#4-votingaveraging) + - [Detailed Working Mechanism](#detailed-working-mechanism) + - [Step 3: Aggregation:](#step-3-aggregation) + - [Advantages and Disadvantages](#advantages-and-disadvantages) + - [Advantages](#advantages) + - [Disadvantages](#disadvantages) + - [Hyperparameters](#hyperparameters) + - [Key Hyperparameters](#key-hyperparameters) + - [Tuning Hyperparameters](#tuning-hyperparameters) + - [Code Examples](#code-examples) + - [Classification Example](#classification-example) + - [Feature Importance](#feature-importance) + - [Hyperparameter Tuning](#hyperparameter-tuning) + - [Regression Example](#regression-example) + - [Conclusion](#conclusion) + - [References](#references) + ## Introduction Random Forest is an ensemble learning method used for classification and regression tasks. It is built from multiple decision trees and combines their outputs to improve the model's accuracy and control over-fitting. From 0185122fb9ae4aada0feff6cd45f1e688644ad1c Mon Sep 17 00:00:00 2001 From: Soubeer Koley Date: Mon, 27 May 2024 19:48:33 +0530 Subject: [PATCH 266/656] fourth --- .vscode/settings.json | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 .vscode/settings.json diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000..7a73a41b --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,2 @@ +{ +} \ No newline at end of file From 91a2b6d4b81bc46098d6c5007a45b0d0df3b572e Mon Sep 17 00:00:00 2001 From: Soubeer Koley Date: Mon, 27 May 2024 21:22:06 +0530 Subject: [PATCH 267/656] Added Random Forest --- contrib/machine-learning/Random_Forest.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/contrib/machine-learning/Random_Forest.md b/contrib/machine-learning/Random_Forest.md index d2d7f5c3..59c44eff 100644 --- a/contrib/machine-learning/Random_Forest.md +++ b/contrib/machine-learning/Random_Forest.md @@ -23,8 +23,8 @@ Random Forest is a versatile machine learning algorithm capable of performing bo - [Feature Importance](#feature-importance) - [Hyperparameter Tuning](#hyperparameter-tuning) - [Regression Example](#regression-example) - - [Conclusion](#conclusion) - - [References](#references) + - [Conclusion](#conclusion) + - [References](#references) ## Introduction @@ -185,10 +185,10 @@ r2 = r2_score(y_test, y_pred) print(f"Mean Squared Error: {mse:.2f}") print(f"R^2 Score: {r2:.2f}") ``` -### Conclusion +## Conclusion Random Forest is a powerful and flexible machine learning algorithm that can handle both classification and regression tasks. Its ability to create an ensemble of decision trees leads to robust and accurate models. However, it is important to be mindful of the computational cost associated with training multiple trees. -### References +## References Scikit-learn Random Forest Documentation Wikipedia: Random Forest Machine Learning Mastery: Introduction to Random Forest From b5f682559bd50495ab44dd46d75eea7693882c27 Mon Sep 17 00:00:00 2001 From: piyush-poddar Date: Tue, 28 May 2024 02:45:47 +0530 Subject: [PATCH 268/656] Added Content: Bar Plots in Matplotlib --- contrib/plotting-visualization/bar-plots.md | 216 ++++++++++++++++++ .../images/bar_colors_and_legends.png | Bin 0 -> 22079 bytes .../images/bar_labels.png | Bin 0 -> 24793 bytes .../images/basic_bar_plot.png | Bin 0 -> 11890 bytes .../images/horizontal_bar_plot_1.png | Bin 0 -> 25704 bytes .../images/horizontal_bar_plot_2.png | Bin 0 -> 25689 bytes .../images/title_and_axis_labels.png | Bin 0 -> 18124 bytes contrib/plotting-visualization/index.md | 1 + 8 files changed, 217 insertions(+) create mode 100644 contrib/plotting-visualization/bar-plots.md create mode 100644 contrib/plotting-visualization/images/bar_colors_and_legends.png create mode 100644 contrib/plotting-visualization/images/bar_labels.png create mode 100644 contrib/plotting-visualization/images/basic_bar_plot.png create mode 100644 contrib/plotting-visualization/images/horizontal_bar_plot_1.png create mode 100644 contrib/plotting-visualization/images/horizontal_bar_plot_2.png create mode 100644 contrib/plotting-visualization/images/title_and_axis_labels.png diff --git a/contrib/plotting-visualization/bar-plots.md b/contrib/plotting-visualization/bar-plots.md new file mode 100644 index 00000000..104001d9 --- /dev/null +++ b/contrib/plotting-visualization/bar-plots.md @@ -0,0 +1,216 @@ +# Bar Plots in Matplotlib +A bar plot or a bar chart is a type of data visualisation that represents data in the form of rectangular bars, with lengths or heights proportional to the values and data which they represent. The bar plots can be plotted both vertically and horizontally. + +It is one of the most widely used type of data visualisation as it is easy to interpret and is pleasing to the eyes. + +Matplotlib provides a very easy and intuitive method to create highly customized bar plots. + +## Prerequisites + +Before creating bar plots in matplotlib you must ensure that you have Python as well as Matplotlib installed on your system. + +## Creating a simple Bar Plot with `bar()` method + +A very basic Bar Plot can be created with `bar()` method in `matplotlib.pyplot` + +```Python +import matplotlib.pyplot as plt + +# Creating dataset +x = ["A", "B", "C", "D"] +y = [2, 7, 9, 11] + +# Creating bar plot +plt.bar(x,y) +plt.show() # Shows the plot +``` +When executed, this would show the following bar plot: + +![Basic Bar Plot](images/basic_bar_plot.png) + +The `bar()` function takes arguments that describes the layout of the bars. + +Here, `plt.bar(x,y)` is used to specify that the bar chart is to be plotted by taking the `x` array as X-axis and `y` array as Y-axis. You can customize the graph further like adding labels to the axes, color of the bars, etc. These will be explored in the upcoming sections. + +Additionally, you can also use `numpy` arrays for faster generation when handling large datasets. + +```Python +import matplotlib.pyplot as plt +import numpy as np + +# Using numpy array +x = np.array(["A", "B", "C", "D"]) +y = np.array([2, 7, 9, 11]) + +plt.bar(x,y) +plt.show() +``` +Its output would be the same as above. + +## Customizing Bar Plots + +For creating customized bar plots, it is **highly recommended** to create the plots using `matplotlib.pyplot.subplots()`, otherwise it is difficult to apply the customizations in the newer versions of Matplotlib. + +### Adding title to the graph and labeling the axes + +Let us create an imaginary graph of number of cars sold in a various years. + +```Python +import matplotlib.pyplot as plt + +fig, ax = plt.subplots() + +years = ['1999', '2000', '2001', '2002'] +num_of_cars_sold = [300, 500, 700, 1000] + +# Creating bar plot +ax.bar(years, num_of_cars_sold) + +# Adding axis labels +ax.set_xlabel("Years") +ax.set_ylabel("Number of cars sold") + +# Adding plot title +ax.set_title("Number of cars sold in various years") + +plt.show() +``` + +![Title and axis labels](images/title_and_axis_labels.png) + +Here, we have created a `matplotlib.pyplot.subplots()` object which returns a `Figure` object `fig` as well as an `Axes` object `ax` both of which are used for customizing the bar plot. `ax.set_xlabel`, `ax.set_ylabel` and `ax.set_title` are respectively used for adding labels of X, Y axis and adding title to the graph. + +### Adding bar colors and legends + +Let us consider our previous example of number of cars sold in various years and suppose that we want to add different colors to the bars from different centuries and respective legends for better interpretation. + +This can be achieved by creating two separate arrays `bar_colors` for bar colors and `bar_labels` for legend labels and passing them as arguments to parameters color and label respectively in `ax.bar` method. + +```Python +import matplotlib.pyplot as plt + +fig, ax = plt.subplots() + +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] +bar_colors = ['tab:green', 'tab:green', 'tab:blue', 'tab:blue', 'tab:blue'] +bar_labels = ['1900s', '_1900s', '2000s', '_2000s', '_2000s'] + +# Creating the customized bar plot +ax.bar(years, num_of_cars_sold, color=bar_colors, label=bar_labels) + +# Adding axis labels +ax.set_xlabel("Years") +ax.set_ylabel("Number of cars sold") + +# Adding plot title +ax.set_title("Number of cars sold in various years") + +# Adding legend title +ax.legend(title='Centuries') + +plt.show() +``` + +![Bar colors and Legends](images/bar_colors_and_legends.png) + +Note that the labels with a preceding underscore won't show up in the legend. Legend titles can be added by simply passing `title` argument in `ax.legend()`, as shown. Also, you can have a different color for all the bars by passing the `HEX` value of that color in the `color` parameter. + +### Adding labels to bars + +We may want to add labels to bars representing their absolute (or truncated) values for instant and accurate reading. This can be achieved by passing the `BarContainer` object (returned by `ax.bar()` method) which is basically a aontainer with all the bars and optionally errorbars to `ax.bar_label` method. + +```Python +import matplotlib.pyplot as plt + +fig, ax = plt.subplots() + +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] +bar_colors = ['tab:green', 'tab:green', 'tab:blue', 'tab:blue', 'tab:blue'] +bar_labels = ['1900s', '_1900s', '2000s', '_2000s', '_2000s'] + +# BarContainer object +bar_container = ax.bar(years, num_of_cars_sold, color=bar_colors, label=bar_labels) + +ax.set_xlabel("Years") +ax.set_ylabel("Number of cars sold") +ax.set_title("Number of cars sold in various years") +ax.legend(title='Centuries') + +# Adding bar labels +ax.bar_label(bar_container) + +plt.show() +``` + +![Bar Labels](images/bar_labels.png) + +**Note:** There are various other methods of adding bar labels in matplotlib. + +## Horizontal Bar Plot + +We can create horizontal bar plots by using the `barh()` method in `matplotlib.pyplot`. All the relevant customizations are applicable here also. + +```Python +import matplotlib.pyplot as plt + +fig, ax = plt.subplots(figsize=(10,5)) # figsize is used to alter the size of figure + +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] +bar_colors = ['tab:green', 'tab:green', 'tab:blue', 'tab:blue', 'tab:blue'] +bar_labels = ['1900s', '_1900s', '2000s', '_2000s', '_2000s'] + +# Creating horizontal bar plot +bar_container = ax.barh(years, num_of_cars_sold, color=bar_colors, label=bar_labels) + +# Adding axis labels +ax.set_xlabel("Years") +ax.set_ylabel("Number of cars sold") + +# Adding Title +ax.set_title("Number of cars sold in various years") +ax.legend(title='Centuries') + +# Adding bar labels +ax.bar_label(bar_container) + +plt.show() +``` + +![Horizontal Bar Plot-1](images/horizontal_bar_plot_1.png) + +We can also invert the Y-axis labels here to show the top values first. + +```Python +import matplotlib.pyplot as plt + +fig, ax = plt.subplots(figsize=(10,5)) # figsize is used to alter the size of figure + +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] +bar_colors = ['tab:green', 'tab:green', 'tab:blue', 'tab:blue', 'tab:blue'] +bar_labels = ['1900s', '_1900s', '2000s', '_2000s', '_2000s'] + +# Creating horizontal bar plot +bar_container = ax.barh(years, num_of_cars_sold, color=bar_colors, label=bar_labels) + +# Adding axis labels +ax.set_xlabel("Years") +ax.set_ylabel("Number of cars sold") + +# Adding Title +ax.set_title("Number of cars sold in various years") +ax.legend(title='Centuries') + +# Adding bar labels +ax.bar_label(bar_container) + +# Inverting Y-axis +ax.invert_yaxis() + +plt.show() +``` + +![Horizontal Bar Plot-2](images/horizontal_bar_plot_2.png) \ No newline at end of file diff --git a/contrib/plotting-visualization/images/bar_colors_and_legends.png b/contrib/plotting-visualization/images/bar_colors_and_legends.png new file mode 100644 index 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a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -1,3 +1,4 @@ # List of sections - [Installing Matplotlib](matplotlib_installation.md) +- [Bar Plots in Matplotlib](bar-plots.md) From 6f9708595d58bbf6945dd9f66355f47b5b56d94e Mon Sep 17 00:00:00 2001 From: piyush-poddar Date: Tue, 28 May 2024 03:19:55 +0530 Subject: [PATCH 269/656] Renamed bar-plots.md file to matplotlib_bar_plots.md --- contrib/plotting-visualization/index.md | 2 +- .../{bar-plots.md => matplotlib_bar_plots.md} | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename contrib/plotting-visualization/{bar-plots.md => matplotlib_bar_plots.md} (100%) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 896e10a2..e7c57452 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -1,4 +1,4 @@ # List of sections - [Installing Matplotlib](matplotlib_installation.md) -- [Bar Plots in Matplotlib](bar-plots.md) +- [Bar Plots in Matplotlib](matplotlib_bar_plots.md) diff --git a/contrib/plotting-visualization/bar-plots.md b/contrib/plotting-visualization/matplotlib_bar_plots.md similarity index 100% rename from contrib/plotting-visualization/bar-plots.md rename to contrib/plotting-visualization/matplotlib_bar_plots.md From d7608ec46647f127219a9ece2aab5a22704e360e Mon Sep 17 00:00:00 2001 From: Manmita Das <34617961+manmita@users.noreply.github.com> Date: Tue, 28 May 2024 16:15:12 +0530 Subject: [PATCH 270/656] Create concatenation-of-arrays.md --- contrib/numpy/concatenation-of-arrays.md | 195 +++++++++++++++++++++++ 1 file changed, 195 insertions(+) create mode 100644 contrib/numpy/concatenation-of-arrays.md diff --git a/contrib/numpy/concatenation-of-arrays.md b/contrib/numpy/concatenation-of-arrays.md new file mode 100644 index 00000000..a0c50511 --- /dev/null +++ b/contrib/numpy/concatenation-of-arrays.md @@ -0,0 +1,195 @@ +# Concatenation of Arrays + +Concatenation of arrays in NumPy refers to combining multiple arrays into a single array, either along existing axes or by adding new axes. NumPy provides several functions for this purpose. + +# Functions of Concatenation + +## np.concatenate + +Joins two or more arrays along an existing axis. + +### Syntax + +```python +numpy.concatenate((arr1, arr2, ...), axis) +``` + +Args: +- arr1, arr2, ...: Sequence of arrays to concatenate. +- axis: Axis along which the arrays will be joined. Default is 0. + +### Example + +```python +import numpy as np +#creating 2 arrays +arr1 = np.array([1 2 3],[7 8 9]) +arr2 = np.array([4 5 6],[10 11 12]) + +#concatenate along axis 0 +result_1 = np.concatenate((arr1, arr2), axis=0) +print("Concatenation along axis 0:") +print(result_1) +""" Output- Concatenation along axis 0: +[[ 1 2 3] + [ 7 8 9] + [ 4 5 6] + [10 11 12]] """ + +#concatenate along axis 1 +result_2 = np.concatenate((arr1, arr2), axis=1) +print("Concatenation along axis 1:") +print(result_2) +""" Output- Concatenation along axis 1: +[[ 1 2 3 4 5 6 ] + [ 7 8 9 10 11 12]] """ + +``` +## np.vstack + +Vertical stacking of arrays (row-wise). + +### Syntax + +```python +numpy.vstack(arrays) +``` + +Args: +- arrays: Sequence of arrays to stack. + +### Example +```python +import numpy as np +#create arrays +arr1= np.array([1 2 3], [7 8 9]) +arr2 = np.array([4 5 6],[10 11 12]) + +result = np.vstack((arr1, arr2)) +print(result) +"""output- +[[ 1 2 3] + [ 7 8 9] + [ 4 5 6] + [10 11 12]] """ +``` +## 3. np.hstack + +Stacks arrays horizontally (column-wise). + +### Syntax + +```python +numpy.hstack(arrays) +``` + +Args: +- arrays: Sequence of arrays to stack. + +### Example + +```python +import numpy as np +#create arrays +arr1= np.array([1 2 3], [7 8 9]) +arr2 = np.array([4 5 6],[10 11 12]) + +result = np.hstack((arr1, arr2)) +print(result) +"""output- +[[ 1 2 3] [ 4 5 6] + [ 7 8 9] [10 11 12]] """ +``` + +## np.dstack + +Stacks arrays along the third axis (depth-wise). + +### Syntax + +```python +numpy.dstack(arrays) +``` + +- arrays: Sequence of arrays to stack. + +### Example + +```python +import numpy as np +#create arrays +arr1= np.array([1 2 3], [7 8 9]) +arr2 = np.array([4 5 6],[10 11 12]) + +result = np.dstack((arr1, arr2)) +print(result) +""" output- +[[[ 1 4] + [ 2 5] + [ 3 6]] + + [[ 7 10] + [ 8 11] + [ 9 12]]] """ +``` + +## np.stack + +Joins a sequence of arrays along a new axis. + +```python +numpy.stack(arrays, axis) +``` + +Args: +- arrays: Sequence of arrays to stack. + +### Example + +```python +import numpy as np +#create arrays +arr1= np.array([1 2 3], [7 8 9]) +arr2 = np.array([4 5 6],[10 11 12]) + +result = np.stack((arr1, arr2), axis=0) +print(result) +""" output- +[[[ 1 2 3] + [ 7 8 9]] + + [[ 4 5 6] + [10 11 12]]] """ +``` + +# Concatenation with Mixed Dimensions + +When concatenating arrays with different shapes, it's often necessary to reshape them to have compatible dimensions. + +## Example + +```python +arr1 = np.array([[1, 2, 3], [4, 5, 6]]) +arr2 = np.array([7, 8, 9]) + +# Concatenate along axis 0 +result_0= np.concatenate((arr1, arr2[np.newaxis, :]), axis=0) +print("Concatenation along axis 0:") +print(result_0) +""" output- +Concatenation along axis 0: +[[1 2 3] + [4 5 6] + [7 8 9]] """ + +# Concatenate along axis 1 +result_1 = np.concatenate((arr1, arr2[:, np.newaxis]), axis=1) +print("\nConcatenation along axis 1:") +print(result_1) +""" output- +Concatenation along axis 1: +[[1 2 3 7] + [4 5 6 8]] """ +``` + + From 81992560369b3c2e8a6e940ab14c35797623abcb Mon Sep 17 00:00:00 2001 From: Manmita Das <34617961+manmita@users.noreply.github.com> Date: Tue, 28 May 2024 16:16:08 +0530 Subject: [PATCH 271/656] Update index.md --- contrib/numpy/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index b2d459ad..fb078654 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -8,3 +8,4 @@ - [Loading Arrays from Files](loading_arrays_from_files.md) - [Saving Numpy Arrays into FIles](saving_numpy_arrays_to_files.md) - [Sorting NumPy Arrays](sorting-array.md) +- [Concatenation of Arrays](concatenation-of-arrays.md) From 13829732888a012ee5b198564e211ddd6375f34d Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Tue, 28 May 2024 21:01:23 +0530 Subject: [PATCH 272/656] Create numpy_array_iteration.md Added Introduction Added Basic Iteration Added nditer Added ndenumerate Added flat Added Conclusion --- contrib/pandas/numpy_array_iteration.md | 109 ++++++++++++++++++++++++ 1 file changed, 109 insertions(+) create mode 100644 contrib/pandas/numpy_array_iteration.md diff --git a/contrib/pandas/numpy_array_iteration.md b/contrib/pandas/numpy_array_iteration.md new file mode 100644 index 00000000..ac35d958 --- /dev/null +++ b/contrib/pandas/numpy_array_iteration.md @@ -0,0 +1,109 @@ +# NumPy Array Iteration + +Iterating over arrays in NumPy is a common task when processing data. NumPy provides several ways to iterate over elements of an array efficiently. +Understanding these methods is crucial for performing operations on array elements effectively. + +## 1. Basic Iteration + +- Iterating using basic `for` loop. + +**Single-dimensional array iteration**: + +Iterating over a single-dimensional array is straightforward using a basic `for` loop + +```python +import numpy as np + +arr = np.array([1, 2, 3, 4, 5]) +for i in arr: + print(i) +``` +**Output** : +```python +[ 1 2 3 4 5 ] +``` +**Multi-dimensional array**: + +Iterating over multi-dimensional arrays, each iteration returns a sub-array along the first axis. + +```python +marr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + +for arr in marr: + print(arr) +``` +**Output** : +```python +[1 2 3] +[4 5 6] +[7 8 9] +``` + +## 2. Iterating with nditer + +- `nditer` is a powerful iterator provided by NumPy for iterating over multi-dimensional arrays. +- In each interation it gives each element. + +```python +import numpy as np + +arr = np.array([[1, 2, 3], [4, 5, 6]]) +for i in np.nditer(arr): + print(i) +``` +**Output** : +```python +1 +2 +3 +4 +5 +6 +``` + +## 3. Iterating with ndenumerate + +- `ndenumerate` allows you to iterate with both the index and the value of each element. +- It gives index and value as output in each iteration + +```python +import numpy as np + +arr = np.array([[1, 2], [3, 4]]) +for index,value in np.ndenumerate(arr): + print(index,value) +``` + +**Output** : + +```python +(0, 0) 1 +(0, 1) 2 +(1, 0) 3 +(1, 1) 4 +``` + +## 4. Iterating with flat + +- The `flat` attribute returns a 1-D iterator over the array. +- + +```python +import numpy as np + +arr = np.array([[1, 2], [3, 4]]) +for element in arr.flat: + print(element) +``` + +**Output** : + +```python +1 +2 +3 +4 +``` + +Understanding the various ways to iterate over NumPy arrays can significantly enhance your data processing efficiency. +Whether you are working with single-dimensional or multi-dimensional arrays, NumPy provides versatile tools to iterate and manipulate array elements effectively. From 6c5f9d9fd7acc118339a00b7c33045028f2e895b Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Tue, 28 May 2024 21:03:35 +0530 Subject: [PATCH 273/656] Update index.md Added Numpy Array Iteration --- contrib/pandas/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index c71c3243..0af05c73 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -7,3 +7,4 @@ - [Excel using Pandas DataFrame](excel_with_pandas.md) - [Importing and Exporting Data in Pandas](import-export.md) - [Handling Missing Values in Pandas](handling-missing-values.md) +- [NumPy Array Iteration](numpy-array-iteration.md) From b31bfc61498761a219a3be2896633e3d6624ad67 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Tue, 28 May 2024 21:04:45 +0530 Subject: [PATCH 274/656] Update index.md --- contrib/pandas/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 0af05c73..9bf59a31 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -7,4 +7,4 @@ - [Excel using Pandas DataFrame](excel_with_pandas.md) - [Importing and Exporting Data in Pandas](import-export.md) - [Handling Missing Values in Pandas](handling-missing-values.md) -- [NumPy Array Iteration](numpy-array-iteration.md) +- [NumPy Array Iteration](numpy_array_iteration.md) From 5280fb09a8b8cb7118b1cddee2f045eeea9cc4ee Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Tue, 28 May 2024 21:28:52 +0530 Subject: [PATCH 275/656] Create numpy_array_iteration.md Added Introduction Added Methods Added examples Added Conclusion --- contrib/numpy/numpy_array_iteration.md | 109 +++++++++++++++++++++++++ 1 file changed, 109 insertions(+) create mode 100644 contrib/numpy/numpy_array_iteration.md diff --git a/contrib/numpy/numpy_array_iteration.md b/contrib/numpy/numpy_array_iteration.md new file mode 100644 index 00000000..ac35d958 --- /dev/null +++ b/contrib/numpy/numpy_array_iteration.md @@ -0,0 +1,109 @@ +# NumPy Array Iteration + +Iterating over arrays in NumPy is a common task when processing data. NumPy provides several ways to iterate over elements of an array efficiently. +Understanding these methods is crucial for performing operations on array elements effectively. + +## 1. Basic Iteration + +- Iterating using basic `for` loop. + +**Single-dimensional array iteration**: + +Iterating over a single-dimensional array is straightforward using a basic `for` loop + +```python +import numpy as np + +arr = np.array([1, 2, 3, 4, 5]) +for i in arr: + print(i) +``` +**Output** : +```python +[ 1 2 3 4 5 ] +``` +**Multi-dimensional array**: + +Iterating over multi-dimensional arrays, each iteration returns a sub-array along the first axis. + +```python +marr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + +for arr in marr: + print(arr) +``` +**Output** : +```python +[1 2 3] +[4 5 6] +[7 8 9] +``` + +## 2. Iterating with nditer + +- `nditer` is a powerful iterator provided by NumPy for iterating over multi-dimensional arrays. +- In each interation it gives each element. + +```python +import numpy as np + +arr = np.array([[1, 2, 3], [4, 5, 6]]) +for i in np.nditer(arr): + print(i) +``` +**Output** : +```python +1 +2 +3 +4 +5 +6 +``` + +## 3. Iterating with ndenumerate + +- `ndenumerate` allows you to iterate with both the index and the value of each element. +- It gives index and value as output in each iteration + +```python +import numpy as np + +arr = np.array([[1, 2], [3, 4]]) +for index,value in np.ndenumerate(arr): + print(index,value) +``` + +**Output** : + +```python +(0, 0) 1 +(0, 1) 2 +(1, 0) 3 +(1, 1) 4 +``` + +## 4. Iterating with flat + +- The `flat` attribute returns a 1-D iterator over the array. +- + +```python +import numpy as np + +arr = np.array([[1, 2], [3, 4]]) +for element in arr.flat: + print(element) +``` + +**Output** : + +```python +1 +2 +3 +4 +``` + +Understanding the various ways to iterate over NumPy arrays can significantly enhance your data processing efficiency. +Whether you are working with single-dimensional or multi-dimensional arrays, NumPy provides versatile tools to iterate and manipulate array elements effectively. From 7ef8cb226d414c75cb62167aa20d7fa4b78eb051 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Tue, 28 May 2024 21:29:29 +0530 Subject: [PATCH 276/656] Delete contrib/pandas/numpy_array_iteration.md Wrong folder --- contrib/pandas/numpy_array_iteration.md | 109 ------------------------ 1 file changed, 109 deletions(-) delete mode 100644 contrib/pandas/numpy_array_iteration.md diff --git a/contrib/pandas/numpy_array_iteration.md b/contrib/pandas/numpy_array_iteration.md deleted file mode 100644 index ac35d958..00000000 --- a/contrib/pandas/numpy_array_iteration.md +++ /dev/null @@ -1,109 +0,0 @@ -# NumPy Array Iteration - -Iterating over arrays in NumPy is a common task when processing data. NumPy provides several ways to iterate over elements of an array efficiently. -Understanding these methods is crucial for performing operations on array elements effectively. - -## 1. Basic Iteration - -- Iterating using basic `for` loop. - -**Single-dimensional array iteration**: - -Iterating over a single-dimensional array is straightforward using a basic `for` loop - -```python -import numpy as np - -arr = np.array([1, 2, 3, 4, 5]) -for i in arr: - print(i) -``` -**Output** : -```python -[ 1 2 3 4 5 ] -``` -**Multi-dimensional array**: - -Iterating over multi-dimensional arrays, each iteration returns a sub-array along the first axis. - -```python -marr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) - -for arr in marr: - print(arr) -``` -**Output** : -```python -[1 2 3] -[4 5 6] -[7 8 9] -``` - -## 2. Iterating with nditer - -- `nditer` is a powerful iterator provided by NumPy for iterating over multi-dimensional arrays. -- In each interation it gives each element. - -```python -import numpy as np - -arr = np.array([[1, 2, 3], [4, 5, 6]]) -for i in np.nditer(arr): - print(i) -``` -**Output** : -```python -1 -2 -3 -4 -5 -6 -``` - -## 3. Iterating with ndenumerate - -- `ndenumerate` allows you to iterate with both the index and the value of each element. -- It gives index and value as output in each iteration - -```python -import numpy as np - -arr = np.array([[1, 2], [3, 4]]) -for index,value in np.ndenumerate(arr): - print(index,value) -``` - -**Output** : - -```python -(0, 0) 1 -(0, 1) 2 -(1, 0) 3 -(1, 1) 4 -``` - -## 4. Iterating with flat - -- The `flat` attribute returns a 1-D iterator over the array. -- - -```python -import numpy as np - -arr = np.array([[1, 2], [3, 4]]) -for element in arr.flat: - print(element) -``` - -**Output** : - -```python -1 -2 -3 -4 -``` - -Understanding the various ways to iterate over NumPy arrays can significantly enhance your data processing efficiency. -Whether you are working with single-dimensional or multi-dimensional arrays, NumPy provides versatile tools to iterate and manipulate array elements effectively. From 866d28dfdd63539e570e5eead81afc2f1c7e9b06 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Tue, 28 May 2024 21:30:02 +0530 Subject: [PATCH 277/656] Update index.md Removed unwanted section --- contrib/pandas/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 9bf59a31..2b3149af 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -7,4 +7,4 @@ - [Excel using Pandas DataFrame](excel_with_pandas.md) - [Importing and Exporting Data in Pandas](import-export.md) - [Handling Missing Values in Pandas](handling-missing-values.md) -- [NumPy Array Iteration](numpy_array_iteration.md) + From 8a2b838e7bd47122c18cb4877399ebc98e559482 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Tue, 28 May 2024 21:30:37 +0530 Subject: [PATCH 278/656] Update index.md Added numpy array iteration section --- contrib/numpy/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index b2d459ad..5e88e1d6 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -8,3 +8,4 @@ - [Loading Arrays from Files](loading_arrays_from_files.md) - [Saving Numpy Arrays into FIles](saving_numpy_arrays_to_files.md) - [Sorting NumPy Arrays](sorting-array.md) +- [NumPy Array Iteration](numpy_array_iteration.md) From 1f4f3abd20eac2ad0c04e8372c2254a12b2e55d6 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Tue, 28 May 2024 21:34:59 +0530 Subject: [PATCH 279/656] Update index.md --- contrib/pandas/index.md | 1 - 1 file changed, 1 deletion(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 2b3149af..c71c3243 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -7,4 +7,3 @@ - [Excel using Pandas DataFrame](excel_with_pandas.md) - [Importing and Exporting Data in Pandas](import-export.md) - [Handling Missing Values in Pandas](handling-missing-values.md) - From 58cfc5b9021b44c461e14ab309e29e4a28a6be8d Mon Sep 17 00:00:00 2001 From: Ritesh Date: Wed, 29 May 2024 10:34:20 +0530 Subject: [PATCH 280/656] Create Logistic_Regression.md --- .../machine-learning/Logistic_Regression.md | 121 ++++++++++++++++++ 1 file changed, 121 insertions(+) create mode 100644 contrib/machine-learning/Logistic_Regression.md diff --git a/contrib/machine-learning/Logistic_Regression.md b/contrib/machine-learning/Logistic_Regression.md new file mode 100644 index 00000000..31115507 --- /dev/null +++ b/contrib/machine-learning/Logistic_Regression.md @@ -0,0 +1,121 @@ +# Logistic Regression + +Logistic Regression is a statistical method used for binary classification problems. It is a type of regression analysis where the dependent variable is categorical. This README provides an overview of logistic regression, including its fundamental concepts, assumptions, and how to implement it using Python. + +## Table of Contents + +1. [Introduction](#introduction) +2. [Concepts](#concepts) +3. [Assumptions](#assumptions) +4. [Implementation](#implementation) + - [Using Scikit-learn](#using-scikit-learn) + - [Code Example](#code-example) +5. [Evaluation Metrics](#evaluation-metrics) +6. [Conclusion](#conclusion) +7. [References](#references) + +## Introduction + +Logistic Regression is used to model the probability of a binary outcome based on one or more predictor variables (features). It is widely used in various fields such as medical research, social sciences, and machine learning for tasks such as spam detection, fraud detection, and predicting user behavior. + +## Concepts + +### Sigmoid Function + +The logistic regression model uses the sigmoid function to map predicted values to probabilities. The sigmoid function is defined as: + +$$ +\sigma(z) = \frac{1}{1 + e^{-z}} +$$ + +Where \( z \) is a linear combination of the input features. + +### Odds and Log-Odds + +- **Odds**: The odds represent the ratio of the probability of an event occurring to the probability of it not occurring. + +$$\text{Odds} = \frac{P(Y=1)}{P(Y=0)}$$ + +- **Log-Odds**: The log-odds is the natural logarithm of the odds. + + $$\text{Log-Odds} = \log \left( \frac{P(Y=1)}{P(Y=0)} \right)$$ + +Logistic regression models the log-odds as a linear combination of the input features. + +### Model Equation + +The logistic regression model equation is: + +$$ +\log \left( \frac{P(Y=1)}{P(Y=0)} \right) = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \cdots + \beta_n X_n +$$ + +Where: +- β₀ is the intercept. +- βi are the coefficients for the predictor variables Xi. + + +## Assumptions + +1. **Linearity**: The log-odds of the response variable are a linear combination of the predictor variables. +2. **Independence**: Observations should be independent of each other. +3. **No Multicollinearity**: Predictor variables should not be highly correlated with each other. +4. **Large Sample Size**: Logistic regression requires a large sample size to provide reliable results. + +## Implementation + +### Using Scikit-learn + +Scikit-learn is a popular machine learning library in Python that provides tools for logistic regression. + +### Code Example + +```python +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import accuracy_score, confusion_matrix, classification_report + +# Load dataset +data = pd.read_csv('path/to/your/dataset.csv') + +# Define features and target variable +X = data[['feature1', 'feature2', 'feature3']] +y = data['target'] + +# Split data into training and testing sets +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) + +# Initialize and train logistic regression model +model = LogisticRegression() +model.fit(X_train, y_train) + +# Make predictions +y_pred = model.predict(X_test) + +# Evaluate the model +accuracy = accuracy_score(y_test, y_pred) +conf_matrix = confusion_matrix(y_test, y_pred) +class_report = classification_report(y_test, y_pred) + +print("Accuracy:", accuracy) +print("Confusion Matrix:\n", conf_matrix) +print("Classification Report:\n", class_report) +``` + +## Evaluation Metrics + +- **Accuracy**: The proportion of correctly classified instances among all instances. +- **Confusion Matrix**: A table showing the number of true positives, true negatives, false positives, and false negatives. +- **Precision, Recall, and F1-Score**: Metrics to evaluate the performance of the classification model. + +## Conclusion + +Logistic regression is a fundamental classification technique that is easy to implement and interpret. It is a powerful tool for binary classification problems and provides a probabilistic framework for predicting binary outcomes. + +## References + +- [Scikit-learn Documentation](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression) +- [Wikipedia: Logistic Regression](https://en.wikipedia.org/wiki/Logistic_regression) +- [Towards Data Science: Understanding Logistic Regression](https://towardsdatascience.com/understanding-logistic-regression-9b02c2aec102) From a8c83302b7c7d063d98951799595c7c7b78050f5 Mon Sep 17 00:00:00 2001 From: Ritesh Date: Wed, 29 May 2024 10:35:42 +0530 Subject: [PATCH 281/656] Update index.md --- contrib/machine-learning/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 94ca1e2f..0b9c2b06 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -9,3 +9,4 @@ - [TensorFlow.md](tensorFlow.md) - [PyTorch.md](pytorch.md) - [Types of optimizers](Types_of_optimizers.md) +- [Logistic Regression](Logistic_Regression.md) From cb5fcb4953a9b0a181479fc4b434c64123280497 Mon Sep 17 00:00:00 2001 From: somyasaxena01 <140182178+somyasaxena01@users.noreply.github.com> Date: Thu, 30 May 2024 10:56:55 +0530 Subject: [PATCH 282/656] Added stacks.md --- contrib/ds-algorithms/stacks.md | 131 ++++++++++++++++++++++++++++++++ 1 file changed, 131 insertions(+) create mode 100644 contrib/ds-algorithms/stacks.md diff --git a/contrib/ds-algorithms/stacks.md b/contrib/ds-algorithms/stacks.md new file mode 100644 index 00000000..f9f662b2 --- /dev/null +++ b/contrib/ds-algorithms/stacks.md @@ -0,0 +1,131 @@ +# STACKS IN PYTHON +In Data Structures and Algorithms, a stack is a linear data structure that complies with the Last In, First Out (LIFO) rule. It works by use of two fundamental techniques: *PUSH* which inserts an element on top of the stack and *POP* which takes out the topmost element.This concept is similar to a stack of plates in a cafeteria. Stacks are usually used for handling function calls, expression evaluation, and parsing in programming. Indeed, they are efficient in managing memory as well as tracking program state. + +**POINTS TO BE REMEMBERED :-** +- A stack is a collection of data items that can be accessed at only one end, called *TOP*. +- Items can be inserted and deleted in a stack only at the *TOP*. +- The last item inserted in a stack is the first one to be deleted. +- Therefore, a stack is called a **Last-In-First-Out (LIFO)** data structure. + +## REAL LIFE EXAMPLES OF STACKS + +**PILE OF BOOKS** - Suppose a set of books are placed one over the other in a pile. When you remove books from the pile, the topmost book will be removed first. Similarly, when you have to add a book to the pile, the book will be placed at the top of the file. + +**PILE OF PLATES** - The first plate begins the pile. The second plate is placed on the top of the first plate and the third plate is placed on the top of the second plate, and so on. In general, if you want to add a plate to the pile, you can keep it on the top of the pile. Similarly, if you want to remove a plate, you can remove the plate from the top of the pile. + +**BANGLES IN A HAND** - When a person wears bangles, the last bangle worn is the first one to be removed. + +## APPLICATIONS OF STACKS + +Stacks are widely used in Computer Science: +- *Function call* management +- Maintaining the *UNDO* list for the application +- Web browser *history management* +- Evaluating expressions +- Checking the nesting of parentheses in an expression +- *Backtracking* algorithms (Recursion) + +Understanding these applications is essential for Software Development. + +## OPERATIONS ON A STACK + +Key operations on a stack include: +- **PUSH** - It is the process of inserting a new element on the top of a stack. +- **OVERFLOW** - A situation when we are pushing an item in a stack that is full. +- **POP** - It is the process of deleting an element from the top of a stack. +- **UNDERFLOW** - A situation when we are popping item from an empty stack. +- **PEEK** - It is the process of getting the most recent value of stack *(i.e. the value at the top of the stack)* +- **ISEMPTY** - It is the function which return true if stack is empty else false. +- **SHOW** -Displaying stack items. + +## IMPLEMENTING STACKS IN PYTHON + +```python +def isEmpty(S): + + if len(S) == 0: + return True + + else: + + return False + +def Push(S, item): + S.append(item) + +def Pop(S): + + if isEmpty(S): + return "Underflow" + + else: + val = S.pop() + return val + +def Peek(S): + + if isEmpty(S): + return "Underflow" + + else: + top = len(S) - 1 + return S[top] + +def Show(S): + + if isEmpty(S): + print("Sorry, No items in Stack") + + else: + print("(Top)", end=' ') + t = len(S) - 1 + while t >= 0: + print(S[t], "<", end=' ') + t -= 1 + print() +``` + +This code defines a stack data structure along with functions to manipulate it. To provide output, we would need to use these functions to interact with the stack. + +Here's an example: +```python +stack = [] + +Push(stack, 5) +Push(stack, 10) +Push(stack, 15) + +print("Stack after Push operations:") +Show(stack) + +print("Peek operation:", Peek(stack)) + +print("Pop operation:", Pop(stack)) + +print("Stack after Pop operation:") +Show(stack) +``` + +This would output: + +``` +Stack after Push operations: + +(Top) 15 < 10 < 5 < + +Peek operation: 15 + +Pop operation: 15 + +Stack after Pop operation: + +(Top) 10 < 5 < +``` + +## Complexity Analysis + +- **Worst case**: `O(n)` This occurs when the stack is full, it is dominated by the usage of Show operation. +- **Best case**: `O(1)` When the operations like isEmpty, Push, Pop and Peek are used, they have a constant time complexity of O(1). +- **Average case**: `O(n)` The average complexity is likely to be lower than O(n), as the stack is not always full. + + From 79017403f82fe8cd5e1737ff573eab8506a7fffa Mon Sep 17 00:00:00 2001 From: somyasaxena01 <140182178+somyasaxena01@users.noreply.github.com> Date: Thu, 30 May 2024 10:58:36 +0530 Subject: [PATCH 283/656] Update index.md --- contrib/ds-algorithms/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index c61ca0b5..783f982d 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -9,3 +9,4 @@ - [Greedy Algorithms](greedy-algorithms.md) - [Dynamic Programming](dynamic-programming.md) - [Linked list](linked-list.md) +- [Stacks in Python](stacks.md) From 3ba006417ca0b66db7c42647398a94b05c4bf99c Mon Sep 17 00:00:00 2001 From: iABn0rma1 Date: Thu, 30 May 2024 17:30:59 +0530 Subject: [PATCH 284/656] understanding cnn from scratch --- contrib/machine-learning/IntroToCNNs.md | 453 ++++++++++++++++++++++++ contrib/machine-learning/index.md | 1 + 2 files changed, 454 insertions(+) create mode 100644 contrib/machine-learning/IntroToCNNs.md diff --git a/contrib/machine-learning/IntroToCNNs.md b/contrib/machine-learning/IntroToCNNs.md new file mode 100644 index 00000000..5f81ace5 --- /dev/null +++ b/contrib/machine-learning/IntroToCNNs.md @@ -0,0 +1,453 @@ +# Understanding Convolutional Neural Networks (CNN) + +## Table of Contents + + +## Introduction + +Convolutional Neural Networks (CNNs) are a specialized type of artificial neural network designed primarily for processing structured grid data like images. CNNs are particularly powerful for tasks involving image recognition, classification, and computer vision. They have revolutionized these fields, outperforming traditional neural networks by leveraging their unique architecture to capture spatial hierarchies in images. + +### Why CNNs are Superior to Traditional Neural Networks + +1. **Localized Receptive Fields**: + - CNNs use convolutional layers that apply filters to local regions of the input image. This localized connectivity ensures that the network learns spatial hierarchies and patterns, such as edges and textures, which are essential for image recognition tasks. + +2. **Parameter Sharing**: + - In CNNs, the same filter (set of weights) is used across different parts of the input, significantly reducing the number of parameters compared to fully connected layers in traditional neural networks. This not only lowers the computational cost but also mitigates the risk of overfitting. + +3. **Translation Invariance**: + - Due to the shared weights and pooling operations, CNNs are inherently invariant to translations of the input image. This means that they can recognize objects even when they appear in different locations within the image. + +4. **Hierarchical Feature Learning**: + - CNNs automatically learn a hierarchy of features from low-level features like edges to high-level features like shapes and objects. Traditional neural networks, on the other hand, require manual feature extraction which is less effective and more time-consuming. + +### Use Cases of CNNs + +- **Image Classification**: Identifying objects within an image (e.g., classifying a picture as containing a cat or a dog). +- **Object Detection**: Detecting and locating objects within an image (e.g., finding faces in a photo). +- **Image Segmentation**: Partitioning an image into segments or regions (e.g., dividing an image into different objects and background). +- **Medical Imaging**: Analyzing medical scans like MRI, CT, and X-rays for diagnosis. + +> This guide will walk you through the fundamentals of CNNs and their implementation in Python. We'll build a simple CNN from scratch, explaining each component to help you understand how CNNs process images and extract features. + +### Let's start by understanding the basic architecture of CNNs. + +## CNN Architecture + +Convolution layers, pooling layers, and fully connected layers are just a few of the many building blocks that CNNs use to automatically and adaptively learn spatial hierarchies of information through backpropagation. + +### Convolutional Layer +The convolutional layer is the core building block of a CNN. The layer's parameters consist of a set of learnable filters (or kernels), which have a small receptive field but extend through the full depth of the input volume. + +#### Input Shape +The dimensions of the input image, including the number of channels (e.g., 3 for RGB images & 1 for Grayscale images). +
+ + + + + + + + + + + +
1 and 0
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+ +- The input matrix represents a simplified binary image of handwritten digits, +where '1' denotes the presence of ink and '0' represents the absence of ink. + +- The first matrix shows the represnetation of 1 and 0, which can be depicted as a vertical line and a closed loop. + +- The second matrix represents 9, combining the loop and line. + +
+ +#### Strides +The step size with which the filter moves across the input image. +
+ + + + + + + + + + + +
3
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+ + + + + + + + + + + +
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+ +- This visualization will help you understand how the filter (kernel) moves acroos the input matrix with stride values of 3 and 2. + +- A stride of 1 means the filter moves one step at a time, ensuring it covers the entire input matrix. + +- However, with larger strides (like 3 or 2 in this example), the filter may not cover all elements, potentially missing some information. + +- While this might seem like a drawback, higher strides are often used to reduce computational cost and decrease the output size, which can be beneficial in speeding up the training process and preventing overfitting. + +
+ +#### Padding +Determines whether the output size is the same as the input size ('same') or reduced ('valid'). +
+ + + + + + + + + + + + + +
padding='same'
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+ + + + + + + + + + + +
padding='valid'
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+ +- `Same` padding is preferred in earlier layers to preserve spatial and edge information, as it can help the network learn more detailed features. + +- Choose `valid` padding when focusing on the central input region or requiring specific output dimensions. + +
+ +#### Filters +Small matrices that slide over the input data to extract features. +
+ + + + + + + +
closed loop
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+ + + + + + + +
vertical line
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+ + + + + + + +
both diagonals
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+ +- The first filter aims to detect closed loops within the input image, being highly relevant for recognizing digits with circular or oval shapes, such as '0', '6', '8', or '9'. + +- The next filter helps in detecting vertical lines, crucial for identifying digits like '1', '4', '7', and parts of other digits that contain vertical strokes. + +- The last filter shows how to detect diagonal lines in the input image, useful for identifying the slashes present in digits like '1', '7', or parts of '4' and '9'. + +
+ +#### Output +A set of feature maps that represent the presence of different features in the input. +
+ + + + + + + + + +
('valid', 1)
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('valid', 2)
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+ +- With no padding and a stride of 1, the 3x3 filter moves one step at a time across the 7x5 input matrix. The filter can only move within the original boundaries of the input, resulting in a smaller 5x3 output matrix. This configuration is useful when you want to reduce the spatial dimensions of the feature map while preserving the exact spatial relationships between features. + +- By adding zero padding to the input matrix, it is expanded to 9x7, allowing the 3x3 filter to "fit" fully on the edges and corners. With a stride of 1, the filter still moves one step at a time, but now the output matrix is the same size (7x5) as the original input. Same padding is often preferred in early layers of a CNN to preserve spatial information and avoid rapid feature map shrinkage. + +- Without padding, the 3x3 filter operates within the original input matrix boundaries, but now it moves two steps at a time (stride 2). This significantly reduces the output matrix size to 3x2. Larger strides are employed to decrease computational cost and the output size, which can be beneficial in speeding up the training process and preventing overfitting. However, they might miss some finer details due to the larger jumps. + +
+ +### Pooling Layer +Pooling layers reduce the dimensionality of each feature map while retaining the most critical information. The most common form of pooling is max pooling. +- **Input Shape:** The dimensions of the feature map from the convolutional layer. +- **Pooling Size:** The size of the pooling window (e.g., 2x2). +- **Strides:** The step size for the pooling operation. +- **Output:** A reduced feature map highlighting the most important features. +
+ + + + + + + + + + +
((2,2), 1)
4884
4884
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+ + + + + + + +
((3,3) 2)
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+ +- The high values (8) indicate that the "closed loop" filter found a strong match in those regions. + +- First matrix of size 6x4 represents a downsampled version of the input. + +- While the second matrix with 3x2, resulting in more aggressive downsampling. + + +
+ +### Flatten Layer +The flatten layer converts the 2D matrix data to a 1D vector, which can be fed into a fully connected (dense) layer. +- **Input Shape:** The 2D feature maps from the previous layer. +- **Output:** A 1D vector that represents the same data in a flattened format. +
+ + + + + + + + + + +
After max pooling (with kernel size = 3 and stride = 1)
888888555555333
+
+ +
+ +### Dropout Layer +Dropout is a regularization technique to prevent overfitting in neural networks by randomly setting a fraction of input units to zero at each update during training time. +- **Input Shape:** The data from the previous layer. +- **Dropout Rate:** The fraction of units to drop (e.g., 0.5 for 50% dropout). +- **Output:** The same shape as the input, with some units set to zero. +
+ + + + + +
dropout rate = 0.3
880800 505055 033
+
+ +- The updated 0 values represents the dropped units. + +## Implementation + +Below is the implementation of a simple CNN in Python. Each function within the `CNN` class corresponds to a layer in the network. + +```python +import numpy as np + +class CNN: + def __init__(self): + pass + + def convLayer(self, input_shape, channels, strides, padding, filter_size): + height, width = input_shape + input_shape_with_channels = (height, width, channels) + print("Input Shape (with channels):", input_shape_with_channels) + + # Generate random input and filter matrices + input_matrix = np.random.randint(0, 10, size=input_shape_with_channels) + filter_matrix = np.random.randint(0, 5, size=(filter_size[0], filter_size[1], channels)) + + print("\nInput Matrix:\n", input_matrix[:, :, 0]) + print("\nFilter Matrix:\n", filter_matrix[:, :, 0]) + + padding = padding.lower() + + if padding == 'same': + # Calculate padding needed for each dimension + pad_height = filter_size[0] // 2 + pad_width = filter_size[1] // 2 + + # Apply padding to the input matrix + input_matrix = np.pad(input_matrix, ((pad_height, pad_height), (pad_width, pad_width), (0, 0)), mode='constant') + + # Adjust height and width to consider the padding + height += 2 * pad_height + width += 2 * pad_width + + elif padding == 'valid': + pass + + else: + return "Invalid Padding!!" + + # Output dimensions + conv_height = (height - filter_size[0]) // strides[0] + 1 + conv_width = (width - filter_size[1]) // strides[1] + 1 + + output_matrix = np.zeros((conv_height, conv_width, channels)) + + # Convolution Operation + for i in range(0, height - filter_size[0] + 1, strides[0]): + for j in range(0, width - filter_size[1] + 1, strides[1]): + receptive_field = input_matrix[i:i + filter_size[0], j:j + filter_size[1], :] + output_matrix[i // strides[0], j // strides[1], :] = np.sum(receptive_field * filter_matrix, axis=(0, 1, 2)) + + return output_matrix + + def maxPooling(self, input_matrix, pool_size=(2, 2), strides_pooling=(2, 2)): + input_height, input_width, input_channels = input_matrix.shape + pool_height, pool_width = pool_size + stride_height, stride_width = strides_pooling + + # Calculate output dimensions + pooled_height = (input_height - pool_height) // stride_height + 1 + pooled_width = (input_width - pool_width) // stride_width + 1 + + # Initialize output + pooled_matrix = np.zeros((pooled_height, pooled_width, input_channels)) + + # Perform max pooling + for c in range(input_channels): + for i in range(0, input_height - pool_height + 1, stride_height): + for j in range(0, input_width - pool_width + 1, stride_width): + patch = input_matrix[i:i + pool_height, j:j + pool_width, c] + pooled_matrix[i // stride_height, j // stride_width, c] = np.max(patch) + + return pooled_matrix + + def flatten(self, input_matrix): + return input_matrix.flatten() + + def dropout(self, input_matrix, dropout_rate=0.5): + assert 0 <= dropout_rate < 1, "Dropout rate must be in [0, 1)." + dropout_mask = np.random.binomial(1, 1 - dropout_rate, size=input_matrix.shape) + return input_matrix * dropout_mask +``` + +Run the below command to generate output, based on random input and filter matrices. + +```python +input_shape = (5, 5) +channels = 1 +strides = (1, 1) +padding = 'valid' +filter_size = (3, 3) + +cnn_model = CNN() + +conv_output = cnn_model.convLayer(input_shape, channels, strides, padding, filter_size) +print("\nConvolution Output:\n", conv_output[:, :, 0]) + +pool_size = (2, 2) +strides_pooling = (1, 1) + +maxPool_output = cnn_model.maxPooling(conv_output, pool_size, strides_pooling) +print("\nMax Pooling Output:\n", maxPool_output[:, :, 0]) + +flattened_output = cnn_model.flatten(maxPool_output) +print("\nFlattened Output:\n", flattened_output) + +dropout_output = cnn_model.dropout(flattened_output, dropout_rate=0.3) +print("\nDropout Output:\n", dropout_output) +``` + +Feel free to play around with the parameters! \ No newline at end of file diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 94ca1e2f..ea8b5cb3 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -9,3 +9,4 @@ - [TensorFlow.md](tensorFlow.md) - [PyTorch.md](pytorch.md) - [Types of optimizers](Types_of_optimizers.md) +- [Introduction To Convolutional Neural Networks (CNNs)](IntroToCNNs.md) From f89ffbd58c0082f8533064aa864483799a9c1751 Mon Sep 17 00:00:00 2001 From: Aman Barthwal Date: Thu, 30 May 2024 17:41:16 +0530 Subject: [PATCH 285/656] Update IntroToCNNs.md --- contrib/machine-learning/IntroToCNNs.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/contrib/machine-learning/IntroToCNNs.md b/contrib/machine-learning/IntroToCNNs.md index 5f81ace5..7c0812f4 100644 --- a/contrib/machine-learning/IntroToCNNs.md +++ b/contrib/machine-learning/IntroToCNNs.md @@ -63,7 +63,8 @@ The convolutional layer is the core building block of a CNN. The layer's paramet #### Input Shape The dimensions of the input image, including the number of channels (e.g., 3 for RGB images & 1 for Grayscale images). -
+
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@@ -76,6 +77,8 @@ The dimensions of the input image, including the number of channels (e.g., 3 for
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- The input matrix represents a simplified binary image of handwritten digits, @@ -450,4 +454,4 @@ dropout_output = cnn_model.dropout(flattened_output, dropout_rate=0.3) print("\nDropout Output:\n", dropout_output) ``` -Feel free to play around with the parameters! \ No newline at end of file +Feel free to play around with the parameters! From d9ab12136329bf485af34a2dbf31fa8e933a9ebe Mon Sep 17 00:00:00 2001 From: Aman Barthwal Date: Thu, 30 May 2024 17:42:51 +0530 Subject: [PATCH 286/656] Update IntroToCNNs.md --- contrib/machine-learning/IntroToCNNs.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/contrib/machine-learning/IntroToCNNs.md b/contrib/machine-learning/IntroToCNNs.md index 7c0812f4..802cfdb8 100644 --- a/contrib/machine-learning/IntroToCNNs.md +++ b/contrib/machine-learning/IntroToCNNs.md @@ -25,6 +25,22 @@ + + +
Table 1 Heading 1 Table 1 Heading 2
+ +|Table 1| Middle | Table 2| +|--|--|--| +|a| not b|and c | + + + +|b|1|2|3| +|--|--|--|--| +|a|s|d|f| + +
+ ## Introduction Convolutional Neural Networks (CNNs) are a specialized type of artificial neural network designed primarily for processing structured grid data like images. CNNs are particularly powerful for tasks involving image recognition, classification, and computer vision. They have revolutionized these fields, outperforming traditional neural networks by leveraging their unique architecture to capture spatial hierarchies in images. From 9f0f50fe4803009d1d4968a0e2f4722b061751a2 Mon Sep 17 00:00:00 2001 From: Rupa-Rd Date: Thu, 30 May 2024 19:27:15 +0530 Subject: [PATCH 287/656] Added index --- contrib/numpy/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index b2d459ad..df759d97 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -3,6 +3,7 @@ - [Installing NumPy](installing-numpy.md) - [Introduction](introduction.md) - [NumPy Data Types](datatypes.md) +- [Numpy Array Shape and Reshape](array_reshape.md) - [Basic Mathematics](basic_math.md) - [Operations on Arrays in NumPy](operations-on-arrays.md) - [Loading Arrays from Files](loading_arrays_from_files.md) From ed9e0652008dc706d96fbbc0833d7458bcc2e386 Mon Sep 17 00:00:00 2001 From: Rupa-Rd Date: Thu, 30 May 2024 19:55:02 +0530 Subject: [PATCH 288/656] Numpy array reshape() is added --- contrib/numpy/array_reshape.md | 54 ++++++++++++++++++++++++++++++++++ 1 file changed, 54 insertions(+) create mode 100644 contrib/numpy/array_reshape.md diff --git a/contrib/numpy/array_reshape.md b/contrib/numpy/array_reshape.md new file mode 100644 index 00000000..d3d7f595 --- /dev/null +++ b/contrib/numpy/array_reshape.md @@ -0,0 +1,54 @@ +# Numpy Array Shape and Reshape +In NumPy, the primary data structure is the ndarray (N-dimensional array). An array can have one or more dimensions, and it organizes your data efficiently. + +Code to create a 2D array +``` python +import numpy as np + +numbers = np.array([[1, 2, 3, 4], [5, 6, 7, 8]]) +print(numbers) + +# Output: +# array([[1, 2, 3, 4],[5, 6, 7, 8]]) +``` + +## Changing Array Shape using Reshape() +The `reshape()` function allows you to rearrange the data within a NumPy array. +It take 2 arguements, row and columns. The `reshape()` can add or remove the dimensions. For instance, array can convert a 1D array into a 2D array or vice versa. + +``` python +arr_1d = np.array([1, 2, 3, 4, 5, 6]) # 1D array +arr_2d = arr_1d.reshape(2, 3) # Reshaping with 2rows and 3cols + +print(arr_2d) + +# Output: +# array([[1, 2, 3],[4, 5, 6]]) + +``` + +## Changing Array Shape using Resize() +The `resize()` function allows you to modify the shape of a NumPy array directly. +It take 2 arguements, row and columns. + +``` python +import numpy as np +arr_1d = np.array([1, 2, 3, 4, 5, 6]) + +arr_1d.resize((2, 3)) # 2rows and 3cols +print(arr_1d) + +# Output: +# array([[1, 2, 3],[4, 5, 6]]) + +``` + +## Reshape() VS Resize() + +| Reshape | Resize | +| ----------- | ----------- | +| Does not modify the original array | Modifies the original array in-place | +| Creates a new array | Changes the shape of the array | +| Returns a reshaped array | Doesn't return anything | +| Compatibility between dimensions | Does not compatibility between dimensions | +| Syntax: reshape(row,col) | Syntax: resize((row,col)) | From 002178161db58da11a0b6126d6b17bd27f0d769c Mon Sep 17 00:00:00 2001 From: Arihant Yadav <147732947+arihunter-18@users.noreply.github.com> Date: Thu, 30 May 2024 23:12:41 +0530 Subject: [PATCH 289/656] Add files via upload --- contrib/database/intro_sqlalchemy.md | 105 +++++++++++++++++++++++++++ 1 file changed, 105 insertions(+) create mode 100644 contrib/database/intro_sqlalchemy.md diff --git a/contrib/database/intro_sqlalchemy.md b/contrib/database/intro_sqlalchemy.md new file mode 100644 index 00000000..66063900 --- /dev/null +++ b/contrib/database/intro_sqlalchemy.md @@ -0,0 +1,105 @@ +# SQLAlchemy +SQLAlchemy is a powerful and flexible SQL toolkit and Object-Relational Mapping (ORM) library for Python. It is a versatile library that bridges the gap between Python applications and relational databases.
+ +SQLAlchemy allows the user to write database-agnostic code that can work with a variety of relational databases such as SQLite, MySQL, PostgreSQL, Oracle, and Microsoft SQL Server. The ORM layer in SQLAlchemy allows developers to map Python classes to database tables. This means you can interact with your database using Python objects instead of writing raw SQL queries. + +## Setting up the Environment +* Python and MySQL Server must be installed and configured. +* The library: **mysql-connector-python** and **sqlalchemy** must be installed. + +```bash +pip install sqlalchemy mysql-connector-python +``` +* If not installed, you can install them using the above command in terminal, + +## Establishing Connection with Database +* Create a connection with the database using the following code snippet: +```python +from sqlalchemy import create_engine +from sqlalchemy.orm import declarative_base +from sqlalchemy.orm import sessionmaker + +DATABASE_URL = 'mysql+mysqlconnector://root:12345@localhost/gssoc' + +engine = create_engine(DATABASE_URL) +Session = sessionmaker(bind=engine) +session = Session() + +Base = declarative_base() +``` +* The connection string **DATABASE_URL** is passed as an argument to **create_engine** function which is used to create a connection to the database. This connection string contains the database credentials such as the database type, username, password, and database name. +* The **sessionmaker** function is used to create a session object which is used to interact with the database +* The **declarative_base** function is used to create a base class for all the database models. This base class is used to define the structure of the database tables. + +## Creating Tables +* The following code snippet creates a table named **"products"** in the database: +```python +from sqlalchemy import Column, Integer, String, Float + +class Product(Base): + __tablename__ = 'products' + id = Column(Integer, primary_key=True) + name = Column(String(50)) + category = Column(String(50)) + price = Column(Float) + quantity = Column(Integer) + +Base.metadata.create_all(engine) +``` +* The **Product class** inherits from **Base**, which is a base class for all the database models. +* The **Base.metadata.create_all(engine)** statement is used to create the table in the database. The engine object is a connection to the database that was created earlier. + +## Inserting Data for Aggregation Functions +* The following code snippet inserts data into the **"products"** table: +```python +products = [ + Product(name='Laptop', category='Electronics', price=1000, quantity=50), + Product(name='Smartphone', category='Electronics', price=700, quantity=150), + Product(name='Tablet', category='Electronics', price=400, quantity=100), + Product(name='Headphones', category='Accessories', price=100, quantity=200), + Product(name='Charger', category='Accessories', price=20, quantity=300), +] + +session.add_all(products) +session.commit() +``` +* A list of **Product** objects is created. Each Product object represents a row in the **products table** in the database. +* The **add_all** method of the session object is used to add all the Product objects to the session. This method takes a **list of objects as an argument** and adds them to the session. +* The **commit** method of the session object is used to commit the changes made to the database. + +## Aggregation Functions +SQLAlchemy provides functions that correspond to SQL aggregation functions and are available in the **sqlalchemy.func module**. +### COUNT +The **COUNT** function returns the number of rows in a result set. It can be demonstrated using the following code snippet: +```python +from sqlalchemy import func + +total_products = session.query(func.count(Product.id)).scalar() +print(f'Total products: {total_products}') +``` +### SUM +The **SUM** function returns the sum of all values in a column. It can be demonstrated using the following code snippet: +```python +total_price = session.query(func.sum(Product.price)).scalar() +print(f'Total price of all products: {total_price}') +``` +### AVG +The **AVG** function returns the average of all values in a column. It can be demonstrated by the following code snippet: +```python +average_price = session.query(func.avg(Product.price)).scalar() +print(f'Average price of products: {average_price}') +``` +### MAX +The **MAX** function returns the maximum value in a column. It can be demonstrated using the following code snippet : +```python +max_price = session.query(func.max(Product.price)).scalar() +print(f'Maximum price of products: {max_price}') +``` +### MIN +The **MIN** function returns the minimum value in a column. It can be demonstrated using the following code snippet: +```python +min_price = session.query(func.min(Product.price)).scalar() +print(f'Minimum price of products: {min_price}') +``` + +In general, the aggregation functions can be implemented by utilising the **session** object to execute the desired query on the table present in a database using the **query()** method. The **scalar()** method is called on the query object to execute the query and return a single value \ No newline at end of file From d2e70303e5388e1b69623cabe980b5d440f1c428 Mon Sep 17 00:00:00 2001 From: Arihant Yadav <147732947+arihunter-18@users.noreply.github.com> Date: Thu, 30 May 2024 23:15:58 +0530 Subject: [PATCH 290/656] Update index.md --- contrib/database/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/database/index.md b/contrib/database/index.md index 56cd85b2..2d57e4d2 100644 --- a/contrib/database/index.md +++ b/contrib/database/index.md @@ -1,3 +1,4 @@ # List of sections - [Introduction to MySQL and Queries](intro_mysql_queries.md) +- [SQLAlchemy and Aggregation Functions](sqlalchemy_aggregation.md) From 33e2191bac6eb16ce94dddeb0bdbcfd2d9c44787 Mon Sep 17 00:00:00 2001 From: Arihant Yadav <147732947+arihunter-18@users.noreply.github.com> Date: Thu, 30 May 2024 23:16:57 +0530 Subject: [PATCH 291/656] Update and rename intro_sqlalchemy.md to sqlalchemy_aggregation.md --- .../database/{intro_sqlalchemy.md => sqlalchemy_aggregation.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/database/{intro_sqlalchemy.md => sqlalchemy_aggregation.md} (97%) diff --git a/contrib/database/intro_sqlalchemy.md b/contrib/database/sqlalchemy_aggregation.md similarity index 97% rename from contrib/database/intro_sqlalchemy.md rename to contrib/database/sqlalchemy_aggregation.md index 66063900..c18e8dfb 100644 --- a/contrib/database/intro_sqlalchemy.md +++ b/contrib/database/sqlalchemy_aggregation.md @@ -102,4 +102,4 @@ min_price = session.query(func.min(Product.price)).scalar() print(f'Minimum price of products: {min_price}') ``` -In general, the aggregation functions can be implemented by utilising the **session** object to execute the desired query on the table present in a database using the **query()** method. The **scalar()** method is called on the query object to execute the query and return a single value \ No newline at end of file +In general, the aggregation functions can be implemented by utilising the **session** object to execute the desired query on the table present in a database using the **query()** method. The **scalar()** method is called on the query object to execute the query and return a single value From 1d01704f40d48be974079d84a7511d281caab497 Mon Sep 17 00:00:00 2001 From: iABn0rma1 Date: Thu, 30 May 2024 23:59:27 +0530 Subject: [PATCH 292/656] replaced tables with images --- contrib/machine-learning/IntroToCNNs.md | 230 ++---------------- .../machine-learning/assets/cnn-dropout.png | Bin 0 -> 28288 bytes .../machine-learning/assets/cnn-filters.png | Bin 0 -> 38260 bytes .../machine-learning/assets/cnn-flattened.png | Bin 0 -> 33305 bytes .../assets/cnn-input_shape.png | Bin 0 -> 54890 bytes .../machine-learning/assets/cnn-ouputs.png | Bin 0 -> 94050 bytes .../machine-learning/assets/cnn-padding.png | Bin 0 -> 98306 bytes .../machine-learning/assets/cnn-pooling.png | Bin 0 -> 79460 bytes .../machine-learning/assets/cnn-strides.png | Bin 0 -> 75501 bytes 9 files changed, 19 insertions(+), 211 deletions(-) create mode 100644 contrib/machine-learning/assets/cnn-dropout.png create mode 100644 contrib/machine-learning/assets/cnn-filters.png create mode 100644 contrib/machine-learning/assets/cnn-flattened.png create mode 100644 contrib/machine-learning/assets/cnn-input_shape.png create mode 100644 contrib/machine-learning/assets/cnn-ouputs.png create mode 100644 contrib/machine-learning/assets/cnn-padding.png create mode 100644 contrib/machine-learning/assets/cnn-pooling.png create mode 100644 contrib/machine-learning/assets/cnn-strides.png diff --git a/contrib/machine-learning/IntroToCNNs.md b/contrib/machine-learning/IntroToCNNs.md index 802cfdb8..f8329804 100644 --- a/contrib/machine-learning/IntroToCNNs.md +++ b/contrib/machine-learning/IntroToCNNs.md @@ -25,22 +25,6 @@ - - -
Table 1 Heading 1 Table 1 Heading 2
- -|Table 1| Middle | Table 2| -|--|--|--| -|a| not b|and c | - - - -|b|1|2|3| -|--|--|--|--| -|a|s|d|f| - -
- ## Introduction Convolutional Neural Networks (CNNs) are a specialized type of artificial neural network designed primarily for processing structured grid data like images. CNNs are particularly powerful for tasks involving image recognition, classification, and computer vision. They have revolutionized these fields, outperforming traditional neural networks by leveraging their unique architecture to capture spatial hierarchies in images. @@ -79,35 +63,8 @@ The convolutional layer is the core building block of a CNN. The layer's paramet #### Input Shape The dimensions of the input image, including the number of channels (e.g., 3 for RGB images & 1 for Grayscale images). -
-

- - - - - - - - - - - -
1 and 0
10111
10101
10101
10101
10101
10101
10111
-

-

- - - - - - - - - - - -
9
01110
01010
01110
00010
00010
00010
00010
-

+
+
- The input matrix represents a simplified binary image of handwritten digits, @@ -121,39 +78,8 @@ where '1' denotes the presence of ink and '0' represents the absence of ink. #### Strides The step size with which the filter moves across the input image. -
- - - - - - - - - - - -
3
011 - 10
010 - 10
01110
00010
000 - 10
000 - 10
00010
- - - - - - - - - - - -
2
01 - 110
01 - 010
01110
00 - 010
00 - 010
00010
00010
+
+
- This visualization will help you understand how the filter (kernel) moves acroos the input matrix with stride values of 3 and 2. @@ -168,33 +94,8 @@ The step size with which the filter moves across the input image. #### Padding Determines whether the output size is the same as the input size ('same') or reduced ('valid'). -
- - - - - - - - - - - - - -
padding='same'
0000000
0011100
0010100
0011100
0000100
0000100
0000100
0000100
0000000
- - - - - - - - - - - -
padding='valid'
01110
01010
01110
00010
00010
00010
00010
+
+
- `Same` padding is preferred in earlier layers to preserve spatial and edge information, as it can help the network learn more detailed features. @@ -205,31 +106,8 @@ Determines whether the output size is the same as the input size ('same') or red #### Filters Small matrices that slide over the input data to extract features. -
- - - - - - - -
closed loop
111
101
111
- - - - - - - -
vertical line
010
010
010
- - - - - - - -
both diagonals
101
010
101
+
+
- The first filter aims to detect closed loops within the input image, being highly relevant for recognizing digits with circular or oval shapes, such as '0', '6', '8', or '9'. @@ -242,37 +120,8 @@ Small matrices that slide over the input data to extract features. #### Output A set of feature maps that represent the presence of different features in the input. -
- - - - - - - - - -
('valid', 1)
404
25-3
25-3
032
032
- - - - - - - - - - - -
('same', 1)
22422
34843
22533
12533
00323
00323
00212
- - - - - - - -
('valid', 2)
44
23
02
+
+
- With no padding and a stride of 1, the 3x3 filter moves one step at a time across the 7x5 input matrix. The filter can only move within the original boundaries of the input, resulting in a smaller 5x3 output matrix. This configuration is useful when you want to reduce the spatial dimensions of the feature map while preserving the exact spatial relationships between features. @@ -289,69 +138,29 @@ Pooling layers reduce the dimensionality of each feature map while retaining the - **Pooling Size:** The size of the pooling window (e.g., 2x2). - **Strides:** The step size for the pooling operation. - **Output:** A reduced feature map highlighting the most important features. -
- - - - - - - - - - -
((2,2), 1)
4884
4884
2553
2553
0333
0333
- - - - - - - -
((3,3) 2)
88
55
33
+
+
- The high values (8) indicate that the "closed loop" filter found a strong match in those regions. - - First matrix of size 6x4 represents a downsampled version of the input. - - While the second matrix with 3x2, resulting in more aggressive downsampling. - -
- ### Flatten Layer The flatten layer converts the 2D matrix data to a 1D vector, which can be fed into a fully connected (dense) layer. - **Input Shape:** The 2D feature maps from the previous layer. - **Output:** A 1D vector that represents the same data in a flattened format. -
- - - - - - - - - - -
After max pooling (with kernel size = 3 and stride = 1)
888888555555333
+
+
-
- ### Dropout Layer Dropout is a regularization technique to prevent overfitting in neural networks by randomly setting a fraction of input units to zero at each update during training time. - **Input Shape:** The data from the previous layer. - **Dropout Rate:** The fraction of units to drop (e.g., 0.5 for 50% dropout). - **Output:** The same shape as the input, with some units set to zero. -
- - - - - -
dropout rate = 0.3
880800 505055 033
+
+
- The updated 0 values represents the dropped units. @@ -402,9 +211,8 @@ class CNN: # Output dimensions conv_height = (height - filter_size[0]) // strides[0] + 1 conv_width = (width - filter_size[1]) // strides[1] + 1 - output_matrix = np.zeros((conv_height, conv_width, channels)) - + # Convolution Operation for i in range(0, height - filter_size[0] + 1, strides[0]): for j in range(0, width - filter_size[1] + 1, strides[1]): @@ -443,7 +251,7 @@ class CNN: return input_matrix * dropout_mask ``` -Run the below command to generate output, based on random input and filter matrices. +Run the below command to generate output with random input and filter matrices, depending on the given size. ```python input_shape = (5, 5) @@ -470,4 +278,4 @@ dropout_output = cnn_model.dropout(flattened_output, dropout_rate=0.3) print("\nDropout Output:\n", dropout_output) ``` -Feel free to play around with the parameters! +Feel free to play around with the parameters! \ No newline at end of file diff --git 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zVw?O-X2sTUMAf6+txw=PkC{{7GaEYM`7#n?wh|0|1nd}zaTX^2(=PkkTI?M9XTr^>mHM5!D3CZf%}jv+E0efg*raK>&~LSZEsJA~uh`FXz0JtmX)FBog1jP_&^3&~N2WMX8tH+L4@ z8(&RBkW5cid`senO*uxz=0ueBTz>ahCVSZl9M-&BEd809t1}yk;ZA#-GhA*aSaj%= zPCMdhWz)JJ*PX5e##IVjqPJaBi9YTUc?1N-a0%pup^NgBFJvci(XvBa5)o_=TCjN^ zeRB)6%x;?Z!gs@4Vbmo=4}l7Jq*>j9I9HN$x=}iX6EzJ&_|A* zSVb#FogUtab%!0SX?aX6DwcHzExtnF^DzXDXA+{yS0Mbp!sR0hC0BH_G1NIhfTlVc z{*Vd|nW$&~`~wr(i&TOa=zsA)7+Bs|CMt*Ens4#zSMPj#&P4!&E@Sx?5943G>HE8d zC4@(rMPL5)$G-gu6+V1Yprcv*pZ|?N1VR!4=l H_w@e&&uRUj literal 0 HcmV?d00001 From 814aeadcbb77f6b19eca81599c12d81ee113499c Mon Sep 17 00:00:00 2001 From: iABn0rma1 Date: Fri, 31 May 2024 02:32:02 +0530 Subject: [PATCH 293/656] added formulas --- contrib/machine-learning/IntroToCNNs.md | 57 +++++++++---------------- 1 file changed, 21 insertions(+), 36 deletions(-) diff --git a/contrib/machine-learning/IntroToCNNs.md b/contrib/machine-learning/IntroToCNNs.md index f8329804..fb9027a5 100644 --- a/contrib/machine-learning/IntroToCNNs.md +++ b/contrib/machine-learning/IntroToCNNs.md @@ -26,25 +26,15 @@ ## Introduction - Convolutional Neural Networks (CNNs) are a specialized type of artificial neural network designed primarily for processing structured grid data like images. CNNs are particularly powerful for tasks involving image recognition, classification, and computer vision. They have revolutionized these fields, outperforming traditional neural networks by leveraging their unique architecture to capture spatial hierarchies in images. ### Why CNNs are Superior to Traditional Neural Networks - -1. **Localized Receptive Fields**: - - CNNs use convolutional layers that apply filters to local regions of the input image. This localized connectivity ensures that the network learns spatial hierarchies and patterns, such as edges and textures, which are essential for image recognition tasks. - -2. **Parameter Sharing**: - - In CNNs, the same filter (set of weights) is used across different parts of the input, significantly reducing the number of parameters compared to fully connected layers in traditional neural networks. This not only lowers the computational cost but also mitigates the risk of overfitting. - -3. **Translation Invariance**: - - Due to the shared weights and pooling operations, CNNs are inherently invariant to translations of the input image. This means that they can recognize objects even when they appear in different locations within the image. - -4. **Hierarchical Feature Learning**: - - CNNs automatically learn a hierarchy of features from low-level features like edges to high-level features like shapes and objects. Traditional neural networks, on the other hand, require manual feature extraction which is less effective and more time-consuming. +1. **Localized Receptive Fields**: CNNs use convolutional layers that apply filters to local regions of the input image. This localized connectivity ensures that the network learns spatial hierarchies and patterns, such as edges and textures, which are essential for image recognition tasks. +2. **Parameter Sharing**: In CNNs, the same filter (set of weights) is used across different parts of the input, significantly reducing the number of parameters compared to fully connected layers in traditional neural networks. This not only lowers the computational cost but also mitigates the risk of overfitting. +3. **Translation Invariance**: Due to the shared weights and pooling operations, CNNs are inherently invariant to translations of the input image. This means that they can recognize objects even when they appear in different locations within the image. +4. **Hierarchical Feature Learning**: CNNs automatically learn a hierarchy of features from low-level features like edges to high-level features like shapes and objects. Traditional neural networks, on the other hand, require manual feature extraction which is less effective and more time-consuming. ### Use Cases of CNNs - - **Image Classification**: Identifying objects within an image (e.g., classifying a picture as containing a cat or a dog). - **Object Detection**: Detecting and locating objects within an image (e.g., finding faces in a photo). - **Image Segmentation**: Partitioning an image into segments or regions (e.g., dividing an image into different objects and background). @@ -55,7 +45,6 @@ Convolutional Neural Networks (CNNs) are a specialized type of artificial neural ### Let's start by understanding the basic architecture of CNNs. ## CNN Architecture - Convolution layers, pooling layers, and fully connected layers are just a few of the many building blocks that CNNs use to automatically and adaptively learn spatial hierarchies of information through backpropagation. ### Convolutional Layer @@ -67,31 +56,22 @@ The dimensions of the input image, including the number of channels (e.g., 3 for

-- The input matrix represents a simplified binary image of handwritten digits, -where '1' denotes the presence of ink and '0' represents the absence of ink. - +- The input matrix is a binary image of handwritten digits, +where '1' marks the pixels containing the digit (ink/grayscale area) and '0' marks the background pixels (empty space). - The first matrix shows the represnetation of 1 and 0, which can be depicted as a vertical line and a closed loop. - - The second matrix represents 9, combining the loop and line. -
- #### Strides The step size with which the filter moves across the input image.
-- This visualization will help you understand how the filter (kernel) moves acroos the input matrix with stride values of 3 and 2. - +- This visualization will help you understand how the filter (kernel) moves acroos the input matrix with stride values of (3,3) and (2,2). - A stride of 1 means the filter moves one step at a time, ensuring it covers the entire input matrix. - - However, with larger strides (like 3 or 2 in this example), the filter may not cover all elements, potentially missing some information. - - While this might seem like a drawback, higher strides are often used to reduce computational cost and decrease the output size, which can be beneficial in speeding up the training process and preventing overfitting. -
- #### Padding Determines whether the output size is the same as the input size ('same') or reduced ('valid').
@@ -99,10 +79,8 @@ Determines whether the output size is the same as the input size ('same') or red
- `Same` padding is preferred in earlier layers to preserve spatial and edge information, as it can help the network learn more detailed features. - - Choose `valid` padding when focusing on the central input region or requiring specific output dimensions. - -
+- Padding value can be determined by $ ( f - 1 ) \over 2 $, where f isfilter size #### Filters Small matrices that slide over the input data to extract features. @@ -111,9 +89,7 @@ Small matrices that slide over the input data to extract features.
- The first filter aims to detect closed loops within the input image, being highly relevant for recognizing digits with circular or oval shapes, such as '0', '6', '8', or '9'. - - The next filter helps in detecting vertical lines, crucial for identifying digits like '1', '4', '7', and parts of other digits that contain vertical strokes. - - The last filter shows how to detect diagonal lines in the input image, useful for identifying the slashes present in digits like '1', '7', or parts of '4' and '9'.
@@ -125,12 +101,21 @@ A set of feature maps that represent the presence of different features in the i
- With no padding and a stride of 1, the 3x3 filter moves one step at a time across the 7x5 input matrix. The filter can only move within the original boundaries of the input, resulting in a smaller 5x3 output matrix. This configuration is useful when you want to reduce the spatial dimensions of the feature map while preserving the exact spatial relationships between features. - - By adding zero padding to the input matrix, it is expanded to 9x7, allowing the 3x3 filter to "fit" fully on the edges and corners. With a stride of 1, the filter still moves one step at a time, but now the output matrix is the same size (7x5) as the original input. Same padding is often preferred in early layers of a CNN to preserve spatial information and avoid rapid feature map shrinkage. - - Without padding, the 3x3 filter operates within the original input matrix boundaries, but now it moves two steps at a time (stride 2). This significantly reduces the output matrix size to 3x2. Larger strides are employed to decrease computational cost and the output size, which can be beneficial in speeding up the training process and preventing overfitting. However, they might miss some finer details due to the larger jumps. - -
+- The output dimension of a CNN model is given by, $$ n_{out} = { n_{in} + (2 \cdot p) - k \over s } $$ +where,
+ nin = number of input features
+ p = padding
+ k = kernel size
+ s = stride + +- Also, the number of trainable parameters for each layer is given by, $ (n_c \cdot [k \cdot k] \cdot f) + f $ +
where,
+ nc = number of input channels
+ k x k = kernel size
+ f = number of filters
+ an additional f is added for bias ### Pooling Layer Pooling layers reduce the dimensionality of each feature map while retaining the most critical information. The most common form of pooling is max pooling. From ac4cca21bd61066338d7ee1d424b8c29ed6f04ef Mon Sep 17 00:00:00 2001 From: piyush-poddar Date: Fri, 31 May 2024 03:21:01 +0530 Subject: [PATCH 294/656] Added Content: Pie Charts in Matplotlib --- .../plotting-visualization/images/autopct.png | Bin 0 -> 22307 bytes .../images/basic_pie_chart.png | Bin 0 -> 13967 bytes .../images/coloring_slices.png | Bin 0 -> 13693 bytes .../images/explode_slice.png | Bin 0 -> 22837 bytes .../images/hatch_patterns.png | Bin 0 -> 45297 bytes .../plotting-visualization/images/legends.png | Bin 0 -> 25236 bytes .../plotting-visualization/images/radius.png | Bin 0 -> 17017 bytes .../images/rotating_slices.png | Bin 0 -> 21976 bytes .../plotting-visualization/images/shadow.png | Bin 0 -> 28011 bytes contrib/plotting-visualization/index.md | 1 + .../matplotlib_pie_charts.md | 233 ++++++++++++++++++ 11 files changed, 234 insertions(+) create mode 100644 contrib/plotting-visualization/images/autopct.png create mode 100644 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Model Architecture

-## Architecture +### Encoder +The encoder is composed of a stack of identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, positionwise fully connected feed-forward network. Each encoder consists of two major components: a self-attention mechanism and a feed-forward neural network. The self-attention mechanism accepts input encodings from the previous encoder and weights their relevance to each other to generate output encodings. The feed-forward neural network further processes each output encoding individually. These output encodings are then passed to the next encoder as its input, as well as to the decoders. + +### Decoder +The decoder is also composed of a stack of identical layers. In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack. The decoder functions in a similar fashion to the encoder, but an additional attention mechanism is inserted which instead draws relevant information from the encodings generated by the encoders. This mechanism can also be called the encoder-decoder attention. + +### Attention +#### Scaled Dot-Product Attention +The input consists of queries and keys of dimension dk, and values of dimension dv. We compute the dot products of the query with all keys, divide each by √dk, and apply a softmax function to obtain the weights on the values. + +> Attention(Q, K, V) = softmax(QKT / √dk) * V + +#### Multi-Head Attention +Instead of performing a single attention function with dmodel-dimensional keys, values and queries, it is beneficial to linearly project the queries, keys and values h times with different, learned linear projections to dk, dk and dv dimensions, respectively. + +Multi-head attention allows the model to jointly attend to information from different representation +subspaces at different positions. With a single attention head, averaging inhibits this. + +> MultiHead(Q, K, V) = Concat(head1, ..., headh) * WO + +where, + +> headi = Attention(Q * WiQ, K * WiK, V * WiV) + +where the projections are parameter matrices. + +#### Masked Attention +It may be necessary to cut out attention links between some word-pairs. For example, the decoder for token position +𝑡 should not have access to token position 𝑡+1. + +> MaskedAttention(Q, K, V) = softmax(M + (QKT / √dk)) * V + +### Feed-Forward Network +Each of the layers in the encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically. This +consists of two linear transformations with a ReLU activation in between. +> FFN(x) = (max(0, (x * W1) + b1) * W2) + b2 + +### Positional Encoding +A positional encoding is a fixed-size vector representation that encapsulates the relative positions of tokens within a target sequence: it provides the transformer model with information about where the words are in the input sequence. + +The sine and cosine functions of different frequencies: +> PE(pos,2i) = sin(pos/100002i/dmodel) + +> PE(pos,2i+1) = cos(pos/100002i/dmodel) ## Implementation ### Theory @@ -19,3 +63,14 @@ allowing the signal for key tokens to be amplified and less important tokens to ### Tensorflow and Keras ### PyTorch + +## Application +The transformer has had great success in natural language processing (NLP). Many large language models such as GPT-2, GPT-3, GPT-4, Claude, BERT, XLNet, RoBERTa and ChatGPT demonstrate the ability of transformers to perform a wide variety of such NLP-related tasks, and have the potential to find real-world applications. + +These may include: +- Machine translation +- Document summarization +- Text generation +- Biological sequence analysis +- Computer code generation +- Video analysis From 35357d2fe3f6650faca3d0f19327fe4fa7e8629d Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Fri, 31 May 2024 17:04:07 +0530 Subject: [PATCH 350/656] Update Transformers.md Added Tensorflow implementation --- contrib/machine-learning/Transformers.md | 138 ++++++++++++++++++++++- 1 file changed, 135 insertions(+), 3 deletions(-) diff --git a/contrib/machine-learning/Transformers.md b/contrib/machine-learning/Transformers.md index 49a1b976..a5a56ecf 100644 --- a/contrib/machine-learning/Transformers.md +++ b/contrib/machine-learning/Transformers.md @@ -58,12 +58,144 @@ Text is converted to numerical representations called tokens, and each token is At each layer, each token is then contextualized within the scope of the context window with other tokens via a parallel multi-head attention mechanism allowing the signal for key tokens to be amplified and less important tokens to be diminished. -### HuggingFace - -### Tensorflow and Keras +### Tensorflow +Tensorflow provides the transformer encoder and decoder block that can be implemented by the specification of the user. Although, the transformer is not provided as a standalone to be imported and executed, the user has to create the model first. They also have a tutorial on how to implement the transformer from scratch for machine translation and can be found [here](https://www.tensorflow.org/text/tutorials/transformer). + +More information on [encoder](https://www.tensorflow.org/api_docs/python/tfm/nlp/layers/TransformerEncoderBlock) and [decoder](https://www.tensorflow.org/api_docs/python/tfm/nlp/layers/TransformerDecoderBlock) block mentioned in the code. + +Imports: +``` +import tensorflow as tf +import tensorflow_models as tfm +``` + +Adding word embeddings and positional encoding: +``` +class PositionalEmbedding(tf.keras.layers.Layer): + def __init__(self, vocab_size, d_model): + super().__init__() + self.d_model = d_model + self.embedding = tf.keras.layers.Embedding(vocab_size, d_model, mask_zero=True) + self.pos_encoding = tfm.nlp.layers.RelativePositionEmbedding(hidden_size=d_model) + + def compute_mask(self, *args, **kwargs): + return self.embedding.compute_mask(*args, **kwargs) + + def call(self, x): + length = tf.shape(x)[1] + x = self.embedding(x) + x = x + self.pos_encoding[tf.newaxis, :length, :] + return x +``` + +Creating the encoder for the transformer: +``` +class Encoder(tf.keras.layers.Layer): + def __init__(self, num_layers, d_model, num_heads, + dff, vocab_size, dropout_rate=0.1): + super().__init__() + + self.d_model = d_model + self.num_layers = num_layers + + self.pos_embedding = PositionalEmbedding( + vocab_size=vocab_size, d_model=d_model) + + self.enc_layers = [ + tfm.nlp.layers.TransformerEncoderBlock(output_last_dim=d_model, + num_attention_heads=num_heads, + inner_dim=dff, + inner_activation="relu", + inner_dropout=dropout_rate) + for _ in range(num_layers)] + self.dropout = tf.keras.layers.Dropout(dropout_rate) + + def call(self, x): + x = self.pos_embedding(x, length=2048) + x = self.dropout(x) + + for i in range(self.num_layers): + x = self.enc_layers[i](x) + + return x +``` + +Creating the decoder for the transformer: +``` +class Decoder(tf.keras.layers.Layer): + def __init__(self, num_layers, d_model, num_heads, dff, vocab_size, + dropout_rate=0.1): + super(Decoder, self).__init__() + + self.d_model = d_model + self.num_layers = num_layers + + self.pos_embedding = PositionalEmbedding(vocab_size=vocab_size, + d_model=d_model) + self.dropout = tf.keras.layers.Dropout(dropout_rate) + self.dec_layers = [ + tfm.nlp.layers.TransformerDecoderBlock(num_attention_heads=num_heads, + intermediate_size=dff, + intermediate_activation="relu", + dropout_rate=dropout_rate) + for _ in range(num_layers)] + + def call(self, x, context): + x = self.pos_embedding(x) + x = self.dropout(x) + + for i in range(self.num_layers): + x = self.dec_layers[i](x, context) + + return x +``` + +Combining the encoder and decoder to create the transformer: +``` +class Transformer(tf.keras.Model): + def __init__(self, num_layers, d_model, num_heads, dff, + input_vocab_size, target_vocab_size, dropout_rate=0.1): + super().__init__() + self.encoder = Encoder(num_layers=num_layers, d_model=d_model, + num_heads=num_heads, dff=dff, + vocab_size=input_vocab_size, + dropout_rate=dropout_rate) + + self.decoder = Decoder(num_layers=num_layers, d_model=d_model, + num_heads=num_heads, dff=dff, + vocab_size=target_vocab_size, + dropout_rate=dropout_rate) + + self.final_layer = tf.keras.layers.Dense(target_vocab_size) + + def call(self, inputs): + context, x = inputs + + context = self.encoder(context) + x = self.decoder(x, context) + logits = self.final_layer(x) + + return logits +``` + +Model initialization that be used for training and inference: +``` +transformer = Transformer( + num_layers=num_layers, + d_model=d_model, + num_heads=num_heads, + dff=dff, + input_vocab_size=tokenizers.pt.get_vocab_size().numpy(), + target_vocab_size=tokenizers.en.get_vocab_size().numpy(), + dropout_rate=dropout_rate +) +``` ### PyTorch + +### HuggingFace + ## Application The transformer has had great success in natural language processing (NLP). Many large language models such as GPT-2, GPT-3, GPT-4, Claude, BERT, XLNet, RoBERTa and ChatGPT demonstrate the ability of transformers to perform a wide variety of such NLP-related tasks, and have the potential to find real-world applications. From f56131349d1ad0e6e27209ea3d9217c13e695e8e Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Fri, 31 May 2024 17:13:19 +0530 Subject: [PATCH 351/656] Update Transformers.md Added python tag to code blocks --- contrib/machine-learning/Transformers.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/contrib/machine-learning/Transformers.md b/contrib/machine-learning/Transformers.md index a5a56ecf..ffdea05f 100644 --- a/contrib/machine-learning/Transformers.md +++ b/contrib/machine-learning/Transformers.md @@ -64,13 +64,13 @@ Tensorflow provides the transformer encoder and decoder block that can be implem More information on [encoder](https://www.tensorflow.org/api_docs/python/tfm/nlp/layers/TransformerEncoderBlock) and [decoder](https://www.tensorflow.org/api_docs/python/tfm/nlp/layers/TransformerDecoderBlock) block mentioned in the code. Imports: -``` +```python import tensorflow as tf import tensorflow_models as tfm ``` Adding word embeddings and positional encoding: -``` +```python class PositionalEmbedding(tf.keras.layers.Layer): def __init__(self, vocab_size, d_model): super().__init__() @@ -89,7 +89,7 @@ class PositionalEmbedding(tf.keras.layers.Layer): ``` Creating the encoder for the transformer: -``` +```python class Encoder(tf.keras.layers.Layer): def __init__(self, num_layers, d_model, num_heads, dff, vocab_size, dropout_rate=0.1): @@ -121,7 +121,7 @@ class Encoder(tf.keras.layers.Layer): ``` Creating the decoder for the transformer: -``` +```python class Decoder(tf.keras.layers.Layer): def __init__(self, num_layers, d_model, num_heads, dff, vocab_size, dropout_rate=0.1): @@ -151,7 +151,7 @@ class Decoder(tf.keras.layers.Layer): ``` Combining the encoder and decoder to create the transformer: -``` +```python class Transformer(tf.keras.Model): def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, target_vocab_size, dropout_rate=0.1): @@ -179,7 +179,7 @@ class Transformer(tf.keras.Model): ``` Model initialization that be used for training and inference: -``` +```python transformer = Transformer( num_layers=num_layers, d_model=d_model, From ca9c6a9a1f23d1e6b5901954c8a5c5c772bd80a7 Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Fri, 31 May 2024 17:44:28 +0530 Subject: [PATCH 352/656] Update Transformers.md Added PyTorch implementation --- contrib/machine-learning/Transformers.md | 24 +++++++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/contrib/machine-learning/Transformers.md b/contrib/machine-learning/Transformers.md index ffdea05f..cb90cf10 100644 --- a/contrib/machine-learning/Transformers.md +++ b/contrib/machine-learning/Transformers.md @@ -56,7 +56,9 @@ The sine and cosine functions of different frequencies: ### Theory Text is converted to numerical representations called tokens, and each token is converted into a vector via looking up from a word embedding table. At each layer, each token is then contextualized within the scope of the context window with other tokens via a parallel multi-head attention mechanism -allowing the signal for key tokens to be amplified and less important tokens to be diminished. +allowing the signal for key tokens to be amplified and less important tokens to be diminished. + +The transformer uses an encoder-decoder architecture. The encoder extracts features from an input sentence, and the decoder uses the features to produce an output sentence. Some architectures use full encoders and decoders, autoregressive encoders and decoders, or combination of both. This depends on the usage and context of the input. ### Tensorflow Tensorflow provides the transformer encoder and decoder block that can be implemented by the specification of the user. Although, the transformer is not provided as a standalone to be imported and executed, the user has to create the model first. They also have a tutorial on how to implement the transformer from scratch for machine translation and can be found [here](https://www.tensorflow.org/text/tutorials/transformer). @@ -192,10 +194,30 @@ transformer = Transformer( ``` ### PyTorch +Unlike Tensorflow, PyTorch provides the full implementation of the transformer model that can be executed on the go. More information can be found [here](https://pytorch.org/docs/stable/_modules/torch/nn/modules/transformer.html#Transformer). A full implementation of the model can be found [here](https://github.com/pytorch/examples/tree/master/word_language_model). + +Imports: +```python +import torch +import torch.nn as nn +``` + +Initializing the model: +```python +transformer = nn.Transformer(nhead=16, num_encoder_layers=8) +``` + +Sample Implementation: +```python +src = torch.rand((10, 32, 512)) +tgt = torch.rand((20, 32, 512)) +output = transformer(src, tgt) +``` ### HuggingFace + ## Application The transformer has had great success in natural language processing (NLP). Many large language models such as GPT-2, GPT-3, GPT-4, Claude, BERT, XLNet, RoBERTa and ChatGPT demonstrate the ability of transformers to perform a wide variety of such NLP-related tasks, and have the potential to find real-world applications. From 27dcfba508722dc35a96544e259cd49c9e3a7f9a Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 18:03:35 +0530 Subject: [PATCH 353/656] Update index.md --- contrib/plotting-visualization/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 32261d6e..3f272465 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -3,3 +3,4 @@ - [Installing Matplotlib](matplotlib-installation.md) - [Bar Plots in Matplotlib](matplotlib-bar-plots.md) - [Pie Charts in Matplotlib](matplotlib-pie-charts.md) +- [Introduction to Seaborn and Installation](seaborn-intro.md) From 273515a8c22a17fc9af4eefcab30f42d6065a31c Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 18:04:45 +0530 Subject: [PATCH 354/656] Create seaborn-intro.md --- contrib/plotting-visualization/seaborn-intro.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 contrib/plotting-visualization/seaborn-intro.md diff --git a/contrib/plotting-visualization/seaborn-intro.md b/contrib/plotting-visualization/seaborn-intro.md new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/contrib/plotting-visualization/seaborn-intro.md @@ -0,0 +1 @@ + From 01552110695fc0d9b39797d983946483d2620a3c Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 18:12:32 +0530 Subject: [PATCH 355/656] Update seaborn-intro.md --- contrib/plotting-visualization/seaborn-intro.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/contrib/plotting-visualization/seaborn-intro.md b/contrib/plotting-visualization/seaborn-intro.md index 8b137891..4e28b6a6 100644 --- a/contrib/plotting-visualization/seaborn-intro.md +++ b/contrib/plotting-visualization/seaborn-intro.md @@ -1 +1,4 @@ +Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. +# Seaborn Installation +Before installing Matplotlib, ensure you have Python installed on your system. You can download and install Python from the [official Python website](https://www.python.org/). From b81223f8e8e19c67078d835d7679e66036356fb4 Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 18:12:50 +0530 Subject: [PATCH 356/656] Update seaborn-intro.md --- contrib/plotting-visualization/seaborn-intro.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/plotting-visualization/seaborn-intro.md b/contrib/plotting-visualization/seaborn-intro.md index 4e28b6a6..8b0d869f 100644 --- a/contrib/plotting-visualization/seaborn-intro.md +++ b/contrib/plotting-visualization/seaborn-intro.md @@ -1,4 +1,4 @@ Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. -# Seaborn Installation +## Seaborn Installation Before installing Matplotlib, ensure you have Python installed on your system. You can download and install Python from the [official Python website](https://www.python.org/). From 32fcb9484e26f9ae38e5d6b82a338190b8e83358 Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 18:15:06 +0530 Subject: [PATCH 357/656] Update seaborn-intro.md --- contrib/plotting-visualization/seaborn-intro.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/contrib/plotting-visualization/seaborn-intro.md b/contrib/plotting-visualization/seaborn-intro.md index 8b0d869f..bbbfec89 100644 --- a/contrib/plotting-visualization/seaborn-intro.md +++ b/contrib/plotting-visualization/seaborn-intro.md @@ -2,3 +2,10 @@ Seaborn is a Python data visualization library based on Matplotlib. It provides ## Seaborn Installation Before installing Matplotlib, ensure you have Python installed on your system. You can download and install Python from the [official Python website](https://www.python.org/). +Below are the steps to install and setup seaborn + +1. **Install Seaborn**: Open your terminal or command prompt and run the following command to install Seaborn using `pip`: + +```bash +pip install seaborn +``` From 56467e57bb3ac0b91bde6d504813e44e983a80ab Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 18:19:06 +0530 Subject: [PATCH 358/656] Update seaborn-intro.md --- .../plotting-visualization/seaborn-intro.md | 20 +++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/contrib/plotting-visualization/seaborn-intro.md b/contrib/plotting-visualization/seaborn-intro.md index bbbfec89..0375db28 100644 --- a/contrib/plotting-visualization/seaborn-intro.md +++ b/contrib/plotting-visualization/seaborn-intro.md @@ -2,10 +2,26 @@ Seaborn is a Python data visualization library based on Matplotlib. It provides ## Seaborn Installation Before installing Matplotlib, ensure you have Python installed on your system. You can download and install Python from the [official Python website](https://www.python.org/). -Below are the steps to install and setup seaborn -1. **Install Seaborn**: Open your terminal or command prompt and run the following command to install Seaborn using `pip`: +Below are the steps to install and setup Seaborn: + +1. Open your terminal or command prompt and run the following command to install Seaborn using `pip`: ```bash pip install seaborn ``` + +2. The basic invocation of `pip` will install seaborn and, if necessary, its mandatory dependencies. It is possible to include optional dependencies that give access to a few advanced features: +```bash +pip install seaborn[stats] +``` + +3. The library is also included as part of the Anaconda distribution, and it can be installed with `conda`: +```bash +conda install seaborn +``` + +4. As the main Anaconda repository can be slow to add new releases, you may prefer using the conda-forge channel: +```bash +conda install seaborn -c conda-forge +``` From bdab7d9bb5154dc23e2b9b350900a5b148b6d3e1 Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 18:29:50 +0530 Subject: [PATCH 359/656] Update seaborn-intro.md --- contrib/plotting-visualization/seaborn-intro.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/contrib/plotting-visualization/seaborn-intro.md b/contrib/plotting-visualization/seaborn-intro.md index 0375db28..6e0a7d82 100644 --- a/contrib/plotting-visualization/seaborn-intro.md +++ b/contrib/plotting-visualization/seaborn-intro.md @@ -25,3 +25,17 @@ conda install seaborn ```bash conda install seaborn -c conda-forge ``` + +## Dependencies +### Supported Python versions +- Python 3.8+ + +### Mandatory Dependencies + - [numpy](https://numpy.org/) + - [pandas](https://pandas.pydata.org/) + - [matplotlib](https://matplotlib.org/) + +### Optional Dependencies + - [statsmodels](https://www.statsmodels.org/stable/index.html) for advanced regression plots + - [scipy](https://scipy.org/) for clustering matrices and some advanced options + - [fastcluster](https://pypi.org/project/fastcluster/) for faster clustering of large matrices From d81e875a3235d7c9f50f9a42bb359bf80d3146a1 Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Fri, 31 May 2024 18:32:47 +0530 Subject: [PATCH 360/656] Update Transformers.md Added outputs for PyTorch and HuggingFace implementation --- contrib/machine-learning/Transformers.md | 112 ++++++++++++++++++++++- 1 file changed, 111 insertions(+), 1 deletion(-) diff --git a/contrib/machine-learning/Transformers.md b/contrib/machine-learning/Transformers.md index cb90cf10..e9e50e88 100644 --- a/contrib/machine-learning/Transformers.md +++ b/contrib/machine-learning/Transformers.md @@ -61,6 +61,8 @@ allowing the signal for key tokens to be amplified and less important tokens to The transformer uses an encoder-decoder architecture. The encoder extracts features from an input sentence, and the decoder uses the features to produce an output sentence. Some architectures use full encoders and decoders, autoregressive encoders and decoders, or combination of both. This depends on the usage and context of the input. ### Tensorflow +TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks. It was developed by the Google Brain team for Google's internal use in research and production. + Tensorflow provides the transformer encoder and decoder block that can be implemented by the specification of the user. Although, the transformer is not provided as a standalone to be imported and executed, the user has to create the model first. They also have a tutorial on how to implement the transformer from scratch for machine translation and can be found [here](https://www.tensorflow.org/text/tutorials/transformer). More information on [encoder](https://www.tensorflow.org/api_docs/python/tfm/nlp/layers/TransformerEncoderBlock) and [decoder](https://www.tensorflow.org/api_docs/python/tfm/nlp/layers/TransformerDecoderBlock) block mentioned in the code. @@ -193,7 +195,22 @@ transformer = Transformer( ) ``` +Sample: +```python +src = tf.random.uniform((64, 40)) +tgt = tf.random.uniform((64, 50)) + +output = transformer((src, tgt)) +``` + +O/P: +``` + +``` + ### PyTorch +PyTorch is a machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, originally developed by Meta AI and now part of the Linux Foundation umbrella. + Unlike Tensorflow, PyTorch provides the full implementation of the transformer model that can be executed on the go. More information can be found [here](https://pytorch.org/docs/stable/_modules/torch/nn/modules/transformer.html#Transformer). A full implementation of the model can be found [here](https://github.com/pytorch/examples/tree/master/word_language_model). Imports: @@ -207,7 +224,7 @@ Initializing the model: transformer = nn.Transformer(nhead=16, num_encoder_layers=8) ``` -Sample Implementation: +Sample: ```python src = torch.rand((10, 32, 512)) tgt = torch.rand((20, 32, 512)) @@ -215,8 +232,101 @@ tgt = torch.rand((20, 32, 512)) output = transformer(src, tgt) ``` +O/P: +``` +tensor([[[ 0.2938, -0.4824, -0.7816, ..., 0.0742, 0.5162, 0.3632], + [-0.0786, -0.5241, 0.6384, ..., 0.3462, -0.0618, 0.9943], + [ 0.7827, 0.1067, -0.1637, ..., -1.7730, -0.3322, -0.0029], + ..., + [-0.3202, 0.2341, -0.0896, ..., -0.9714, -0.1251, -0.0711], + [-0.1663, -0.5047, -0.0404, ..., -0.9339, 0.3963, 0.1018], + [ 1.2834, -0.4400, 0.0486, ..., -0.6876, -0.4752, 0.0180]], + + [[ 0.9869, -0.7384, -1.0704, ..., -0.9417, 1.3279, -0.1665], + [ 0.3445, -0.2454, -0.3644, ..., -0.4856, -1.1004, -0.6819], + [ 0.7568, -0.3151, -0.5034, ..., -1.2081, -0.7119, 0.3775], + ..., + [-0.0451, -0.7596, 0.0168, ..., -0.8267, -0.3272, 1.0457], + [ 0.3150, -0.6588, -0.1840, ..., 0.1822, -0.0653, 0.9053], + [ 0.8692, -0.3519, 0.3128, ..., -1.8446, -0.2325, -0.8662]], + + [[ 0.9719, -0.3113, 0.4637, ..., -0.4422, 1.2348, 0.8274], + [ 0.3876, -0.9529, -0.7810, ..., -0.5843, -1.1439, -0.3366], + [-0.5774, 0.3789, -0.2819, ..., -1.4057, 0.4352, 0.1474], + ..., + [ 0.6899, -0.1146, -0.3297, ..., -1.7059, -0.1750, 0.4203], + [ 0.3689, -0.5174, -0.1253, ..., 0.1417, 0.4159, 0.7560], + [ 0.5024, -0.7996, 0.1592, ..., -0.8344, -1.1125, 0.4736]], + + ..., + + [[ 0.0704, -0.3971, -0.2768, ..., -1.9929, 0.8608, 1.2264], + [ 0.4013, -0.0962, -0.0965, ..., -0.4452, -0.8682, -0.4593], + [ 0.1656, 0.5224, -0.1723, ..., -1.5785, 0.3219, 1.1507], + ..., + [-0.9443, 0.4653, 0.2936, ..., -0.9840, -0.0142, -0.1595], + [-0.6544, -0.3294, -0.0803, ..., 0.1623, -0.5061, 0.9824], + [-0.0978, -1.0023, -0.6915, ..., -0.2296, -0.0594, -0.4715]], + + [[ 0.6531, -0.9285, -0.0331, ..., -1.1481, 0.7768, -0.7321], + [ 0.3325, -0.6683, -0.6083, ..., -0.4501, 0.2289, 0.3573], + [-0.6750, 0.4600, -0.8512, ..., -2.0097, -0.5159, 0.2773], + ..., + [-1.4356, -1.0135, 0.0081, ..., -1.2985, -0.3715, -0.2678], + [ 0.0546, -0.2111, -0.0965, ..., -0.3822, -0.4612, 1.6217], + [ 0.7700, -0.5309, -0.1754, ..., -2.2807, -0.0320, -1.5551]], + + [[ 0.2399, -0.9659, 0.1086, ..., -1.1756, 0.4063, 0.0615], + [-0.2202, -0.7972, -0.5024, ..., -0.9126, -1.5248, 0.2418], + [ 0.5215, 0.4540, 0.0036, ..., -0.2135, 0.2145, 0.6638], + ..., + [-0.2190, -0.4967, 0.7149, ..., -0.3324, 0.3502, 1.0624], + [-0.0108, -0.9205, -0.1315, ..., -1.0153, 0.2989, 1.1415], + [ 1.1284, -0.6560, 0.6755, ..., -1.2157, 0.8580, -0.5022]]], + grad_fn=) +``` +``` +>> output.shape +torch.Size([20, 32, 512]) +``` + ### HuggingFace +Hugging Face, Inc. is a French-American company incorporated under the Delaware General Corporation Law and based in New York City that develops computation tools for building applications using machine learning. + +It has a wide-range of models that can implemented in Tensorflow, PyTorch and other development backends as well. The models are already trained on a dataset and can be pretrained on custom dataset for customized use, according to the user. The information for training the model and loading the pretrained model can be found [here](https://huggingface.co/docs/transformers/en/training). + +In HuggingFace, `pipeline` is used to run inference from the trained model available in the Hub. This is very beginner friendly. The model is downloaded to the local system on running the script before running the inference. It has to be made sure that the model downloaded does not exceed your available data plan. +Imports: +```python +from transformers import pipeline +``` + +Initialization: + +The model used here is BART (large) which was trained on MultiNLI dataset, which consist of sentence paired with its textual entailment. +```python +classifier = pipeline(model="facebook/bart-large-mnli") +``` + +Sample: + +The first argument is the sentence which needs to be analyzed. The second argument, `candidate_labels`, is the list of labels which most likely the first argument sentence belongs to. The output dictionary will have a key as `score`, where the highest index is the textual entailment of the sentence with the index of the label in the list. + +```python +output = classifier( + "I need to leave but later", + candidate_labels=["urgent", "not urgent", "sleep"], +) +``` + +O/P: + +``` +{'sequence': 'I need to leave but later', + 'labels': ['not urgent', 'urgent', 'sleep'], + 'scores': [0.8889380097389221, 0.10631518065929413, 0.00474683940410614]} +``` ## Application The transformer has had great success in natural language processing (NLP). Many large language models such as GPT-2, GPT-3, GPT-4, Claude, BERT, XLNet, RoBERTa and ChatGPT demonstrate the ability of transformers to perform a wide variety of such NLP-related tasks, and have the potential to find real-world applications. From 251c4d86894b26e113b1b4426aeaf412c0b4c977 Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Fri, 31 May 2024 18:43:19 +0530 Subject: [PATCH 361/656] Update Transformers.md Added Tensorflow output and fixed wrong mentions --- contrib/machine-learning/Transformers.md | 95 +++++++++++++++++++++++- 1 file changed, 92 insertions(+), 3 deletions(-) diff --git a/contrib/machine-learning/Transformers.md b/contrib/machine-learning/Transformers.md index e9e50e88..d30bd63c 100644 --- a/contrib/machine-learning/Transformers.md +++ b/contrib/machine-learning/Transformers.md @@ -189,8 +189,8 @@ transformer = Transformer( d_model=d_model, num_heads=num_heads, dff=dff, - input_vocab_size=tokenizers.pt.get_vocab_size().numpy(), - target_vocab_size=tokenizers.en.get_vocab_size().numpy(), + input_vocab_size=64, + target_vocab_size=64, dropout_rate=dropout_rate ) ``` @@ -205,7 +205,96 @@ output = transformer((src, tgt)) O/P: ``` - + +``` +``` +>>> output.shape +TensorShape([64, 50, 64]) ``` ### PyTorch From 44303ff8e04e496e0f545d92a60e9758b971e314 Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Fri, 31 May 2024 18:45:07 +0530 Subject: [PATCH 362/656] Update Transformers.md Extended Introduction --- contrib/machine-learning/Transformers.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/contrib/machine-learning/Transformers.md b/contrib/machine-learning/Transformers.md index d30bd63c..97346bdd 100644 --- a/contrib/machine-learning/Transformers.md +++ b/contrib/machine-learning/Transformers.md @@ -1,8 +1,9 @@ # Transformers ## Introduction A transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism. It is based on the softmax-based attention -mechanism. Before transformers, predecessors of attention mechanism were added to gated recurrent neural networks, such as LSTMs and gated recurrent units (GRUs), which -processed datasets sequentially. Dependency on previous token computations prevented them from being able to parallelize the attention mechanism. +mechanism. Before transformers, predecessors of attention mechanism were added to gated recurrent neural networks, such as LSTMs and gated recurrent units (GRUs), which processed datasets sequentially. Dependency on previous token computations prevented them from being able to parallelize the attention mechanism. + +Transformers are a revolutionary approach to natural language processing (NLP). Unlike older models, they excel at understanding long-range connections between words. This "attention" mechanism lets them grasp the context of a sentence, making them powerful for tasks like machine translation, text summarization, and question answering. Introduced in 2017, transformers are now the backbone of many large language models, including tools you might use every day. Their ability to handle complex relationships in language is fueling advancements in AI across various fields. ## Model Architecture

Model Architecture

From a8e60cf4b1121311363846e4ca06d0967e4652fb Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 20:29:42 +0530 Subject: [PATCH 363/656] Update index.md --- contrib/plotting-visualization/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 3f272465..96615db5 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -4,3 +4,4 @@ - [Bar Plots in Matplotlib](matplotlib-bar-plots.md) - [Pie Charts in Matplotlib](matplotlib-pie-charts.md) - [Introduction to Seaborn and Installation](seaborn-intro.md) +- [Getting started with Seaborn](seaborn-basics.md) From 4446a85d1f81f8a18d75f5a29cc9a1733bd800ab Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 20:30:11 +0530 Subject: [PATCH 364/656] Create seaborn-basics.md --- contrib/plotting-visualization/seaborn-basics.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 contrib/plotting-visualization/seaborn-basics.md diff --git a/contrib/plotting-visualization/seaborn-basics.md b/contrib/plotting-visualization/seaborn-basics.md new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/contrib/plotting-visualization/seaborn-basics.md @@ -0,0 +1 @@ + From 6aecf49fd04b8777365b7173fa47d59c86e5e0aa Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 20:47:43 +0530 Subject: [PATCH 365/656] Update seaborn-basics.md --- .../plotting-visualization/seaborn-basics.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/contrib/plotting-visualization/seaborn-basics.md b/contrib/plotting-visualization/seaborn-basics.md index 8b137891..603f0745 100644 --- a/contrib/plotting-visualization/seaborn-basics.md +++ b/contrib/plotting-visualization/seaborn-basics.md @@ -1 +1,19 @@ +Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them. +Here’s an example of what seaborn can do: +```Python +# Import seaborn +import seaborn as sns + +# Apply the default theme +sns.set_theme() + +# Load an example dataset +tips = sns.load_dataset("tips") + +# Create a visualization +sns.relplot( + data=tips, + x="total_bill", y="tip", col="time", + hue="smoker", style="smoker", size="size", +) From 03920cd7501a05316e9a143f0dc7d0406e6117c1 Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 20:48:58 +0530 Subject: [PATCH 366/656] Add files via upload --- .../images/seaborn-basics1.png | Bin 0 -> 53821 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 contrib/plotting-visualization/images/seaborn-basics1.png diff --git a/contrib/plotting-visualization/images/seaborn-basics1.png 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zDmk8}Qj~*(BPl<4Y|CIEh8$?rfXCamM`#dfgzomQtf#N|36N-eUK{#!5Yr_hMD{&# ze4IF^Jy{cbC3O>RUS7yt`wXS6H(#^IsZ!ajRtiq3^6GC7o8GaSVacvWaa3@xgLzk z1?YXXMtf>JR&!mTJ797rcRXkWpE{~QiNFRE%8{!zpRbg;#Q+AT&As~{@!l5*1Bc`8xT|3;0M8lRl^wiL z{Z@wC)p(?*P@UznDwuZO_&npWjIeji0pNX7mw=9v%;ZgfYGi}dRsxK9U#aylRDn-< z_HLyibpNsZfdf48zq+{uxQF8)`$rV-0)iwf(9>=;>S{e-hL5VEaI< zY=j>_eh_1Y9-JOo)}_Y8#Q3Vd2OdKpdN8v<(5TS=?lpw{^9G-u_y5r4cqCE{<~L*P Ss(20qenEt#g-Qi6$ literal 0 HcmV?d00001 From ef42e881851eb20f9a9c8c28a9afdcff89c7fb67 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 20:49:03 +0530 Subject: [PATCH 367/656] Update index.md --- contrib/plotting-visualization/index.md | 1 - 1 file changed, 1 deletion(-) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 92ec1064..32261d6e 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -3,4 +3,3 @@ - [Installing Matplotlib](matplotlib-installation.md) - [Bar Plots in Matplotlib](matplotlib-bar-plots.md) - [Pie Charts in Matplotlib](matplotlib-pie-charts.md) - From f26f8da60ec1814d6165a6cb908aa2a0ea7a1b2c Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 20:53:21 +0530 Subject: [PATCH 368/656] Update grid-search.md --- contrib/machine-learning/grid-search.md | 39 +++++++++++++------------ 1 file changed, 21 insertions(+), 18 deletions(-) diff --git a/contrib/machine-learning/grid-search.md b/contrib/machine-learning/grid-search.md index 3bf53fff..ae44412f 100644 --- a/contrib/machine-learning/grid-search.md +++ b/contrib/machine-learning/grid-search.md @@ -1,20 +1,26 @@ # Grid Search Grid Search is a hyperparameter tuning technique in Machine Learning that helps to find the best combination of hyperparameters for a given model. It works by defining a grid of hyperparameters and then training the model with all the possible combinations of hyperparameters to find the best performing set. + The Grid Search Method considers some hyperparameter combinations and selects the one returning a lower error score. This method is specifically useful when there are only some hyperparameters in order to optimize. However, it is outperformed by other weighted-random search methods when the Machine Learning model grows in complexity. ## Implementation -Before applying Grid Searching on any algorithm, Data is used to divided into training and validation set, a validation set is used to validate the models. A model with all possible combinations of hyperparameters is tested on the validation set to choose the best combination. -Grid Searching can be applied to any hyperparameters algorithm whose performance can be improved by tuning hyperparameter. For example, we can apply grid searching on K-Nearest Neighbors by validating its performance on a set of values of K in it. Same thing we can do with Logistic Regression by using a set of values of learning rate to find the best learning rate at which Logistic Regression achieves the best accurac +Before applying Grid Searching on any algorithm, data is divided into training and validation set, a validation set is used to validate the models. A model with all possible combinations of hyperparameters is tested on the validation set to choose the best combination. + +Grid Searching can be applied to any hyperparameters algorithm whose performance can be improved by tuning hyperparameter. For example, we can apply grid searching on K-Nearest Neighbors by validating its performance on a set of values of K in it. Same thing we can do with Logistic Regression by using a set of values of learning rate to find the best learning rate at which Logistic Regression achieves the best accuracy. + Let us consider that the model accepts the below three parameters in the form of input: -1. Number of hidden layers [2, 4] -2. Number of neurons in every layer [5, 10] -3. Number of epochs [10, 50] +1. Number of hidden layers `[2, 4]` +2. Number of neurons in every layer `[5, 10]` +3. Number of epochs `[10, 50]` + +If we want to try out two options for every parameter input (as specified in square brackets above), it estimates different combinations. For instance, one possible combination can be `[2, 5, 10]`. Finding such combinations manually would be a headache. -If we want to try out two options for every parameter input (as specified in square brackets above), it estimates different combinations. For instance, one possible combination can be [2, 5, 10]. Finding such combinations manually would be a headache. Now, suppose that we had ten different parameters as input, and we would like to try out five possible values for each and every parameter. It would need manual input from the programmer's end every time we like to alter the value of a parameter, re-execute the code, and keep a record of the outputs for every combination of the parameters. + Grid Search automates that process, as it accepts the possible value for every parameter and executes the code in order to try out each and every possible combination outputs the result for the combinations and outputs the combination having the best accuracy. + Higher values of C tell the model, the training data resembles real world information, place a greater weight on the training data. While lower values of C do the opposite. ## Explaination of the Code @@ -32,16 +38,14 @@ The code provided performs hyperparameter tuning for a Logistic Regression model 10. logit.score(X, y) calculates the accuracy of the fitted model on the dataset and appends this score to the scores list. 11. After the loop, the scores list is printed, showing the accuracy for each value of C. -## Python Code +### Python Code +```python from sklearn import datasets - from sklearn.linear_model import LogisticRegression iris = datasets.load_iris() - X = iris['data'] - y = iris['target'] logit = LogisticRegression(max_iter = 10000) @@ -49,20 +53,19 @@ logit = LogisticRegression(max_iter = 10000) C = [0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2] scores = [] - for choice in C: - logit.set_params(C=choice) - logit.fit(X, y) - scores.append(logit.score(X, y)) - print(scores) +``` -## Results +#### Results +``` [0.9666666666666667, 0.9666666666666667, 0.9733333333333334, 0.9733333333333334, 0.98, 0.98, 0.9866666666666667, 0.9866666666666667] +``` + +We can see that the lower values of `C` performed worse than the base parameter of `1`. However, as we increased the value of `C` to `1.75` the model experienced increased accuracy. -We can see that the lower values of C performed worse than the base parameter of 1. However, as we increased the value of C to 1.75 the model experienced increased accuracy. -It seems that increasing C beyond this amount does not help increase model accuracy. +It seems that increasing `C` beyond this amount does not help increase model accuracy. From 5ffcbf21346cc0a56b873c5b11d97e349fa379fc Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 20:57:53 +0530 Subject: [PATCH 369/656] Update seaborn-basics.md --- .../plotting-visualization/seaborn-basics.md | 20 +++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/contrib/plotting-visualization/seaborn-basics.md b/contrib/plotting-visualization/seaborn-basics.md index 603f0745..41d76a4e 100644 --- a/contrib/plotting-visualization/seaborn-basics.md +++ b/contrib/plotting-visualization/seaborn-basics.md @@ -17,3 +17,23 @@ sns.relplot( x="total_bill", y="tip", col="time", hue="smoker", style="smoker", size="size", ) +``` +Below is the output for the above code snippet: + +![Seaborn intro image](images/seaborn-basics.png) + +```Python +# Load an example dataset +tips = sns.load_dataset("tips") +``` +Most code in the docs will use the `load_dataset()` function to get quick access to an example dataset. There’s nothing special about these datasets: they are just pandas data frames, and we could have loaded them with `pandas.read_csv()` or build them by hand. Most of the examples in the documentation will specify data using pandas data frames, but Seaborn is very flexible about the data structures that it accepts. + +```Python +# Create a visualization +sns.relplot( + data=tips, + x="total_bill", y="tip", col="time", + hue="smoker", style="smoker", size="size", +) +``` +This plot shows the relationship between five variables in the tips dataset using a single call to the seaborn function `relplot()`. Notice how we provided only the names of the variables and their roles in the plot. Unlike when using matplotlib directly, it wasn’t necessary to specify attributes of the plot elements in terms of the color values or marker codes. Behind the scenes, seaborn handled the translation from values in the dataframe to arguments that Matplotlib understands. This declarative approach lets you stay focused on the questions that you want to answer, rather than on the details of how to control matplotlib. From 4a7e00b61aa225b133fc702d23d70b3a74b70adf Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 20:58:54 +0530 Subject: [PATCH 370/656] Update seaborn-basics.md --- contrib/plotting-visualization/seaborn-basics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/plotting-visualization/seaborn-basics.md b/contrib/plotting-visualization/seaborn-basics.md index 41d76a4e..147083b2 100644 --- a/contrib/plotting-visualization/seaborn-basics.md +++ b/contrib/plotting-visualization/seaborn-basics.md @@ -20,7 +20,7 @@ sns.relplot( ``` Below is the output for the above code snippet: -![Seaborn intro image](images/seaborn-basics.png) +![Seaborn intro image](images/seaborn-basics1.png) ```Python # Load an example dataset From ccaa3770e7b308fe25410124dedcdea772ebe504 Mon Sep 17 00:00:00 2001 From: Rishi Goswami <92781956+rishig2003@users.noreply.github.com> Date: Fri, 31 May 2024 21:04:10 +0530 Subject: [PATCH 371/656] Update seaborn-basics.md --- contrib/plotting-visualization/seaborn-basics.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/contrib/plotting-visualization/seaborn-basics.md b/contrib/plotting-visualization/seaborn-basics.md index 147083b2..42df5522 100644 --- a/contrib/plotting-visualization/seaborn-basics.md +++ b/contrib/plotting-visualization/seaborn-basics.md @@ -26,7 +26,7 @@ Below is the output for the above code snippet: # Load an example dataset tips = sns.load_dataset("tips") ``` -Most code in the docs will use the `load_dataset()` function to get quick access to an example dataset. There’s nothing special about these datasets: they are just pandas data frames, and we could have loaded them with `pandas.read_csv()` or build them by hand. Most of the examples in the documentation will specify data using pandas data frames, but Seaborn is very flexible about the data structures that it accepts. +Most code in the docs will use the `load_dataset()` function to get quick access to an example dataset. There’s nothing special about these datasets: they are just pandas data frames, and we could have loaded them with `pandas.read_csv()` or build them by hand. Many users specify data using pandas data frames, but Seaborn is very flexible about the data structures that it accepts. ```Python # Create a visualization @@ -36,4 +36,4 @@ sns.relplot( hue="smoker", style="smoker", size="size", ) ``` -This plot shows the relationship between five variables in the tips dataset using a single call to the seaborn function `relplot()`. Notice how we provided only the names of the variables and their roles in the plot. Unlike when using matplotlib directly, it wasn’t necessary to specify attributes of the plot elements in terms of the color values or marker codes. Behind the scenes, seaborn handled the translation from values in the dataframe to arguments that Matplotlib understands. This declarative approach lets you stay focused on the questions that you want to answer, rather than on the details of how to control matplotlib. +This plot shows the relationship between five variables in the tips dataset using a single call to the seaborn function `relplot()`. Notice how only the names of the variables and their roles in the plot are provided. Unlike when using matplotlib directly, it wasn’t necessary to specify attributes of the plot elements in terms of the color values or marker codes. Behind the scenes, seaborn handled the translation from values in the dataframe to arguments that Matplotlib understands. This declarative approach lets you stay focused on the questions that you want to answer, rather than on the details of how to control matplotlib. From f75b2bab640e8b52e94d383ad2805ef487d2984a Mon Sep 17 00:00:00 2001 From: Arihant Yadav <147732947+arihunter-18@users.noreply.github.com> Date: Fri, 31 May 2024 22:22:54 +0530 Subject: [PATCH 372/656] Add files via upload --- contrib/advanced-python/closures.md | 101 ++++++++++++++++++++++++++++ 1 file changed, 101 insertions(+) create mode 100644 contrib/advanced-python/closures.md diff --git a/contrib/advanced-python/closures.md b/contrib/advanced-python/closures.md new file mode 100644 index 00000000..87ad058f --- /dev/null +++ b/contrib/advanced-python/closures.md @@ -0,0 +1,101 @@ +# Closures +In order to have complete understanding of this topic in python, one needs to be crystal clear with the concept of functions and the different types of them which are namely First Class Functions and Nested Functions. + +### First Class Functions +These are the normal functions used by the programmer in routine as they can be assigned to variables, passed as arguments and returned from other functions. +### Nested Functions +These are the functions defined within other functions and involve thorough usage of **Closures**. It is also referred as **Inner Functions** by some books. There are times when it is required to prevent a function or the data it has access to from being accessed from other parts of the code, and this is where Nested Functions come into play. Basically, its usage allows the encapsulation of that particular data/function within another function. This enables it to be virtually hidden from the global scope. + +## Defining Closures +In nested functions, if the outer function basically ends up returning the inner function, in this case the concept of closures comes into play. + +A closure is a function object that remembers values in enclosing scopes even if they are not present in memory. There are certain neccesary condtions required to create a closure in python : +1. The inner function must be defined inside the outer function. +2. The inner function must refer to a value defined in the outer function. +3. The inner function must return a value. + +## Advantages of Closures +* Closures make it possible to pass data to inner functions without first passing them to outer functions +* Closures can be used to create private variables and functions +* They also make it possible to invoke the inner function from outside of the encapsulating outer function. +* It improves code readability and maintainability + +## Examples implementing Closures +### Example 1 : Basic Implementation +```python +def make_multiplier_of(n): + def multiplier(x): + return x * n + return multiplier + +times3 = make_multiplier_of(3) +times5 = make_multiplier_of(5) + +print(times3(9)) +print(times5(3)) +``` +#### Output: +``` +27 +15 +``` +The **multiplier function** is defined inside the **make_multiplier_of function**. It has access to the n variable from the outer scope, even after the make_multiplier_of function has returned. This is an example of a closure. + +### Example 2 : Implementation with Decorators +```python +def decorator_function(original_function): + def wrapper_function(*args, **kwargs): + print(f"Wrapper executed before {original_function.__name__}") + return original_function(*args, **kwargs) + return wrapper_function + +@decorator_function +def display(): + print("Display function executed") + +display() +``` +#### Output: +``` + Wrapper executed before display + Display function executed +``` +The code in the example defines a decorator function: ***decorator_function*** that takes a function as an argument and returns a new function **wrapper_function**. The **wrapper_function** function prints a message to the console before calling the original function which appends the name of the called function as specified in the code. + +The **@decorator_function** syntax is used to apply the decorator_function decorator to the display function. This means that the display function is replaced with the result of calling **decorator_function(display)**. + +When the **display()** function is called, the wrapper_function function is executed instead. The wrapper_function function prints a message to the console and then calls the original display function. +### Example 3 : Implementation with for loop +```python +def create_closures(): + closures = [] + for i in range(5): + def closure(i=i): # Capture current value of i by default argument + return i + closures.append(closure) + return closures + +my_closures = create_closures() +for closure in my_closures: + print(closure()) + +``` +#### Output: +``` +0 +1 +2 +3 +4 +``` +The code in the example defines a function **create_closures** that creates a list of closure functions. Each closure function returns the current value of the loop variable i. + +The closure function is defined inside the **create_closures function**. It has access to the i variable from the **outer scope**, even after the create_closures function has returned. This is an example of a closure. + +The **i**=*i* argument in the closure function is used to capture the current value of *i* by default argument. This is necessary because the ****i** variable in the outer scope is a loop variable, and its value changes in each iteration of the loop. By capturing the current value of *i* in the default argument, we ensure that each closure function returns the correct value of **i**. This is responsible for the generation of output 0,1,2,3,4. + + +For more examples related to closures, [click here](https://dev.to/bshadmehr/understanding-closures-in-python-a-comprehensive-tutorial-11ld). + +## Summary +Closures in Python provide a powerful mechanism for encapsulating state and behavior, enabling more flexible and modular code. Understanding and effectively using closures enables the creation of function factories, allows functions to have state, and facilitates functional programming techniques. \ No newline at end of file From 50f1e2c3deb4d244b052fcc4c9e4b7d3b6836551 Mon Sep 17 00:00:00 2001 From: Arihant Yadav <147732947+arihunter-18@users.noreply.github.com> Date: Fri, 31 May 2024 22:24:16 +0530 Subject: [PATCH 373/656] Updated index.md --- contrib/advanced-python/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index b95e4b91..d9a90568 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -7,3 +7,4 @@ - [Regular Expressions in Python](regular_expressions.md) - [JSON module](json-module.md) - [Map Function](map-function.md) +- [Closures](closures.md) From d20b8efca6cb73ef7543c0db41c34aede656e88f Mon Sep 17 00:00:00 2001 From: Jaya-Prakash-17 Date: Sat, 1 Jun 2024 00:41:56 +0530 Subject: [PATCH 374/656] Content Added: Protocols --- contrib/advanced-python/index.md | 1 + contrib/advanced-python/protocols.md | 243 +++++++++++++++++++++++++++ 2 files changed, 244 insertions(+) create mode 100644 contrib/advanced-python/protocols.md diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index b95e4b91..3945721c 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -7,3 +7,4 @@ - [Regular Expressions in Python](regular_expressions.md) - [JSON module](json-module.md) - [Map Function](map-function.md) +- [Protocols](protocols.md) diff --git a/contrib/advanced-python/protocols.md b/contrib/advanced-python/protocols.md new file mode 100644 index 00000000..9b5e74a3 --- /dev/null +++ b/contrib/advanced-python/protocols.md @@ -0,0 +1,243 @@ +# Protocols in Python +Python can establish informal interfaces using protocols In order to improve code structure, reusability, and type checking. Protocols allow for progressive adoption and are more flexible than standard interfaces in other programming languages like JAVA, which are tight contracts that specify the methods and attributes a class must implement. + +>Before going into depth of this topic let's understand another topic which is pre-requisite od this topic \#TypingModule + +## Typing Module +This is a module in python which provides +1. Provides classes, functions, and type aliases. +2. Allows adding type annotations to our code. +3. Enhances code readability. +4. Helps in catching errors early. + +### Type Hints in Python: +Type hints allow you to specify the expected data types of variables, function parameters, and return values. This can improve code readability and help with debugging. + +Here is a simple function that adds two numbers: +```python +def add(a,b): + return a + b +add(10,20) +``` +>Output: 30 + +While this works fine, adding type hints makes the code more understandable and serves as documentation: + +```python +def add(a:int, b:int)->int: + return a + b +print(add(1,10)) +``` +>Output: 11 + +In this version, `a` and `b` are expected to be integers, and the function is expected to return an integer. This makes the function's purpose and usage clearer. + +#### let's see another example + +The function given below takes an iterable (it can be any off list, tuple, dict, set, frozeset, String... etc) and print it's content in a single line along with it's type. + +```python +from typing import Iterable +# type alias + +def print_all(l: Iterable)->None: + print(type(l),end=' ') + for i in l: + print(i,end=' ') + print() + +l = [1,2,3,4,5] # type: List[int] +s = {1,2,3,4,5} # type: Set[int] +t = (1,2,3,4,5) # type: Tuple[int] + +for iter_obj in [l,s,t]: + print_all(iter_obj) + +``` +Output: +> 1 2 3 4 5 +> 1 2 3 4 5 +> 1 2 3 4 5 + +and now lets try calling the function `print_all` using a non-iterable object `int` as argument. + +```python +a = 10 +print_all(a) # This will raise an error +``` +Output: +>TypeError: 'int' object is not iterable + +This error occurs because `a` is an `integer`, and the `integer` class does not have any methods or attributes that make it work like an iterable. In other words, the integer class does not conform to the `Iterable` protocol. + +**Benefits of Type Hints** +Using type hints helps in several ways: + +1. **Error Detection**: Tools like mypy can catch type-related problems during development, decreasing runtime errors. +2. **Code Readability**: Type hints serve as documentation, making it easy to comprehend what data types are anticipated and returned. +3. **Improved Maintenance**: With unambiguous type expectations, maintaining and updating code becomes easier, especially in huge codebases. + +Now that we have understood about type hints and typing module let's dive deep into protocols. + +## Understanding Protocols + +In Python, protocols define interfaces similar to Java interfaces. They let you specify methods and attributes that an object must implement without requiring inheritance from a base class. Protocols are part of the `typing` module and provide a way to enforce certain structures in your classes, enhancing type safety and code clarity. + +### What is a Protocol? + +A protocol specifies one or more method signatures that a class must implement to be considered as conforming to the protocol. + This concept is often referred to as "structural subtyping" or "duck typing," meaning that if an object implements the required methods and attributes, it can be treated as an instance of the protocol. + +Let's write our own protocol: + +```python +from typing import Protocol + +# Define a Printable protocol +class Printable(Protocol): + def print(self) -> None: + """Print the object""" + pass + +# Book class implements the Printable protocol +class Book: + def __init__(self, title: str): + self.title = title + + def print(self) -> None: + print(f"Book Title: {self.title}") + +# print_object function takes a Printable object and calls its print method +def print_object(obj: Printable) -> None: + obj.print() + +book = Book("Python Programming") +print_object(book) +``` +Output: +> Book Title: Python Programming + +In this example: + +1. **Printable Protocol:** Defines an interface with a single method print. +2. **Book Class:** Implements the Printable protocol by providing a print method. +3. **print_object Function:** Accepts any object that conforms to the Printable protocol and calls its print method. + +we got our output because the class `Book` confirms to the protocols `printable`. +similarly When you pass an object to `print_object` that does not conform to the Printable protocol, an error will occur. This is because the object does not implement the required `print` method. +Let's see an example: +```python +class Team: + def huddle(self) -> None: + print("Team Huddle") + +c = Team() +print_object(c) # This will raise an error +``` +Output: +>AttributeError: 'Team' object has no attribute 'print' + +In this case: +- The `Team` class has a `huddle` method but does not have a `print` method. +- When `print_object` tries to call the `print` method on a `Team` instance, it raises an `AttributeError`. + +> This is an important aspect of using protocols: they ensure that objects provide the necessary methods, leading to more predictable and reliable code. + +**Ensuring Protocol Conformance** +To avoid such errors, you need to ensure that any object passed to `print_object` implements the `Printable` protocol. Here's how you can modify the `Team` class to conform to the protocol: +```python +class Team: + def __init__(self, name: str): + self.name = name + + def huddle(self) -> None: + print("Team Huddle") + + def print(self) -> None: + print(f"Team Name: {self.name}") + +c = Team("Dream Team") +print_object(c) +``` +Output: +>Team Name: Dream Team + +The `Team` class now implements the `print` method, conforming to the `Printable` protocol. and hence, no longer raises an error. + +### Protocols and Inheritance: +Protocols can also be used in combination with inheritance to create more complex interfaces. +we can do that by following these steps: +**Step 1 - Base protocol**: Define a base protocol that specifies a common set of methods and attributes. +**Step 2 - Derived Protocols**: Create derives protocols that extends the base protocol with addition requirements +**Step 3 - Polymorphism**: Objects can then conform to multiple protocols, allowing for Polymorphic behavior. + +Let's see an example on this as well: + +```python +from typing import Protocol + +# Base Protocols +class Printable(Protocol): + def print(self) -> None: + """Print the object""" + pass + +# Base Protocols-2 +class Serializable(Protocol): + def serialize(self) -> str: + pass + +# Derived Protocol +class PrintableAndSerializable(Printable, Serializable): + pass + +# class with implementation of both Printable and Serializable +class Book_serialize: + def __init__(self, title: str): + self.title = title + + def print(self) -> None: + print(f"Book Title: {self.title}") + + def serialize(self) -> None: + print(f"serialize: {self.title}") + +# function accepts the object which implements PrintableAndSerializable +def test(obj: PrintableAndSerializable): + obj.print() + obj.serialize() + +book = Book_serialize("lean-in") +test(book) +``` +Output: +> Book Title: lean-in +serialize: lean-in + +In this example: + +**Printable Protocol:** Specifies a `print` method. +**Serializable Protocol:** Specifies a `serialize` method. +**PrintableAndSerializable Protocol:** Combines both `Printable` and `Serializable`. +**Book Class**: Implements both `print` and `serialize` methods, conforming to `PrintableAndSerializable`. +**test Function:** Accepts any object that implements the `PrintableAndSerializable` protocol. + +If you try to pass an object that does not conform to the `PrintableAndSerializable` protocol to the test function, it will raise an `error`. Let's see an example: + +```python +class Team: + def huddle(self) -> None: + print("Team Huddle") + +c = Team() +test(c) # This will raise an error +``` +output: +> AttributeError: 'Team' object has no attribute 'print' + +In this case: +The `Team` class has a `huddle` method but does not implement `print` or `serialize` methods. +When test tries to call `print` and `serialize` on a `Team` instance, it raises an `AttributeError`. + +**In Conclusion:** +>Python protocols offer a versatile and powerful means of defining interfaces, encouraging the decoupling of code, improving readability, and facilitating static type checking. They are particularly handy for scenarios involving file-like objects, bespoke containers, and any case where you wish to enforce certain behaviors without requiring inheritance from a specific base class. Ensuring that classes conform to protocols reduces runtime problems and makes your code more robust and maintainable. \ No newline at end of file From 9a504138c2b4f1ccb412e4e05cd0d2f76f864448 Mon Sep 17 00:00:00 2001 From: somyasaxena01 <140182178+somyasaxena01@users.noreply.github.com> Date: Sat, 1 Jun 2024 10:12:12 +0530 Subject: [PATCH 375/656] Delete contrib/ds-algorithms/stacks.md --- contrib/ds-algorithms/stacks.md | 131 -------------------------------- 1 file changed, 131 deletions(-) delete mode 100644 contrib/ds-algorithms/stacks.md diff --git a/contrib/ds-algorithms/stacks.md b/contrib/ds-algorithms/stacks.md deleted file mode 100644 index f9f662b2..00000000 --- a/contrib/ds-algorithms/stacks.md +++ /dev/null @@ -1,131 +0,0 @@ -# STACKS IN PYTHON -In Data Structures and Algorithms, a stack is a linear data structure that complies with the Last In, First Out (LIFO) rule. It works by use of two fundamental techniques: *PUSH* which inserts an element on top of the stack and *POP* which takes out the topmost element.This concept is similar to a stack of plates in a cafeteria. Stacks are usually used for handling function calls, expression evaluation, and parsing in programming. Indeed, they are efficient in managing memory as well as tracking program state. - -**POINTS TO BE REMEMBERED :-** -- A stack is a collection of data items that can be accessed at only one end, called *TOP*. -- Items can be inserted and deleted in a stack only at the *TOP*. -- The last item inserted in a stack is the first one to be deleted. -- Therefore, a stack is called a **Last-In-First-Out (LIFO)** data structure. - -## REAL LIFE EXAMPLES OF STACKS - -**PILE OF BOOKS** - Suppose a set of books are placed one over the other in a pile. When you remove books from the pile, the topmost book will be removed first. Similarly, when you have to add a book to the pile, the book will be placed at the top of the file. - -**PILE OF PLATES** - The first plate begins the pile. The second plate is placed on the top of the first plate and the third plate is placed on the top of the second plate, and so on. In general, if you want to add a plate to the pile, you can keep it on the top of the pile. Similarly, if you want to remove a plate, you can remove the plate from the top of the pile. - -**BANGLES IN A HAND** - When a person wears bangles, the last bangle worn is the first one to be removed. - -## APPLICATIONS OF STACKS - -Stacks are widely used in Computer Science: -- *Function call* management -- Maintaining the *UNDO* list for the application -- Web browser *history management* -- Evaluating expressions -- Checking the nesting of parentheses in an expression -- *Backtracking* algorithms (Recursion) - -Understanding these applications is essential for Software Development. - -## OPERATIONS ON A STACK - -Key operations on a stack include: -- **PUSH** - It is the process of inserting a new element on the top of a stack. -- **OVERFLOW** - A situation when we are pushing an item in a stack that is full. -- **POP** - It is the process of deleting an element from the top of a stack. -- **UNDERFLOW** - A situation when we are popping item from an empty stack. -- **PEEK** - It is the process of getting the most recent value of stack *(i.e. the value at the top of the stack)* -- **ISEMPTY** - It is the function which return true if stack is empty else false. -- **SHOW** -Displaying stack items. - -## IMPLEMENTING STACKS IN PYTHON - -```python -def isEmpty(S): - - if len(S) == 0: - return True - - else: - - return False - -def Push(S, item): - S.append(item) - -def Pop(S): - - if isEmpty(S): - return "Underflow" - - else: - val = S.pop() - return val - -def Peek(S): - - if isEmpty(S): - return "Underflow" - - else: - top = len(S) - 1 - return S[top] - -def Show(S): - - if isEmpty(S): - print("Sorry, No items in Stack") - - else: - print("(Top)", end=' ') - t = len(S) - 1 - while t >= 0: - print(S[t], "<", end=' ') - t -= 1 - print() -``` - -This code defines a stack data structure along with functions to manipulate it. To provide output, we would need to use these functions to interact with the stack. - -Here's an example: -```python -stack = [] - -Push(stack, 5) -Push(stack, 10) -Push(stack, 15) - -print("Stack after Push operations:") -Show(stack) - -print("Peek operation:", Peek(stack)) - -print("Pop operation:", Pop(stack)) - -print("Stack after Pop operation:") -Show(stack) -``` - -This would output: - -``` -Stack after Push operations: - -(Top) 15 < 10 < 5 < - -Peek operation: 15 - -Pop operation: 15 - -Stack after Pop operation: - -(Top) 10 < 5 < -``` - -## Complexity Analysis - -- **Worst case**: `O(n)` This occurs when the stack is full, it is dominated by the usage of Show operation. -- **Best case**: `O(1)` When the operations like isEmpty, Push, Pop and Peek are used, they have a constant time complexity of O(1). -- **Average case**: `O(n)` The average complexity is likely to be lower than O(n), as the stack is not always full. - - From 1f54c5a7ccb5ba49d5f49728837521796f2b4de7 Mon Sep 17 00:00:00 2001 From: somyasaxena01 <140182178+somyasaxena01@users.noreply.github.com> Date: Sat, 1 Jun 2024 10:12:55 +0530 Subject: [PATCH 376/656] Add files via upload --- contrib/ds-algorithms/stacks.md | 116 ++++++++++++++++++++++++++++++++ 1 file changed, 116 insertions(+) create mode 100644 contrib/ds-algorithms/stacks.md diff --git a/contrib/ds-algorithms/stacks.md b/contrib/ds-algorithms/stacks.md new file mode 100644 index 00000000..428a1938 --- /dev/null +++ b/contrib/ds-algorithms/stacks.md @@ -0,0 +1,116 @@ +# Stacks in Python + +In Data Structures and Algorithms, a stack is a linear data structure that complies with the Last In, First Out (LIFO) rule. It works by use of two fundamental techniques: **PUSH** which inserts an element on top of the stack and **POP** which takes out the topmost element.This concept is similar to a stack of plates in a cafeteria. Stacks are usually used for handling function calls, expression evaluation, and parsing in programming. Indeed, they are efficient in managing memory as well as tracking program state. + +## Points to be Remebered + +- A stack is a collection of data items that can be accessed at only one end, called **TOP**. +- Items can be inserted and deleted in a stack only at the TOP. +- The last item inserted in a stack is the first one to be deleted. +- Therefore, a stack is called a **Last-In-First-Out (LIFO)** data structure. + +## Real Life Examples of Stacks + +- **PILE OF BOOKS** - Suppose a set of books are placed one over the other in a pile. When you remove books from the pile, the topmost book will be removed first. Similarly, when you have to add a book to the pile, the book will be placed at the top of the file. + +- **PILE OF PLATES** - The first plate begins the pile. The second plate is placed on the top of the first plate and the third plate is placed on the top of the second plate, and so on. In general, if you want to add a plate to the pile, you can keep it on the top of the pile. Similarly, if you want to remove a plate, you can remove the plate from the top of the pile. + +- **BANGLES IN A HAND** - When a person wears bangles, the last bangle worn is the first one to be removed. + +## Applications of Stacks + +Stacks are widely used in Computer Science: + +- Function call management +- Maintaining the UNDO list for the application +- Web browser *history management* +- Evaluating expressions +- Checking the nesting of parentheses in an expression +- Backtracking algorithms (Recursion) + +Understanding these applications is essential for Software Development. + +## Operations on a Stack + +Key operations on a stack include: + +- **PUSH** - It is the process of inserting a new element on the top of a stack. +- **OVERFLOW** - A situation when we are pushing an item in a stack that is full. +- **POP** - It is the process of deleting an element from the top of a stack. +- **UNDERFLOW** - A situation when we are popping item from an empty stack. +- **PEEK** - It is the process of getting the most recent value of stack *(i.e. the value at the top of the stack)* +- **isEMPTY** - It is the function which return true if stack is empty else false. +- **SHOW** -Displaying stack items. + +## Implementing Stacks in Python + +```python +def isEmpty(S): + if len(S) == 0: + return True + else: + return False + +def Push(S, item): + S.append(item) + +def Pop(S): + if isEmpty(S): + return "Underflow" + else: + val = S.pop() + return val + +def Peek(S): + if isEmpty(S): + return "Underflow" + else: + top = len(S) - 1 + return S[top] + +def Show(S): + if isEmpty(S): + print("Sorry, No items in Stack") + else: + print("(Top)", end=' ') + t = len(S) - 1 + while t >= 0: + print(S[t], "<", end=' ') + t -= 1 + print() + +stack = [] # initially stack is empty + +Push(stack, 5) +Push(stack, 10) +Push(stack, 15) + +print("Stack after Push operations:") +Show(stack) +print("Peek operation:", Peek(stack)) +print("Pop operation:", Pop(stack)) +print("Stack after Pop operation:") +Show(stack) +``` + +## Output + +```markdown +Stack after Push operations: + +(Top) 15 < 10 < 5 < + +Peek operation: 15 + +Pop operation: 15 + +Stack after Pop operation: + +(Top) 10 < 5 < +``` + +## Complexity Analysis + +- **Worst case**: `O(n)` This occurs when the stack is full, it is dominated by the usage of Show operation. +- **Best case**: `O(1)` When the operations like isEmpty, Push, Pop and Peek are used, they have a constant time complexity of O(1). +- **Average case**: `O(n)` The average complexity is likely to be lower than O(n), as the stack is not always full. From d83b6e1deaa84b2d0d2411c7a5eb8b4284ab5351 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sat, 1 Jun 2024 20:56:34 +0530 Subject: [PATCH 377/656] Update CONTRIBUTING.md --- CONTRIBUTING.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 4a366da8..86880091 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -24,8 +24,8 @@ The list of topics for which we are looking for content are provided below along - Web Scrapping - [Link](https://github.com/animator/learn-python/tree/main/contrib/web-scrapping) - API Development - [Link](https://github.com/animator/learn-python/tree/main/contrib/api-development) - Data Structures & Algorithms - [Link](https://github.com/animator/learn-python/tree/main/contrib/ds-algorithms) -- Python Mini Projects - [Link](https://github.com/animator/learn-python/tree/main/contrib/mini-projects) -- Python Question Bank - [Link](https://github.com/animator/learn-python/tree/main/contrib/question-bank) +- Python Mini Projects - [Link](https://github.com/animator/learn-python/tree/main/contrib/mini-projects) **(Not accepting)** +- Python Question Bank - [Link](https://github.com/animator/learn-python/tree/main/contrib/question-bank) **(Not accepting)** You can check out some content ideas below. From d372975f6c7accb7dd3e7a7852ba61d3e24ea724 Mon Sep 17 00:00:00 2001 From: Mohammed Ahmed Majid <109688855+PilotAxis@users.noreply.github.com> Date: Sat, 1 Jun 2024 21:45:43 +0530 Subject: [PATCH 378/656] Added file exception-handling.md --- contrib/advanced-python/exception-handling.md | 192 ++++++++++++++++++ 1 file changed, 192 insertions(+) create mode 100644 contrib/advanced-python/exception-handling.md diff --git a/contrib/advanced-python/exception-handling.md b/contrib/advanced-python/exception-handling.md new file mode 100644 index 00000000..bddcac46 --- /dev/null +++ b/contrib/advanced-python/exception-handling.md @@ -0,0 +1,192 @@ +# Exception Handling in Python + +Exception handling is a way of managing errors that may occur during program execution, through which you can handle exceptions gracefully. Python's exception handling mechanism has been designed to avoid unexpected termination of the program and offer a means to either regain control after an error or display meaningful messages to the user. + +- **Error** - An error is a mistake or an incorrect result produced by a program. It can be a syntax error, a logical error, or a runtime error. Errors are typically fatal, meaning they prevent the program from continuing to execute. +- **Exception** - An exception is an event that occurs during the execution of a program that disrupts the normal flow of instructions. Exceptions are typically unexpected and can be handled by the program to prevent it from crashing or terminating abnormally. It can be runtime, input/output or system exceptions. Exceptions are designed to be handled by the program, allowing it to recover from the error and continue executing. + +## Python Built-in Exceptions + +There are plenty of built-in exceptions in Python that are raised when a corresponding error occur. +We can view all the built-in exceptions using the built-in `local()` function as follows: + +```python +print(dir(locals()['__builtins__'])) +``` + +|**S.No**|**Exception**|**Description**| +|---|---|---| +|1|SyntaxError|A syntax error occurs when the code we write violates the grammatical rules such as misspelled keywords, missing colon, mismatched parentheses etc.| +|2|TypeError|A type error occurs when we try to perform an operation or use a function with objects that are of incompatible data types.| +|3|NameError|A name error occurs when we try to use a variable, function, module or string without quotes that hasn't been defined or isn't used in a valid way.| +|4|IndexError|A index error occurs when we try to access an element in a sequence (like a list, tuple or string) using an index that's outside the valid range of indices for that sequence.| +|5|KeyError|A key error occurs when we try to access a key that doesn't exist in a dictionary. Attempting to retrieve a value using a non-existent key results this error.| +|6|ValueError|A value error occurs when we provide an argument or value that's inappropriate for a specific operation or function such as doing mathematical operations with incompatible types (e.g., dividing a string by an integer.)| +|7|AttributeError|An attribute error occurs when we try to access an attribute (like a variable or method) on an object that doesn't possess that attribute.| +|8|IOError|An IO (Input/Output) error occurs when an operation involving file or device interaction fails. It signifies that there's an issue during communication between your program and the external system.| +|9|ZeroDivisionError|A ZeroDivisionError occurs when we attempt to divide a number by zero. This operation is mathematically undefined, and Python raises this error to prevent nonsensical results.| +|10|ImportError|An import error occurs when we try to use a module or library that Python can't find or import succesfully.| + +## Try and Except Statement - Catching Exception + +The `try-except` statement allows us to anticipate potential errors during program execution and define what actions to take when those errors occur. This prevents the program from crashing unexpectedly and makes it more robust. + +Here's an example to explain this: + +```python +try: + # code that might raise an exception + result = 10 / 0 +except: + print("An error occured!") +``` + +Output + +```markdown +An error occured! +``` + +In this example, the `try` block contains the code that you suspect might raise an exception. Python attempts to execute the code within this block. If an exception occurs, Python jumps to the `except` block and executes the code within it. + +## Specific Exception Handling + +You can specify the type of expection you want to catch using the `except` keyword followed by the exception class name. You can also have multiple `except` blocks to handle different exception types. + +Here's an example: + +```python +try: + # Code that might raise ZeroDivisionError or NameError + result = 10 / 0 + name = undefined_variable +except ZeroDivisionError: + print("Oops! You tried to divide by zero.") +except NameError: + print("There's a variable named 'undefined_variable' that hasn't been defined yet.") +``` + +Output + +```markdown +Oops! You tried to divide by zero. +``` + +If you comment on the line `result = 10 / 0`, then the output will be + +```markdown +There's a variable named 'undefined_variable' that hasn't been defined yet. +``` + +## Important Note + +In this code, the `except` block are specific to each type of expection. If you want to catch both exceptions with a single `except` block, you can use of tuple of exceptions, like this: + +```python +try: + # Code that might raise ZeroDivisionError or NameError + result = 10 / 0 + name = undefined_variable +except (ZeroDivisionError, NameError): + print("An error occured!") +``` + +Output + +```markdown +An error occured! +``` + +## Try with Else Clause + +The `else` clause in a Python `try-except` block provides a way to execute code only when the `try` block succeeds without raising any exceptions. It's like having a section of code that runs exclusively under the condition that no errors occur during the main operation in the `try` block. + +Here's an example to understand this: + +```python +def calculate_average(numbers): + if len(numbers) == 0: # Handle empty list case seperately (optional) + return None + try: + total = sum(numbers) + average = total / len(numbers) + except ZeroDivisionError: + print("Cannot calculate average for a list containing zero.") + else: + print("The average is:", average) + return average #Optionally return the average here + +# Example usage +numbers = [10, 20, 30] +result = calculate_average(numbers) + +if result is not None: # Check if result is available (handles empty list case) + print("Calculation succesfull!") +``` + +Output + +```markdown +The average is: 20.0 +``` + +## Finally Keyword in Python + +The `finally` keyword in Python is used within `try-except` statements to execute a block of code **always**, regardless of whether an exception occurs in the `try` block or not. + +To understand this, let us take an example: + +```python +try: + a = 10 // 0 + print(a) +except ZeroDivisionError: + print("Cannot be divided by zero.") +finally: + print("Program executed!") +``` + +Output + +```markdown +Cannot be divided by zero. +Program executed! +``` + +## Raise Keyword in Python + +In Python, raising an exception allows you to signal that an error condition has occured during your program's execution. The `raise` keyword is used to explicity raise an exception. + +Let us take an example: + +```python +def divide(x, y): + if y == 0: + raise ZeroDivisionError("Can't divide by zero!") # Raise an exception with a message + result = x / y + return result + +try: + division_result = divide(10, 0) + print("Result:", division_result) +except ZeroDivisionError as e: + print("An error occured:", e) # Handle the exception and print the message +``` + +Output + +```markdown +An error occured: Can't divide by zero! +``` + +## Advantages of Exception Handling + +- **Improved Error Handling** - It allows you to gracefully handle unexpected situations that arise during program execution. Instead of crashing abruptly, you can define specific actions to take when exceptions occur, providing a smoother experience. +- **Code Robustness** - Exception Handling helps you to write more resilient programs by anticipating potential issues and providing approriate responses. +- **Enhanced Code Readability** - By seperating error handling logic from the core program flow, your code becomes more readable and easier to understand. The `try-except` blocks clearly indicate where potential errors might occur and how they'll be addressed. + +## Disadvantages of Exception Handling + +- **Hiding Logic Errors** - Relying solely on exception handling might mask underlying logic error in your code. It's essential to write clear and well-tested logic to minimize the need for excessive exception handling. +- **Performance Overhead** - In some cases, using `try-except` blocks can introduce a slight performance overhead compared to code without exception handling. Howerer, this is usually negligible for most applications. +- **Overuse of Exceptions** - Overusing exceptions for common errors or control flow can make code less readable and harder to maintain. It's important to use exceptions judiciously for unexpected situations. From a9e3a4673c62076ba2a764cf6d77d5acda177fc3 Mon Sep 17 00:00:00 2001 From: Mohammed Ahmed Majid Date: Sun, 2 Jun 2024 00:39:56 +0530 Subject: [PATCH 379/656] Updated Files --- contrib/advanced-python/exception-handling.md | 6 +++--- contrib/advanced-python/index.md | 1 + 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/contrib/advanced-python/exception-handling.md b/contrib/advanced-python/exception-handling.md index bddcac46..3e0c6726 100644 --- a/contrib/advanced-python/exception-handling.md +++ b/contrib/advanced-python/exception-handling.md @@ -1,6 +1,6 @@ # Exception Handling in Python -Exception handling is a way of managing errors that may occur during program execution, through which you can handle exceptions gracefully. Python's exception handling mechanism has been designed to avoid unexpected termination of the program and offer a means to either regain control after an error or display meaningful messages to the user. +Exception Handling is a way of managing the errors that may occur during a program execution. Python's exception handling mechanism has been designed to avoid the unexpected termination of the program, and offer to either regain control after an error or display a meaningful message to the user. - **Error** - An error is a mistake or an incorrect result produced by a program. It can be a syntax error, a logical error, or a runtime error. Errors are typically fatal, meaning they prevent the program from continuing to execute. - **Exception** - An exception is an event that occurs during the execution of a program that disrupts the normal flow of instructions. Exceptions are typically unexpected and can be handled by the program to prevent it from crashing or terminating abnormally. It can be runtime, input/output or system exceptions. Exceptions are designed to be handled by the program, allowing it to recover from the error and continue executing. @@ -35,7 +35,7 @@ Here's an example to explain this: ```python try: - # code that might raise an exception + # Code that might raise an exception result = 10 / 0 except: print("An error occured!") @@ -72,7 +72,7 @@ Output Oops! You tried to divide by zero. ``` -If you comment on the line `result = 10 / 0`, then the output will be +If you comment on the line `result = 10 / 0`, then the output will be: ```markdown There's a variable named 'undefined_variable' that hasn't been defined yet. diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index b95e4b91..febcbbe1 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -7,3 +7,4 @@ - [Regular Expressions in Python](regular_expressions.md) - [JSON module](json-module.md) - [Map Function](map-function.md) +- [Exception Handling in Python](exception-handling.md) From 3a8ac54d7cd94a7ceda1552d8be823129230ff78 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:05:00 +0530 Subject: [PATCH 380/656] Update clustering.md --- contrib/machine-learning/clustering.md | 19 ------------------- 1 file changed, 19 deletions(-) diff --git a/contrib/machine-learning/clustering.md b/contrib/machine-learning/clustering.md index 7d5577ca..bc02d374 100644 --- a/contrib/machine-learning/clustering.md +++ b/contrib/machine-learning/clustering.md @@ -2,19 +2,6 @@ Clustering is an unsupervised machine learning technique that groups a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups (clusters). This README provides an overview of clustering, including its fundamental concepts, types, algorithms, and how to implement it using Python. -## Table of Contents - -1. [Introduction](#introduction) -2. [Concepts](#concepts) -3. [Types of Clustering](#types-of-clustering) -4. [Clustering Algorithms](#clustering-algorithms) -5. [Implementation](#implementation) - - [Using Scikit-learn](#using-scikit-learn) - - [Code Example](#code-example) -6. [Evaluation Metrics](#evaluation-metrics) -7. [Conclusion](#conclusion) -8. [References](#references) - ## Introduction Clustering is a technique used to find inherent groupings within data without pre-labeled targets. It is widely used in exploratory data analysis, pattern recognition, image analysis, information retrieval, and bioinformatics. @@ -107,9 +94,3 @@ print(data.head()) ## Conclusion Clustering is a powerful technique for discovering structure in data. Understanding different clustering algorithms and their evaluation metrics is crucial for selecting the appropriate method for a given problem. - -## References - -- [Scikit-learn Documentation](https://scikit-learn.org/stable/modules/clustering.html) -- [Wikipedia: Cluster Analysis](https://en.wikipedia.org/wiki/Cluster_analysis) -- [Towards Data Science: A Comprehensive Guide to Clustering](https://towardsdatascience.com/a-comprehensive-guide-to-clustering-9789897f8b88) From 03a168d202babc9839e00b9b9563dc49f39bed39 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:11:03 +0530 Subject: [PATCH 381/656] Update and rename IntroToCNNs.md to intro-to-cnn.md --- .../{IntroToCNNs.md => intro-to-cnn.md} | 75 +++++-------------- 1 file changed, 17 insertions(+), 58 deletions(-) rename contrib/machine-learning/{IntroToCNNs.md => intro-to-cnn.md} (89%) diff --git a/contrib/machine-learning/IntroToCNNs.md b/contrib/machine-learning/intro-to-cnn.md similarity index 89% rename from contrib/machine-learning/IntroToCNNs.md rename to contrib/machine-learning/intro-to-cnn.md index fb9027a5..0221ca10 100644 --- a/contrib/machine-learning/IntroToCNNs.md +++ b/contrib/machine-learning/intro-to-cnn.md @@ -1,30 +1,5 @@ # Understanding Convolutional Neural Networks (CNN) -## Table of Contents -
-Click to expand - -- [Introduction](#introduction) -- [CNN Architecture](#cnn-architecture) - -
- Convolutional Layer - - - [Input Shape](#input-shape) - - [Stride](#strides) - - [Padding](#padding) - - [Filter](#filters) - - [Output](#output) - -
- -- [Pooling Layer](#pooling-layer) -- [Flatten Layer](#flatten-layer) -- [Dropout Layer](#dropout-layer) - -- [Implementation](#implementation) - -
- ## Introduction Convolutional Neural Networks (CNNs) are a specialized type of artificial neural network designed primarily for processing structured grid data like images. CNNs are particularly powerful for tasks involving image recognition, classification, and computer vision. They have revolutionized these fields, outperforming traditional neural networks by leveraging their unique architecture to capture spatial hierarchies in images. @@ -52,9 +27,7 @@ The convolutional layer is the core building block of a CNN. The layer's paramet #### Input Shape The dimensions of the input image, including the number of channels (e.g., 3 for RGB images & 1 for Grayscale images). -
- -
+![image](assets/cnn-input_shape.png) - The input matrix is a binary image of handwritten digits, where '1' marks the pixels containing the digit (ink/grayscale area) and '0' marks the background pixels (empty space). @@ -63,9 +36,7 @@ where '1' marks the pixels containing the digit (ink/grayscale area) and '0' mar #### Strides The step size with which the filter moves across the input image. -
- -
+![image](assets/cnn-strides.png) - This visualization will help you understand how the filter (kernel) moves acroos the input matrix with stride values of (3,3) and (2,2). - A stride of 1 means the filter moves one step at a time, ensuring it covers the entire input matrix. @@ -74,9 +45,7 @@ The step size with which the filter moves across the input image. #### Padding Determines whether the output size is the same as the input size ('same') or reduced ('valid'). -
- -
+![image](assets/cnn-padding.png) - `Same` padding is preferred in earlier layers to preserve spatial and edge information, as it can help the network learn more detailed features. - Choose `valid` padding when focusing on the central input region or requiring specific output dimensions. @@ -84,37 +53,31 @@ Determines whether the output size is the same as the input size ('same') or red #### Filters Small matrices that slide over the input data to extract features. -
- -
+![image](assets/cnn-filters.png) - The first filter aims to detect closed loops within the input image, being highly relevant for recognizing digits with circular or oval shapes, such as '0', '6', '8', or '9'. - The next filter helps in detecting vertical lines, crucial for identifying digits like '1', '4', '7', and parts of other digits that contain vertical strokes. - The last filter shows how to detect diagonal lines in the input image, useful for identifying the slashes present in digits like '1', '7', or parts of '4' and '9'. -
- #### Output A set of feature maps that represent the presence of different features in the input. -
- -
+![image](assets/cnn-ouputs.png) - With no padding and a stride of 1, the 3x3 filter moves one step at a time across the 7x5 input matrix. The filter can only move within the original boundaries of the input, resulting in a smaller 5x3 output matrix. This configuration is useful when you want to reduce the spatial dimensions of the feature map while preserving the exact spatial relationships between features. - By adding zero padding to the input matrix, it is expanded to 9x7, allowing the 3x3 filter to "fit" fully on the edges and corners. With a stride of 1, the filter still moves one step at a time, but now the output matrix is the same size (7x5) as the original input. Same padding is often preferred in early layers of a CNN to preserve spatial information and avoid rapid feature map shrinkage. - Without padding, the 3x3 filter operates within the original input matrix boundaries, but now it moves two steps at a time (stride 2). This significantly reduces the output matrix size to 3x2. Larger strides are employed to decrease computational cost and the output size, which can be beneficial in speeding up the training process and preventing overfitting. However, they might miss some finer details due to the larger jumps. - The output dimension of a CNN model is given by, $$ n_{out} = { n_{in} + (2 \cdot p) - k \over s } $$ -where,
- nin = number of input features
- p = padding
- k = kernel size
+where, + nin = number of input features + p = padding + k = kernel size s = stride -- Also, the number of trainable parameters for each layer is given by, $ (n_c \cdot [k \cdot k] \cdot f) + f $ -
where,
- nc = number of input channels
- k x k = kernel size
- f = number of filters
+- Also, the number of trainable parameters for each layer is given by, $ (n_c \cdot [k \cdot k] \cdot f) + f $ +where, + nc = number of input channels + k x k = kernel size + f = number of filters an additional f is added for bias ### Pooling Layer @@ -135,18 +98,14 @@ Pooling layers reduce the dimensionality of each feature map while retaining the The flatten layer converts the 2D matrix data to a 1D vector, which can be fed into a fully connected (dense) layer. - **Input Shape:** The 2D feature maps from the previous layer. - **Output:** A 1D vector that represents the same data in a flattened format. -
- -
+![image](assets/cnn-flattened.png) ### Dropout Layer Dropout is a regularization technique to prevent overfitting in neural networks by randomly setting a fraction of input units to zero at each update during training time. - **Input Shape:** The data from the previous layer. - **Dropout Rate:** The fraction of units to drop (e.g., 0.5 for 50% dropout). - **Output:** The same shape as the input, with some units set to zero. -
- -
+![image](assets/cnn-dropout.png) - The updated 0 values represents the dropped units. @@ -263,4 +222,4 @@ dropout_output = cnn_model.dropout(flattened_output, dropout_rate=0.3) print("\nDropout Output:\n", dropout_output) ``` -Feel free to play around with the parameters! \ No newline at end of file +Feel free to play around with the parameters! From e4dd80f1ce9e18679d00ba97bbe99a337a15b73e Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:11:26 +0530 Subject: [PATCH 382/656] Update index.md --- contrib/machine-learning/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index ea8b5cb3..988a27f8 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -9,4 +9,4 @@ - [TensorFlow.md](tensorFlow.md) - [PyTorch.md](pytorch.md) - [Types of optimizers](Types_of_optimizers.md) -- [Introduction To Convolutional Neural Networks (CNNs)](IntroToCNNs.md) +- [Introduction To Convolutional Neural Networks (CNNs)](intro-to-cnn.md) From 33543fd13c21329a4656f360a96d29c6483e8c71 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:15:14 +0530 Subject: [PATCH 383/656] Delete .vscode/settings.json --- .vscode/settings.json | 2 -- 1 file changed, 2 deletions(-) delete mode 100644 .vscode/settings.json diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index 7a73a41b..00000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,2 +0,0 @@ -{ -} \ No newline at end of file From 3c95166575d6e25c2544a0be7d8d8cae4ab9b45e Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:15:39 +0530 Subject: [PATCH 384/656] Rename Random_Forest.md to random-forest.md --- contrib/machine-learning/{Random_Forest.md => random-forest.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/machine-learning/{Random_Forest.md => random-forest.md} (99%) diff --git a/contrib/machine-learning/Random_Forest.md b/contrib/machine-learning/random-forest.md similarity index 99% rename from contrib/machine-learning/Random_Forest.md rename to contrib/machine-learning/random-forest.md index 59c44eff..0abd1ab0 100644 --- a/contrib/machine-learning/Random_Forest.md +++ b/contrib/machine-learning/random-forest.md @@ -193,4 +193,4 @@ Scikit-learn Random Forest Documentation Wikipedia: Random Forest Machine Learning Mastery: Introduction to Random Forest Kaggle: Random Forest Guide -Towards Data Science: Understanding Random Forests \ No newline at end of file +Towards Data Science: Understanding Random Forests From 86e7c0d80613c236d05b4cd0895f0ee6df7601cb Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:16:18 +0530 Subject: [PATCH 385/656] Update index.md --- contrib/machine-learning/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 073bca93..3b8c95b2 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -9,4 +9,4 @@ - [TensorFlow.md](tensorFlow.md) - [PyTorch.md](pytorch.md) - [Types of optimizers](Types_of_optimizers.md) -- [Random Forest](Random_Forest.md) \ No newline at end of file +- [Random Forest](random-forest.md) From 0189c285cc983d2a19e306967bfe4d1f0dda43da Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:18:23 +0530 Subject: [PATCH 386/656] Update random-forest.md --- contrib/machine-learning/random-forest.md | 37 ++++------------------- 1 file changed, 6 insertions(+), 31 deletions(-) diff --git a/contrib/machine-learning/random-forest.md b/contrib/machine-learning/random-forest.md index 0abd1ab0..feaaa7a7 100644 --- a/contrib/machine-learning/random-forest.md +++ b/contrib/machine-learning/random-forest.md @@ -2,31 +2,6 @@ Random Forest is a versatile machine learning algorithm capable of performing both regression and classification tasks. It is an ensemble method that operates by constructing a multitude of decision trees during training and outputting the average prediction of the individual trees (for regression) or the mode of the classes (for classification). - -- [Random Forest](#random-forest) - - [Introduction](#introduction) - - [How Random Forest Works](#how-random-forest-works) - - [1. Bootstrap Sampling:](#1-bootstrap-sampling) - - [2. Decision Trees:](#2-decision-trees) - - [3. Feature Selection:](#3-feature-selection) - - [4. Voting/Averaging:](#4-votingaveraging) - - [Detailed Working Mechanism](#detailed-working-mechanism) - - [Step 3: Aggregation:](#step-3-aggregation) - - [Advantages and Disadvantages](#advantages-and-disadvantages) - - [Advantages](#advantages) - - [Disadvantages](#disadvantages) - - [Hyperparameters](#hyperparameters) - - [Key Hyperparameters](#key-hyperparameters) - - [Tuning Hyperparameters](#tuning-hyperparameters) - - [Code Examples](#code-examples) - - [Classification Example](#classification-example) - - [Feature Importance](#feature-importance) - - [Hyperparameter Tuning](#hyperparameter-tuning) - - [Regression Example](#regression-example) - - [Conclusion](#conclusion) - - [References](#references) - - ## Introduction Random Forest is an ensemble learning method used for classification and regression tasks. It is built from multiple decision trees and combines their outputs to improve the model's accuracy and control over-fitting. @@ -41,9 +16,9 @@ Random Forest is an ensemble learning method used for classification and regress For classification, the mode of the classes predicted by individual trees is taken (majority vote). For regression, the average of the outputs of the individual trees is taken. ### Detailed Working Mechanism -* #### Step 1: Bootstrap Sampling: +#### Step 1: Bootstrap Sampling: Each tree is trained on a random sample of the original data, drawn with replacement (bootstrap sample). This means some data points may appear multiple times in a sample while others may not appear at all. -* #### Step 2: Tree Construction: +#### Step 2: Tree Construction: Each node in the tree is split using the best split among a random subset of the features. This process adds an additional layer of randomness, contributing to the robustness of the model. #### Step 3: Aggregation: For classification tasks, the final prediction is based on the majority vote from all the trees. For regression tasks, the final prediction is the average of all the tree predictions. @@ -73,7 +48,7 @@ Hyperparameter tuning can significantly improve the performance of a Random Fore #### Classification Example Below is a simple example of using Random Forest for a classification task with the Iris dataset. -``` +```python import numpy as np import pandas as pd from sklearn.datasets import load_iris @@ -109,7 +84,7 @@ print("Classification Report:\n", classification_report(y_test, y_pred)) Random Forest provides a way to measure the importance of each feature in making predictions. -``` +```python import matplotlib.pyplot as plt # Get feature importances @@ -132,7 +107,7 @@ plt.show() #### Hyperparameter Tuning Using Grid Search for hyperparameter tuning. -``` +```python from sklearn.model_selection import GridSearchCV # Define the parameter grid @@ -155,7 +130,7 @@ print("Best parameters found: ", grid_search.best_params_) #### Regression Example Below is a simple example of using Random Forest for a regression task with the Boston housing dataset. -``` +```python import numpy as np import pandas as pd from sklearn.datasets import load_boston From 56e7774648e3dbccd68dbdc1d8e61521524b83aa Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:20:23 +0530 Subject: [PATCH 387/656] Update index.md --- contrib/ds-algorithms/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index d3c3c271..e3fc3783 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -10,4 +10,4 @@ - [Greedy Algorithms](greedy-algorithms.md) - [Dynamic Programming](dynamic-programming.md) - [Linked list](linked-list.md) -- [Sliding Window Technique](Sliding_Window.md) +- [Sliding Window Technique](sliding-window.md) From 234df0ee0b4b4d29a799eac61b942cea57a785a9 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:20:46 +0530 Subject: [PATCH 388/656] Rename Sliding_Window.md to sliding-window.md --- contrib/ds-algorithms/{Sliding_Window.md => sliding-window.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/ds-algorithms/{Sliding_Window.md => sliding-window.md} (100%) diff --git a/contrib/ds-algorithms/Sliding_Window.md b/contrib/ds-algorithms/sliding-window.md similarity index 100% rename from contrib/ds-algorithms/Sliding_Window.md rename to contrib/ds-algorithms/sliding-window.md From be0b5b69324ee8e4cb997d327e4ac753e1499093 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:22:13 +0530 Subject: [PATCH 389/656] Update index.md --- contrib/ds-algorithms/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 653cc986..a07dc23d 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -10,4 +10,4 @@ - [Greedy Algorithms](greedy-algorithms.md) - [Dynamic Programming](dynamic-programming.md) - [Linked list](linked-list.md) -- [Trie](Trie.md) +- [Trie](trie.md) From 1b0bd26cbe9aebbdfbf9135384f4928371a61a19 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:22:32 +0530 Subject: [PATCH 390/656] Rename Trie.md to trie.md --- contrib/ds-algorithms/{Trie.md => trie.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/ds-algorithms/{Trie.md => trie.md} (100%) diff --git a/contrib/ds-algorithms/Trie.md b/contrib/ds-algorithms/trie.md similarity index 100% rename from contrib/ds-algorithms/Trie.md rename to contrib/ds-algorithms/trie.md From 04bc0893a2c596c042dd6dae1fe401714310b582 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:23:47 +0530 Subject: [PATCH 391/656] Update trie.md --- contrib/ds-algorithms/trie.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/contrib/ds-algorithms/trie.md b/contrib/ds-algorithms/trie.md index 43f04157..0ccfbaad 100644 --- a/contrib/ds-algorithms/trie.md +++ b/contrib/ds-algorithms/trie.md @@ -27,7 +27,7 @@ Node Class have mainly two components Code Block of Node Class : -``` +```python class Node: def __init__(self): self.alphabets = [None] * 26 @@ -40,7 +40,7 @@ Now we need to implement Trie. We create another class named Trie with some meth Code Implementation of Initialization: -``` +```python class Trie: def __init__(self): self.root = Node() @@ -51,7 +51,7 @@ class Trie: 1. **Insertion**: Inserts the word into the Trie. This method takes `word` as parameter. For each character in the word, it checks if there is a corresponding child node. If not, it creates a new `Node`. After processing all the characters in word, it increments the `end_of_word` value of the last node. Code Implementation of Insertion: -``` +```python def insert(self, word): node = self.root for char in word: @@ -69,7 +69,7 @@ There are two cases in Searching: - *Word found*: It happens when the search word is present in the Trie. This case will occur, when the `end_of_word` value is greater than `0` of the node after traversing the whole word. Code Implementation of Searching: -``` +```python def Search(self, word): node = self.root for char in word: @@ -84,7 +84,7 @@ Code Implementation of Searching: Code Implementation of Deletion: -``` +```python def delete(self, word): node = self.root for char in word: @@ -96,7 +96,7 @@ def delete(self, word): Python Code to implement Trie: -``` +```python class Node: def __init__(self): self.alphabets = [None] * 26 From f20baa60d2614be595098b5a2d0fe4497dbfdf7e Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:28:14 +0530 Subject: [PATCH 392/656] Update and rename matplotlib.md to matplotlib-introduction.md --- .../{matplotlib.md => matplotlib-introduction.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/plotting-visualization/{matplotlib.md => matplotlib-introduction.md} (100%) diff --git a/contrib/plotting-visualization/matplotlib.md b/contrib/plotting-visualization/matplotlib-introduction.md similarity index 100% rename from contrib/plotting-visualization/matplotlib.md rename to contrib/plotting-visualization/matplotlib-introduction.md From 0d420ef4a6914d35a986e65db90b6329d0c30d88 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:28:42 +0530 Subject: [PATCH 393/656] Update index.md --- contrib/plotting-visualization/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 84d07f9d..d20340bc 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -1,6 +1,6 @@ # List of sections - [Installing Matplotlib](matplotlib-installation.md) +- [Introducing Matplotlib](matplotlib-introduction.md) - [Bar Plots in Matplotlib](matplotlib-bar-plots.md) - [Pie Charts in Matplotlib](matplotlib-pie-charts.md) -- [Introducing Matplotlib](matplotlib.md) From 31eaf9948dc9f4f2417b0b91b69a852bc5618fea Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:29:56 +0530 Subject: [PATCH 394/656] Rename bar-plots.md to matplotlib-bar-plots.md --- .../{bar-plots.md => matplotlib-bar-plots.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/plotting-visualization/{bar-plots.md => matplotlib-bar-plots.md} (100%) diff --git a/contrib/plotting-visualization/bar-plots.md b/contrib/plotting-visualization/matplotlib-bar-plots.md similarity index 100% rename from contrib/plotting-visualization/bar-plots.md rename to contrib/plotting-visualization/matplotlib-bar-plots.md From 358d8b05e4792ff9e819c36e14df3a3d2459c41f Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:32:56 +0530 Subject: [PATCH 395/656] Update matplotlib-introduction.md --- .../matplotlib-introduction.md | 30 ++++++------------- 1 file changed, 9 insertions(+), 21 deletions(-) diff --git a/contrib/plotting-visualization/matplotlib-introduction.md b/contrib/plotting-visualization/matplotlib-introduction.md index 02f17f56..691d1f8a 100644 --- a/contrib/plotting-visualization/matplotlib-introduction.md +++ b/contrib/plotting-visualization/matplotlib-introduction.md @@ -12,8 +12,9 @@ To make this process of data visualization easier and clearer, matplotlib librar ## Starting with Matplotlib -### 1.] Install and import the neccasary libraries - mayplotlib.pylplot -```python +### 1. Install and import the neccasary libraries - mayplotlib.pylplot + +```bash pip install matplotlib ``` @@ -22,9 +23,7 @@ import maptplotlib.pyplot as plt import numpy as np ``` - - -### 2.] Scatter plot +### 2. Scatter plot Scatter plot is a type of plot that uses the cartesian coordinates between x and y to describe the relation between them. It uses dots to represent relation between the data variables of the data set. ```python @@ -41,12 +40,9 @@ plt.tight_layout() plt.show() ``` +![scatterplot](images/scatterplot.png) - -![scatterplot](https://github.com/Harshita297/learn-python/assets/122027571/aa57eb31-4377-4f75-9a97-e4fa7a54a938) - - -### 3.] Bar plot +### 3. Bar plot Bar plot is a type of plot that plots the frequency distrubution of the categorical variables. Each entity of the categoric variable is represented as a bar. The size of the bar represents its numeric value. ```python @@ -61,11 +57,10 @@ plt.ylabel("Y values") plt.show() ``` -![barplot](https://github.com/Harshita297/learn-python/assets/122027571/00cea3cd-a962-4bfe-b158-921389ee564c) - +![barplot](images/barplot.png) -### 4.] Histogram +### 4. Histogram Histogram is the representation of frequency distribution of qualitative data. The height of each rectangle defines the amount, or how often that variable appears. ```python @@ -79,14 +74,7 @@ plt.ylabel("Frequency Distribution") plt.show() ``` -![histogram](https://github.com/Harshita297/learn-python/assets/122027571/32470819-bac5-4bb2-9f36-79521fa40bf3) - - - - - - - +![histogram](images/histogram.png) From 4468dd6e91b4481fd15249bed93988ea82acfe89 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:35:40 +0530 Subject: [PATCH 396/656] Rename Descriptive_Statistics.md to descriptive-statistics.md --- .../{Descriptive_Statistics.md => descriptive-statistics.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/pandas/{Descriptive_Statistics.md => descriptive-statistics.md} (100%) diff --git a/contrib/pandas/Descriptive_Statistics.md b/contrib/pandas/descriptive-statistics.md similarity index 100% rename from contrib/pandas/Descriptive_Statistics.md rename to contrib/pandas/descriptive-statistics.md From 06935da991e2412c60f3b750b3dfe2955e2c4f5e Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:36:05 +0530 Subject: [PATCH 397/656] Rename GroupBy_Functions_Pandas.md to groupby-functions.md --- .../pandas/{GroupBy_Functions_Pandas.md => groupby-functions.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/pandas/{GroupBy_Functions_Pandas.md => groupby-functions.md} (100%) diff --git a/contrib/pandas/GroupBy_Functions_Pandas.md b/contrib/pandas/groupby-functions.md similarity index 100% rename from contrib/pandas/GroupBy_Functions_Pandas.md rename to contrib/pandas/groupby-functions.md From df9ed4453f022c338fdfc4b588ac6fee861ece0b Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:36:22 +0530 Subject: [PATCH 398/656] Rename excel_with_pandas.md to excel-with-pandas.md --- contrib/pandas/{excel_with_pandas.md => excel-with-pandas.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/pandas/{excel_with_pandas.md => excel-with-pandas.md} (100%) diff --git a/contrib/pandas/excel_with_pandas.md b/contrib/pandas/excel-with-pandas.md similarity index 100% rename from contrib/pandas/excel_with_pandas.md rename to contrib/pandas/excel-with-pandas.md From 6df31671efe29800d369ec7c9d74dcb297a18b6a Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:36:42 +0530 Subject: [PATCH 399/656] Rename pandas_series_vs_numpy_ndarray.md to pandas-series-vs-numpy-ndarray.md --- ...ries_vs_numpy_ndarray.md => pandas-series-vs-numpy-ndarray.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/pandas/{pandas_series_vs_numpy_ndarray.md => pandas-series-vs-numpy-ndarray.md} (100%) diff --git a/contrib/pandas/pandas_series_vs_numpy_ndarray.md b/contrib/pandas/pandas-series-vs-numpy-ndarray.md similarity index 100% rename from contrib/pandas/pandas_series_vs_numpy_ndarray.md rename to contrib/pandas/pandas-series-vs-numpy-ndarray.md From ccd5dea51ebf9da139f85762c98f42ec3f26f9cc Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:37:36 +0530 Subject: [PATCH 400/656] Update index.md --- contrib/pandas/index.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index c17d8393..33c73479 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -1,10 +1,10 @@ # List of sections - [Pandas Introduction and Dataframes in Pandas](introduction.md) -- [Pandas Series Vs NumPy ndarray](pandas_series_vs_numpy_ndarray.md) -- [Pandas Descriptive Statistics](Descriptive_Statistics.md) -- [Group By Functions with Pandas](GroupBy_Functions_Pandas.md) -- [Excel using Pandas DataFrame](excel_with_pandas.md) +- [Pandas Series Vs NumPy ndarray](pandas-series-vs-numpy-ndarray.md) +- [Pandas Descriptive Statistics](descriptive-statistics.md) +- [Group By Functions with Pandas](groupby-functions.md) +- [Excel using Pandas DataFrame](excel-with-pandas.md) - [Working with Date & Time in Pandas](datetime.md) - [Importing and Exporting Data in Pandas](import-export.md) - [Handling Missing Values in Pandas](handling-missing-values.md) From 59773b20eed7cff5a85b5785f96edfe4e90a7139 Mon Sep 17 00:00:00 2001 From: "Kaza.Sunitha" <133480054+Kazasunitha@users.noreply.github.com> Date: Sun, 2 Jun 2024 03:49:19 +0530 Subject: [PATCH 401/656] Added viewing_data_in_pandas.md file (#660) * Add files via upload * Update classes.md * Update classes.md * Update classes.md * Update index.md * Update classes.md * Update index.md * Add files via upload * Update viewing_data_in_pandas.md * Delete contrib/advanced-python/classes.md * Delete contrib/advanced-python/index.md * Revert "Delete contrib/advanced-python/index.md" This reverts commit 55fe958c1c57daf431b3dce1d5d103bb8361612a. * Update index.md * Update viewing_data_in_pandas.md Updated the heading format * Update index.md * Update and rename viewing_data_in_pandas.md to viewing-data.md * Update viewing-data.md --------- Co-authored-by: Ankit Mahato --- contrib/pandas/index.md | 1 + contrib/pandas/viewing-data.md | 67 ++++++++++++++++++++++++++++++++++ 2 files changed, 68 insertions(+) create mode 100644 contrib/pandas/viewing-data.md diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 33c73479..e5a83533 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -1,6 +1,7 @@ # List of sections - [Pandas Introduction and Dataframes in Pandas](introduction.md) +- [Viewing data in pandas](viewing-data.md) - [Pandas Series Vs NumPy ndarray](pandas-series-vs-numpy-ndarray.md) - [Pandas Descriptive Statistics](descriptive-statistics.md) - [Group By Functions with Pandas](groupby-functions.md) diff --git a/contrib/pandas/viewing-data.md b/contrib/pandas/viewing-data.md new file mode 100644 index 00000000..8aaa4ae4 --- /dev/null +++ b/contrib/pandas/viewing-data.md @@ -0,0 +1,67 @@ +# Viewing rows of the frame + +## `head()` method + +The pandas library in Python provides a convenient method called `head()` that allows you to view the first few rows of a DataFrame. Let me explain how it works: +- The `head()` function returns the first n rows of a DataFrame or Series. +- By default, it displays the first 5 rows, but you can specify a different number of rows using the n parameter. + +### Syntax + +```python +dataframe.head(n) +``` + +`n` is the Optional value. The number of rows to return. Default value is `5`. + +### Example + +```python +import pandas as pd +df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion','tiger','rabit','dog','fox','monkey','elephant']}) +df.head(n=5) +``` + +#### Output + +``` + animal +0 alligator +1 bee +2 falcon +3 lion +4 tiger +``` + +## `tail()` method + +The `tail()` function in Python displays the last five rows of the dataframe by default. It takes in a single parameter: the number of rows. We can use this parameter to display the number of rows of our choice. +- The `tail()` function returns the last n rows of a DataFrame or Series. +- By default, it displays the last 5 rows, but you can specify a different number of rows using the n parameter. + +### Syntax + +```python +dataframe.tail(n) +``` + +`n` is the Optional value. The number of rows to return. Default value is `5`. + +### Example + +```python +import pandas as pd +df = pd.DataFrame({'fruits': ['mongo', 'orange', 'apple', 'lemon','banana','water melon','papaya','grapes','cherry','coconut']}) +df.tail(n=5) +``` + +#### Output + +``` + fruits +5 water melon +6 papaya +7 grapes +8 cherry +9 coconut +``` From 839211767809e4c7d53f38dd21dafd90ccd9bd5a Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:56:26 +0530 Subject: [PATCH 402/656] Update matplotlib-line-plot.md --- .../matplotlib-line-plot.md | 56 +++++++++++-------- 1 file changed, 33 insertions(+), 23 deletions(-) diff --git a/contrib/plotting-visualization/matplotlib-line-plot.md b/contrib/plotting-visualization/matplotlib-line-plot.md index 566fdbba..b7488e6f 100644 --- a/contrib/plotting-visualization/matplotlib-line-plot.md +++ b/contrib/plotting-visualization/matplotlib-line-plot.md @@ -1,24 +1,27 @@ # Line Chart in Matplotlib A line chart is a simple way to visualize data where we connect individual data points. It helps us to see trends and patterns over time or across categories. -
This type of chart is particularly useful for:
-* Comparing Data: Comparing multiple datasets on the same axes. -* Highlighting Changes: Illustrating changes and patterns in data. -* Visualizing Trends: Showing trends over time or other continuous variables. + +This type of chart is particularly useful for: +- Comparing Data: Comparing multiple datasets on the same axes. +- Highlighting Changes: Illustrating changes and patterns in data. +- Visualizing Trends: Showing trends over time or other continuous variables. ## Prerequisites -Line plots can be created in Python with Matplotlib's ``pyplot`` library. To build a line plot, first import ``Matplotlib``. It is a standard convention to import Matplotlib's pyplot library as ``plt``. -``` -import matplotlib.pyplot as plt +Line plots can be created in Python with Matplotlib's `pyplot` library. To build a line plot, first import `matplotlib`. It is a standard convention to import Matplotlib's pyplot library as `plt`. + +```python +import matplotlib.pyplot as plt ``` ## Creating a simple Line Plot First import matplotlib and numpy, these are useful for charting. -
You can use the ``plot(x,y)`` method to create a line chart.
-``` +You can use the `plot(x,y)` method to create a line chart. + +```python import matplotlib.pyplot as plt import numpy as np @@ -29,6 +32,7 @@ y = 2*x + 1 plt.plot(x, y) plt.show() ``` + When executed, this will show the following line plot: ![Basic line Chart](images/simple_line.png) @@ -36,8 +40,9 @@ When executed, this will show the following line plot: ## Curved line -The ``plot()`` method also works for other types of line charts. It doesn’t need to be a straight line, y can have any type of values. -``` +The `plot()` method also works for other types of line charts. It doesn’t need to be a straight line, y can have any type of values. + +```python import matplotlib.pyplot as plt import numpy as np @@ -47,6 +52,7 @@ y = 2**x + 1 plt.plot(x, y) plt.show() ``` + When executed, this will show the following Curved line plot: ![Curved line](images/line-curve.png) @@ -54,8 +60,9 @@ When executed, this will show the following Curved line plot: ## Line with Labels -To know what you are looking at, you need meta data. Labels are a type of meta data. They show what the chart is about. The chart has an ``x label``, ``y label`` and ``title``. -``` +To know what you are looking at, you need meta data. Labels are a type of meta data. They show what the chart is about. The chart has an `x label`, `y label` and `title`. + +```python import matplotlib.pyplot as plt import numpy as np @@ -72,15 +79,16 @@ plt.title("With Labels") plt.show() ``` + When executed, this will show the following line with labels plot: ![line with labels](images/line-labels.png) ## Multiple lines -More than one line can be in the plot. To add another line, just call the ``plot(x,y)`` function again. In the example below we have two different values for ``y(y1,y2)`` that are plotted onto the chart. +More than one line can be in the plot. To add another line, just call the `plot(x,y)` function again. In the example below we have two different values for `y(y1,y2)` that are plotted onto the chart. -``` +```python import matplotlib.pyplot as plt import numpy as np @@ -98,6 +106,7 @@ plt.plot(x, y1, plt.show() ``` + When executed, this will show the following Multiple lines plot: ![multiple lines](images/two-lines.png) @@ -105,9 +114,9 @@ When executed, this will show the following Multiple lines plot: ## Dotted line -Lines can be in the form of dots like the image below. Instead of calling ``plot(x,y)`` call the ``scatter(x,y)`` method. The ``scatter(x,y)`` method can also be used to (randomly) plot points onto the chart. +Lines can be in the form of dots like the image below. Instead of calling `plot(x,y)` call the `scatter(x,y)` method. The `scatter(x,y)` method can also be used to (randomly) plot points onto the chart. -``` +```python import matplotlib.pyplot as plt import numpy as np @@ -130,9 +139,9 @@ When executed, this will show the following Dotted line plot: ## Line ticks -You can change the ticks on the plot. Set them on the ``x-axis``, ``y-axis`` or even change their color. The line can be more thick and have an alpha value. +You can change the ticks on the plot. Set them on the `x-axis`, `y-axis` or even change their color. The line can be more thick and have an alpha value. -``` +```python import matplotlib.pyplot as plt import numpy as np @@ -166,9 +175,9 @@ When executed, this will show the following line ticks plot: ## Line with asymptote -An asymptote can be added to the plot. To do that, use ``plt.annotate()``. There’s lso a dotted line in the plot below. You can play around with the code to see how it works. +An asymptote can be added to the plot. To do that, use `plt.annotate()`. There’s lso a dotted line in the plot below. You can play around with the code to see how it works. -``` +```python import matplotlib.pyplot as plt import numpy as np @@ -222,9 +231,9 @@ When executed, this will show the following Line with asymptote plot: ## Line with text scale -It doesn’t have to be a numeric scale. The scale can also contain textual words like the example below. In ``plt.yticks()`` we just pass a list with text values. These values are then show against the ``y axis``. +It doesn’t have to be a numeric scale. The scale can also contain textual words like the example below. In `plt.yticks()` we just pass a list with text values. These values are then show against the `y axis`. -``` +```python import matplotlib.pyplot as plt import numpy as np @@ -261,6 +270,7 @@ ax.spines['left'].set_position(('data', 0)) plt.show() ``` + When executed, this will show the following Line with text scale plot: ![Line with text scale](images/line-with-text-scale.png) From aeb947bb1a0c3a44ff3b2ece398649bae6a9e5df Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:59:12 +0530 Subject: [PATCH 403/656] Rename ArtificialNeuralNetwork.md to ann.md --- contrib/machine-learning/{ArtificialNeuralNetwork.md => ann.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/machine-learning/{ArtificialNeuralNetwork.md => ann.md} (100%) diff --git a/contrib/machine-learning/ArtificialNeuralNetwork.md b/contrib/machine-learning/ann.md similarity index 100% rename from contrib/machine-learning/ArtificialNeuralNetwork.md rename to contrib/machine-learning/ann.md From e3c2799e6f8a4e58ba00778d7d0cedc76b1495b3 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:59:31 +0530 Subject: [PATCH 404/656] Rename Decision-Tree.md to decision-tree.md --- contrib/machine-learning/{Decision-Tree.md => decision-tree.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/machine-learning/{Decision-Tree.md => decision-tree.md} (99%) diff --git a/contrib/machine-learning/Decision-Tree.md b/contrib/machine-learning/decision-tree.md similarity index 99% rename from contrib/machine-learning/Decision-Tree.md rename to contrib/machine-learning/decision-tree.md index 6563a223..8159bcf2 100644 --- a/contrib/machine-learning/Decision-Tree.md +++ b/contrib/machine-learning/decision-tree.md @@ -254,4 +254,4 @@ The final decision tree classifies instances based on the following rules: - If Outlook is Rain and Wind is Weak, PlayTennis is Yes - If Outlook is Rain and Wind is Strong, PlayTennis is No -> Note that the calculated entropies and information gains may vary slightly depending on the specific implementation and rounding methods used. \ No newline at end of file +> Note that the calculated entropies and information gains may vary slightly depending on the specific implementation and rounding methods used. From 038bc20a2805baf4165129ebdda4dc059cdcb223 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 03:59:44 +0530 Subject: [PATCH 405/656] Rename Regression.md to regression.md --- contrib/machine-learning/{Regression.md => regression.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/machine-learning/{Regression.md => regression.md} (100%) diff --git a/contrib/machine-learning/Regression.md b/contrib/machine-learning/regression.md similarity index 100% rename from contrib/machine-learning/Regression.md rename to contrib/machine-learning/regression.md From b19af0241afc2e239dfe31039d743ff888ec59ec Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:00:07 +0530 Subject: [PATCH 406/656] Rename tensorFlow.md to tensorflow.md --- contrib/machine-learning/{tensorFlow.md => tensorflow.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/machine-learning/{tensorFlow.md => tensorflow.md} (99%) diff --git a/contrib/machine-learning/tensorFlow.md b/contrib/machine-learning/tensorflow.md similarity index 99% rename from contrib/machine-learning/tensorFlow.md rename to contrib/machine-learning/tensorflow.md index 1d9357d0..b2c847c6 100644 --- a/contrib/machine-learning/tensorFlow.md +++ b/contrib/machine-learning/tensorflow.md @@ -61,4 +61,4 @@ TensorFlow is a great choice if you: ## Example Use Cases - Building and deploying complex neural networks for image recognition, natural language processing, or recommendation systems. -- Developing models that need to be run on mobile or embedded devices. \ No newline at end of file +- Developing models that need to be run on mobile or embedded devices. From f909c3a2b92d6e1379b9ee276edf78b06592c6a5 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:00:31 +0530 Subject: [PATCH 407/656] Rename binomial_distribution.md to binomial-distribution.md --- .../{binomial_distribution.md => binomial-distribution.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/machine-learning/{binomial_distribution.md => binomial-distribution.md} (100%) diff --git a/contrib/machine-learning/binomial_distribution.md b/contrib/machine-learning/binomial-distribution.md similarity index 100% rename from contrib/machine-learning/binomial_distribution.md rename to contrib/machine-learning/binomial-distribution.md From 3b161f57fabb3c1665ccec773578dfcd3377dbe5 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:00:53 +0530 Subject: [PATCH 408/656] Rename Types_of_optimizers.md to types-of-optimizers.md --- .../{Types_of_optimizers.md => types-of-optimizers.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/machine-learning/{Types_of_optimizers.md => types-of-optimizers.md} (100%) diff --git a/contrib/machine-learning/Types_of_optimizers.md b/contrib/machine-learning/types-of-optimizers.md similarity index 100% rename from contrib/machine-learning/Types_of_optimizers.md rename to contrib/machine-learning/types-of-optimizers.md From 87b2d056531182f69dea33798807b895214c731c Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:01:46 +0530 Subject: [PATCH 409/656] Update index.md --- contrib/machine-learning/index.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index e56691d8..a0dd9997 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -1,16 +1,16 @@ # List of sections -- [Binomial Distribution](binomial_distribution.md) -- [Regression in Machine Learning](Regression.md) +- [Binomial Distribution](binomial-distribution.md) +- [Regression in Machine Learning](regression.md) - [Confusion Matrix](confusion-matrix.md) -- [Decision Tree Learning](Decision-Tree.md) +- [Decision Tree Learning](decision-tree.md) - [Random Forest](random-forest.md) - [Support Vector Machine Algorithm](support-vector-machine.md) -- [Artificial Neural Network from the Ground Up](ArtificialNeuralNetwork.md) +- [Artificial Neural Network from the Ground Up](ann.md) - [Introduction To Convolutional Neural Networks (CNNs)](intro-to-cnn.md) -- [TensorFlow.md](tensorFlow.md) +- [TensorFlow.md](tensorflow.md) - [PyTorch.md](pytorch.md) -- [Types of optimizers](Types_of_optimizers.md) +- [Types of optimizers](types-of-optimizers.md) - [Logistic Regression](logistic-regression.md) - [Clustering](clustering.md) - [Grid Search](grid-search.md) From d6e610d8cc3505c806687db1d4dfb9ad0f4f5e66 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:03:36 +0530 Subject: [PATCH 410/656] Rename sklearn.md to sklearn-introduction.md --- .../machine-learning/{sklearn.md => sklearn-introduction.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/machine-learning/{sklearn.md => sklearn-introduction.md} (99%) diff --git a/contrib/machine-learning/sklearn.md b/contrib/machine-learning/sklearn-introduction.md similarity index 99% rename from contrib/machine-learning/sklearn.md rename to contrib/machine-learning/sklearn-introduction.md index 8174d938..7bb5aa8d 100644 --- a/contrib/machine-learning/sklearn.md +++ b/contrib/machine-learning/sklearn-introduction.md @@ -141,4 +141,4 @@ print(f"Accuracy: {accuracy * 100:.2f}%") ## Conclusion -scikit-learn is a powerful and versatile library that can be used for a wide range of machine learning tasks. It is particularly well-suited for beginners due to its easy-to-use interface and extensive documentation. Whether you are working on a simple classification task or a more complex clustering problem, scikit-learn provides the tools you need to build and evaluate your models effectively. \ No newline at end of file +scikit-learn is a powerful and versatile library that can be used for a wide range of machine learning tasks. It is particularly well-suited for beginners due to its easy-to-use interface and extensive documentation. Whether you are working on a simple classification task or a more complex clustering problem, scikit-learn provides the tools you need to build and evaluate your models effectively. From f671f4d3e9229ba6c622bad87a3972d4e74fea39 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:10:58 +0530 Subject: [PATCH 411/656] Rename matplotlib-line-plot.md to matplotlib-line-plots.md --- .../{matplotlib-line-plot.md => matplotlib-line-plots.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/plotting-visualization/{matplotlib-line-plot.md => matplotlib-line-plots.md} (100%) diff --git a/contrib/plotting-visualization/matplotlib-line-plot.md b/contrib/plotting-visualization/matplotlib-line-plots.md similarity index 100% rename from contrib/plotting-visualization/matplotlib-line-plot.md rename to contrib/plotting-visualization/matplotlib-line-plots.md From c7746086b91d44369aa6f8e7f3c4a863cd655da2 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:18:14 +0530 Subject: [PATCH 412/656] Rename Types_of_Cost_Functions.md to cost-functions.md --- .../{Types_of_Cost_Functions.md => cost-functions.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/machine-learning/{Types_of_Cost_Functions.md => cost-functions.md} (100%) diff --git a/contrib/machine-learning/Types_of_Cost_Functions.md b/contrib/machine-learning/cost-functions.md similarity index 100% rename from contrib/machine-learning/Types_of_Cost_Functions.md rename to contrib/machine-learning/cost-functions.md From f125cf4a33de46d7f8fee1066a347551f71ea13a Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:26:45 +0530 Subject: [PATCH 413/656] Update cost-functions.md --- contrib/machine-learning/cost-functions.md | 41 ++++++++++------------ 1 file changed, 19 insertions(+), 22 deletions(-) diff --git a/contrib/machine-learning/cost-functions.md b/contrib/machine-learning/cost-functions.md index f6507268..c1fe2170 100644 --- a/contrib/machine-learning/cost-functions.md +++ b/contrib/machine-learning/cost-functions.md @@ -14,9 +14,9 @@ MSE is one of the most commonly used cost functions, particularly in regression The MSE is defined as: $$MSE = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$$ Where: -- \( n \) is the number of samples. -- \( y_i \) is the actual value. -- \( y^i\) is the predicted value. +- `n` is the number of samples. +- $y_i$ is the actual value. +- $\hat{y}_i$ is the predicted value. **Advantages:** - Sensitive to large errors due to squaring. @@ -43,9 +43,9 @@ MAE is another commonly used cost function for regression tasks. It measures the The MAE is defined as: $$MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i|$$ Where: -- \( n \) is the number of samples. -- \( y_i \) is the actual value. -- \( y^i\) is the predicted value. +- `n` is the number of samples. +- $y_i$ is the actual value. +- $\hat{y}_i$ is the predicted value. **Advantages:** - Less sensitive to outliers compared to MSE. @@ -76,9 +76,9 @@ For binary classification, the cross-entropy loss is defined as: $$\text{Cross-Entropy} = -\frac{1}{n} \sum_{i=1}^{n} [y_i \log(\hat{y}_i) + (1 - y_i) \log(1 - \hat{y}_i)]$$ Where: -- \( n \) is the number of samples. -- \( y_i \) is the actual class label (0 or 1). -- \( y^i\) is the predicted probability of the positive class. +- `n` is the number of samples. +- $y_i$ is the actual class label (0 or 1). +- $\hat{y}_i$ is the predicted probability of the positive class. **Advantages:** @@ -109,11 +109,10 @@ The multiclass cross-entropy loss is defined as: $$\text{Cross-Entropy} = -\frac{1}{n} \sum_{i=1}^{n} \sum_{c=1}^{C} y_{i,c} \log(\hat{y}_{i,c})$$ Where: -- \( n \) is the number of samples. -- \( C \) is the number of classes. -- \( y_{i,c} \) is the indicator function for the true class of sample \( i \). - -- (y^i,c) is the predicted probability of sample \( i \) belonging to class \( c \). +- `n` is the number of samples. +- `C` is the number of classes. +- $y_{i,c}$ is the indicator function for the true class of sample `i`. +- $\hat{y}_{i,c}$ is the predicted probability of sample `i` belonging to class `c`. **Advantages:** - Handles multiple classes effectively. @@ -143,9 +142,9 @@ For binary classification, the hinge loss is defined as: $$\text{Hinge Loss} = \frac{1}{n} \sum_{i=1}^{n} \max(0, 1 - y_i \cdot \hat{y}_i)$$ Where: -- \( n \) is the number of samples. -- \( y_i \) is the actual class label (-1 or 1). -- \( \hat{y}_i \) is the predicted score for sample \( i \). +- `n` is the number of samples. +- $y_i$ is the actual class label (-1 or 1). +- $\hat{y}_i$ is the predicted score for sample \( i \). **Advantages:** - Encourages margin maximization in SVMs. @@ -182,8 +181,8 @@ $$\text{Huber Loss} = \frac{1}{n} \sum_{i=1}^{n} \left\{ \right.$$ Where: -- \( n \) is the number of samples. -- \(delta\) is a threshold parameter. +- `n` is the number of samples. +- $\delta$ is a threshold parameter. **Advantages:** - Provides a smooth loss function. @@ -214,7 +213,7 @@ The Log-Cosh loss is defined as: $$\text{Log-Cosh Loss} = \frac{1}{n} \sum_{i=1}^{n} \log(\cosh(y_i - \hat{y}_i))$$ Where: -- \( n \) is the number of samples. +- `n` is the number of samples. **Advantages:** - Smooth and differentiable everywhere. @@ -234,5 +233,3 @@ def logcosh_loss(y_true, y_pred): ``` These implementations provide various options for cost functions suitable for different machine learning tasks. Each function has its advantages and disadvantages, making them suitable for different scenarios and problem domains. - ---- From 15fed7ee6e448637b27ecdc23ef5157ed2ef5ce4 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 04:31:21 +0530 Subject: [PATCH 414/656] Update index.md --- contrib/api-development/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/api-development/index.md b/contrib/api-development/index.md index 72789070..8d4dc595 100644 --- a/contrib/api-development/index.md +++ b/contrib/api-development/index.md @@ -1,4 +1,4 @@ # List of sections - [API Methods](api-methods.md) -- [FastAPI](fast-api.md) \ No newline at end of file +- [FastAPI](fast-api.md) From f65c949564f003591c746088bf8c25be44a65853 Mon Sep 17 00:00:00 2001 From: Ritesh Date: Sun, 2 Jun 2024 09:17:22 +0530 Subject: [PATCH 415/656] Create hierarchical-clustering.md --- .../hierarchical-clustering.md | 119 ++++++++++++++++++ 1 file changed, 119 insertions(+) create mode 100644 contrib/machine-learning/hierarchical-clustering.md diff --git a/contrib/machine-learning/hierarchical-clustering.md b/contrib/machine-learning/hierarchical-clustering.md new file mode 100644 index 00000000..3f9e606b --- /dev/null +++ b/contrib/machine-learning/hierarchical-clustering.md @@ -0,0 +1,119 @@ +# Hierarchical Clustering + +Hierarchical Clustering is a method of cluster analysis that seeks to build a hierarchy of clusters. This README provides an overview of the hierarchical clustering algorithm, including its fundamental concepts, types, steps, and how to implement it using Python. + +## Table of Contents + +1. [Introduction](#introduction) +2. [Concepts](#concepts) +3. [Types of Hierarchical Clustering](#types-of-hierarchical-clustering) +4. [Steps in Hierarchical Clustering](#steps-in-hierarchical-clustering) +5. [Linkage Criteria](#linkage-criteria) +6. [Implementation](#implementation) + - [Using Scikit-learn](#using-scikit-learn) + - [Code Example](#code-example) +7. [Evaluation Metrics](#evaluation-metrics) +8. [Conclusion](#conclusion) +9. [References](#references) + +## Introduction + +Hierarchical Clustering is an unsupervised learning method used to group similar objects into clusters. Unlike other clustering techniques, hierarchical clustering does not require the number of clusters to be specified beforehand. It produces a tree-like structure called a dendrogram, which displays the arrangement of the clusters and their sub-clusters. + +## Concepts + +### Dendrogram + +A dendrogram is a tree-like diagram that records the sequences of merges or splits. It is a useful tool for visualizing the process of hierarchical clustering. + +### Distance Measure + +Distance measures are used to quantify the similarity or dissimilarity between data points. Common distance measures include Euclidean distance, Manhattan distance, and cosine similarity. + +### Linkage Criteria + +Linkage criteria determine how the distance between clusters is calculated. Different linkage criteria include single linkage, complete linkage, average linkage, and Ward's linkage. + +## Types of Hierarchical Clustering + +1. **Agglomerative Clustering (Bottom-Up Approach)**: + - Starts with each data point as a separate cluster. + - Repeatedly merges the closest pairs of clusters until only one cluster remains or a stopping criterion is met. + +2. **Divisive Clustering (Top-Down Approach)**: + - Starts with all data points in a single cluster. + - Repeatedly splits clusters into smaller clusters until each data point is its own cluster or a stopping criterion is met. + +## Steps in Hierarchical Clustering + +1. **Calculate Distance Matrix**: Compute the distance between each pair of data points. +2. **Create Clusters**: Treat each data point as a single cluster. +3. **Merge Closest Clusters**: Find the two clusters that are closest to each other and merge them into a single cluster. +4. **Update Distance Matrix**: Update the distance matrix to reflect the distance between the new cluster and the remaining clusters. +5. **Repeat**: Repeat steps 3 and 4 until all data points are merged into a single cluster or the desired number of clusters is achieved. + +## Linkage Criteria + +1. **Single Linkage (Minimum Linkage)**: The distance between two clusters is defined as the minimum distance between any single data point in the first cluster and any single data point in the second cluster. +2. **Complete Linkage (Maximum Linkage)**: The distance between two clusters is defined as the maximum distance between any single data point in the first cluster and any single data point in the second cluster. +3. **Average Linkage**: The distance between two clusters is defined as the average distance between all pairs of data points, one from each cluster. +4. **Ward's Linkage**: The distance between two clusters is defined as the increase in the sum of squared deviations from the mean when the two clusters are merged. + +## Implementation + +### Using Scikit-learn + +Scikit-learn is a popular machine learning library in Python that provides tools for hierarchical clustering. + +### Code Example + +```python +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from scipy.cluster.hierarchy import dendrogram, linkage +from sklearn.cluster import AgglomerativeClustering +from sklearn.preprocessing import StandardScaler + +# Load dataset +data = pd.read_csv('path/to/your/dataset.csv') + +# Preprocess the data +scaler = StandardScaler() +data_scaled = scaler.fit_transform(data) + +# Perform hierarchical clustering +Z = linkage(data_scaled, method='ward') + +# Plot the dendrogram +plt.figure(figsize=(10, 7)) +dendrogram(Z) +plt.title('Dendrogram') +plt.xlabel('Data Points') +plt.ylabel('Distance') +plt.show() + +# Perform Agglomerative Clustering +agg_clustering = AgglomerativeClustering(n_clusters=3, affinity='euclidean', linkage='ward') +labels = agg_clustering.fit_predict(data_scaled) + +# Add cluster labels to the original data +data['Cluster'] = labels +print(data.head()) +``` + +## Evaluation Metrics + +- **Silhouette Score**: Measures how similar a data point is to its own cluster compared to other clusters. +- **Cophenetic Correlation Coefficient**: Measures how faithfully a dendrogram preserves the pairwise distances between the original data points. +- **Dunn Index**: Ratio of the minimum inter-cluster distance to the maximum intra-cluster distance. + +## Conclusion + +Hierarchical clustering is a versatile and intuitive method for clustering data. It is particularly useful when the number of clusters is not known beforehand. By understanding the different linkage criteria and evaluation metrics, one can effectively apply hierarchical clustering to various types of data. + +## References + +- [Scikit-learn Documentation](https://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering) +- [Wikipedia: Hierarchical Clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering) +- [Towards Data Science: Hierarchical Clustering Explained](https://towardsdatascience.com/hierarchical-clustering-explained-925d9e1600c1) From 5b6cd66f79e2cc0341f94bd0ec3ee935ccf8a20c Mon Sep 17 00:00:00 2001 From: Ritesh Date: Sun, 2 Jun 2024 09:21:45 +0530 Subject: [PATCH 416/656] Update index.md --- contrib/machine-learning/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index b6945cd2..20305dce 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -15,4 +15,5 @@ - [Logistic Regression](logistic-regression.md) - [Types_of_Cost_Functions](cost-functions.md) - [Clustering](clustering.md) +- [Hierarchical Clustering](hierarchical-clustering.md) - [Grid Search](grid-search.md) From 8ef6a2febc2ec303ffdc0702d838e2caa1462b17 Mon Sep 17 00:00:00 2001 From: Soubeer Koley Date: Sun, 2 Jun 2024 09:27:46 +0530 Subject: [PATCH 417/656] added ensemble learning --- contrib/machine-learning/ensemble_learning.md | 140 ++++++++++++++++++ contrib/machine-learning/index.md | 3 +- 2 files changed, 142 insertions(+), 1 deletion(-) create mode 100644 contrib/machine-learning/ensemble_learning.md diff --git a/contrib/machine-learning/ensemble_learning.md b/contrib/machine-learning/ensemble_learning.md new file mode 100644 index 00000000..940bd09e --- /dev/null +++ b/contrib/machine-learning/ensemble_learning.md @@ -0,0 +1,140 @@ +# Ensemble Learning + +Ensemble Learning is a powerful machine learning paradigm that combines multiple models to achieve better performance than any individual model. The idea is to leverage the strengths of different models to improve overall accuracy, robustness, and generalization. + + + +## Introduction + +Ensemble Learning is a technique that combines the predictions from multiple machine learning models to make more accurate and robust predictions than a single model. It leverages the diversity of different models to reduce errors and improve performance. + +## Types of Ensemble Learning + +### Bagging + +Bagging, or Bootstrap Aggregating, involves training multiple versions of the same model on different subsets of the training data and averaging their predictions. The most common example of bagging is the `RandomForest` algorithm. + +### Boosting + +Boosting focuses on training models sequentially, where each new model corrects the errors made by the previous ones. This way, the ensemble learns from its mistakes, leading to improved performance. `AdaBoost` and `Gradient Boosting` are popular examples of boosting algorithms. + +### Stacking + +Stacking involves training multiple models (the base learners) and a meta-model that combines their predictions. The base learners are trained on the original dataset, while the meta-model is trained on the outputs of the base learners. This approach allows leveraging the strengths of different models. + +## Advantages and Disadvantages + +### Advantages + +- **Improved Accuracy**: Combines the strengths of multiple models. +- **Robustness**: Reduces the risk of overfitting and model bias. +- **Versatility**: Can be applied to various machine learning tasks, including classification and regression. + +### Disadvantages + +- **Complexity**: More complex than individual models, making interpretation harder. +- **Computational Cost**: Requires more computational resources and training time. +- **Implementation**: Can be challenging to implement and tune effectively. + +## Key Concepts + +- **Diversity**: The models in the ensemble should be diverse to benefit from their different strengths. +- **Voting/Averaging**: For classification, majority voting is used to combine predictions. For regression, averaging is used. +- **Weighting**: In some ensembles, models are weighted based on their accuracy or other metrics. + +## Code Examples + +### Bagging with Random Forest + +Below is an example of using Random Forest for classification on the Iris dataset. + +```python +import numpy as np +import pandas as pd +from sklearn.datasets import load_iris +from sklearn.ensemble import RandomForestClassifier +from sklearn.model_selection import train_test_split +from sklearn.metrics import accuracy_score, classification_report + +# Load dataset +iris = load_iris() +X, y = iris.data, iris.target + +# Split dataset +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) + +# Initialize Random Forest model +clf = RandomForestClassifier(n_estimators=100, random_state=42) + +# Train the model +clf.fit(X_train, y_train) + +# Make predictions +y_pred = clf.predict(X_test) + +# Evaluate the model +accuracy = accuracy_score(y_test, y_pred) +print(f"Accuracy: {accuracy * 100:.2f}%") +print("Classification Report:\n", classification_report(y_test, y_pred)) +``` + +### Boosting with AdaBoost +Below is an example of using AdaBoost for classification on the Iris dataset. + +``` +from sklearn.ensemble import AdaBoostClassifier +from sklearn.tree import DecisionTreeClassifier + +# Initialize base model +base_model = DecisionTreeClassifier(max_depth=1) + +# Initialize AdaBoost model +ada_clf = AdaBoostClassifier(base_estimator=base_model, n_estimators=50, random_state=42) + +# Train the model +ada_clf.fit(X_train, y_train) + +# Make predictions +y_pred = ada_clf.predict(X_test) + +# Evaluate the model +accuracy = accuracy_score(y_test, y_pred) +print(f"Accuracy: {accuracy * 100:.2f}%") +print("Classification Report:\n", classification_report(y_test, y_pred)) +``` + +### Stacking with Multiple Models +Below is an example of using stacking with multiple models for classification on the Iris dataset. + +``` +from sklearn.linear_model import LogisticRegression +from sklearn.neighbors import KNeighborsClassifier +from sklearn.svm import SVC +from sklearn.ensemble import StackingClassifier + +# Define base models +base_models = [ + ('knn', KNeighborsClassifier(n_neighbors=5)), + ('svc', SVC(kernel='linear', probability=True)) +] + +# Define meta-model +meta_model = LogisticRegression() + +# Initialize Stacking model +stacking_clf = StackingClassifier(estimators=base_models, final_estimator=meta_model, cv=5) + +# Train the model +stacking_clf.fit(X_train, y_train) + +# Make predictions +y_pred = stacking_clf.predict(X_test) + +# Evaluate the model +accuracy = accuracy_score(y_test, y_pred) +print(f"Accuracy: {accuracy * 100:.2f}%") +print("Classification Report:\n", classification_report(y_test, y_pred)) +``` + +## Conclusion +Ensemble Learning is a powerful technique that combines multiple models to improve overall performance. By leveraging the strengths of different models, it provides better accuracy, robustness, and generalization. However, it comes with increased complexity and computational cost. Understanding and implementing ensemble methods can significantly enhance machine learning solutions. \ No newline at end of file diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 073bca93..a46cd7af 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -9,4 +9,5 @@ - [TensorFlow.md](tensorFlow.md) - [PyTorch.md](pytorch.md) - [Types of optimizers](Types_of_optimizers.md) -- [Random Forest](Random_Forest.md) \ No newline at end of file +- [Random Forest](Random_Forest.md) +- [Ensemble Learning](ensemble_learning.md) \ No newline at end of file From 402289d3ceae6add26458f41d33364eff322f8e2 Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Sun, 2 Jun 2024 10:23:09 +0530 Subject: [PATCH 418/656] Rename Transformers.md to transformers.md --- contrib/machine-learning/{Transformers.md => transformers.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/machine-learning/{Transformers.md => transformers.md} (100%) diff --git a/contrib/machine-learning/Transformers.md b/contrib/machine-learning/transformers.md similarity index 100% rename from contrib/machine-learning/Transformers.md rename to contrib/machine-learning/transformers.md From e13fb234f2829a1b918aac3c044e4feef5066d34 Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Sun, 2 Jun 2024 10:23:56 +0530 Subject: [PATCH 419/656] Add transformers.md --- contrib/machine-learning/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index b6945cd2..13d94405 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -16,3 +16,4 @@ - [Types_of_Cost_Functions](cost-functions.md) - [Clustering](clustering.md) - [Grid Search](grid-search.md) +- [Transformers](transformers.md) From 229458043245de432c96f05457dd79330d568a92 Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Sun, 2 Jun 2024 10:39:37 +0530 Subject: [PATCH 420/656] Update transformers.md Added source for the image --- contrib/machine-learning/transformers.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/contrib/machine-learning/transformers.md b/contrib/machine-learning/transformers.md index 97346bdd..9fbfa6d3 100644 --- a/contrib/machine-learning/transformers.md +++ b/contrib/machine-learning/transformers.md @@ -6,7 +6,10 @@ mechanism. Before transformers, predecessors of attention mechanism were added t Transformers are a revolutionary approach to natural language processing (NLP). Unlike older models, they excel at understanding long-range connections between words. This "attention" mechanism lets them grasp the context of a sentence, making them powerful for tasks like machine translation, text summarization, and question answering. Introduced in 2017, transformers are now the backbone of many large language models, including tools you might use every day. Their ability to handle complex relationships in language is fueling advancements in AI across various fields. ## Model Architecture -

Model Architecture

+

+ Model Architecture
+Source: Attention Is All You Need - Figure 1 +

### Encoder The encoder is composed of a stack of identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, positionwise fully connected feed-forward network. Each encoder consists of two major components: a self-attention mechanism and a feed-forward neural network. The self-attention mechanism accepts input encodings from the previous encoder and weights their relevance to each other to generate output encodings. The feed-forward neural network further processes each output encoding individually. These output encodings are then passed to the next encoder as its input, as well as to the decoders. From 07a1e737606d10d7e96e2291cec21003309bfbf0 Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Sun, 2 Jun 2024 10:46:21 +0530 Subject: [PATCH 421/656] Update transformers.md Added bibliography --- contrib/machine-learning/transformers.md | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/contrib/machine-learning/transformers.md b/contrib/machine-learning/transformers.md index 9fbfa6d3..0799b7f2 100644 --- a/contrib/machine-learning/transformers.md +++ b/contrib/machine-learning/transformers.md @@ -430,4 +430,11 @@ These may include: - Text generation - Biological sequence analysis - Computer code generation -- Video analysis + +## Bibliography +- [Attention Is All You Need](https://arxiv.org/pdf/1706.03762) +- [Tensorflow Tutorial](https://www.tensorflow.org/text/tutorials/transformer) +- [Tensorflow Models Docs](https://www.tensorflow.org/api_docs/python/tfm/nlp/layers) +- [Wikipedia](https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)) +- [HuggingFace](https://huggingface.co/docs/transformers/en/index) +- [PyTorch](https://pytorch.org/docs/stable/generated/torch.nn.Transformer.html) From cbc72ef9d3c36fba38480709de60ec8ba06f578c Mon Sep 17 00:00:00 2001 From: Niyonika Gaur <83643952+niyonikagaur@users.noreply.github.com> Date: Sun, 2 Jun 2024 11:31:01 +0530 Subject: [PATCH 422/656] Visualizing_nifti_files.md --- .../Visualizing_nifti_files.md | 90 +++++++++++++++++++ 1 file changed, 90 insertions(+) create mode 100644 contrib/plotting-visualization/Visualizing_nifti_files.md diff --git a/contrib/plotting-visualization/Visualizing_nifti_files.md b/contrib/plotting-visualization/Visualizing_nifti_files.md new file mode 100644 index 00000000..4baaca34 --- /dev/null +++ b/contrib/plotting-visualization/Visualizing_nifti_files.md @@ -0,0 +1,90 @@ +## NIfTI Files +Neuroimaging Informatics Technology Initiative is a data format for storage. Functional Magnetic Resonance Imaging (fMRI) and other medical images. +## In this, we'll be using different packages: +1. Matplotlib +2. Nibabel: It gives access to a variety of imaging formats, it provides a common interface for various formats produced by different scanners. + +## Various Methods for viewing a NIfTI file +### 1. Load NIfTI file +``` +brain_vol = nib.load("") #Enter the path to NIfTI file here +type(brain_vol) +``` +### 2. View Metadata +``` +print(brain_vol.header) +``` +### Output +``` + object, endian='<' +sizeof_hdr : 348 +data_type : b'' +db_name : b'' +extents : 0 +session_error : 0 +regular : b'r' +dim_info : 54 +dim : [ 3 192 256 256 1 1 1 1] +intent_p1 : 0.0 +intent_p2 : 0.0 +intent_p3 : 0.0 +intent_code : none +datatype : int16 +bitpix : 16 +slice_start : 0 +pixdim : [1. 1. 1. 1. 2.3 0. 0. 0. ] +vox_offset : 0.0 +scl_slope : nan +scl_inter : nan +slice_end : 0 +slice_code : unknown +xyzt_units : 10 +cal_max : 0.0 +cal_min : 0.0 +slice_duration : 0.0 +toffset : 0.0 +glmax : 0 +glmin : 0 +descrip : b'TE=2.7;Time=150734.828;phase=1' +aux_file : b'' +qform_code : scanner +sform_code : scanner +quatern_b : 0.00042526424 +quatern_c : -0.027039066 +quatern_d : 0.01571919 +qoffset_x : -81.5222 +qoffset_y : -130.72974 +qoffset_z : -144.47054 +srow_x : [ 9.9804670e-01 -3.1445995e-02 -5.4038301e-02 -8.1522202e+01] +srow_y : [ 3.1400096e-02 9.9950546e-01 -1.7001767e-03 -1.3072974e+02] +srow_z : [ 5.4065209e-02 4.7863640e-08 9.9853742e-01 -1.4447054e+02] +intent_name : b'' +magic : b'n+1' +``` +### 3. Access data in the NIfTI object +``` +brain_vol_data = brain_vol.get_fdata() +type(brain_vol_data) +``` +### Output +``` +numpy.ndarray +``` +### Dimensions of the image +``` +brain_vol_data.shape +``` +### Output +``` +(192, 256, 256) +``` +### 4. 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z9b2o*fm=w5VBL!L^s|Nmq2L11XzpYhX5=t!hLzu?n1z{(q}_0lmkD%K(BSlkGt5Vh zGi5D#n1!bbcFcE133u5CsV2+6(=M@;=2Q&Q(%wYHr1W?>q~W#Ze(0{DnQ9$QD4{&y zwZIuiCCN5Hg`s<@pA|4?zg(s?-L%|eyhk(rPUE0cv>_GQFVvWkLF5su<`en0ca*l? zdp5)&&@eCnLzoBUPUtsU?b+}g_GcZ+o6-%@7GPfp1xx`Qgymf|h>vjYbBfN?<@Ntk zo5riM%j~F#GMoZObb{B&jQQ#%&daCb!kRl0BYAocXW36Sv7?z~SeOke?Jn=V407;# z(pZS!o8kk(l4CPbZ-(in%Rmv(laSDs7zXR!*j$lb-9Zn*y!CB6HQxyCm+YeP_ zGqoO5b_}r^$lO{MLRW-!r@1hS)LyNtfjv;H#akYHiGqda;L{gVw(N9W*)pL^!}rB1 z&1NA7Mk#%U``6T4U(&TVfi-Si$yH+0G^L({iFTcR`PbSrR-v|;mSJ^xLG#IMQ_`;^ zd#NT`zDMb%Rl$+hsF{ql*C<|1jvHXcWLXdw^IxB&AqeySg89v$?FQmzED;)ax%UXR zJ>p`rwDfDe-uC&8ZDByu(eFRvhw0!B$D`Il{&hcoWfPj={?9*4`|o3Xw%dr%v;X%$ zU)iw#xv%X1+_&bbRQ!K<;eX!NpNNb9G(7+Fy+sHA_l^Fi)dUyyf3IlXfB%X9_pQTH f|9^i*w-CB(w@e=&@;Lk*_-AQ)`C`TSKkxk?I=MGT From dbe3af270ac69849229c450609d23dd25eb8f3e2 Mon Sep 17 00:00:00 2001 From: Niyonika Gaur <83643952+niyonikagaur@users.noreply.github.com> Date: Sun, 2 Jun 2024 11:51:34 +0530 Subject: [PATCH 427/656] Visualizing_nifti_files.md --- .../Visualizing_nifti_files.md | 31 +++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/contrib/plotting-visualization/Visualizing_nifti_files.md b/contrib/plotting-visualization/Visualizing_nifti_files.md index 4baaca34..bcd26196 100644 --- a/contrib/plotting-visualization/Visualizing_nifti_files.md +++ b/contrib/plotting-visualization/Visualizing_nifti_files.md @@ -84,6 +84,37 @@ plt.imshow(brain_vol_data[96], cmap='bone') plt.axis('off') plt.show() ``` +The image can be rotated +``` +plt.imshow(ndi.rotate(brain_vol_data[96], 90), cmap='bone') +plt.axis('off') +plt.show() +``` +### Plot a series of slices +``` +fig_rows = 4 +fig_cols = 4 +n_subplots = fig_rows * fig_cols +n_slice = brain_vol_data.shape[0] +step_size = n_slice // n_subplots +plot_range = n_subplots * step_size +start_stop = int((n_slice - plot_range) / 2) + +fig, axs = plt.subplots(fig_rows, fig_cols, figsize=[10, 10]) + +for idx, img in enumerate(range(start_stop, plot_range, step_size)): + axs.flat[idx].imshow(ndi.rotate(brain_vol_data[img, :, :], 90), cmap='gray') + axs.flat[idx].axis('off') + +plt.tight_layout() +plt.show() +``` +### Multiples slices at once +``` +plotting.plot_img(brain_vol, display_mode='mosaic', cmap='gray') +plt.show() +``` + From cd3e090e703ab3b3812614a4590a062ce8e81ce3 Mon Sep 17 00:00:00 2001 From: Niyonika Gaur <83643952+niyonikagaur@users.noreply.github.com> Date: Sun, 2 Jun 2024 11:53:31 +0530 Subject: [PATCH 428/656] index.md --- contrib/plotting-visualization/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 67bee6dc..74558c42 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -1,3 +1,4 @@ # List of sections - [Installing Matplotlib](matplotlib_installation.md) +- [Visualizing NIfTI files](Visualizing_NIfTI_files.md) From 643cadfa330d4fa5443ba1f4044ecc5942e2b7f3 Mon Sep 17 00:00:00 2001 From: manishh12 Date: Sun, 2 Jun 2024 11:54:06 +0530 Subject: [PATCH 429/656] Added Generators under Advance python --- contrib/advanced-python/Generators.md | 87 +++++++++++++++++++++++++++ contrib/advanced-python/index.md | 1 + 2 files changed, 88 insertions(+) create mode 100644 contrib/advanced-python/Generators.md diff --git a/contrib/advanced-python/Generators.md b/contrib/advanced-python/Generators.md new file mode 100644 index 00000000..ff5226e4 --- /dev/null +++ b/contrib/advanced-python/Generators.md @@ -0,0 +1,87 @@ +# Generators Under Advanced Python + +## Introduction + +Generators in Python are a sophisticated feature that enables the creation of iterators without the need to construct a full list in memory. They allow you to generate values on-the-fly, which is particularly beneficial for working with large datasets or infinite sequences. In this README, we'll explore generators in depth, covering their types, mathematical formulation, advantages, disadvantages, and implementation examples. + +## Function Generators + +Function generators are created using the `yield` keyword within a function. When invoked, a function generator returns a generator iterator, allowing you to iterate over the values generated by the function. + +### Mathematical Formulation + +Function generators can be represented mathematically using set-builder notation. The general form is: + +``` +{expression | variable in iterable, condition} +``` + +Where: +- `expression` is the expression to generate values. +- `variable` is the variable used in the expression. +- `iterable` is the sequence of values to iterate over. +- `condition` is an optional condition that filters the values. + +### Advantages of Function Generators + +1. **Memory Efficiency**: Function generators produce values lazily, meaning they generate values only when needed, saving memory compared to constructing an entire sequence upfront. + +2. **Lazy Evaluation**: Values are generated on-the-fly as they are consumed, leading to improved performance and reduced overhead, especially when dealing with large datasets. + +3. **Infinite Sequences**: Function generators can represent infinite sequences, such as the Fibonacci sequence, allowing you to work with data streams of arbitrary length without consuming excessive memory. + +### Disadvantages of Function Generators + +1. **Single Iteration**: Once a function generator is exhausted, it cannot be reused. If you need to iterate over the sequence again, you'll have to create a new generator. + +2. **Limited Random Access**: Function generators do not support random access like lists. They only allow sequential access, which might be a limitation depending on the use case. + +### Implementation Example + +```python +def fibonacci(): + a, b = 0, 1 + while True: + yield a + a, b = b, a + b + +# Usage +fib_gen = fibonacci() +for _ in range(10): + print(next(fib_gen)) +``` + +## Generator Expressions + +Generator expressions are similar to list comprehensions but return a generator object instead of a list. They offer a concise way to create generators without the need for a separate function. + +### Mathematical Formulation + +Generator expressions can also be represented mathematically using set-builder notation. The general form is the same as for function generators. + +### Advantages of Generator Expressions + +1. **Memory Efficiency**: Generator expressions produce values lazily, similar to function generators, resulting in memory savings. + +2. **Lazy Evaluation**: Values are generated on-the-fly as they are consumed, providing improved performance and reduced overhead. + +### Disadvantages of Generator Expressions + +1. **Single Iteration**: Like function generators, once a generator expression is exhausted, it cannot be reused. + +2. **Limited Random Access**: Generator expressions, similar to function generators, do not support random access. + +### Implementation Example + +```python +# Generate squares of numbers from 0 to 9 +square_gen = (x**2 for x in range(10)) + +# Usage +for num in square_gen: + print(num) +``` + +## Conclusion + +Generators offer a powerful mechanism for creating iterators efficiently in Python. By understanding the differences between function generators and generator expressions, along with their mathematical formulation, advantages, and disadvantages, you can leverage them effectively in various scenarios. Whether you're dealing with large datasets or need to work with infinite sequences, generators provide a memory-efficient solution with lazy evaluation capabilities, contributing to more elegant and scalable code. \ No newline at end of file diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index fa2fd7b9..df4b5458 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -9,3 +9,4 @@ - [Map Function](map-function.md) - [Protocols](protocols.md) - [Exception Handling in Python](exception-handling.md) +- [Generators](Generators.md) From b519834b34a998805d9fc76343b224ad7220f7ff Mon Sep 17 00:00:00 2001 From: Niyonika Gaur <83643952+niyonikagaur@users.noreply.github.com> Date: Sun, 2 Jun 2024 11:54:24 +0530 Subject: [PATCH 430/656] index.md --- contrib/plotting-visualization/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 74558c42..03994ec4 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -1,4 +1,4 @@ # List of sections - [Installing Matplotlib](matplotlib_installation.md) -- [Visualizing NIfTI files](Visualizing_NIfTI_files.md) +- [Visualizing NIfTI files](Visualizing_nifti_files.md) From 14f425286e720804c7b1e8c606aa50c5cdee279c Mon Sep 17 00:00:00 2001 From: yashasvi232 <154299201+yashasvi232@users.noreply.github.com> Date: Sun, 2 Jun 2024 15:58:06 +0530 Subject: [PATCH 431/656] Create Dijkstra's_algorithm.md --- contrib/ds-algorithms/Dijkstra's_algorithm.md | 88 +++++++++++++++++++ 1 file changed, 88 insertions(+) create mode 100644 contrib/ds-algorithms/Dijkstra's_algorithm.md diff --git a/contrib/ds-algorithms/Dijkstra's_algorithm.md b/contrib/ds-algorithms/Dijkstra's_algorithm.md new file mode 100644 index 00000000..18845f6b --- /dev/null +++ b/contrib/ds-algorithms/Dijkstra's_algorithm.md @@ -0,0 +1,88 @@ + +# Dijkstra's Algorithm +Dijkstra's algorithm is a graph algorithm that gives the shortest distance of each node from the given node in a weighted, undirected graph. It operates by continually choosing the closest unvisited node and determining the distance to all its unvisited neighboring nodes. This algorithm is similar to BFS in graphs, with the difference being it gives priority to nodes with shorter distances by using a priority queue(min-heap) instead of a FIFO queue. The data structures required would be a distance list (to store the minimum distance of each node), a priority queue or a set, and we assume the adjacency list will be provided. + +## Working +- We will store the minimum distance of each node in the distance list, which has a length equal to the number of nodes in the graph. Thus, the minimum distance of the 2nd node will be stored in the 2nd index of the distance list. We initialize the list with the maximum number possible, say infinity. + +- We now start the traversal from the starting node given and mark its distance as 0. We push this node to the priority queue along with its minimum distance, which is 0, so the structure pushed will be (0, node), a tuple. + +- Now, with the help of the adjacency list, we will add the neighboring nodes to the priority queue with the distance equal to (edge weight + current node distance), and this should be less than the distance list value. We will also update the distance list in the process. + +- When all the nodes are added, we will select the node with the shortest distance and repeat the process. + +## Dry Run +We will now do a manual simulation using an example graph given. First, (0, a) is pushed to the priority queue (pq). +![gssoc photo 1](https://github.com/animator/learn-python/assets/154299201/713cf7dd-fa8a-4f81-829f-00ac33eb12f4) +- **Step1:** The lowest element is popped from the pq, which is (0, a), and all its neighboring nodes are added to the pq while simultaneously checking the distance list. Thus (3, b), (7, c), (1, d) are added to the pq. +![gssoc photo2](https://github.com/animator/learn-python/assets/154299201/9456b701-7877-44dc-854b-da416c5e2e9b) +- **Step2:** Again, the lowest element is popped from the pq, which is (1, d). It has two neighboring nodes, a and e, from which + (0 + 1, a) will not be added to the pq as dist[a] = 0 is less than 1. +![gssoc photo3](https://github.com/animator/learn-python/assets/154299201/83990a40-f3fc-4173-9e21-10eb9e2003cb) + +- **Step3:** Now, the lowest element is popped from the pq, which is (3, b). It has two neighboring nodes, a and c, from which + (0 + 1, a) will not be added to the pq. But the new distance to reach c is 5 (3 + 2), which is less than dist[c] = 7. So (5, c) is added to the pq. +![gssoc photo4](https://github.com/animator/learn-python/assets/154299201/b47eb063-8ce7-4e68-85a5-da98f5ac9a3d) + +- **Step4:** The next smallest element is (5, c). It has neighbors a and e. The new distance to reach a will be 5 + 7 = 12, which is more than dist[a], so it will not be considered. Similarly, the new distance for e is 5 + 3 = 8, which again will not be considered. So, no new tuple has been added to the pq. +![gssoc photo5](https://github.com/animator/learn-python/assets/154299201/c0ef3c2a-1938-4c33-9e07-0929e8e5e631) + +- **Step5:** Similarly, both the elements of the pq will be popped one by one without any new addition. +![gssoc photo6](https://github.com/animator/learn-python/assets/154299201/828739c6-413b-41f6-a09f-b702802b20e1) +![gssoc photo7](https://github.com/animator/learn-python/assets/154299201/488d4c7b-d7bf-4887-a9c5-04accc6c6c89) + +- The distance list we get at the end will be our answer. +- `Output` `dist=[1, 3, 7, 1, 6]` + +## Python Code +```python +import heapq + +def dijkstra(graph, start): + # Create a priority queue + pq = [] + heapq.heappush(pq, (0, start)) + + # Create a dictionary to store distances to each node + dist = {node: float('inf') for node in graph} + dist[start] = 0 + + while pq: + # Get the node with the smallest distance + current_distance, current_node = heapq.heappop(pq) + + # If the current distance is greater than the recorded distance, skip it + if current_distance > dist[current_node]: + continue + + # Update the distances to the neighboring nodes + for neighbor, weight in graph[current_node].items(): + distance = current_distance + weight + # Only consider this new path if it's better + if distance < dist[neighbor]: + dist[neighbor] = distance + heapq.heappush(pq, (distance, neighbor)) + + return dist + +# Example usage: +graph = { + 'A': {'B': 1, 'C': 4}, + 'B': {'A': 1, 'C': 2, 'D': 5}, + 'C': {'A': 4, 'B': 2, 'D': 1}, + 'D': {'B': 5, 'C': 1} +} + +start_node = 'A' +dist = dijkstra(graph, start_node) +print(dist) +``` + +## Complexity Analysis + +- **Time Complexity**: \(O((V + E) log V)\) +- **Space Complexity**: \(O(V + E)\) + + + + From 3e35783d59919de603e6d11e81df54fdf47662b2 Mon Sep 17 00:00:00 2001 From: SAM <8dmasters@gmail.com> Date: Sun, 2 Jun 2024 16:19:11 +0530 Subject: [PATCH 432/656] Create transformer-architecture.png --- .../assets/transformer-architecture.png | 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Date: Sun, 2 Jun 2024 16:22:50 +0530 Subject: [PATCH 433/656] Update transformers.md Added image location --- contrib/machine-learning/transformers.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/machine-learning/transformers.md b/contrib/machine-learning/transformers.md index 0799b7f2..0dcf6976 100644 --- a/contrib/machine-learning/transformers.md +++ b/contrib/machine-learning/transformers.md @@ -7,7 +7,7 @@ Transformers are a revolutionary approach to natural language processing (NLP). ## Model Architecture

    - Model Architecture
    + Model Architecture
    Source:     Attention Is All You Need - Figure 1

    From f84da4252f1a80405a98451389d70c0ec2d1d3e0 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 21:39:17 +0530 Subject: [PATCH 434/656] Rename OOPs.md to oops.md --- contrib/advanced-python/{OOPs.md => oops.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/advanced-python/{OOPs.md => oops.md} (100%) diff --git a/contrib/advanced-python/OOPs.md b/contrib/advanced-python/oops.md similarity index 100% rename from contrib/advanced-python/OOPs.md rename to contrib/advanced-python/oops.md From 18003e05e2d9c9f019601c0bddd89e7eab456d90 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 21:39:35 +0530 Subject: [PATCH 435/656] Update index.md --- contrib/advanced-python/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index fa2fd7b9..16210c24 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -1,6 +1,6 @@ # List of sections -- [OOPs](OOPs.md) +- [OOPs](oops.md) - [Decorators/\*args/**kwargs](decorator-kwargs-args.md) - [Lambda Function](lambda-function.md) - [Working with Dates & Times in Python](dates_and_times.md) From 20494f222f48f91209a112475961cde7d1f01175 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 21:47:03 +0530 Subject: [PATCH 436/656] Update index.md --- contrib/advanced-python/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index df4b5458..0ce294f9 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -9,4 +9,4 @@ - [Map Function](map-function.md) - [Protocols](protocols.md) - [Exception Handling in Python](exception-handling.md) -- [Generators](Generators.md) +- [Generators](generators.md) From bb245f09792e9853a7545503d56bcb027754ac19 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Sun, 2 Jun 2024 21:48:10 +0530 Subject: [PATCH 437/656] Update and rename Generators.md to generators.md --- contrib/advanced-python/{Generators.md => generators.md} | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) rename contrib/advanced-python/{Generators.md => generators.md} (94%) diff --git a/contrib/advanced-python/Generators.md b/contrib/advanced-python/generators.md similarity index 94% rename from contrib/advanced-python/Generators.md rename to contrib/advanced-python/generators.md index ff5226e4..96287efc 100644 --- a/contrib/advanced-python/Generators.md +++ b/contrib/advanced-python/generators.md @@ -1,8 +1,8 @@ -# Generators Under Advanced Python +# Generators ## Introduction -Generators in Python are a sophisticated feature that enables the creation of iterators without the need to construct a full list in memory. They allow you to generate values on-the-fly, which is particularly beneficial for working with large datasets or infinite sequences. In this README, we'll explore generators in depth, covering their types, mathematical formulation, advantages, disadvantages, and implementation examples. +Generators in Python are a sophisticated feature that enables the creation of iterators without the need to construct a full list in memory. They allow you to generate values on-the-fly, which is particularly beneficial for working with large datasets or infinite sequences. We will explore generators in depth, covering their types, mathematical formulation, advantages, disadvantages, and implementation examples. ## Function Generators @@ -84,4 +84,4 @@ for num in square_gen: ## Conclusion -Generators offer a powerful mechanism for creating iterators efficiently in Python. By understanding the differences between function generators and generator expressions, along with their mathematical formulation, advantages, and disadvantages, you can leverage them effectively in various scenarios. Whether you're dealing with large datasets or need to work with infinite sequences, generators provide a memory-efficient solution with lazy evaluation capabilities, contributing to more elegant and scalable code. \ No newline at end of file +Generators offer a powerful mechanism for creating iterators efficiently in Python. By understanding the differences between function generators and generator expressions, along with their mathematical formulation, advantages, and disadvantages, you can leverage them effectively in various scenarios. Whether you're dealing with large datasets or need to work with infinite sequences, generators provide a memory-efficient solution with lazy evaluation capabilities, contributing to more elegant and scalable code. From 843eb9b01959f199b6bba37ac3839356c3023f6d Mon Sep 17 00:00:00 2001 From: anamika123 Date: Sun, 2 Jun 2024 22:04:58 +0530 Subject: [PATCH 438/656] eval function --- contrib/advanced-python/eval_function.md | 75 ++++++++++++++++++++++++ contrib/advanced-python/index.md | 1 + 2 files changed, 76 insertions(+) create mode 100644 contrib/advanced-python/eval_function.md diff --git a/contrib/advanced-python/eval_function.md b/contrib/advanced-python/eval_function.md new file mode 100644 index 00000000..9e3ec54b --- /dev/null +++ b/contrib/advanced-python/eval_function.md @@ -0,0 +1,75 @@ +# Understanding the `eval` Function in Python +## Introduction + +The `eval` function in Python allows you to execute a string-based Python expression dynamically. This can be useful in various scenarios where you need to evaluate expressions that are not known until runtime. + +## Syntax +```python +eval(expression, globals=None, locals=None) +``` + +### Parameters: + +* expression: String is parsed and evaluated as a Python expression +* globals [optional]: Dictionary to specify the available global methods and variables. +* locals [optional]: Another dictionary to specify the available local methods and variables. + +## Examples +Example 1: +```python +result = eval('2 + 3 * 4') +print(result) # Output: 14 +``` +Example 2: + +```python +x = 10 +expression = 'x * 2' +result = eval(expression, {'x': x}) +print(result) # Output: 20 +``` +Example 3: +```python +x = 10 +def multiply(a, b): + return a * b +expression = 'multiply(x, 5) + 2' +result = eval(expression) +print("Result:",result) # Output: Result:52 +``` +Example 4: +```python +expression = input("Enter a Python expression: ") +result = eval(expression) +print("Result:", result) +#input= "3+2" +#Output: Result:5 +``` + +Example 5: +```python +import numpy as np +a=np.random.randint(1,9) +b=np.random.randint(1,9) +operations=["*","-","+"] +op=np.random.choice(operations) + +expression=str(a)+op+str(b) +correct_answer=eval(expression) +given_answer=int(input(str(a)+" "+op+" "+str(b)+" = ")) + +if given_answer==correct_answer: + print("Correct") +else: + print("Incorrect") + print("correct answer is :" ,correct_answer) + +#2 * 1 = 8 +#Incorrect +#correct answer is : 2 +#or +#3 * 2 = 6 +#Correct +``` +## Conclusion +The eval function is a powerful tool in Python that allows for dynamic evaluation of expressions. \ No newline at end of file diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index b31544eb..94194808 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -10,3 +10,4 @@ - [Protocols](protocols.md) - [Exception Handling in Python](exception-handling.md) - [Generators](generators.md) +- [Eval Function](eval_function.md) From ea37cbb25457eaf0c650cd6822ff62fdb63da3a9 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Sun, 2 Jun 2024 22:07:01 +0530 Subject: [PATCH 439/656] Create list_comprehension.md Added Introduction Added Syntax Added Examples Added Conclusion --- contrib/advanced-python/list_comprehension.md | 73 +++++++++++++++++++ 1 file changed, 73 insertions(+) create mode 100644 contrib/advanced-python/list_comprehension.md diff --git a/contrib/advanced-python/list_comprehension.md b/contrib/advanced-python/list_comprehension.md new file mode 100644 index 00000000..d9ab589d --- /dev/null +++ b/contrib/advanced-python/list_comprehension.md @@ -0,0 +1,73 @@ +# List Comprehension + +Creating lists concisely and expressively is what list comprehension in Python does. You can generate lists from already existing iterables like lists, tuples or strings with a short form. +This boosts the readability of code and reduces necessity of using explicit looping constructs. + +## Syntax : + +### Basic syntax + +```python +new_list = [expression for item in iterable] +``` +- **new_list**: This is the name given to the list that will be created using the list comprehension. +- **expression**: This is the expression that defines how each element of the new list will be generated or transformed. +- **item**: This variable represents each individual element from the iterable. It takes on the value of each element in the iterable during each iteration. +- **iterable**: This is the sequence-like object over which the iteration will take place. It provides the elements that will be processed by the expression. + +This list comprehension syntax `[expression for item in iterable]` allows you to generate a new list by applying a specific expression to each element in an iterable. + +### Syntax including condition + +```python +new_list = [expression for item in iterable if condition] +``` +- **new_list**: This is the name given to the list that will be created using the list comprehension. +- **expression**: This is the expression that defines how each element of the new list will be generated or transformed. +- **item**: This variable represents each individual element from the iterable. It takes on the value of each element in the iterable during each iteration. +- **iterable**: This is the sequence-like object over which the iteration will take place. It provides the elements that will be processed by the expression. +- **if condition**: This is an optional part of the syntax. It allows for conditional filtering of elements from the iterable. Only items that satisfy the condition + will be included in the new list. + + +## Examples: + +1. Generating a list of squares of numbers from 1 to 5: + +```python +squares = [x ** 2 for x in range(1, 6)] +print(squares) +``` + +- **Output** : +```python +[1, 4, 9, 16, 25] +``` + +2. Filtering even numbers from a list: + +```python +nums = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +even = [x for x in nums if x % 2 == 0] +print(even) +``` + +- **Output** : +```python +[2, 4, 6, 8, 10] +``` + +3. Flattening a list of lists: +```python +matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] +flat = [x for sublist in matrix for x in sublist] +print(flat) +``` + +- **Output** : +```python +[1, 2, 3, 4, 5, 6, 7, 8, 9] +``` + +List comprehension is a powerful feature in Python for creating lists based on existing iterables with a concise syntax. +By mastering list comprehension, developers can write cleaner, more expressive code and leverage Python's functional programming capabilities effectively. From b1f8d147a2abd103e4029c4e366011c1831fe784 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Sun, 2 Jun 2024 22:08:52 +0530 Subject: [PATCH 440/656] Update index.md Added List Comprehension section --- contrib/advanced-python/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index b31544eb..7ae2edeb 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -10,3 +10,4 @@ - [Protocols](protocols.md) - [Exception Handling in Python](exception-handling.md) - [Generators](generators.md) +- [List Comprehension](#list_comprehension.md) From 53efed98073fba08e3c2867676273f654882c9cb Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Sun, 2 Jun 2024 22:11:33 +0530 Subject: [PATCH 441/656] Update index.md made a small change --- contrib/advanced-python/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 7ae2edeb..631423a6 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -10,4 +10,4 @@ - [Protocols](protocols.md) - [Exception Handling in Python](exception-handling.md) - [Generators](generators.md) -- [List Comprehension](#list_comprehension.md) +- [List Comprehension](list_comprehension.md) From cc85ae3802f2f015a353a81b48f33f1ff5cb6a10 Mon Sep 17 00:00:00 2001 From: yashasvi232 <154299201+yashasvi232@users.noreply.github.com> Date: Sun, 2 Jun 2024 22:54:56 +0530 Subject: [PATCH 442/656] Update index.md --- contrib/ds-algorithms/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 1d7293bb..2263620d 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -13,3 +13,4 @@ - 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It's useful for identifying relationships, trends, and correlations, as well as spotting clusters and outliers. +* The dots on the plot shows how the variables are related. A scatter plot is made with the matplotlib library's `scatter() method`. +## Syntax +**Here's how to write code for the scatter() method:** +``` +matplotlib.pyplot.scatter (x_axis_value, y_axis_value, s = None, c = None, vmin = None, vmax = None, marker = None, cmap = None, alpha = None, linewidths = None, edgecolors = None) + +``` +## Prerequisites +Scatter plots can be created in Python with Matplotlib's pyplot library. To build a Scatter plot, first import matplotlib. It is a standard convention to import Matplotlib's pyplot library as plt. +``` +import matplotlib.pyplot as plt + +``` +## Creating a simple Scatter Plot +With Pyplot, you can use the `scatter()` function to draw a scatter plot. + +The `scatter()` function plots one dot for each observation. It needs two arrays of the same length, one for the values of the x-axis, and one for values on the y-axis: +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6]) +y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86]) + +plt.scatter(x, y) +plt.show() +``` + +When executed, this will show the following Scatter plot: + + + +## Compare Plots + +In a scatter plot, comparing plots involves examining multiple sets of points to identify differences or similarities in patterns, trends, or correlations between the data sets. + +``` +import matplotlib.pyplot as plt +import numpy as np + +#day one, the age and speed of 13 cars: +x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6]) +y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86]) +plt.scatter(x, y) + +#day two, the age and speed of 15 cars: +x = np.array([2,2,8,1,15,8,12,9,7,3,11,4,7,14,12]) +y = np.array([100,105,84,105,90,99,90,95,94,100,79,112,91,80,85]) +plt.scatter(x, y) + +plt.show() +``` + +When executed, this will show the following Compare Scatter plot: + +## Colors in Scatter plot +You can set your own color for each scatter plot with the `color` or the `c` argument: + +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6]) +y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86]) +plt.scatter(x, y, color = 'hotpink') + +x = np.array([2,2,8,1,15,8,12,9,7,3,11,4,7,14,12]) +y = np.array([100,105,84,105,90,99,90,95,94,100,79,112,91,80,85]) +plt.scatter(x, y, color = '#88c999') + +plt.show() +``` + +When executed, this will show the following Colors Scatter plot: + +## Color Each Dot +You can even set a specific color for each dot by using an array of colors as value for the `c` argument: + +``**Note:** You cannot use the `color` argument for this, only the `c` argument.`` + +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6]) +y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86]) +colors = np.array(["red","green","blue","yellow","pink","black","orange","purple","beige","brown","gray","cyan","magenta"]) + +plt.scatter(x, y, c=colors) + +plt.show() +``` + +When executed, this will show the following Color Each Dot: + +## ColorMap +The Matplotlib module has a number of available colormaps. + +A colormap is like a list of colors, where each color has a value that ranges from 0 to 100. + +Here is an example of a colormap: + + + + +This colormap is called 'viridis' and as you can see it ranges from 0, which is a purple color, up to 100, which is a yellow color. + +## How to Use the ColorMap +You can specify the colormap with the keyword argument `cmap` with the value of the colormap, in this case `'viridis'` which is one of the built-in colormaps available in Matplotlib. + +In addition you have to create an array with values (from 0 to 100), one value for each point in the scatter plot: + +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6]) +y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86]) +colors = np.array([0, 10, 20, 30, 40, 45, 50, 55, 60, 70, 80, 90, 100]) + +plt.scatter(x, y, c=colors, cmap='viridis') + +plt.show() +``` + +When executed, this will show the following Scatter ColorMap: + + + +You can include the colormap in the drawing by including the `plt.colorbar()` statement: + +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6]) +y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86]) +colors = np.array([0, 10, 20, 30, 40, 45, 50, 55, 60, 70, 80, 90, 100]) + +plt.scatter(x, y, c=colors, cmap='viridis') + +plt.colorbar() + +plt.show() +``` + +When executed, this will show the following Scatter ColorMap using `plt.colorbar()`: + + + + + + From edd8aec3a2b1f9855e7456d0065520d9a43243e2 Mon Sep 17 00:00:00 2001 From: rohit Date: Tue, 4 Jun 2024 00:28:41 +0530 Subject: [PATCH 445/656] Added K-Nearest Neighbors (KNN).md file --- .../K-nearest neighbor (KNN).md | 122 ++++++++++++++++++ contrib/machine-learning/index.md | 1 + 2 files changed, 123 insertions(+) create mode 100644 contrib/machine-learning/K-nearest neighbor (KNN).md diff --git a/contrib/machine-learning/K-nearest neighbor (KNN).md b/contrib/machine-learning/K-nearest neighbor (KNN).md new file mode 100644 index 00000000..748f8087 --- /dev/null +++ b/contrib/machine-learning/K-nearest neighbor (KNN).md @@ -0,0 +1,122 @@ +# K-Nearest Neighbors (KNN) Machine Learning Algorithm in Python + +## Introduction +K-Nearest Neighbors (KNN) is a simple, yet powerful, supervised machine learning algorithm used for both classification and regression tasks. It assumes that similar things exist in close proximity. In other words, similar data points are near to each other. + +## How KNN Works +KNN works by finding the distances between a query and all the examples in the data, selecting the specified number of examples (K) closest to the query, then voting for the most frequent label (in classification) or averaging the labels (in regression). + +### Steps: +1. **Choose the number K of neighbors** +2. **Calculate the distance** between the query-instance and all the training samples +3. **Sort the distances** and determine the nearest neighbors based on the K-th minimum distance +4. **Gather the labels** of the nearest neighbors +5. **Vote for the most frequent label** (in case of classification) or **average the labels** (in case of regression) + +## When to Use KNN +### Advantages: +- **Simple and easy to understand:** KNN is intuitive and easy to implement. +- **No training phase:** KNN is a lazy learner, meaning there is no explicit training phase. +- **Effective with a small dataset:** KNN performs well with a small number of input variables. + +### Disadvantages: +- **Computationally expensive:** The algorithm becomes significantly slower as the number of examples and/or predictors/independent variables increase. +- **Sensitive to irrelevant features:** All features contribute to the distance equally. +- **Memory-intensive:** Storing all the training data can be costly. + +### Use Cases: +- **Recommender Systems:** Suggest items based on similarity to user preferences. +- **Image Recognition:** Classify images by comparing new images to the training set. +- **Finance:** Predict credit risk or fraud detection based on historical data. + +## KNN in Python + +### Required Libraries +To implement KNN, we need the following Python libraries: +- `numpy` +- `pandas` +- `scikit-learn` +- `matplotlib` (for visualization) + +### Installation +```bash +pip install numpy pandas scikit-learn matplotlib +``` + +### Example Code +Let's implement a simple KNN classifier using the Iris dataset. + +#### Step 1: Import Libraries +```python +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.neighbors import KNeighborsClassifier +from sklearn.metrics import accuracy_score +import matplotlib.pyplot as plt +``` + +#### Step 2: Load Dataset +```python +from sklearn.datasets import load_iris +iris = load_iris() +X = iris.data +y = iris.target +``` + +#### Step 3: Split Dataset +```python +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) +``` + +#### Step 4: Train KNN Model +```python +knn = KNeighborsClassifier(n_neighbors=3) +knn.fit(X_train, y_train) +``` + +#### Step 5: Make Predictions +```python +y_pred = knn.predict(X_test) +``` + +#### Step 6: Evaluate the Model +```python +accuracy = accuracy_score(y_test, y_pred) +print(f'Accuracy: {accuracy}') +``` + +### Visualization (Optional) +```python +# Plotting the decision boundary for visualization (for 2D data) +h = .02 # step size in the mesh +# Create color maps +cmap_light = plt.cm.RdYlBu +cmap_bold = plt.cm.RdYlBu + +# For simplicity, we take only the first two features of the dataset +X_plot = X[:, :2] +x_min, x_max = X_plot[:, 0].min() - 1, X_plot[:, 0].max() + 1 +y_min, y_max = X_plot[:, 1].min() - 1, y_plot[:, 1].max() + 1 +xx, yy = np.meshgrid(np.arange(x_min, x_max, h), + np.arange(y_min, y_max, h)) + +Z = knn.predict(np.c_[xx.ravel(), yy.ravel()]) +Z = Z.reshape(xx.shape) +plt.figure() +plt.pcolormesh(xx, yy, Z, cmap=cmap_light) + +# Plot also the training points +plt.scatter(X_plot[:, 0], X_plot[:, 1], c=y, edgecolor='k', cmap=cmap_bold) +plt.xlim(xx.min(), xx.max()) +plt.ylim(yy.min(), yy.max()) +plt.title("3-Class classification (k = 3)") +plt.show() +``` + +## Generalization and Considerations +- **Choosing K:** The choice of K is critical. Smaller values of K can lead to noisy models, while larger values make the algorithm computationally expensive and might oversimplify the model. +- **Feature Scaling:** Since KNN relies on distance calculations, features should be scaled (standardized or normalized) to ensure that all features contribute equally to the distance computation. +- **Distance Metrics:** The choice of distance metric (Euclidean, Manhattan, etc.) can affect the performance of the algorithm. + +In conclusion, KNN is a versatile and easy-to-implement algorithm suitable for various classification and regression tasks, particularly when working with small datasets and well-defined features. However, careful consideration should be given to the choice of K, feature scaling, and distance metrics to optimize its performance. \ No newline at end of file diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index b6945cd2..e5f53715 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -16,3 +16,4 @@ - [Types_of_Cost_Functions](cost-functions.md) - [Clustering](clustering.md) - [Grid Search](grid-search.md) +- [K-nearest neighbor (KNN)](K-nearest neighbor (KNN).md) \ No newline at end of file From c812fa29ac87b29857bb3c95ffbaeb766c5b5f8e Mon Sep 17 00:00:00 2001 From: Dishika Vaishkiyar <152963337+Dishika18@users.noreply.github.com> Date: Tue, 4 Jun 2024 00:35:40 +0530 Subject: [PATCH 446/656] Update matplotlib-scatter-plot.md --- .../matplotlib-scatter-plot.md | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/contrib/plotting-visualization/matplotlib-scatter-plot.md b/contrib/plotting-visualization/matplotlib-scatter-plot.md index a9fa500a..535a3a35 100644 --- a/contrib/plotting-visualization/matplotlib-scatter-plot.md +++ b/contrib/plotting-visualization/matplotlib-scatter-plot.md @@ -2,7 +2,7 @@ * A scatter plot is a type of data visualization that uses dots to show values for two variables, with one variable on the x-axis and the other on the y-axis. It's useful for identifying relationships, trends, and correlations, as well as spotting clusters and outliers. * The dots on the plot shows how the variables are related. A scatter plot is made with the matplotlib library's `scatter() method`. ## Syntax -**Here's how to write code for the scatter() method:** +**Here's how to write code for the `scatter() method`:** ``` matplotlib.pyplot.scatter (x_axis_value, y_axis_value, s = None, c = None, vmin = None, vmax = None, marker = None, cmap = None, alpha = None, linewidths = None, edgecolors = None) @@ -30,7 +30,7 @@ plt.show() When executed, this will show the following Scatter plot: - +![Basic line Chart](images/simple_scatter.png) ## Compare Plots @@ -55,6 +55,8 @@ plt.show() When executed, this will show the following Compare Scatter plot: +![Compare Plots](images/scatter_compare.png) + ## Colors in Scatter plot You can set your own color for each scatter plot with the `color` or the `c` argument: @@ -75,10 +77,12 @@ plt.show() When executed, this will show the following Colors Scatter plot: +![Colors in Scatter plot](images/scatter_color.png) + ## Color Each Dot You can even set a specific color for each dot by using an array of colors as value for the `c` argument: -``**Note:** You cannot use the `color` argument for this, only the `c` argument.`` +``Note: You cannot use the `color` argument for this, only the `c` argument.`` ``` import matplotlib.pyplot as plt @@ -95,6 +99,8 @@ plt.show() When executed, this will show the following Color Each Dot: +![Color Each Dot](images/scatter_coloreachdot.png) + ## ColorMap The Matplotlib module has a number of available colormaps. @@ -102,8 +108,7 @@ A colormap is like a list of colors, where each color has a value that ranges fr Here is an example of a colormap: - - +![ColorMap](images/img_colorbar.png) This colormap is called 'viridis' and as you can see it ranges from 0, which is a purple color, up to 100, which is a yellow color. @@ -127,7 +132,7 @@ plt.show() When executed, this will show the following Scatter ColorMap: - +![Scatter ColorMap](images/scatter_colormap1.png) You can include the colormap in the drawing by including the `plt.colorbar()` statement: @@ -148,7 +153,7 @@ plt.show() When executed, this will show the following Scatter ColorMap using `plt.colorbar()`: - +![Scatter ColorMap1](images/scatter_colormap2.png) From 93eae0c0c908f7563c096c4563e9ac37a96a9b5e Mon Sep 17 00:00:00 2001 From: Dishika Vaishkiyar <152963337+Dishika18@users.noreply.github.com> Date: Tue, 4 Jun 2024 00:38:23 +0530 Subject: [PATCH 447/656] Update index.md --- contrib/plotting-visualization/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 3192f03d..7e43d9b2 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -5,5 +5,6 @@ - [Bar Plots in Matplotlib](matplotlib-bar-plots.md) - [Pie Charts in Matplotlib](matplotlib-pie-charts.md) - [Line Charts in Matplotlib](matplotlib-line-plots.md) +- [Scatter Plots in Matplotlib](matplotlib-scatter-plot.md) - [Introduction to Seaborn and Installation](seaborn-intro.md) - [Getting started with Seaborn](seaborn-basics.md) From ff13658e4ab3cf70a592b27a8a9562847f12f624 Mon Sep 17 00:00:00 2001 From: SAM <8dmasters@gmail.com> Date: Tue, 4 Jun 2024 01:07:24 +0530 Subject: [PATCH 448/656] Create match_case_statement.md --- contrib/advanced-python/match_case_statement.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 contrib/advanced-python/match_case_statement.md diff --git a/contrib/advanced-python/match_case_statement.md b/contrib/advanced-python/match_case_statement.md new file mode 100644 index 00000000..e69de29b From 3ac978f6a97fa53d1a111d3fb0132984a13ce6de Mon Sep 17 00:00:00 2001 From: SAM <8dmasters@gmail.com> Date: Tue, 4 Jun 2024 01:07:36 +0530 Subject: [PATCH 449/656] Update index.md Added match_case_statement index --- contrib/advanced-python/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index b31544eb..c262aaa3 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -10,3 +10,4 @@ - [Protocols](protocols.md) - [Exception Handling in Python](exception-handling.md) - [Generators](generators.md) +- [Match Case Statement](match_case_statement.md) \ No newline at end of file From 5678bc9cf3bfb93c1d938d56b06c11a36e3f94b1 Mon Sep 17 00:00:00 2001 From: Kosuri Indu Date: Tue, 4 Jun 2024 08:19:32 +0530 Subject: [PATCH 450/656] Added Hashing through linear probing --- .../ds-algorithms/hashing-linear-probing.md | 139 ++++++++++++++++++ 1 file changed, 139 insertions(+) create mode 100644 contrib/ds-algorithms/hashing-linear-probing.md diff --git a/contrib/ds-algorithms/hashing-linear-probing.md b/contrib/ds-algorithms/hashing-linear-probing.md new file mode 100644 index 00000000..0d27db47 --- /dev/null +++ b/contrib/ds-algorithms/hashing-linear-probing.md @@ -0,0 +1,139 @@ +# Hashing with Linear Probing + +In Data Structures and Algorithms, hashing is used to map data of arbitrary size to fixed-size values. A common approach to handle collisions in hashing is **linear probing**. In linear probing, if a collision occurs (i.e., the hash value points to an already occupied slot), we linearly probe through the table to find the next available slot. This method ensures that every element can be inserted or found in the hash table. + +## Points to be Remembered + +- **Hash Function**: A function that converts an input (or 'key') into an index in a hash table. +- **Collision**: When two keys hash to the same index. +- **Linear Probing**: A method to resolve collisions by checking the next slot (i.e., index + 1) until an empty slot is found. + +## Real Life Examples of Hashing with Linear Probing + +- **Student Record System**: Each student record is stored in a table where the student's ID number is hashed to an index. If two students have the same hash index, linear probing finds the next available slot. +- **Library System**: Books are indexed by their ISBN numbers. If two books hash to the same slot, linear probing helps find another spot for the book in the catalog. + +## Applications of Hashing + +Hashing is widely used in Computer Science: + +- **Database Indexing** +- **Caches** (like CPU caches, web caches) +- **Associative Arrays** (or dictionaries in Python) +- **Sets** (unordered collections of unique elements) + +Understanding these applications is essential for Software Development. + +## Operations in Hash Table with Linear Probing + +Key operations include: + +- **INSERT**: Insert a new element into the hash table. +- **SEARCH**: Find the position of an element in the hash table. +- **DELETE**: Remove an element from the hash table. + +## Implementing Hash Table with Linear Probing in Python + +```python +class HashTable: + def __init__(self, size): + self.size = size + self.table = [None] * size + + def hash_function(self, key): + return key % self.size + + def insert(self, key, value): + hash_index = self.hash_function(key) + + if self.table[hash_index] is None: + self.table[hash_index] = (key, value) + else: + while self.table[hash_index] is not None: + hash_index = (hash_index + 1) % self.size + self.table[hash_index] = (key, value) + + def search(self, key): + hash_index = self.hash_function(key) + + while self.table[hash_index] is not None: + if self.table[hash_index][0] == key: + return self.table[hash_index][1] + hash_index = (hash_index + 1) % self.size + + return None + + def delete(self, key): + hash_index = self.hash_function(key) + + while self.table[hash_index] is not None: + if self.table[hash_index][0] == key: + self.table[hash_index] = None + return True + hash_index = (hash_index + 1) % self.size + + return False + + def display(self): + for index, item in enumerate(self.table): + print(f"Index {index}: {item}") + +# Example usage +hash_table = HashTable(10) + +hash_table.insert(1, 'A') +hash_table.insert(11, 'B') +hash_table.insert(21, 'C') + +print("Hash Table after Insert operations:") +hash_table.display() + +print("Search operation for key 11:", hash_table.search(11)) + +hash_table.delete(11) + +print("Hash Table after Delete operation:") +hash_table.display() +``` + +## Output + +```markdown +Hash Table after Insert operations: +Index 0: None +Index 1: (1, 'A') +Index 2: None +Index 3: None +Index 4: None +Index 5: None +Index 6: None +Index 7: None +Index 8: None +Index 9: None +Index 10: None +Index 11: (11, 'B') +Index 12: (21, 'C') + +Search operation for key 11: B + +Hash Table after Delete operation: +Index 0: None +Index 1: (1, 'A') +Index 2: None +Index 3: None +Index 4: None +Index 5: None +Index 6: None +Index 7: None +Index 8: None +Index 9: None +Index 10: None +Index 11: None +Index 12: (21, 'C') +``` + +## Complexity Analysis + +- **Insertion**: Average case O(1), Worst case O(n) when many collisions occur. +- **Search**: Average case O(1), Worst case O(n) when many collisions occur. +- **Deletion**: Average case O(1), Worst case O(n) when many collisions occur. \ No newline at end of file From 96752204ead3ad9412e2da4fdd01abbbca860349 Mon Sep 17 00:00:00 2001 From: Kosuri Indu Date: Tue, 4 Jun 2024 08:28:18 +0530 Subject: [PATCH 451/656] Added Hashing through Chaining --- contrib/ds-algorithms/hashing-chaining.md | 153 ++++++++++++++++++++++ 1 file changed, 153 insertions(+) create mode 100644 contrib/ds-algorithms/hashing-chaining.md diff --git a/contrib/ds-algorithms/hashing-chaining.md b/contrib/ds-algorithms/hashing-chaining.md new file mode 100644 index 00000000..34086b5e --- /dev/null +++ b/contrib/ds-algorithms/hashing-chaining.md @@ -0,0 +1,153 @@ +# Hashing with Chaining + +In Data Structures and Algorithms, hashing is used to map data of arbitrary size to fixed-size values. A common approach to handle collisions in hashing is **chaining**. In chaining, each slot of the hash table contains a linked list, and all elements that hash to the same slot are stored in that list. + +## Points to be Remembered + +- **Hash Function**: A function that converts an input (or 'key') into an index in a hash table. +- **Collision**: When two keys hash to the same index. +- **Chaining**: A method to resolve collisions by maintaining a linked list for each hash table slot. + +## Real Life Examples of Hashing with Chaining + +- **Phone Directory**: Contacts are stored in a hash table where the contact's name is hashed to an index. If multiple names hash to the same index, they are stored in a linked list at that index. +- **Library Catalog**: Books are indexed by their titles. If multiple books have titles that hash to the same index, they are stored in a linked list at that index. + +## Applications of Hashing + +Hashing is widely used in Computer Science: + +- **Database Indexing** +- **Caches** (like CPU caches, web caches) +- **Associative Arrays** (or dictionaries in Python) +- **Sets** (unordered collections of unique elements) + +Understanding these applications is essential for Software Development. + +## Operations in Hash Table with Chaining + +Key operations include: + +- **INSERT**: Insert a new element into the hash table. +- **SEARCH**: Find the position of an element in the hash table. +- **DELETE**: Remove an element from the hash table. + +## Implementing Hash Table with Chaining in Python + +```python +class Node: + def __init__(self, key, value): + self.key = key + self.value = value + self.next = None + +class HashTable: + def __init__(self, size): + self.size = size + self.table = [None] * size + + def hash_function(self, key): + return key % self.size + + def insert(self, key, value): + hash_index = self.hash_function(key) + new_node = Node(key, value) + + if self.table[hash_index] is None: + self.table[hash_index] = new_node + else: + current = self.table[hash_index] + while current.next is not None: + current = current.next + current.next = new_node + + def search(self, key): + hash_index = self.hash_function(key) + current = self.table[hash_index] + + while current is not None: + if current.key == key: + return current.value + current = current.next + + return None + + def delete(self, key): + hash_index = self.hash_function(key) + current = self.table[hash_index] + prev = None + + while current is not None: + if current.key == key: + if prev is None: + self.table[hash_index] = current.next + else: + prev.next = current.next + return True + prev = current + current = current.next + + return False + + def display(self): + for index, item in enumerate(self.table): + print(f"Index {index}:", end=" ") + current = item + while current is not None: + print(f"({current.key}, {current.value})", end=" -> ") + current = current.next + print("None") + +# Example usage +hash_table = HashTable(10) + +hash_table.insert(1, 'A') +hash_table.insert(11, 'B') +hash_table.insert(21, 'C') + +print("Hash Table after Insert operations:") +hash_table.display() + +print("Search operation for key 11:", hash_table.search(11)) + +hash_table.delete(11) + +print("Hash Table after Delete operation:") +hash_table.display() +``` + +## Output + +```markdown +Hash Table after Insert operations: +Index 0: None +Index 1: (1, 'A') -> (11, 'B') -> (21, 'C') -> None +Index 2: None +Index 3: None +Index 4: None +Index 5: None +Index 6: None +Index 7: None +Index 8: None +Index 9: None + +Search operation for key 11: B + +Hash Table after Delete operation: +Index 0: None +Index 1: (1, 'A') -> (21, 'C') -> None +Index 2: None +Index 3: None +Index 4: None +Index 5: None +Index 6: None +Index 7: None +Index 8: None +Index 9: None +``` + +## Complexity Analysis + +- **Insertion**: Average case O(1), Worst case O(n) when many elements hash to the same slot. +- **Search**: Average case O(1), Worst case O(n) when many elements hash to the same slot. +- **Deletion**: Average case O(1), Worst case O(n) when many elements hash to the same slot. \ No newline at end of file From 0699376c4b30dbe5c767c58f184a58e2ecdb6674 Mon Sep 17 00:00:00 2001 From: Mangalya Phaye <141892171+mdphaye@users.noreply.github.com> Date: Tue, 4 Jun 2024 11:07:11 +0530 Subject: [PATCH 452/656] Rename Queues.md to queues.md --- contrib/ds-algorithms/{Queues.md => queues.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/ds-algorithms/{Queues.md => queues.md} (100%) diff --git a/contrib/ds-algorithms/Queues.md b/contrib/ds-algorithms/queues.md similarity index 100% rename from contrib/ds-algorithms/Queues.md rename to contrib/ds-algorithms/queues.md From a2f8b49b1e77b1d4d9fcc376509e401955b468b7 Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Tue, 4 Jun 2024 15:47:49 +0530 Subject: [PATCH 453/656] Update match_case_statement.md Added introduction and syntax --- .../advanced-python/match_case_statement.md | 22 +++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/contrib/advanced-python/match_case_statement.md b/contrib/advanced-python/match_case_statement.md index e69de29b..d4ad8616 100644 --- a/contrib/advanced-python/match_case_statement.md +++ b/contrib/advanced-python/match_case_statement.md @@ -0,0 +1,22 @@ +# Match Case Statements +## Introduction +Match and case statements are introduced in Python 3.10 for structural pattern matching of patterns with associated actions. It offers more readible and +cleaniness to the code as opposed to the traditional `if-else` statements. They also have destructuring, pattern matching and checks for specific properties in +addition to the traditional `switch-case` statements in other languages, which makes them more versatile. + +## Syntax +``` +match : + case : + + case : + + case _: + +``` +A match statement takes a statement which compares it to the various cases and their patterns. If any of the pattern is matched successively, the task is performed accordingly. If an exact match is not confirmed, the last case, a wildcard `_`, if provided, will be used as the matching case. + +## Pattern Matching +As discussed earlier, match case statements use pattern matching where the patterns consist of sequences, mappings, primitive data types as well as class instances. The structural pattern matching uses declarative approach and it nexplicitly states the conditions for the patterns to match with the data. + +### Patterns with a Literal From f0c59b288c5e5877eb1f6d69a7f995cbdf08f2f9 Mon Sep 17 00:00:00 2001 From: mankalavaishnavi Date: Tue, 4 Jun 2024 15:58:52 +0530 Subject: [PATCH 454/656] Filter definition , syntax is done --- contrib/advanced-python/filter-function.md | 86 ++++++++++++++++++++++ contrib/advanced-python/index.md | 2 + contrib/advanced-python/reduce-function.md | 72 ++++++++++++++++++ 3 files changed, 160 insertions(+) create mode 100644 contrib/advanced-python/filter-function.md create mode 100644 contrib/advanced-python/reduce-function.md diff --git a/contrib/advanced-python/filter-function.md b/contrib/advanced-python/filter-function.md new file mode 100644 index 00000000..cbf9463e --- /dev/null +++ b/contrib/advanced-python/filter-function.md @@ -0,0 +1,86 @@ +# Filter Function + +## Definition +The filter function is a built-in Python function used for constructing an iterator from elements of an iterable for which a function returns true. + +**Syntax**: + ```python +filter(function, iterable) +``` +**Parameters**:
    +*function*: A function that tests if each element of an iterable returns True or False.
    +*iterable*: An iterable like sets, lists, tuples, etc., whose elements are to be filtered.
    +*Returns* : An iterator that is already filtered. + +## Basic Usage +**Example 1: Filtering a List of Numbers**: +```python +# Define a function that returns True for even numbers +def is_even(n): + return n % 2 == 0 + +numbers = [1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +even_numbers = filter(is_even, numbers) + +# Convert the filter object to a list +print(list(even_numbers)) # Output: [2, 4, 6, 8, 10] +``` + +**Example 2: Filtering with a Lambda Function**: +```python +numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +odd_numbers = filter(lambda x: x % 2 != 0, numbers) + +print(list(odd_numbers)) # Output: [1, 3, 5, 7, 9] +``` + +**Example 3: Filtering Strings**: +```python +words = ["apple", "banana", "cherry", "date", "elderberry", "fig", "grape" , "python"] +long_words = filter(lambda word: len(word) > 5, words) + +print(list(long_words)) # Output: ['banana', 'cherry', 'elderberry', 'python'] +``` + +## Advanced Usage +**Example 4: Filtering Objects with Attributes**: +```python +class Person: + def __init__(self, name, age): + self.name = name + self.age = age + +people = [ + Person("Alice", 30), + Person("Bob", 15), + Person("Charlie", 25), + Person("David", 35) +] + +adults = filter(lambda person: person.age >= 18, people) +adult_names = map(lambda person: person.name, adults) + +print(list(adult_names)) # Output: ['Alice', 'Charlie', 'David'] +``` + +**Example 5: Using None as the Function**: +```python +numbers = [0, 1, 2, 3, 0, 4, 0, 5] +non_zero_numbers = filter(None, numbers) + +print(list(non_zero_numbers)) # Output: [1, 2, 3, 4, 5] +``` +**NOTE**: When None is passed as the function, filter removes all items that are false. + +## Time Complexity: +- The time complexity of filter() depends on two factors: + 1. The time complexity of the filtering function (the one you provide as an argument). + 2. The size of the iterable being filtered. +- If the filtering function has a constant time complexity (e.g., O(1)), the overall time complexity of filter() is linear (O(n)), where ‘n’ is the number of elements in the iterable. + +## Space Complexity: +- The space complexity of filter() is also influenced by the filtering function and the size of the iterable. +- Since filter() returns an iterator, it doesn’t create a new list in memory. Instead, it generates filtered elements on-the-fly as you iterate over it. Therefore, the space complexity is O(1). + +## Conclusion: +Python’s filter() allows you to perform filtering operations on iterables. This kind of operation consists of applying a Boolean function to the items in an iterable and keeping only those values for which the function returns a true result. In general, you can use filter() to process existing iterables and produce new iterables containing the values that you currently need.Both versions of Python support filter(), but Python 3’s approach is more memory-efficient due to the use of iterators. \ No newline at end of file diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index b31544eb..5d6a0b6f 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -10,3 +10,5 @@ - [Protocols](protocols.md) - [Exception Handling in Python](exception-handling.md) - [Generators](generators.md) +- [Filter](filter-function.md) +- [Reduce](reduce-function.md) \ No newline at end of file diff --git a/contrib/advanced-python/reduce-function.md b/contrib/advanced-python/reduce-function.md new file mode 100644 index 00000000..5c0c81b4 --- /dev/null +++ b/contrib/advanced-python/reduce-function.md @@ -0,0 +1,72 @@ +# Reduce Function + +## Definition: +The reduce() function is part of the functools module and is used to apply a binary function (a function that takes two arguments) cumulatively to the items of an iterable (e.g., a list, tuple, or string). It reduces the iterable to a single value by successively combining elements. + +**Syntax**: +```python +from functools import reduce +reduce(function, iterable, initial=None) +``` +**Parameters**:
    +*function* : The binary function to apply. It takes two arguments and returns a single value.
    +*iterable* : The sequence of elements to process.
    +*initial (optional)*: An initial value. If provided, the function is applied to the initial value and the first element of the iterable. Otherwise, the first two elements are used as the initial values. + +## Working: +- Intially , first two elements of iterable are picked and the result is obtained. +- Next step is to apply the same function to the previously attained result and the number just succeeding the second element and the result is again stored. +- This process continues till no more elements are left in the container. +- The final returned result is returned and printed on console. + +## Examples: + +**Example 1:** +```python +numbers = [1, 2, 3, 4, 10] +total = reduce(lambda x, y: x + y, numbers) +print(total) # Output: 20 +``` +**Example 2:** +```python +numbers = [11, 7, 8, 20, 1] +max_value = reduce(lambda x, y: x if x > y else y, numbers) +print(max_value) # Output: 20 +``` +**Example 3:** +```python +# Importing reduce function from functools +from functools import reduce + +# Creating a list +my_list = [10, 20, 30, 40, 50] + +# Calculating the product of the numbers in my_list +# using reduce and lambda functions together +product = reduce(lambda x, y: x * y, my_list) + +# Printing output +print(f"Product = {product}") # Output : Product = 12000000 +``` + +## Difference Between reduce() and accumulate(): +- **Behavior:** + - reduce() stores intermediate results and only returns the final summation value. + - accumulate() returns an iterator containing all intermediate results. The last value in the iterator is the summation value of the list. + +- **Use Cases:** + - Use reduce() when you need a single result (e.g., total sum, product) from the iterable. + - Use accumulate() when you want to access intermediate results during the reduction process. + +- **Initial Value:** + - reduce() allows an optional initial value. + - accumulate() also accepts an optional initial value since Python 3.8. + +- **Order of Arguments:** + - reduce() takes the function first, followed by the iterable. + - accumulate() takes the iterable first, followed by the function. + +## Conclusion: +Python's Reduce function enables us to apply reduction operations to iterables using lambda and callable functions. A +function called reduce() reduces the elements of an iterable to a single cumulative value. The reduce function in +Python solves various straightforward issues, including adding and multiplying iterables of numbers. \ No newline at end of file From 55717a47dee55e6d62aa161e52840b7075dc0689 Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Tue, 4 Jun 2024 16:48:03 +0530 Subject: [PATCH 455/656] Update match_case_statement.md Added patterns with a literal section --- .../advanced-python/match_case_statement.md | 57 +++++++++++++++++++ 1 file changed, 57 insertions(+) diff --git a/contrib/advanced-python/match_case_statement.md b/contrib/advanced-python/match_case_statement.md index d4ad8616..691d23b1 100644 --- a/contrib/advanced-python/match_case_statement.md +++ b/contrib/advanced-python/match_case_statement.md @@ -20,3 +20,60 @@ A match statement takes a statement which compares it to the various cases and t As discussed earlier, match case statements use pattern matching where the patterns consist of sequences, mappings, primitive data types as well as class instances. The structural pattern matching uses declarative approach and it nexplicitly states the conditions for the patterns to match with the data. ### Patterns with a Literal +#### Generic Case +`sample text` is passed as a literal in the `match` block. There are two cases and a wildcard case mentioned. +```python +match 'sample text': + case 'sample text': + print('sample text') + case 'sample': + print('sample') + case _: + print('None found') +``` +The `sample text` case is satisfied as it matches with the literal `sample text` described in the `match` block. + +O/P: +``` +sample text +``` + +#### Using OR +Taking another example, `|` can be used as OR to include multiple patterns in a single case statement where the multiple patterns all lead to a similar task. + +The below code snippets can be used interchangebly and generate the similar output. The latter is more consive and readible. +```python +match 'e': + case 'a': + print('vowel') + case 'e': + print('vowel') + case 'i': + print('vowel') + case 'o': + print('vowel') + case 'u': + print('vowel') + case _: + print('consonant') +``` +```python +match 'e': + case 'a' | 'e' | 'i' | 'o' | 'u': + print('vowel') + case _: + print('consonant') +``` +O/P: +``` +vowel +``` + +#### Without wildcard +When in a `match` block, there is no wildcard case present there are be two cases of match being present or not. If the match doesn't exist, the behaviour is a no-op. +```python +match 'c': + case 'a' | 'e' | 'i' | 'o' | 'u': + print('vowel') +``` +The output will be blank as a no-op occurs. From 43455d1b56fdbd35a291999f94303373c1c5ee59 Mon Sep 17 00:00:00 2001 From: Yogesh Vishwakarma Date: Tue, 4 Jun 2024 17:08:51 +0530 Subject: [PATCH 456/656] Added two-pointer-technique.md and updated index.md --- contrib/ds-algorithms/index.md | 1 + .../ds-algorithms/two-pointer-technique.md | 134 ++++++++++++++++++ 2 files changed, 135 insertions(+) create mode 100644 contrib/ds-algorithms/two-pointer-technique.md diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 1d7293bb..52293d97 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -13,3 +13,4 @@ - [Stacks in Python](stacks.md) - [Sliding Window Technique](sliding-window.md) - [Trie](trie.md) +- [Two Pointer Technique](two-pointer-technique.md) diff --git a/contrib/ds-algorithms/two-pointer-technique.md b/contrib/ds-algorithms/two-pointer-technique.md new file mode 100644 index 00000000..cec588f0 --- /dev/null +++ b/contrib/ds-algorithms/two-pointer-technique.md @@ -0,0 +1,134 @@ +# Two-Pointer Technique + +--- + +- The two-pointer technique is a popular algorithmic strategy used to solve various problems efficiently. This technique involves using two pointers (or indices) to traverse through data structures such as arrays or linked lists. +- The pointers can move in different directions, allowing for efficient processing of elements to achieve the desired results. + +## Common Use Cases + +1. **Finding pairs in a sorted array that sum to a target**: One pointer starts at the beginning and the other at the end. +2. **Reversing a linked list**: One pointer starts at the head, and the other at the next node, progressing through the list. +3. **Removing duplicates from a sorted array**: One pointer keeps track of the unique elements, and the other traverses the array. +4. **Merging two sorted arrays**: Two pointers are used to iterate through the arrays and merge them. + +## Example 1: Finding Pairs with a Given Sum + +### Problem Statement + +Given a sorted array of integers and a target sum, find all pairs in the array that sum up to the target. + +### Approach + +1. Initialize two pointers: one at the beginning (`left`) and one at the end (`right`) of the array. +2. Calculate the sum of the elements at the `left` and `right` pointers. +3. If the sum is equal to the target, record the pair and move both pointers inward. +4. If the sum is less than the target, move the `left` pointer to the right to increase the sum. +5. If the sum is greater than the target, move the `right` pointer to the left to decrease the sum. +6. Repeat the process until the `left` pointer is not less than the `right` pointer. + +### Example Code + +```python +def find_pairs_with_sum(arr, target): + left = 0 + right = len(arr) - 1 + pairs = [] + + while left < right: + current_sum = arr[left] + arr[right] + + if current_sum == target: + pairs.append((arr[left], arr[right])) + left += 1 + right -= 1 + elif current_sum < target: + left += 1 + else: + right -= 1 + + return pairs + +# Example usage +arr = [1, 2, 3, 4, 5, 6, 7, 8, 9] +target = 10 +result = find_pairs_with_sum(arr, target) +print("Pairs with sum", target, "are:", result) + ``` + +## Example 2: Removing Duplicates from a Sorted Array + +### Problem Statement +Given a sorted array, remove the duplicates in place such that each element appears only once and return the new length of the array. + +### Approach +1. If the array is empty, return 0. +2. Initialize a slow pointer at the beginning of the array. +3. Use a fast pointer to traverse through the array. +4. Whenever the element at the fast pointer is different from the element at the slow pointer, increment the slow pointer and update the element at the slow pointer with the element at the fast pointer. +5. Continue this process until the fast pointer reaches the end of the array. +6. The slow pointer will indicate the position of the last unique element. + +### Example Code + +```python +def remove_duplicates(arr): + if not arr: + return 0 + + slow = 0 + + for fast in range(1, len(arr)): + if arr[fast] != arr[slow]: + slow += 1 + arr[slow] = arr[fast] + + return slow + 1 + +# Example usage +arr = [1, 1, 2, 2, 3, 4, 4, 5] +new_length = remove_duplicates(arr) +print("Array after removing duplicates:", arr[:new_length]) +print("New length of array:", new_length) +``` +# Advantages of the Two-Pointer Technique + +Here are some key benefits of using the two-pointer technique: + +## 1. **Improved Time Complexity** + +It often reduces the time complexity from O(n^2) to O(n), making it significantly faster for many problems. + +### Example +- **Finding pairs with a given sum**: Efficiently finds pairs in O(n) time. + +## 2. **Simplicity** + +The implementation is straightforward, using basic operations like incrementing or decrementing pointers. + +### Example +- **Removing duplicates from a sorted array**: Easy to implement and understand. + +## 3. **In-Place Solutions** + +Many problems can be solved in place, requiring no extra space beyond the input data. + +### Example +- **Reversing a linked list**: Adjusts pointers within the existing nodes. + +## 4. **Versatility** + +Applicable to a wide range of problems, from arrays and strings to linked lists. + +### Example +- **Merging two sorted arrays**: Efficiently merges using two pointers. + +## 5. **Efficiency** + +Minimizes redundant operations and enhances performance, especially with large data sets. + +### Example +- **Partitioning problems**: Efficiently partitions elements with minimal operations. + + +These advantages make the two-pointer technique a valuable approach for many algorithmic challenges. From a4c93b95adbca63d75c05d5754ea81e540cba34c Mon Sep 17 00:00:00 2001 From: Yogesh Vishwakarma <103316955+Yogeshkarma@users.noreply.github.com> Date: Tue, 4 Jun 2024 17:13:27 +0530 Subject: [PATCH 457/656] Update two-pointer-technique.md Removed the footer line --- contrib/ds-algorithms/two-pointer-technique.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/contrib/ds-algorithms/two-pointer-technique.md b/contrib/ds-algorithms/two-pointer-technique.md index cec588f0..6b8720a5 100644 --- a/contrib/ds-algorithms/two-pointer-technique.md +++ b/contrib/ds-algorithms/two-pointer-technique.md @@ -130,5 +130,3 @@ Minimizes redundant operations and enhances performance, especially with large d ### Example - **Partitioning problems**: Efficiently partitions elements with minimal operations. - -These advantages make the two-pointer technique a valuable approach for many algorithmic challenges. From 5f57b24ac03006216a366219e09ea0566e231ab8 Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Tue, 4 Jun 2024 17:44:13 +0530 Subject: [PATCH 458/656] Update match_case_statement.md Added two more sections. Patterns with a literal and a variable, and patterns with classes. --- .../advanced-python/match_case_statement.md | 83 +++++++++++++++++++ 1 file changed, 83 insertions(+) diff --git a/contrib/advanced-python/match_case_statement.md b/contrib/advanced-python/match_case_statement.md index 691d23b1..57f6bfba 100644 --- a/contrib/advanced-python/match_case_statement.md +++ b/contrib/advanced-python/match_case_statement.md @@ -77,3 +77,86 @@ match 'c': print('vowel') ``` The output will be blank as a no-op occurs. + +### Patterns with a Literal and a Variable +Pattern matching can be done by unpacking the assignments and also bind variables with it. +```python +def get_names(names: str) -> None: + match names: + case ('Bob', y): + print(f'Hello {y}') + case (x, 'John'): + print(f'Hello {x}') + case (x, y): + print(f'Hello {x} and {y}') + case _: + print('Invalid') +``` +Here, the `names` is a tuple that contains two names. The `match` block unpacks the tuple and binds `x` and `y` based on the patterns. A wildcard case prints `Invalid` if the condition is not satisfied. + +O/P: + +In this example, the above code snippet with the parameter `names` as below and the respective output. +``` +>>> get_names(('Bob', 'Max')) +Hello Max + +>>> get_names(('Rob', 'John')) +Hello Rob + +>>> get_names(('Rob', 'Max')) +Hello Rob and Max + +>>> get_names(('Rob', 'Max', 'Bob')) +Invalid +``` + +### Patterns with Classes +Class structures can be used in `match` block for pattern matching. The class members can also be binded with a variable to perform certain operations. For the class structure: +```python +class Person: + def __init__(self, name, age): + self.name = name + self.age = age +``` +The match case example illustrates the generic working as well as the binding of variables with the class members. +```python +def get_class(cls: Person) -> None: + match cls: + case Person(name='Bob', age=18): + print('Hello Bob with age 18') + case Person(name='Max', age=y): + print(f'Age is {y}') + case Person(name=x, age=18): + print(f'Name is {x}') + case Person(name=x, age=y): + print(f'Name and age is {x} and {y}') + case _: + print('Invalid') +``` +O/P: +``` +>>> get_class(Person('Bob', 18)) +Hello Bob with age 18 + +>>> get_class(Person('Max', 21)) +Age is 21 + +>>> get_class(Person('Rob', 18)) +Name is Rob + +>>> get_class(Person('Rob', 21)) +Name and age is Rob and 21 +``` +Now, if a new class is introduced in the above code snippet like below. +```python +class Pet: + def __init__(self, name, animal): + self.name = name + self.animal = animal +``` +The patterns will not match the cases and will trigger the wildcard case for the original code snippet above with `get_class` function. +``` +>>> get_class(Pet('Tommy', 'Dog')) +Invalid +``` From e5c88286d5b72b0e0e51e4f1c889544b41c91ef9 Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Tue, 4 Jun 2024 18:02:50 +0530 Subject: [PATCH 459/656] Update match_case_statement.md Added remaining topic headings --- contrib/advanced-python/match_case_statement.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/contrib/advanced-python/match_case_statement.md b/contrib/advanced-python/match_case_statement.md index 57f6bfba..6941d98e 100644 --- a/contrib/advanced-python/match_case_statement.md +++ b/contrib/advanced-python/match_case_statement.md @@ -160,3 +160,14 @@ The patterns will not match the cases and will trigger the wildcard case for the >>> get_class(Pet('Tommy', 'Dog')) Invalid ``` + +### Nested Patterns + +### Complex Patterns +#### Wildcard + +#### Guard + +## Summary + +## Further Reading From ffcc45ad986b70a5b1b39cc515bb7e7230db3c4d Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Tue, 4 Jun 2024 18:28:39 +0530 Subject: [PATCH 460/656] Update match_case_statement.md Added nested patterns --- .../advanced-python/match_case_statement.md | 27 +++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/contrib/advanced-python/match_case_statement.md b/contrib/advanced-python/match_case_statement.md index 6941d98e..22eeef98 100644 --- a/contrib/advanced-python/match_case_statement.md +++ b/contrib/advanced-python/match_case_statement.md @@ -162,6 +162,33 @@ Invalid ``` ### Nested Patterns +The patterns can be nested via various means. It can include the mix of the patterns mentioned earlier or can be symmetrical across. A basic of the nested pattern of a list with Patterns with a Literal and Variable is taken. Classes and Iterables can laso be included. +```python +def get_points(points: list) -> None: + match points: + case []: + print('Empty') + case [x]: + print(f'One point {x}') + case [x, y]: + print(f'Two points {x} and {y}') + case _: + print('More than two points') +``` +O/P: +``` +>>> get_points([]) +Empty + +>>> get_points([1]) +One point 1 + +>>> get_points([1, 2]) +Two points 1 and 2 + +>>> get_points([1, 2, 3]) +More than two points +``` ### Complex Patterns #### Wildcard From d345d0339f7a2bd5eea818f5a42bc4e0c5d9f5f7 Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Tue, 4 Jun 2024 19:14:09 +0530 Subject: [PATCH 461/656] Update match_case_statement.md Added the content to remaining sections --- .../advanced-python/match_case_statement.md | 50 +++++++++++++++++++ 1 file changed, 50 insertions(+) diff --git a/contrib/advanced-python/match_case_statement.md b/contrib/advanced-python/match_case_statement.md index 22eeef98..cd7bc641 100644 --- a/contrib/advanced-python/match_case_statement.md +++ b/contrib/advanced-python/match_case_statement.md @@ -191,10 +191,60 @@ More than two points ``` ### Complex Patterns +Complex patterns are also supported in the pattern matching sequence. The complex does not mean complex numbers but rather the structure which makes the readibility to seem complex. + #### Wildcard +The wildcard used till now are in the form of `case _` where the wildcard case is used if no match is found. Furthermore, the wildcard `_` can also be used as a placeholder in complex patterns. + +```python +def wildcard(value: tuple) -> None: + match value: + case ('Bob', age, 'Mechanic'): + print(f'Bob is mechanic of age {age}') + case ('Bob', age, _): + print(f'Bob is not a mechanic of age {age}') +``` +O/P: + +The value in the above snippet is a tuple with `(Name, Age, Job)`. If the job is Mechanic and the name is Bob, the first case is triggered. But if the job is different and not a mechanic, then the other case is triggered with the wildcard. +``` +>>> wildcard(('Bob', 18, 'Mechanic')) +Bob is mechanic of age 18 + +>>> wildcard(('Bob', 21, 'Engineer')) +Bob is not a mechanic of age 21 +``` #### Guard +A `guard` is when an `if` is added to a pattern. The evaluation depends on the truth value of the guard. + +`nums` is the tuple which contains two integers. A guard is the first case where it checks whether the first number is greater or equal to the second number in the tuple. If it is false, then it moves to the second case, where it concludes that the first number is smaller than the second number. +```python +def guard(nums: tuple) -> None: + match nums: + case (x, y) if x >= y: + print(f'{x} is greater or equal than {y}') + case (x, y): + print(f'{x} is smaller than {y}') + case _: + print('Invalid') +``` +O/P: +``` +>>> guard((1, 2)) +1 is smaller than 2 + +>>> guard((2, 1)) +2 is greater or equal than 1 + +>>> guard((1, 1)) +1 is greater or equal than 1 +``` ## Summary +The match case statements provide an elegant and readible format to perform operations on pattern matching as compared to `if-else` statements. They are also more versatile as they provide additional functionalities on the pattern matching operations like unpacking, class matching, iterables and iterators. It can also use positional arguments for checking the patterns. They provide a powerful and concise way to handle multiple conditions and perform pattern matching ## Further Reading +- [Python Match Case Statement - GeeksForGeeks](https://www.geeksforgeeks.org/python-match-case-statement/) +- [PEP 634 – Structural Pattern Matching: Specification](https://peps.python.org/pep-0634/) +- [PEP 636 – Structural Pattern Matching: Tutorial](https://peps.python.org/pep-0636/) From fe4ce4a34df72c800d12c624385069b27b689d11 Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Tue, 4 Jun 2024 19:16:29 +0530 Subject: [PATCH 462/656] Update match_case_statement.md Added further reading topic summary --- contrib/advanced-python/match_case_statement.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/contrib/advanced-python/match_case_statement.md b/contrib/advanced-python/match_case_statement.md index cd7bc641..b617f7a1 100644 --- a/contrib/advanced-python/match_case_statement.md +++ b/contrib/advanced-python/match_case_statement.md @@ -245,6 +245,8 @@ O/P: The match case statements provide an elegant and readible format to perform operations on pattern matching as compared to `if-else` statements. They are also more versatile as they provide additional functionalities on the pattern matching operations like unpacking, class matching, iterables and iterators. It can also use positional arguments for checking the patterns. They provide a powerful and concise way to handle multiple conditions and perform pattern matching ## Further Reading +This article provides a brief introduction to the match case statements and the overview on the pattern matching operations. To know more, the below articles can be used for in-depth understanding of the topic. + - [Python Match Case Statement - GeeksForGeeks](https://www.geeksforgeeks.org/python-match-case-statement/) - [PEP 634 – Structural Pattern Matching: Specification](https://peps.python.org/pep-0634/) - [PEP 636 – Structural Pattern Matching: Tutorial](https://peps.python.org/pep-0636/) From b3c31ea9499b068a41c10d2a444edb3c439eae5c Mon Sep 17 00:00:00 2001 From: Yogesh Vishwakarma Date: Tue, 4 Jun 2024 19:57:15 +0530 Subject: [PATCH 463/656] added Universal-Functions.md and updated the index.md --- contrib/numpy/Universal-Functions.md | 130 +++++++++++++++++++++++++++ contrib/numpy/index.md | 1 + 2 files changed, 131 insertions(+) create mode 100644 contrib/numpy/Universal-Functions.md diff --git a/contrib/numpy/Universal-Functions.md b/contrib/numpy/Universal-Functions.md new file mode 100644 index 00000000..a06b0d1d --- /dev/null +++ b/contrib/numpy/Universal-Functions.md @@ -0,0 +1,130 @@ +# Universal functions (ufunc) + +--- + +A `ufunc`, short for "`universal function`," is a fundamental concept in NumPy, a powerful library for numerical computing in Python. Universal functions are highly optimized, element-wise functions designed to perform operations on data stored in NumPy arrays. + + + +## Uses of Ufuncs in NumPy + +Universal functions (ufuncs) in NumPy provide a wide range of functionalities for efficient and powerful numerical computations. Below is a detailed explanation of their uses: + +### 1. **Element-wise Operations** +Ufuncs perform operations on each element of the arrays independently. + +```python +import numpy as np + +A = np.array([1, 2, 3, 4]) +B = np.array([5, 6, 7, 8]) + +# Element-wise addition +np.add(A, B) # Output: array([ 6, 8, 10, 12]) +``` + +### 2. **Broadcasting** +Ufuncs support broadcasting, allowing operations on arrays with different shapes, making it possible to perform operations without explicitly reshaping arrays. + +```python +C = np.array([1, 2, 3]) +D = np.array([[1], [2], [3]]) + +# Broadcasting addition +np.add(C, D) # Output: array([[2, 3, 4], [3, 4, 5], [4, 5, 6]]) +``` + +### 3. **Vectorization** +Ufuncs are vectorized, meaning they are implemented in low-level C code, allowing for fast execution and avoiding the overhead of Python loops. + +```python +# Vectorized square root +np.sqrt(A) # Output: array([1., 1.41421356, 1.73205081, 2.]) +``` + +### 4. **Type Flexibility** +Ufuncs handle various data types and perform automatic type casting as needed. + +```python +E = np.array([1.0, 2.0, 3.0]) +F = np.array([4, 5, 6]) + +# Addition with type casting +np.add(E, F) # Output: array([5., 7., 9.]) +``` + +### 5. **Reduction Operations** +Ufuncs support reduction operations, such as summing all elements of an array or finding the product of all elements. + +```python +# Summing all elements +np.add.reduce(A) # Output: 10 + +# Product of all elements +np.multiply.reduce(A) # Output: 24 +``` + +### 6. **Accumulation Operations** +Ufuncs can perform accumulation operations, which keep a running tally of the computation. + +```python +# Cumulative sum +np.add.accumulate(A) # Output: array([ 1, 3, 6, 10]) +``` + +### 7. **Reduceat Operations** +Ufuncs can perform segmented reductions using the `reduceat` method, which applies the ufunc at specified intervals. + +```python +G = np.array([0, 1, 2, 3, 4, 5, 6, 7]) +indices = [0, 2, 5] +np.add.reduceat(G, indices) # Output: array([ 1, 9, 18]) +``` + +### 8. **Outer Product** +Ufuncs can compute the outer product of two arrays, producing a matrix where each element is the result of applying the ufunc to each pair of elements from the input arrays. + +```python +# Outer product +np.multiply.outer([1, 2, 3], [4, 5, 6]) +# Output: array([[ 4, 5, 6], +# [ 8, 10, 12], +# [12, 15, 18]]) +``` + +### 9. **Out Parameter** +Ufuncs can use the `out` parameter to store results in a pre-allocated array, saving memory and improving performance. + +```python +result = np.empty_like(A) +np.multiply(A, B, out=result) # Output: array([ 5, 12, 21, 32]) +``` + +# Create Your Own Ufunc + +You can create custom ufuncs for specific needs using np.frompyfunc or np.vectorize, allowing Python functions to behave like ufuncs. + +Here, we are using `frompyfunc()` which takes three argument: + +1. function - the name of the function. +2. inputs - the number of input (arrays). +3. outputs - the number of output arrays. + +```python +def my_add(x, y): + return x + y + +my_add_ufunc = np.frompyfunc(my_add, 2, 1) +my_add_ufunc(A, B) # Output: array([ 6, 8, 10, 12], dtype=object) +``` +# Some Common Ufunc are + +Here are some commonly used ufuncs in NumPy: + +- **Arithmetic**: `np.add`, `np.subtract`, `np.multiply`, `np.divide` +- **Trigonometric**: `np.sin`, `np.cos`, `np.tan` +- **Exponential and Logarithmic**: `np.exp`, `np.log`, `np.log10` +- **Comparison**: `np.maximum`, `np.minimum`, `np.greater`, `np.less` +- **Logical**: `np.logical_and`, `np.logical_or`, `np.logical_not` + +For more such Ufunc, address to [Universal functions (ufunc) — NumPy](https://numpy.org/doc/stable/reference/ufuncs.html) \ No newline at end of file diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 50e80460..2b0c357e 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -11,3 +11,4 @@ - [Sorting NumPy Arrays](sorting-array.md) - [NumPy Array Iteration](array-iteration.md) - [Concatenation of Arrays](concatenation-of-arrays.md) +- [Universal Functions (Ufunc)](Universal-Functions.md) From 0eefe006fadf5646c0be4c8283743ae6051ea5c0 Mon Sep 17 00:00:00 2001 From: Yogesh Vishwakarma <103316955+Yogeshkarma@users.noreply.github.com> Date: Tue, 4 Jun 2024 20:07:56 +0530 Subject: [PATCH 464/656] Delete contrib/numpy/Universal-Functions.md for creating two PR separately --- contrib/numpy/Universal-Functions.md | 130 --------------------------- 1 file changed, 130 deletions(-) delete mode 100644 contrib/numpy/Universal-Functions.md diff --git a/contrib/numpy/Universal-Functions.md b/contrib/numpy/Universal-Functions.md deleted file mode 100644 index a06b0d1d..00000000 --- a/contrib/numpy/Universal-Functions.md +++ /dev/null @@ -1,130 +0,0 @@ -# Universal functions (ufunc) - ---- - -A `ufunc`, short for "`universal function`," is a fundamental concept in NumPy, a powerful library for numerical computing in Python. Universal functions are highly optimized, element-wise functions designed to perform operations on data stored in NumPy arrays. - - - -## Uses of Ufuncs in NumPy - -Universal functions (ufuncs) in NumPy provide a wide range of functionalities for efficient and powerful numerical computations. Below is a detailed explanation of their uses: - -### 1. **Element-wise Operations** -Ufuncs perform operations on each element of the arrays independently. - -```python -import numpy as np - -A = np.array([1, 2, 3, 4]) -B = np.array([5, 6, 7, 8]) - -# Element-wise addition -np.add(A, B) # Output: array([ 6, 8, 10, 12]) -``` - -### 2. **Broadcasting** -Ufuncs support broadcasting, allowing operations on arrays with different shapes, making it possible to perform operations without explicitly reshaping arrays. - -```python -C = np.array([1, 2, 3]) -D = np.array([[1], [2], [3]]) - -# Broadcasting addition -np.add(C, D) # Output: array([[2, 3, 4], [3, 4, 5], [4, 5, 6]]) -``` - -### 3. **Vectorization** -Ufuncs are vectorized, meaning they are implemented in low-level C code, allowing for fast execution and avoiding the overhead of Python loops. - -```python -# Vectorized square root -np.sqrt(A) # Output: array([1., 1.41421356, 1.73205081, 2.]) -``` - -### 4. **Type Flexibility** -Ufuncs handle various data types and perform automatic type casting as needed. - -```python -E = np.array([1.0, 2.0, 3.0]) -F = np.array([4, 5, 6]) - -# Addition with type casting -np.add(E, F) # Output: array([5., 7., 9.]) -``` - -### 5. **Reduction Operations** -Ufuncs support reduction operations, such as summing all elements of an array or finding the product of all elements. - -```python -# Summing all elements -np.add.reduce(A) # Output: 10 - -# Product of all elements -np.multiply.reduce(A) # Output: 24 -``` - -### 6. **Accumulation Operations** -Ufuncs can perform accumulation operations, which keep a running tally of the computation. - -```python -# Cumulative sum -np.add.accumulate(A) # Output: array([ 1, 3, 6, 10]) -``` - -### 7. **Reduceat Operations** -Ufuncs can perform segmented reductions using the `reduceat` method, which applies the ufunc at specified intervals. - -```python -G = np.array([0, 1, 2, 3, 4, 5, 6, 7]) -indices = [0, 2, 5] -np.add.reduceat(G, indices) # Output: array([ 1, 9, 18]) -``` - -### 8. **Outer Product** -Ufuncs can compute the outer product of two arrays, producing a matrix where each element is the result of applying the ufunc to each pair of elements from the input arrays. - -```python -# Outer product -np.multiply.outer([1, 2, 3], [4, 5, 6]) -# Output: array([[ 4, 5, 6], -# [ 8, 10, 12], -# [12, 15, 18]]) -``` - -### 9. **Out Parameter** -Ufuncs can use the `out` parameter to store results in a pre-allocated array, saving memory and improving performance. - -```python -result = np.empty_like(A) -np.multiply(A, B, out=result) # Output: array([ 5, 12, 21, 32]) -``` - -# Create Your Own Ufunc - -You can create custom ufuncs for specific needs using np.frompyfunc or np.vectorize, allowing Python functions to behave like ufuncs. - -Here, we are using `frompyfunc()` which takes three argument: - -1. function - the name of the function. -2. inputs - the number of input (arrays). -3. outputs - the number of output arrays. - -```python -def my_add(x, y): - return x + y - -my_add_ufunc = np.frompyfunc(my_add, 2, 1) -my_add_ufunc(A, B) # Output: array([ 6, 8, 10, 12], dtype=object) -``` -# Some Common Ufunc are - -Here are some commonly used ufuncs in NumPy: - -- **Arithmetic**: `np.add`, `np.subtract`, `np.multiply`, `np.divide` -- **Trigonometric**: `np.sin`, `np.cos`, `np.tan` -- **Exponential and Logarithmic**: `np.exp`, `np.log`, `np.log10` -- **Comparison**: `np.maximum`, `np.minimum`, `np.greater`, `np.less` -- **Logical**: `np.logical_and`, `np.logical_or`, `np.logical_not` - -For more such Ufunc, address to [Universal functions (ufunc) — NumPy](https://numpy.org/doc/stable/reference/ufuncs.html) \ No newline at end of file From 6b35fb7a8ebcbd423f5e6d4a8f95c39c3550cdfa Mon Sep 17 00:00:00 2001 From: Yogesh Vishwakarma <103316955+Yogeshkarma@users.noreply.github.com> Date: Tue, 4 Jun 2024 20:08:24 +0530 Subject: [PATCH 465/656] Update index.md for two different pRs --- contrib/numpy/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 2b0c357e..3caac1d4 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -11,4 +11,4 @@ - [Sorting NumPy Arrays](sorting-array.md) - [NumPy Array Iteration](array-iteration.md) - [Concatenation of Arrays](concatenation-of-arrays.md) -- [Universal Functions (Ufunc)](Universal-Functions.md) + From 4a9287f6f0f57a0f4490f41037e5c7489fe089ff Mon Sep 17 00:00:00 2001 From: Yogesh Vishwakarma <103316955+Yogeshkarma@users.noreply.github.com> Date: Tue, 4 Jun 2024 20:10:18 +0530 Subject: [PATCH 466/656] Update index.md --- contrib/numpy/index.md | 1 - 1 file changed, 1 deletion(-) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 3caac1d4..50e80460 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -11,4 +11,3 @@ - [Sorting NumPy Arrays](sorting-array.md) - [NumPy Array Iteration](array-iteration.md) - [Concatenation of Arrays](concatenation-of-arrays.md) - From 218c09f481ff14f9bbecd2280561a06c82cd9de6 Mon Sep 17 00:00:00 2001 From: Saimanjari777 Date: Tue, 4 Jun 2024 20:45:53 +0530 Subject: [PATCH 467/656] Update sorting-algorithms.md I have added radix and counting sort --- contrib/ds-algorithms/sorting-algorithms.md | 96 +++++++++++++++++++++ 1 file changed, 96 insertions(+) diff --git a/contrib/ds-algorithms/sorting-algorithms.md b/contrib/ds-algorithms/sorting-algorithms.md index 2edc265c..55367b82 100644 --- a/contrib/ds-algorithms/sorting-algorithms.md +++ b/contrib/ds-algorithms/sorting-algorithms.md @@ -465,3 +465,99 @@ print("Sorted array:", arr) # Output: [1, 2, 3, 5, 8] - **Worst Case:** `𝑂(𝑛log𝑛)`. Building the heap takes `𝑂(𝑛)` time, and each of the 𝑛 element extractions takes `𝑂(log𝑛)` time. - **Best Case:** `𝑂(𝑛log𝑛)`. Even if the array is already sorted, heap sort will still build the heap and perform the extractions. - **Average Case:** `𝑂(𝑛log𝑛)`. Similar to the worst-case, the overall complexity remains `𝑂(𝑛log𝑛)` because each insertion and deletion in a heap takes `𝑂(log𝑛)` time, and these operations are performed 𝑛 times. + + ## 7. Radix Sort +Radix Sort is a non-comparative integer sorting algorithm that sorts numbers by processing individual digits. It processes digits from the least significant digit (LSD) to the most significant digit (MSD) or vice versa. This algorithm is efficient for sorting numbers with a fixed number of digits. + +**Algorithm Overview:** +- **Digit by Digit sorting:** Radix sort processes the digits of the numbers starting from either the least significant digit (LSD) or the most significant digit (MSD). Typically, LSD is used. +- **Stable Sort:** A stable sorting algorithm like Counting Sort or Bucket Sort is used as an intermediate sorting technique. Radix Sort relies on this stability to maintain the relative order of numbers with the same digit value. +- **Multiple passes:** The algorithm performs multiple passes over the numbers, one for each digit, from the least significant to the most significant. + +### Radix Sort Code in Python + +```python +def counting_sort(arr, exp): + n = len(arr) + output = [0] * n + count = [0] * 10 + + for i in range(n): + index = arr[i] // exp + count[index % 10] += 1 + + for i in range(1, 10): + count[i] += count[i - 1] + + i = n - 1 + while i >= 0: + index = arr[i] // exp + output[count[index % 10] - 1] = arr[i] + count[index % 10] -= 1 + i -= 1 + + for i in range(n): + arr[i] = output[i] + +def radix_sort(arr): + max_num = max(arr) + exp = 1 + while max_num // exp > 0: + counting_sort(arr, exp) + exp *= 10 + +# Example usage +arr = [170, 45, 75, 90] +print("Original array:", arr) +radix_sort(arr) +print("Sorted array:", arr) +``` + +### Complexity Analysis + - **Time Complexity:** O(d * (n + k)) for all cases. Radix Sort always processes each digit of every number in the array. + - **Space Complexity:** O(n + k). This is due to the space required for: +- The output array used in Counting Sort, which is of size n. +- The count array used in Counting Sort, which is of size k. + +## 8. Counting Sort +Counting sort is a sorting technique based on keys between a specific range. It works by counting the number of objects having distinct key values (kind of hashing). Then do some arithmetic to calculate the position of each object in the output sequence. + +**Algorithm Overview:** +- Convert the input string into a list of characters. +- Count the occurrence of each character in the list using the collections.Counter() method. +- Sort the keys of the resulting Counter object to get the unique characters in the list in sorted order. +- For each character in the sorted list of keys, create a list of repeated characters using the corresponding count from the Counter object. +- Concatenate the lists of repeated characters to form the sorted output list. + + +### Counting Sort Code in Python using counter method. + +```python +from collections import Counter + +def counting_sort(arr): + count = Counter(arr) + output = [] + for c in sorted(count.keys()): + output += * count + return output + +arr = "geeksforgeeks" +arr = list(arr) +arr = counting_sort(arr) +output = ''.join(arr) +print("Sorted character array is", output) + +``` +### Counting Sort Code in Python using sorted() and reduce(): + +```python +from functools import reduce +string = "geeksforgeeks" +sorted_str = reduce(lambda x, y: x+y, sorted(string)) +print("Sorted string:", sorted_str) +``` + +### Complexity Analysis + - **Time Complexity:** O(n+k) for all cases.No matter how the elements are placed in the array, the algorithm goes through n+k times + - **Space Complexity:** O(max). Larger the range of elements, larger is the space complexity. From 5542f6ba9d665838d273257859992efe958eca6a Mon Sep 17 00:00:00 2001 From: Revanth <109272714+revanth1718@users.noreply.github.com> Date: Tue, 4 Jun 2024 22:18:48 +0530 Subject: [PATCH 468/656] Added K-Means --- .../machine-learning/K-Means_Clustering.md | 84 +++++++++++++++++++ 1 file changed, 84 insertions(+) create mode 100644 contrib/machine-learning/K-Means_Clustering.md diff --git a/contrib/machine-learning/K-Means_Clustering.md b/contrib/machine-learning/K-Means_Clustering.md new file mode 100644 index 00000000..f90e2901 --- /dev/null +++ b/contrib/machine-learning/K-Means_Clustering.md @@ -0,0 +1,84 @@ +# K-Means Clustering +Unsupervised Learning Algorithm for Grouping Similar Data. +## Introduction +K-means clustering is a fundamental unsupervised machine learning algorithm that excels at grouping similar data points together. It's a popular choice due to its simplicity and efficiency in uncovering hidden patterns within unlabeled datasets. +## Unsupervised Learning +Unlike supervised learning algorithms that rely on labeled data for training, unsupervised algorithms, like K-means, operate solely on input data (without predefined categories). Their objective is to discover inherent structures or groupings within the data. +## The K-Means Objective +Organize similar data points into clusters to unveil underlying patterns. The main objective is to minimize total intra-cluster variance or the squared function. + +![enter image description here](https://encrypted-tbn2.gstatic.com/images?q=tbn:ANd9GcQnDTFfxrrAttYMwwjZV0RAA8J8rmUxgrnWc2gU_ArEqrxEMlHm) +## Clusters and Centroids +A cluster represents a collection of data points that share similar characteristics. K-means identifies a pre-determined number (k) of clusters within the dataset. Each cluster is represented by a centroid, which acts as its central point (imaginary or real). +## Minimizing In-Cluster Variation +The K-means algorithm strategically assigns each data point to a cluster such that the total variation within each cluster (measured by the sum of squared distances between points and their centroid) is minimized. In simpler terms, K-means strives to create clusters where data points are close to their respective centroids. +## The Meaning Behind "K-Means" +The "means" in K-means refers to the averaging process used to compute the centroid, essentially finding the center of each cluster. +## K-Means Algorithm in Action +![enter image description here](https://d3i71xaburhd42.cloudfront.net/2f49631bb3103a61fbc3045dd035c3d8f2175887/11-Figure2-1.png) +The K-means algorithm follows an iterative approach to optimize cluster formation: + +1. **Initial Centroid Placement:** The process begins with randomly selecting k centroids to serve as initial reference points for each cluster. +2. **Data Point Assignment:** Each data point is assigned to the closest centroid, effectively creating a preliminary clustering. +3. **Centroid Repositioning:** Once data points are assigned, the centroids are recalculated by averaging the positions of the points within their respective clusters. These new centroids represent the refined centers of the clusters. +4. **Iteration Until Convergence:** Steps 2 and 3 are repeated iteratively until a stopping criterion is met. This criterion can be either: + - **Centroid Stability:** No significant change occurs in the centroids' positions, indicating successful clustering. + - **Reaching Maximum Iterations:** A predefined number of iterations is completed. + ## Code + Following is a simple implementation of K-Means. + + + # Generate and Visualize Sample Data + # import the necessary Libraries + + import numpy as np + import matplotlib.pyplot as plt + + # Create data points for cluster 1 and cluster 2 + X = -2 * np.random.rand(100, 2) + X1 = 1 + 2 * np.random.rand(50, 2) + + # Combine data points from both clusters + X[50:100, :] = X1 + + # Plot data points and display the plot + plt.scatter(X[:, 0], X[:, 1], s=50, c='b') + plt.show() + + # K-Means Model Creation and Training + from sklearn.cluster import KMeans + + # Create KMeans object with 2 clusters + kmeans = KMeans(n_clusters=2) + kmeans.fit(X) # Train the model on the data + + # Visualize Data Points with Centroids + centroids = kmeans.cluster_centers_ # Get centroids (cluster centers) + + plt.scatter(X[:, 0], X[:, 1], s=50, c='b') # Plot data points again + plt.scatter(centroids[0, 0], centroids[0, 1], s=200, c='g', marker='s') # Plot centroid 1 + plt.scatter(centroids[1, 0], centroids[1, 1], s=200, c='r', marker='s') # Plot centroid 2 + plt.show() # Display the plot with centroids + + # Predict Cluster Label for New Data Point + new_data = np.array([-3.0, -3.0]) + new_data_reshaped = new_data.reshape(1, -1) + predicted_cluster = kmeans.predict(new_data_reshaped) + print("Predicted cluster for new data:", predicted_cluster) + + ### Output: + Before Implementing K-Means Clustering : +![Before Implementing K-Means Clustering](https://miro.medium.com/v2/resize:fit:640/format:webp/1*jnyQxmEj7rFhazeMH7KXVg.png) + + + After Implementing K-Means Clustering:![enter image description here](https://miro.medium.com/v2/resize:fit:640/format:webp/1*H3L3EH3Jh6kWFmbec0ewKA.png) +## Conclusion +**K-Means** can be applied to data that has a smaller number of dimensions, is numeric, and is continuous or can be used to find groups that have not been explicitly labeled in the data. As an example, it can be used for Document Classification, Delivery Store Optimization, or Customer Segmentation. + + + + + + + + From 7259dc7047a0576721b852fc4aeb08ddf5bd8b84 Mon Sep 17 00:00:00 2001 From: Revanth <109272714+revanth1718@users.noreply.github.com> Date: Tue, 4 Jun 2024 22:24:02 +0530 Subject: [PATCH 469/656] Update K-Means_Clustering.md --- contrib/machine-learning/K-Means_Clustering.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/contrib/machine-learning/K-Means_Clustering.md b/contrib/machine-learning/K-Means_Clustering.md index f90e2901..ab5fba93 100644 --- a/contrib/machine-learning/K-Means_Clustering.md +++ b/contrib/machine-learning/K-Means_Clustering.md @@ -67,11 +67,12 @@ The K-means algorithm follows an iterative approach to optimize cluster formatio print("Predicted cluster for new data:", predicted_cluster) ### Output: - Before Implementing K-Means Clustering : + Before Implementing K-Means Clustering ![Before Implementing K-Means Clustering](https://miro.medium.com/v2/resize:fit:640/format:webp/1*jnyQxmEj7rFhazeMH7KXVg.png) - After Implementing K-Means Clustering:![enter image description here](https://miro.medium.com/v2/resize:fit:640/format:webp/1*H3L3EH3Jh6kWFmbec0ewKA.png) + After Implementing K-Means Clustering + ![enter image description here](https://miro.medium.com/v2/resize:fit:640/format:webp/1*H3L3EH3Jh6kWFmbec0ewKA.png) ## Conclusion **K-Means** can be applied to data that has a smaller number of dimensions, is numeric, and is continuous or can be used to find groups that have not been explicitly labeled in the data. As an example, it can be used for Document Classification, Delivery Store Optimization, or Customer Segmentation. From a2c1d8bcc484eafcb381f404d290a1792db527fe Mon Sep 17 00:00:00 2001 From: Shraman Jain <60072287+Shraman-jain@users.noreply.github.com> Date: Tue, 4 Jun 2024 22:42:07 +0530 Subject: [PATCH 470/656] Update index.md add threading.md --- contrib/advanced-python/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index b31544eb..552025a1 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -10,3 +10,4 @@ - [Protocols](protocols.md) - [Exception Handling in Python](exception-handling.md) - [Generators](generators.md) +- [Threading](threading.md) From ca4ce0620a9840af9e39f649e87166d4c0a895eb Mon Sep 17 00:00:00 2001 From: Shraman Jain <60072287+Shraman-jain@users.noreply.github.com> Date: Tue, 4 Jun 2024 22:44:27 +0530 Subject: [PATCH 471/656] Create threading.md --- contrib/advanced-python/threading.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 contrib/advanced-python/threading.md diff --git a/contrib/advanced-python/threading.md b/contrib/advanced-python/threading.md new file mode 100644 index 00000000..85b5f4bf --- /dev/null +++ b/contrib/advanced-python/threading.md @@ -0,0 +1 @@ +#Threading From e9eb68ab3832ec651264b967de709ee8ae41af5f Mon Sep 17 00:00:00 2001 From: Revanth <109272714+revanth1718@users.noreply.github.com> Date: Tue, 4 Jun 2024 23:28:27 +0530 Subject: [PATCH 472/656] Update K-Means_Clustering.md --- contrib/machine-learning/K-Means_Clustering.md | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/contrib/machine-learning/K-Means_Clustering.md b/contrib/machine-learning/K-Means_Clustering.md index ab5fba93..c6aee6a7 100644 --- a/contrib/machine-learning/K-Means_Clustering.md +++ b/contrib/machine-learning/K-Means_Clustering.md @@ -15,7 +15,6 @@ The K-means algorithm strategically assigns each data point to a cluster such th ## The Meaning Behind "K-Means" The "means" in K-means refers to the averaging process used to compute the centroid, essentially finding the center of each cluster. ## K-Means Algorithm in Action -![enter image description here](https://d3i71xaburhd42.cloudfront.net/2f49631bb3103a61fbc3045dd035c3d8f2175887/11-Figure2-1.png) The K-means algorithm follows an iterative approach to optimize cluster formation: 1. **Initial Centroid Placement:** The process begins with randomly selecting k centroids to serve as initial reference points for each cluster. @@ -66,13 +65,13 @@ The K-means algorithm follows an iterative approach to optimize cluster formatio predicted_cluster = kmeans.predict(new_data_reshaped) print("Predicted cluster for new data:", predicted_cluster) - ### Output: - Before Implementing K-Means Clustering -![Before Implementing K-Means Clustering](https://miro.medium.com/v2/resize:fit:640/format:webp/1*jnyQxmEj7rFhazeMH7KXVg.png) + ### Output: +![Before Implementing K-Means Clustering](data:image/png;base64,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) - After Implementing K-Means Clustering - ![enter image description here](https://miro.medium.com/v2/resize:fit:640/format:webp/1*H3L3EH3Jh6kWFmbec0ewKA.png) + + ![After Implementing K-Means Clustering](data:image/png;base64,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+ Predicted cluster for new data: [0] ## Conclusion **K-Means** can be applied to data that has a smaller number of dimensions, is numeric, and is continuous or can be used to find groups that have not been explicitly labeled in the data. As an example, it can be used for Document Classification, Delivery Store Optimization, or Customer Segmentation. 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z!s>5(N}+(ZYo14FzRtNg^TU^uiyqHBaGpQV4i=4`3*N;iYFt-VS!I<~R#|10RaRLg arT+(~s&-lbfZ;O$0000 Date: Tue, 4 Jun 2024 23:41:27 +0530 Subject: [PATCH 474/656] Update K-Means_Clustering.md --- contrib/machine-learning/K-Means_Clustering.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/contrib/machine-learning/K-Means_Clustering.md b/contrib/machine-learning/K-Means_Clustering.md index c6aee6a7..82a5c83e 100644 --- a/contrib/machine-learning/K-Means_Clustering.md +++ b/contrib/machine-learning/K-Means_Clustering.md @@ -7,7 +7,7 @@ Unlike supervised learning algorithms that rely on labeled data for training, un ## The K-Means Objective Organize similar data points into clusters to unveil underlying patterns. The main objective is to minimize total intra-cluster variance or the squared function. -![enter image description here](https://encrypted-tbn2.gstatic.com/images?q=tbn:ANd9GcQnDTFfxrrAttYMwwjZV0RAA8J8rmUxgrnWc2gU_ArEqrxEMlHm) +![image](assets/Knm.png) ## Clusters and Centroids A cluster represents a collection of data points that share similar characteristics. K-means identifies a pre-determined number (k) of clusters within the dataset. Each cluster is represented by a centroid, which acts as its central point (imaginary or real). ## Minimizing In-Cluster Variation @@ -15,6 +15,7 @@ The K-means algorithm strategically assigns each data point to a cluster such th ## The Meaning Behind "K-Means" The "means" in K-means refers to the averaging process used to compute the centroid, essentially finding the center of each cluster. ## K-Means Algorithm in Action +![image](assets/Km_.png) The K-means algorithm follows an iterative approach to optimize cluster formation: 1. **Initial Centroid Placement:** The process begins with randomly selecting k centroids to serve as initial reference points for each cluster. @@ -66,11 +67,11 @@ The K-means algorithm follows an iterative approach to optimize cluster formatio print("Predicted cluster for new data:", predicted_cluster) ### Output: -![Before Implementing K-Means Clustering](data:image/png;base64,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) + Before Implementing K-Means Clustering +![Before Implementing K-Means Clustering](assets/Km_2.png) - - - ![After Implementing K-Means Clustering](data:image/png;base64,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) + After Implementing K-Means Clustering + ![After Implementing K-Means Clustering](assets/Km_3.png) Predicted cluster for new data: [0] ## Conclusion **K-Means** can be applied to data that has a smaller number of dimensions, is numeric, and is continuous or can be used to find groups that have not been explicitly labeled in the data. As an example, it can be used for Document Classification, Delivery Store Optimization, or Customer Segmentation. From d65dce72e10873a2554cbc37b00fab868fdfb0d5 Mon Sep 17 00:00:00 2001 From: Revanth <109272714+revanth1718@users.noreply.github.com> Date: Tue, 4 Jun 2024 23:46:20 +0530 Subject: [PATCH 475/656] update k-means --- contrib/machine-learning/K-Means_Clustering.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/contrib/machine-learning/K-Means_Clustering.md b/contrib/machine-learning/K-Means_Clustering.md index 82a5c83e..bf72ffd5 100644 --- a/contrib/machine-learning/K-Means_Clustering.md +++ b/contrib/machine-learning/K-Means_Clustering.md @@ -7,7 +7,7 @@ Unlike supervised learning algorithms that rely on labeled data for training, un ## The K-Means Objective Organize similar data points into clusters to unveil underlying patterns. The main objective is to minimize total intra-cluster variance or the squared function. -![image](assets/Knm.png) +![image](assets\knm.png) ## Clusters and Centroids A cluster represents a collection of data points that share similar characteristics. K-means identifies a pre-determined number (k) of clusters within the dataset. Each cluster is represented by a centroid, which acts as its central point (imaginary or real). ## Minimizing In-Cluster Variation @@ -15,7 +15,7 @@ The K-means algorithm strategically assigns each data point to a cluster such th ## The Meaning Behind "K-Means" The "means" in K-means refers to the averaging process used to compute the centroid, essentially finding the center of each cluster. ## K-Means Algorithm in Action -![image](assets/Km_.png) +![image](assets\km_.png) The K-means algorithm follows an iterative approach to optimize cluster formation: 1. **Initial Centroid Placement:** The process begins with randomly selecting k centroids to serve as initial reference points for each cluster. @@ -68,10 +68,10 @@ The K-means algorithm follows an iterative approach to optimize cluster formatio ### Output: Before Implementing K-Means Clustering -![Before Implementing K-Means Clustering](assets/Km_2.png) +![Before Implementing K-Means Clustering](assets\km_2.png) After Implementing K-Means Clustering - ![After Implementing K-Means Clustering](assets/Km_3.png) + ![After Implementing K-Means Clustering](assets\km_3.png) Predicted cluster for new data: [0] ## Conclusion **K-Means** can be applied to data that has a smaller number of dimensions, is numeric, and is continuous or can be used to find groups that have not been explicitly labeled in the data. As an example, it can be used for Document Classification, Delivery Store Optimization, or Customer Segmentation. From f7dc61226c88c3f877d473cb71ca4142a5e7cad5 Mon Sep 17 00:00:00 2001 From: Pradnya Gaitonde <116059908+PradnyaGaitonde@users.noreply.github.com> Date: Wed, 5 Jun 2024 00:33:22 +0530 Subject: [PATCH 476/656] Create Type_Hinting.md Added Content on Type hinting in python --- contrib/advanced-python/Type_Hinting.md | 106 ++++++++++++++++++++++++ 1 file changed, 106 insertions(+) create mode 100644 contrib/advanced-python/Type_Hinting.md diff --git a/contrib/advanced-python/Type_Hinting.md b/contrib/advanced-python/Type_Hinting.md new file mode 100644 index 00000000..fcf1e1c0 --- /dev/null +++ b/contrib/advanced-python/Type_Hinting.md @@ -0,0 +1,106 @@ +# Introduction to Type Hinting in Python +Type hinting is a feature in Python that allows you to specify the expected data types of variables, function arguments, and return values. It was introduced +in Python 3.5 via PEP 484 and has since become a standard practice to improve code readability and facilitate static analysis tools. + +**Benefits of Type Hinting** + +1. Improved Readability: Type hints make it clear what type of data is expected, making the code easier to understand for others and your future self. +2. Error Detection: Static analysis tools like MyPy can use type hints to detect type errors before runtime, reducing bugs and improving code quality. +3.Better Tooling Support: Modern IDEs and editors can leverage type hints to provide better autocompletion, refactoring, and error checking features. +4. Documentation: Type hints serve as a form of documentation, indicating the intended usage of functions and classes. + +**Syntax of Type Hinting**
    +Type hints can be added to variables, function arguments, and return values using annotations. + +1. Variable Annotations: + +```bash +age: int = 25 +name: str = "Alice" +is_student: bool = True +``` + +2. Function Annotations: + +```bash +def greet(name: str) -> str: + return f"Hello, {name}!" +``` + +3. Multiple Arguments and Return Types: + +```bash +def add(a: int, b: int) -> int: + return a + b +``` + +4. Optional Types: Use the Optional type from the typing module for values that could be None. + +```bash +from typing import Optional + +def get_user_name(user_id: int) -> Optional[str]: + # Function logic here + return None # Example return value +``` + +5. Union Types: Use the Union type when a variable can be of multiple types. + +```bash +from typing import Union + +def get_value(key: str) -> Union[int, str]: + # Function logic here + return "value" # Example return value +``` + +6. List and Dictionary Types: Use the List and Dict types from the typing module for collections. + +```bash +from typing import List, Dict + +def process_data(data: List[int]) -> Dict[str, int]: + # Function logic here + return {"sum": sum(data)} # Example return value +``` + +7. Type Aliases: Create type aliases for complex types to make the code more readable. + +```bash +from typing import List, Tuple + +Coordinates = List[Tuple[int, int]] + +def draw_shape(points: Coordinates) -> None: + # Function logic here + pass +``` + +**Example of Type Hinting in a Class**
    +Here is a more comprehensive example using type hints in a class: + +```bash +from typing import List + +class Student: + def __init__(self, name: str, age: int, grades: List[int]) -> None: + self.name = name + self.age = age + self.grades = grades + + def average_grade(self) -> float: + return sum(self.grades) / len(self.grades) + + def add_grade(self, grade: int) -> None: + self.grades.append(grade) + +# Example usage +student = Student("Alice", 20, [90, 85, 88]) +print(student.average_grade()) # Output: 87.66666666666667 +student.add_grade(92) +print(student.average_grade()) # Output: 88.75 +``` + +### Conclusion +Type hinting in Python enhances code readability, facilitates error detection through static analysis, and improves tooling support. By adopting +type hinting, you can write clearer and more maintainable code, reducing the likelihood of bugs and making your codebase easier to navigate for yourself and others. From 23a7577e92f4d1d618ef6784feec46a4294640f2 Mon Sep 17 00:00:00 2001 From: Pradnya Gaitonde <116059908+PradnyaGaitonde@users.noreply.github.com> Date: Wed, 5 Jun 2024 00:35:53 +0530 Subject: [PATCH 477/656] Update index.md Updated index for Type hinting content --- contrib/advanced-python/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 5ea5081c..e2179e72 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -1,3 +1,4 @@ # List of sections - [Decorators/\*args/**kwargs](decorator-kwargs-args.md) +- [Type Hinting/\*args/**kwargs](Type_Hinting.md) From a3961ff390a95c24b33bca10b047f89ec63a7201 Mon Sep 17 00:00:00 2001 From: Pradnya Gaitonde <116059908+PradnyaGaitonde@users.noreply.github.com> Date: Wed, 5 Jun 2024 00:44:22 +0530 Subject: [PATCH 478/656] Create Itertools_module.md Added content for itertools module in python. --- contrib/advanced-python/Itertools_module.md | 144 ++++++++++++++++++++ 1 file changed, 144 insertions(+) create mode 100644 contrib/advanced-python/Itertools_module.md diff --git a/contrib/advanced-python/Itertools_module.md b/contrib/advanced-python/Itertools_module.md new file mode 100644 index 00000000..501a1274 --- /dev/null +++ b/contrib/advanced-python/Itertools_module.md @@ -0,0 +1,144 @@ +# The 'itertools' Module in Python +The itertools module in Python provides a collection of fast, memory-efficient tools that are useful for creating and working with iterators. These functions +allow you to iterate over data in various ways, often combining, filtering, or extending iterators to generate complex sequences efficiently. + +## Benefits of itertools +1. Efficiency: Functions in itertools are designed to be memory-efficient, often generating elements on the fly and avoiding the need to store large intermediate results. +2. Conciseness: Using itertools can lead to more readable and concise code, reducing the need for complex loops and temporary variables. +3. Composability: Functions from itertools can be easily combined, allowing you to build complex iterator pipelines from simple building blocks. + +## Useful Functions in itertools
    +Here are some of the most useful functions in the itertools module, along with examples of how to use them: + +1. 'count': Generates an infinite sequence of numbers, starting from a specified value. + +```bash +import itertools + +counter = itertools.count(start=10, step=2) +for _ in range(5): + print(next(counter)) +# Output: 10, 12, 14, 16, 18 +``` + +2. 'cycle': Cycles through an iterable indefinitely. + +```bash +import itertools + +cycler = itertools.cycle(['A', 'B', 'C']) +for _ in range(6): + print(next(cycler)) +# Output: A, B, C, A, B, C +``` + +3.'repeat': Repeats an object a specified number of times or indefinitely. + +```bash +import itertools + +repeater = itertools.repeat('Hello', 3) +for item in repeater: + print(item) +# Output: Hello, Hello, Hello +``` + +4. 'chain': Combines multiple iterables into a single iterable. + +```bash +import itertools + +combined = itertools.chain([1, 2, 3], ['a', 'b', 'c']) +for item in combined: + print(item) +# Output: 1, 2, 3, a, b, c +``` + +5. 'islice': Slices an iterator, similar to slicing a list. + +```bash +import itertools + +sliced = itertools.islice(range(10), 2, 8, 2) +for item in sliced: + print(item) +# Output: 2, 4, 6 +``` + +6. 'compress': Filters elements in an iterable based on a corresponding selector iterable. + +```bash +import itertools + +data = ['A', 'B', 'C', 'D'] +selectors = [1, 0, 1, 0] +result = itertools.compress(data, selectors) +for item in result: + print(item) +# Output: A, C +``` + +7. 'permutations': Generates all possible permutations of an iterable. + +```bash +import itertools + +perms = itertools.permutations('ABC', 2) +for item in perms: + print(item) +# Output: ('A', 'B'), ('A', 'C'), ('B', 'A'), ('B', 'C'), ('C', 'A'), ('C', 'B') +``` + +8. 'combinations': Generates all possible combinations of a specified length from an iterable. + +```bash +import itertools + +combs = itertools.combinations('ABC', 2) +for item in combs: + print(item) +# Output: ('A', 'B'), ('A', 'C'), ('B', 'C') +``` + +9. 'product': Computes the Cartesian product of input iterables. + +```bash +import itertools + +prod = itertools.product('AB', '12') +for item in prod: + print(item) +# Output: ('A', '1'), ('A', '2'), ('B', '1'), ('B', '2') +``` + +10. 'groupby': Groups elements of an iterable by a specified key function. + +```bash +import itertools + +data = [{'name': 'Alice', 'age': 25}, {'name': 'Bob', 'age': 25}, {'name': 'Charlie', 'age': 30}] +sorted_data = sorted(data, key=lambda x: x['age']) +grouped = itertools.groupby(sorted_data, key=lambda x: x['age']) +for key, group in grouped: + print(key, list(group)) +# Output: +# 25 [{'name': 'Alice', 'age': 25}, {'name': 'Bob', 'age': 25}] +# 30 [{'name': 'Charlie', 'age': 30}] +``` + +11. 'accumulate': Makes an iterator that returns accumulated sums, or accumulated results of other binary functions specified via the optional func argument. + +```bash +import itertools +import operator + +data = [1, 2, 3, 4, 5] +acc = itertools.accumulate(data, operator.mul) +for item in acc: + print(item) +# Output: 1, 2, 6, 24, 120 +``` + +## Conclusion +The itertools module is a powerful toolkit for working with iterators in Python. Its functions enable efficient and concise handling of iterable data, allowing you to create complex data processing pipelines with minimal memory overhead. +By leveraging itertools, you can improve the readability and performance of your code, making it a valuable addition to your Python programming arsenal. From 294afa2d325da4ce3afff3bf193a286f72832be0 Mon Sep 17 00:00:00 2001 From: Pradnya Gaitonde <116059908+PradnyaGaitonde@users.noreply.github.com> Date: Wed, 5 Jun 2024 00:46:53 +0530 Subject: [PATCH 479/656] Update index.md Updated index for itertools module content. --- contrib/advanced-python/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 5ea5081c..f8a366fa 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -1,3 +1,4 @@ # List of sections - [Decorators/\*args/**kwargs](decorator-kwargs-args.md) +- ['itertools' module](Itertools_module.md) From 7a83f1524a4c4e5a1c6b27401d98b53fcc5492c4 Mon Sep 17 00:00:00 2001 From: Pradnya Gaitonde <116059908+PradnyaGaitonde@users.noreply.github.com> Date: Wed, 5 Jun 2024 00:48:20 +0530 Subject: [PATCH 480/656] Update index.md --- contrib/advanced-python/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index e2179e72..36bd1039 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -1,4 +1,4 @@ # List of sections - [Decorators/\*args/**kwargs](decorator-kwargs-args.md) -- [Type Hinting/\*args/**kwargs](Type_Hinting.md) +- [Type Hinting](Type_Hinting.md) From 77b313abe8c4357496d4e9f3ccfb6ec0a027deb0 Mon Sep 17 00:00:00 2001 From: Arihant Yadav <147732947+arihunter-18@users.noreply.github.com> Date: Wed, 5 Jun 2024 02:39:44 +0530 Subject: [PATCH 481/656] Update closures.md --- contrib/advanced-python/closures.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/advanced-python/closures.md b/contrib/advanced-python/closures.md index 87ad058f..e363c15f 100644 --- a/contrib/advanced-python/closures.md +++ b/contrib/advanced-python/closures.md @@ -98,4 +98,4 @@ The **i**=*i* argument in the closure function is used to capture the current va For more examples related to closures, [click here](https://dev.to/bshadmehr/understanding-closures-in-python-a-comprehensive-tutorial-11ld). ## Summary -Closures in Python provide a powerful mechanism for encapsulating state and behavior, enabling more flexible and modular code. Understanding and effectively using closures enables the creation of function factories, allows functions to have state, and facilitates functional programming techniques. \ No newline at end of file +Closures in Python provide a powerful mechanism for encapsulating state and behavior, enabling more flexible and modular code. Understanding and effectively using closures enables the creation of function factories, allows functions to have state, and facilitates functional programming techniques From 7bc7b0621eb59fa6e85c076885d1e18d2f103847 Mon Sep 17 00:00:00 2001 From: Revanth <109272714+revanth1718@users.noreply.github.com> Date: Wed, 5 Jun 2024 11:26:57 +0530 Subject: [PATCH 482/656] Update K-Means_Clustering.md --- contrib/machine-learning/K-Means_Clustering.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/contrib/machine-learning/K-Means_Clustering.md b/contrib/machine-learning/K-Means_Clustering.md index bf72ffd5..859636bf 100644 --- a/contrib/machine-learning/K-Means_Clustering.md +++ b/contrib/machine-learning/K-Means_Clustering.md @@ -7,7 +7,7 @@ Unlike supervised learning algorithms that rely on labeled data for training, un ## The K-Means Objective Organize similar data points into clusters to unveil underlying patterns. The main objective is to minimize total intra-cluster variance or the squared function. -![image](assets\knm.png) +![image](assets/knm.png) ## Clusters and Centroids A cluster represents a collection of data points that share similar characteristics. K-means identifies a pre-determined number (k) of clusters within the dataset. Each cluster is represented by a centroid, which acts as its central point (imaginary or real). ## Minimizing In-Cluster Variation @@ -15,7 +15,7 @@ The K-means algorithm strategically assigns each data point to a cluster such th ## The Meaning Behind "K-Means" The "means" in K-means refers to the averaging process used to compute the centroid, essentially finding the center of each cluster. ## K-Means Algorithm in Action -![image](assets\km_.png) +![image](assets/km_.png) The K-means algorithm follows an iterative approach to optimize cluster formation: 1. **Initial Centroid Placement:** The process begins with randomly selecting k centroids to serve as initial reference points for each cluster. @@ -68,10 +68,11 @@ The K-means algorithm follows an iterative approach to optimize cluster formatio ### Output: Before Implementing K-Means Clustering -![Before Implementing K-Means Clustering](assets\km_2.png) +![Before Implementing K-Means Clustering](assets/km_2.png) After Implementing K-Means Clustering - ![After Implementing K-Means Clustering](assets\km_3.png) + ![After Implementing K-Means Clustering](assets/km_3.png) + Predicted cluster for new data: [0] ## Conclusion **K-Means** can be applied to data that has a smaller number of dimensions, is numeric, and is continuous or can be used to find groups that have not been explicitly labeled in the data. As an example, it can be used for Document Classification, Delivery Store Optimization, or Customer Segmentation. From 92571f619700a05019853aff0230015fe4b85a2d Mon Sep 17 00:00:00 2001 From: saiumasankar Date: Wed, 5 Jun 2024 11:29:23 +0530 Subject: [PATCH 483/656] update dynamic programming Added LIS problem and Tabulation code of LCS and 0-1 Knapsack --- contrib/ds-algorithms/dynamic-programming.md | 124 ++++++++++++++++++- 1 file changed, 121 insertions(+), 3 deletions(-) diff --git a/contrib/ds-algorithms/dynamic-programming.md b/contrib/ds-algorithms/dynamic-programming.md index 43149f86..54c6fcbf 100644 --- a/contrib/ds-algorithms/dynamic-programming.md +++ b/contrib/ds-algorithms/dynamic-programming.md @@ -84,8 +84,32 @@ Y = "GXTXAYB" print("Length of Longest Common Subsequence:", longest_common_subsequence(X, Y, len(X), len(Y))) ``` +## Longest Common Subsequence Code in Python (Bottom-Up Approach) + +```python + +def longestCommonSubsequence(X, Y, m, n): + L = [[None]*(n+1) for i in range(m+1)] + for i in range(m+1): + for j in range(n+1): + if i == 0 or j == 0: + L[i][j] = 0 + elif X[i-1] == Y[j-1]: + L[i][j] = L[i-1][j-1]+1 + else: + L[i][j] = max(L[i-1][j], L[i][j-1]) + return L[m][n] + + +S1 = "AGGTAB" +S2 = "GXTXAYB" +m = len(S1) +n = len(S2) +print("Length of LCS is", longestCommonSubsequence(S1, S2, m, n)) +``` + ## Complexity Analysis -- **Time Complexity**: O(m * n) for the top-down approach, where m and n are the lengths of the input sequences +- **Time Complexity**: O(m * n) for both approaches, where m and n are the lengths of the input sequences - **Space Complexity**: O(m * n) for the memoization table
    @@ -122,11 +146,105 @@ capacity = 50 n = len(weights) print("Maximum value that can be obtained:", knapsack(weights, values, capacity, n)) ``` +## 0-1 Knapsack Problem Code in Python (Bottom-up Approach) + +```python +def knapSack(capacity, weights, values, n): + K = [[0 for x in range(capacity + 1)] for x in range(n + 1)] + for i in range(n + 1): + for w in range(capacity + 1): + if i == 0 or w == 0: + K[i][w] = 0 + elif weights[i-1] <= w: + K[i][w] = max(values[i-1] + + K[i-1][w-weights[i-1]], + K[i-1][w]) + else: + K[i][w] = K[i-1][w] + + return K[n][capacity] +values = [60, 100, 120] +weights = [10, 20, 30] +capacity = 50 +n = len(weights) +print(knapSack(capacity, weights, values, n)) +``` ## Complexity Analysis -- **Time Complexity**: O(n * W) for the top-down approach, where n is the number of items and W is the capacity of the knapsack +- **Time Complexity**: O(n * W) for both approaches, where n is the number of items and W is the capacity of the knapsack - **Space Complexity**: O(n * W) for the memoization table
    -
    \ No newline at end of file +
    + +# 4. Longest Increasing Subsequence + +The Longest Increasing Subsequence (LIS) is a task is to find the longest subsequence that is strictly increasing, meaning each element in the subsequence is greater than the one before it. This subsequence must maintain the order of elements as they appear in the original sequence but does not need to be contiguous. The goal is to identify the subsequence with the maximum possible length. + +**Algorithm Overview:** +- **Base cases:** If the sequence is empty, the LIS length is 0. +- **Memoization:** Store the results of previously computed subproblems to avoid redundant computations. +- **Recurrence relation:** Compute the LIS length by comparing characters of the sequences and making decisions based on their values. + +## Longest Increasing Subsequence Code in Python (Top-Down Approach using Memoization) + +```python + +import sys + +def f(idx, prev_idx, n, a, dp): + if (idx == n): + return 0 + + if (dp[idx][prev_idx + 1] != -1): + return dp[idx][prev_idx + 1] + + notTake = 0 + f(idx + 1, prev_idx, n, a, dp) + take = -sys.maxsize - 1 + if (prev_idx == -1 or a[idx] > a[prev_idx]): + take = 1 + f(idx + 1, idx, n, a, dp) + + dp[idx][prev_idx + 1] = max(take, notTake) + return dp[idx][prev_idx + 1] + +def longestSubsequence(n, a): + + dp = [[-1 for i in range(n + 1)]for j in range(n + 1)] + return f(0, -1, n, a, dp) + +a = [3, 10, 2, 1, 20] +n = len(a) + +print("Length of lis is", longestSubsequence(n, a)) + +``` + +## Longest Increasing Subsequence Code in Python (Bottom-Up Approach) + +```python +def lis(arr): + n = len(arr) + lis = [1]*n + + for i in range(1, n): + for j in range(0, i): + if arr[i] > arr[j] and lis[i] < lis[j] + 1: + lis[i] = lis[j]+1 + + maximum = 0 + for i in range(n): + maximum = max(maximum, lis[i]) + + return maximum + +arr = [10, 22, 9, 33, 21, 50, 41, 60] +print("Length of lis is", lis(arr)) +``` +## Complexity Analysis +- **Time Complexity**: O(n * n) for both approaches, where n is the length of the array. +- **Space Complexity**: O(n * n) for the memoization table in Top-Down Approach, O(n) in Bottom-Up Approach. + +
    +
    +
    From f742fddff366a0eff24520c8ee292b81d2188d45 Mon Sep 17 00:00:00 2001 From: saiumasankar Date: Wed, 5 Jun 2024 17:28:33 +0530 Subject: [PATCH 484/656] Adding the BeautifulSoup web scrapping --- contrib/web-scrapping/beautifulsoup.md | 206 +++++++++++++++++++++++++ contrib/web-scrapping/index.md | 1 + 2 files changed, 207 insertions(+) create mode 100644 contrib/web-scrapping/beautifulsoup.md diff --git a/contrib/web-scrapping/beautifulsoup.md b/contrib/web-scrapping/beautifulsoup.md new file mode 100644 index 00000000..372b8f29 --- /dev/null +++ b/contrib/web-scrapping/beautifulsoup.md @@ -0,0 +1,206 @@ +# Beautiful Soup Library in Python + +## Table of Contents +1. Introduction +2. Prerequisites +3. Setting Up Environment +4. Beautiful Soup Objects +5. Tag object +6. Children, Parents and Siblings +7. Filter: Findall method +8. Web Scraping the Contents of a Web Page + +## 1. Introduction +Beautiful Soup is a Python library used for web scraping purposes to extract data from HTML and XML files. It creates a parse tree for parsed pages that can be used to extract data easily. + +## 2. Prerequisites +- Understanding of HTTP Requests and Responses. +- Understanding of HTTP methods (GET method). +- Basic Knowledge on HTML. +- Python installed on your machine (version 3.6 or higher). +- pip (Python package installer) installed. + +## 3. Setting Up Environment +1.**Install latest version of Python:** Go to Python.org and Download the latest version of Python. + +2.**Install a IDE:** Install any IDE of your choice to code. VS code is preferred. +>Note: You can use Google Colab without installing Python and IDE. + +3.**Install Beautiful Soup:** +```python +!pip install bs4 +``` + +## 4. Beautiful Soup Objects +Beautiful Soup is a Python library for pulling data out of HTML and XML files. This can be done by presenting the HTML as a set of objects. + +Take the below HTML page as input +``` + + + +Page Title + + +

    Title

    +

    This is the main context of the page

    + + +``` + +lets store the HTML code in a variable +``` +html= " + + + Page Title + + +

    Title

    +

    This is the main context of the page

    + + " +``` + +To parse the HTML document, pass the variable to the `BeautifulSoup` Constructor. +``` +soup = BeautifulSoup(html,'html.parser') +``` +Beautiful Soup transforms a complex HTML document into a complex tree of Python objects. + +## 5. Tags + +The Tag object corresponds to an HTML tag in the original document. + +``` +tag_obj = soup.title +print("tag object:", tag_obj) +print("tag object type:",type(tag_obj)) +``` +``` +Result: +tag object: Page Title + +tag object type: +``` + +## 6. Children, Parent and Siblings + +We can access the child of the tag or navigate down. + +``` +tag_obj = soup.h3 +child = tag_obj.b +print(child) +parent = child.parent +print(parent) +sib = tag_obj.next_sibling +print(sib); +``` +``` +Result: + Title +

    Title

    +

    This is the main context of the page

    +``` + +> We need to mention the child to which we want to navigate. It is because there can be more than one child to a tag but only one parent and next sibling. + +**Navigable String:** A string corresponds to a bit of text or content within a tag. Beautiful Soup uses the NavigableString class to contain this text. + +``` +tagstr = child.string +print(tagstr) +``` +``` +Result: +Title +``` + +## 7. Filter + +Filters allow you to find complex patterns, the simplest filter is a string. + +Consider the following HTML code: + +``` + + + + + + Sample Table + + +

    Sample Table

    + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameAgeCity
    John Doe28New York
    Jane Smith34Los Angeles
    Emily Jones23Chicago
    + + + +``` +Store the above code in a variable. +``` +table = " + + + + + Sample Table +.............. + +table_bs = BeautifulSoup(table, 'html.parser') +" +``` +**Find All:** The find_all() method looks through a tag’s descendants and retrieves all descendants that match your filters. It places all the objects in a list. We can access by indexing. + +## 8. Web Scraping the Contents of a Web Page + +1. Get the URL of the webpage we need to scrape. + +2. Get the HTML content by using `get` method in HTTP request. + +3. Pass the HTML content to the BeautifulSoup Constructor to prepare an object. + +4. Access the HTML elements using Tag name. + +Below is the code to extract the img tags in a wikipedia page. + +``` +import requests +!pip install bs4 +from bs4 import BeautifulSoup + +url = "https://en.wikipedia.org/wiki/Encyclopedia" +data = requests.get(url).text +soup = BeautifulSoup(data,"html.parser") + +for link in soup.find_all('a'): + print(link) + print(link.get('src')) +``` +We can retrieve any data in the webpage by access through the tags. diff --git a/contrib/web-scrapping/index.md b/contrib/web-scrapping/index.md index 276014ea..29f558aa 100644 --- a/contrib/web-scrapping/index.md +++ b/contrib/web-scrapping/index.md @@ -2,3 +2,4 @@ - [Section title](filename.md) - [Introduction to Flask](flask.md) +- [Web Scrapping Using Beautiful Soup](beautifulsoup.md) From bccf4eacf8e1364ac1a4229f08c7c9d8d0327f1f Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Thu, 6 Jun 2024 05:55:26 +0530 Subject: [PATCH 485/656] Update hierarchical-clustering.md --- .../hierarchical-clustering.md | 20 ------------------- 1 file changed, 20 deletions(-) diff --git a/contrib/machine-learning/hierarchical-clustering.md b/contrib/machine-learning/hierarchical-clustering.md index 3f9e606b..93822703 100644 --- a/contrib/machine-learning/hierarchical-clustering.md +++ b/contrib/machine-learning/hierarchical-clustering.md @@ -2,20 +2,6 @@ Hierarchical Clustering is a method of cluster analysis that seeks to build a hierarchy of clusters. This README provides an overview of the hierarchical clustering algorithm, including its fundamental concepts, types, steps, and how to implement it using Python. -## Table of Contents - -1. [Introduction](#introduction) -2. [Concepts](#concepts) -3. [Types of Hierarchical Clustering](#types-of-hierarchical-clustering) -4. [Steps in Hierarchical Clustering](#steps-in-hierarchical-clustering) -5. [Linkage Criteria](#linkage-criteria) -6. [Implementation](#implementation) - - [Using Scikit-learn](#using-scikit-learn) - - [Code Example](#code-example) -7. [Evaluation Metrics](#evaluation-metrics) -8. [Conclusion](#conclusion) -9. [References](#references) - ## Introduction Hierarchical Clustering is an unsupervised learning method used to group similar objects into clusters. Unlike other clustering techniques, hierarchical clustering does not require the number of clusters to be specified beforehand. It produces a tree-like structure called a dendrogram, which displays the arrangement of the clusters and their sub-clusters. @@ -111,9 +97,3 @@ print(data.head()) ## Conclusion Hierarchical clustering is a versatile and intuitive method for clustering data. It is particularly useful when the number of clusters is not known beforehand. By understanding the different linkage criteria and evaluation metrics, one can effectively apply hierarchical clustering to various types of data. - -## References - -- [Scikit-learn Documentation](https://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering) -- [Wikipedia: Hierarchical Clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering) -- [Towards Data Science: Hierarchical Clustering Explained](https://towardsdatascience.com/hierarchical-clustering-explained-925d9e1600c1) From c6c8ffe1f694f2795c468afac8655059146a2d1c Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Thu, 6 Jun 2024 06:01:22 +0530 Subject: [PATCH 486/656] Update transformers.md --- contrib/machine-learning/transformers.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/contrib/machine-learning/transformers.md b/contrib/machine-learning/transformers.md index 0dcf6976..7d4fa389 100644 --- a/contrib/machine-learning/transformers.md +++ b/contrib/machine-learning/transformers.md @@ -6,10 +6,12 @@ mechanism. Before transformers, predecessors of attention mechanism were added t Transformers are a revolutionary approach to natural language processing (NLP). Unlike older models, they excel at understanding long-range connections between words. This "attention" mechanism lets them grasp the context of a sentence, making them powerful for tasks like machine translation, text summarization, and question answering. Introduced in 2017, transformers are now the backbone of many large language models, including tools you might use every day. Their ability to handle complex relationships in language is fueling advancements in AI across various fields. ## Model Architecture -     + +![Model Architecture](assets/transformer-architecture.png) + + +Source: [Attention Is All You Need](https://arxiv.org/pdf/1706.03762) + ### Encoder The encoder is composed of a stack of identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, positionwise fully connected feed-forward network. Each encoder consists of two major components: a self-attention mechanism and a feed-forward neural network. The self-attention mechanism accepts input encodings from the previous encoder and weights their relevance to each other to generate output encodings. The feed-forward neural network further processes each output encoding individually. These output encodings are then passed to the next encoder as its input, as well as to the decoders. From 3d52c978c52da5c0120cb73b4be8ee77d9810613 Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Thu, 6 Jun 2024 13:56:27 +0530 Subject: [PATCH 487/656] Update transformers.md Replaced html with latex equation outside the blockquotes --- contrib/machine-learning/transformers.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/contrib/machine-learning/transformers.md b/contrib/machine-learning/transformers.md index 7d4fa389..95ccada9 100644 --- a/contrib/machine-learning/transformers.md +++ b/contrib/machine-learning/transformers.md @@ -9,7 +9,6 @@ Transformers are a revolutionary approach to natural language processing (NLP). ![Model Architecture](assets/transformer-architecture.png) - Source: [Attention Is All You Need](https://arxiv.org/pdf/1706.03762) @@ -21,12 +20,12 @@ The decoder is also composed of a stack of identical layers. In addition to the ### Attention #### Scaled Dot-Product Attention -The input consists of queries and keys of dimension dk, and values of dimension dv. We compute the dot products of the query with all keys, divide each by √dk, and apply a softmax function to obtain the weights on the values. +The input consists of queries and keys of dimension $d_k$ , and values of dimension $d_v$. We compute the dot products of the query with all keys, divide each by $\sqrt d_k$ , and apply a softmax function to obtain the weights on the values. > Attention(Q, K, V) = softmax(QKT / √dk) * V #### Multi-Head Attention -Instead of performing a single attention function with dmodel-dimensional keys, values and queries, it is beneficial to linearly project the queries, keys and values h times with different, learned linear projections to dk, dk and dv dimensions, respectively. +Instead of performing a single attention function with $d_{model}$-dimensional keys, values and queries, it is beneficial to linearly project the queries, keys and values h times with different, learned linear projections to $d_k$ , $d_k$ and $d_v$ dimensions, respectively. Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this. @@ -41,7 +40,7 @@ where the projections are parameter matrices. #### Masked Attention It may be necessary to cut out attention links between some word-pairs. For example, the decoder for token position -𝑡 should not have access to token position 𝑡+1. +$t$ should not have access to token position $t+1$. > MaskedAttention(Q, K, V) = softmax(M + (QKT / √dk)) * V From b230028124bcc03f56ed8f87ebeb437a38bce21c Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Thu, 6 Jun 2024 14:17:40 +0530 Subject: [PATCH 488/656] Update transformers.md Mathematical equations are converted to LaTex mathematical equations. --- contrib/machine-learning/transformers.md | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/contrib/machine-learning/transformers.md b/contrib/machine-learning/transformers.md index 95ccada9..5a276885 100644 --- a/contrib/machine-learning/transformers.md +++ b/contrib/machine-learning/transformers.md @@ -20,9 +20,9 @@ The decoder is also composed of a stack of identical layers. In addition to the ### Attention #### Scaled Dot-Product Attention -The input consists of queries and keys of dimension $d_k$ , and values of dimension $d_v$. We compute the dot products of the query with all keys, divide each by $\sqrt d_k$ , and apply a softmax function to obtain the weights on the values. +The input consists of queries and keys of dimension $d_k$ , and values of dimension $d_v$. We compute the dot products of the query with all keys, divide each by $\sqrt {d_k}$ , and apply a softmax function to obtain the weights on the values. -> Attention(Q, K, V) = softmax(QKT / √dk) * V +$$Attention(Q, K, V) = softmax(\dfrac{QK^T}{\sqrt{d_k}}) \times V$$ #### Multi-Head Attention Instead of performing a single attention function with $d_{model}$-dimensional keys, values and queries, it is beneficial to linearly project the queries, keys and values h times with different, learned linear projections to $d_k$ , $d_k$ and $d_v$ dimensions, respectively. @@ -30,11 +30,11 @@ Instead of performing a single attention function with $d_{model}$-dimensional k Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this. -> MultiHead(Q, K, V) = Concat(head1, ..., headh) * WO +$$MultiHead(Q, K, V) = Concat(head_1, _{...}, head_h) \times W^O$$ where, -> headi = Attention(Q * WiQ, K * WiK, V * WiV) +$$head_i = Attention(QW_i^Q, KW_i^K, VW_i^V)$$ where the projections are parameter matrices. @@ -42,20 +42,22 @@ where the projections are parameter matrices. It may be necessary to cut out attention links between some word-pairs. For example, the decoder for token position $t$ should not have access to token position $t+1$. -> MaskedAttention(Q, K, V) = softmax(M + (QKT / √dk)) * V +$$MaskedAttention(Q, K, V) = softmax(M + \dfrac{QK^T}{\sqrt{d_k}}) \times V$$ ### Feed-Forward Network Each of the layers in the encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically. This consists of two linear transformations with a ReLU activation in between. -> FFN(x) = (max(0, (x * W1) + b1) * W2) + b2 + +$$FFN(x) = max(0, xW_1 + b_1)W_2 + b_2$$ ### Positional Encoding A positional encoding is a fixed-size vector representation that encapsulates the relative positions of tokens within a target sequence: it provides the transformer model with information about where the words are in the input sequence. The sine and cosine functions of different frequencies: -> PE(pos,2i) = sin(pos/100002i/dmodel) -> PE(pos,2i+1) = cos(pos/100002i/dmodel) +$$PE(pos,2i) = \sin({\dfrac{pos}{10000^{\dfrac{2i}{d_{model}}}}})$$ + +$$PE(pos,2i) = \cos({\dfrac{pos}{10000^{\dfrac{2i}{d_{model}}}}})$$ ## Implementation ### Theory From 76e07d587bc12188361ed05a65dae893792f2bfa Mon Sep 17 00:00:00 2001 From: Kosuri Indu Date: Thu, 6 Jun 2024 23:53:19 +0530 Subject: [PATCH 489/656] Added in index.md --- contrib/ds-algorithms/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 1d7293bb..d39c33fb 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -13,3 +13,4 @@ - [Stacks in Python](stacks.md) - [Sliding Window Technique](sliding-window.md) - [Trie](trie.md) +- [Hashing through Chaining](hashing-chaining.md) From 35c8620187ea011e49025d414c0e82fa33d31f62 Mon Sep 17 00:00:00 2001 From: Kosuri Indu Date: Thu, 6 Jun 2024 23:56:03 +0530 Subject: [PATCH 490/656] Added in index.md --- contrib/ds-algorithms/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 1d7293bb..30881acf 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -13,3 +13,4 @@ - [Stacks in Python](stacks.md) - [Sliding Window Technique](sliding-window.md) - [Trie](trie.md) +- [Hashing through Linear Probing](hashing-linear-probing.md) From 8bafaaa091b06d8cbcd49b607633276ea70d3b01 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 8 Jun 2024 10:20:05 +0530 Subject: [PATCH 491/656] Rename K-nearest neighbor (KNN).md to knn.md --- .../machine-learning/{K-nearest neighbor (KNN).md => knn.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/machine-learning/{K-nearest neighbor (KNN).md => knn.md} (99%) diff --git a/contrib/machine-learning/K-nearest neighbor (KNN).md b/contrib/machine-learning/knn.md similarity index 99% rename from contrib/machine-learning/K-nearest neighbor (KNN).md rename to contrib/machine-learning/knn.md index 748f8087..85578f3f 100644 --- a/contrib/machine-learning/K-nearest neighbor (KNN).md +++ b/contrib/machine-learning/knn.md @@ -119,4 +119,4 @@ plt.show() - **Feature Scaling:** Since KNN relies on distance calculations, features should be scaled (standardized or normalized) to ensure that all features contribute equally to the distance computation. - **Distance Metrics:** The choice of distance metric (Euclidean, Manhattan, etc.) can affect the performance of the algorithm. -In conclusion, KNN is a versatile and easy-to-implement algorithm suitable for various classification and regression tasks, particularly when working with small datasets and well-defined features. However, careful consideration should be given to the choice of K, feature scaling, and distance metrics to optimize its performance. \ No newline at end of file +In conclusion, KNN is a versatile and easy-to-implement algorithm suitable for various classification and regression tasks, particularly when working with small datasets and well-defined features. However, careful consideration should be given to the choice of K, feature scaling, and distance metrics to optimize its performance. From 906333954390d915e4c85b92e6ccc95919a878d8 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 8 Jun 2024 10:20:27 +0530 Subject: [PATCH 492/656] Update index.md --- contrib/machine-learning/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index e5f53715..deafddad 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -16,4 +16,4 @@ - [Types_of_Cost_Functions](cost-functions.md) - [Clustering](clustering.md) - [Grid Search](grid-search.md) -- [K-nearest neighbor (KNN)](K-nearest neighbor (KNN).md) \ No newline at end of file +- [K-nearest neighbor (KNN)](knn.md) From c7adf3b7c6154849e621124963a7810edcd0c6c0 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 8 Jun 2024 10:28:51 +0530 Subject: [PATCH 493/656] Rename ensemble_learning.md to ensemble-learning.md --- .../{ensemble_learning.md => ensemble-learning.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/machine-learning/{ensemble_learning.md => ensemble-learning.md} (98%) diff --git a/contrib/machine-learning/ensemble_learning.md b/contrib/machine-learning/ensemble-learning.md similarity index 98% rename from contrib/machine-learning/ensemble_learning.md rename to contrib/machine-learning/ensemble-learning.md index 940bd09e..508f45e7 100644 --- a/contrib/machine-learning/ensemble_learning.md +++ b/contrib/machine-learning/ensemble-learning.md @@ -137,4 +137,4 @@ print("Classification Report:\n", classification_report(y_test, y_pred)) ``` ## Conclusion -Ensemble Learning is a powerful technique that combines multiple models to improve overall performance. By leveraging the strengths of different models, it provides better accuracy, robustness, and generalization. However, it comes with increased complexity and computational cost. Understanding and implementing ensemble methods can significantly enhance machine learning solutions. \ No newline at end of file +Ensemble Learning is a powerful technique that combines multiple models to improve overall performance. By leveraging the strengths of different models, it provides better accuracy, robustness, and generalization. However, it comes with increased complexity and computational cost. Understanding and implementing ensemble methods can significantly enhance machine learning solutions. From 26b6691bf4de1f29fcf7475f1368a4571a0226ac Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 8 Jun 2024 10:29:21 +0530 Subject: [PATCH 494/656] Update index.md --- contrib/machine-learning/index.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index e64c95db..8e2d84e1 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -11,8 +11,7 @@ - [TensorFlow.md](tensorFlow.md) - [PyTorch.md](pytorch.md) - [Types of optimizers](Types_of_optimizers.md) -- [Random Forest](Random_Forest.md) -- [Ensemble Learning](ensemble_learning.md) +- [Ensemble Learning](ensemble-learning.md) - [Logistic Regression](logistic-regression.md) - [Clustering](clustering.md) - [Grid Search](grid-search.md) From f637a28ba1c2326adedeea7fdf633f48e2199e2f Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 8 Jun 2024 11:00:46 +0530 Subject: [PATCH 495/656] Update index.md --- contrib/advanced-python/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 631423a6..f61fdf0c 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -10,4 +10,4 @@ - [Protocols](protocols.md) - [Exception Handling in Python](exception-handling.md) - [Generators](generators.md) -- [List Comprehension](list_comprehension.md) +- [List Comprehension](list-comprehension.md) From f48a2d70fd60d69d8ad033526f06a0d4a79e4b27 Mon Sep 17 00:00:00 2001 From: rohit Date: Sat, 8 Jun 2024 11:01:07 +0530 Subject: [PATCH 496/656] Added Neural Network Regression in Python --- .../Neural network regression.md | 98 +++++++++++++++++++ contrib/machine-learning/index.md | 1 + 2 files changed, 99 insertions(+) create mode 100644 contrib/machine-learning/Neural network regression.md diff --git a/contrib/machine-learning/Neural network regression.md b/contrib/machine-learning/Neural network regression.md new file mode 100644 index 00000000..36c3b9a9 --- /dev/null +++ b/contrib/machine-learning/Neural network regression.md @@ -0,0 +1,98 @@ +# Neural Network Regression in Python + +## Overview + +Neural Network Regression is a type of machine learning algorithm used to predict continuous values. Unlike classification, where the goal is to predict a category or class, regression aims to predict a specific numerical value. Neural networks are particularly powerful for regression tasks when dealing with complex, non-linear relationships in data. + +## When to Use Neural Network Regression + +### Suitable Scenarios + +1. **Complex Relationships**: When the relationship between input features and the target variable is non-linear and complex. +2. **Large Datasets**: When you have a large dataset that can support the training of a neural network. +3. **Feature Engineering**: When you can leverage the feature extraction capabilities of neural networks, especially in domains like image or text data. + +### Unsuitable Scenarios + +1. **Small Datasets**: Neural networks require substantial amounts of data to train effectively. For small datasets, simpler models like linear regression or decision trees might perform better. +2. **Real-time Predictions**: If low-latency predictions are required and computational resources are limited, simpler models might be more efficient. +3. **Interpretability**: If model interpretability is crucial, neural networks might not be the best choice due to their "black-box" nature. + +## Implementing Neural Network Regression in Python + +We'll use TensorFlow and Keras, popular libraries for building and training neural networks in Python. + +### Step-by-Step Implementation + +1. **Import Libraries** + +```python +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from tensorflow.keras.models import Sequential +from tensorflow.keras.layers import Dense +from tensorflow.keras.optimizers import Adam +``` + +2. **Load and Prepare Data** + +For illustration, let's use a synthetic dataset. + +```python +# Generate synthetic data +np.random.seed(42) +X = np.random.rand(1000, 3) +y = X[:, 0] * 3 + X[:, 1] * -2 + X[:, 2] * 0.5 + np.random.randn(1000) * 0.1 + +# Split the data +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + +# Standardize the data +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_test = scaler.transform(X_test) +``` + +3. **Build the Neural Network Model** + +```python +model = Sequential() +model.add(Dense(64, input_dim=X_train.shape[1], activation='relu')) +model.add(Dense(64, activation='relu')) +model.add(Dense(1)) # Output layer for regression + +model.compile(optimizer=Adam(learning_rate=0.001), loss='mse', metrics=['mae']) +``` + +4. **Train the Model** + +```python +history = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.2) +``` + +5. **Evaluate the Model** + +```python +loss, mae = model.evaluate(X_test, y_test) +print(f"Test Mean Absolute Error: {mae}") +``` + +6. **Make Predictions** + +```python +predictions = model.predict(X_test) +``` + +### Explanation + +- **Data Generation and Preparation**: Synthetic data is created and split into training and test sets. The data is standardized to improve the neural network's training efficiency. +- **Model Construction**: A simple feedforward neural network is built using Keras. It consists of two hidden layers with 64 neurons each and ReLU activation functions. The output layer has a single neuron for regression. +- **Training**: The model is trained for 100 epochs with a batch size of 32. The Adam optimizer is used to adjust the weights. +- **Evaluation**: The model's performance is evaluated on the test set, using Mean Absolute Error (MAE) as a metric. +- **Prediction**: Predictions are made on the test data. + +## Conclusion + +Neural Network Regression is a powerful tool for predicting continuous values, particularly in cases involving complex, non-linear relationships. However, they require large datasets and significant computational resources. For smaller datasets or scenarios requiring model interpretability, simpler models might be preferable. By following the steps outlined, you can build, train, and evaluate a neural network for regression tasks in Python using TensorFlow and Keras. \ No newline at end of file diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 0bf39f98..82fbbcc9 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -20,3 +20,4 @@ - [Grid Search](grid-search.md) - [Transformers](transformers.md) - [K-nearest neighbor (KNN)](knn.md) +- [Neural network regression](Neural network regression.md) From c2740251604764cb12945841c828dc0033fbb151 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 8 Jun 2024 11:01:08 +0530 Subject: [PATCH 497/656] Rename list_comprehension.md to list-comprehension.md --- .../{list_comprehension.md => list-comprehension.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/advanced-python/{list_comprehension.md => list-comprehension.md} (100%) diff --git a/contrib/advanced-python/list_comprehension.md b/contrib/advanced-python/list-comprehension.md similarity index 100% rename from contrib/advanced-python/list_comprehension.md rename to contrib/advanced-python/list-comprehension.md From 98ad137d915cdb5039045aa0ee7f0c696c02ade5 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 8 Jun 2024 11:34:42 +0530 Subject: [PATCH 498/656] Rename Type_Hinting.md to type-hinting.md --- contrib/advanced-python/{Type_Hinting.md => type-hinting.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/advanced-python/{Type_Hinting.md => type-hinting.md} (100%) diff --git a/contrib/advanced-python/Type_Hinting.md b/contrib/advanced-python/type-hinting.md similarity index 100% rename from contrib/advanced-python/Type_Hinting.md rename to contrib/advanced-python/type-hinting.md From 3e346a29a6bcddd25fe0c7af7b8b2dbffe74a3da Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 8 Jun 2024 11:37:03 +0530 Subject: [PATCH 499/656] Rename Itertools_module.md to itertools.md --- contrib/advanced-python/{Itertools_module.md => itertools.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/advanced-python/{Itertools_module.md => itertools.md} (100%) diff --git a/contrib/advanced-python/Itertools_module.md b/contrib/advanced-python/itertools.md similarity index 100% rename from contrib/advanced-python/Itertools_module.md rename to contrib/advanced-python/itertools.md From 2647dbc3678d8380da938bc2ff02cb2243fc37b1 Mon Sep 17 00:00:00 2001 From: yashasvi232 <154299201+yashasvi232@users.noreply.github.com> Date: Sat, 8 Jun 2024 11:48:48 +0530 Subject: [PATCH 500/656] Update index.md --- contrib/ds-algorithms/index.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 2263620d..b18b79ac 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -13,4 +13,6 @@ - [Stacks in Python](stacks.md) - [Sliding Window Technique](sliding-window.md) - [Trie](trie.md) +- [Two Pointer Technique](two-pointer-technique.md) +- [Hashing through Linear Probing](hashing-linear-probing.md) - [Dijkstra's Algorithm](Dijkstra's_algorithm.md) From 7c5e89c0baf3612bd1af5916cc7571d6d36e516c Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 8 Jun 2024 13:24:12 +0530 Subject: [PATCH 501/656] Add files via upload --- .../machine-learning/PyTorch_Fundamentals.md | 427 ++++++++++++++++++ 1 file changed, 427 insertions(+) create mode 100644 contrib/machine-learning/PyTorch_Fundamentals.md diff --git a/contrib/machine-learning/PyTorch_Fundamentals.md b/contrib/machine-learning/PyTorch_Fundamentals.md new file mode 100644 index 00000000..a504ffc1 --- /dev/null +++ b/contrib/machine-learning/PyTorch_Fundamentals.md @@ -0,0 +1,427 @@ +# PyTorch Fundamentals + + +```python +# Import pytorch in our codespace +import torch +print(torch.__version__) +``` + + 2.3.0+cu121 + + +2.3.0 is the pytorch version and 121 is the cuda version + +Now you have already seen how to create a tensor in pytorch. In this notebook i am going to show you the operations which can be applied on a tensor with a quick previous revision. + +### 1. Creating tensors + + +```python +# Scalar tensor ( a zero dimension tensor) +scalar = torch.tensor(7) +print(scalar) +``` + + tensor(7) + + + +```python +# Check the dimension of the above tensor +print(scalar.ndim) +``` + + 0 + + + +```python +# To retrieve the number from the tensor we use `item()` +print(scalar.item()) +``` + + 7 + + + +```python +# Vector (It is a single dimension tensor but contain many numbers) +vector = torch.tensor([1,2]) +print(vector) +``` + + tensor([1, 2]) + + + +```python +# Check the dimensions +print(vector.ndim) +``` + + 1 + + + +```python +# Check the shape of the vector +print(vector.shape) +``` + + torch.Size([2]) + + +The above returns torch.Size([2]) which means our vector has a shape of [2]. This is because of the two elements we placed inside the square brackets ([1,2]) + +Note: +I'll let you in on a trick. + +You can tell the number of dimensions a tensor in PyTorch has by the number of square brackets on the outside ([) and you only need to count one side. + + +```python +# Let's create a matrix +MATRIX = torch.tensor([[1,2], + [4,5]]) +print(MATRIX) +``` + + tensor([[1, 2], + [4, 5]]) + + +There are two brackets so it must be 2 dimensions , lets check + + +```python +print(MATRIX.ndim) +``` + + 2 + + + +```python +# Shape +print(MATRIX.shape) +``` + + torch.Size([2, 2]) + + +It means MATRIX has 2 rows and 2 columns. + + +```python +# Let's create a TENSOR +TENSOR = torch.tensor([[[1,2,3], + [4,5,6], + [7,8,9]]]) +print(TENSOR) +``` + + tensor([[[1, 2, 3], + [4, 5, 6], + [7, 8, 9]]]) + + + +```python +# Let's check the dimensions +print(TENSOR.ndim) +``` + + 3 + + + +```python +# shape? +print(TENSOR.shape) +``` + + torch.Size([1, 3, 3]) + + +The dimensions go outer to inner. + +That means there's 1 dimension of 3 by 3. + +##### Let's summarise + +* scalar -> a single number having 0 dimension. +* vector -> have many numbers but having 1 dimension. +* matrix -> a array of numbers having 2 dimensions. +* tensor -> a array of numbers having n dimensions. + +### Random Tensors + +We can create them using `torch.rand()` and passing in the `size` parameter. + + +```python +# creating a random tensor of size (3,4) +rand_tensor = torch.rand(size = (3,4)) +print(rand_tensor) +``` + + tensor([[0.7462, 0.4950, 0.7851, 0.8277], + [0.6112, 0.5159, 0.1728, 0.6847], + [0.4472, 0.1612, 0.6481, 0.3236]]) + + + +```python +# Check the dimensions +print(rand_tensor.ndim) +``` + + 2 + + + +```python +# Shape +print(rand_tensor.shape) +``` + + torch.Size([3, 4]) + + + +```python +# datatype +print(rand_tensor.dtype) +``` + + torch.float32 + + +### Zeros and ones + +Here we will create a tensor of any shape filled with zeros and ones + + +```python +# Create a tensor of all zeros +zeros = torch.zeros(size = (3,4)) +print(zeros) +``` + + tensor([[0., 0., 0., 0.], + [0., 0., 0., 0.], + [0., 0., 0., 0.]]) + + + +```python +# create a tensor of ones +ones = torch.ones(size = (3,4)) +print(ones) +``` + + tensor([[1., 1., 1., 1.], + [1., 1., 1., 1.], + [1., 1., 1., 1.]]) + + +### Create a tensor having range of numbers + +You can use `torch.arange(start, end, step)` to do so. + +Where: + +* start = start of range (e.g. 0) +* end = end of range (e.g. 10) +* step = how many steps in between each value (e.g. 1) + +> Note: In Python, you can use range() to create a range. However in PyTorch, torch.range() is deprecated show error, show use `torch.arange()` + + +```python +zero_to_ten = torch.arange(start = 0, + end = 10, + step = 1) +print(zero_to_ten) +``` + + tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) + + +# 2. Manipulating tensors (tensor operations) + +The operations are : + +* Addition +* Substraction +* Multiplication (element-wise) +* Division +* Matrix multiplication + +### 1. Addition + + +```python +tensor = torch.tensor([1,2,3]) +print(tensor+10) +``` + + tensor([11, 12, 13]) + + +We have add 10 to each tensor element. + + +```python +tensor1 = torch.tensor([4,5,6]) +print(tensor+tensor1) +``` + + tensor([5, 7, 9]) + + +We have added two tensors , remember that addition takes place element wise. + +### 2. Subtraction + + +```python +print(tensor-8) +``` + + tensor([-7, -6, -5]) + + +We've subtracted 8 from the above tensor. + + +```python +print(tensor-tensor1) +``` + + tensor([-3, -3, -3]) + + +### 3. Multiplication + + +```python +# Multiply the tensor with 10 (element wise) +print(tensor*10) +``` + + tensor([10, 20, 30]) + + +Each element of tensor gets multiplied by 10. + +Note: + +PyTorch also has a bunch of built-in functions like `torch.mul()` (short for multiplication) and `torch.add()` to perform basic operations. + + +```python +# let's see them +print(torch.add(tensor,10)) +``` + + tensor([11, 12, 13]) + + + +```python +print(torch.mul(tensor,10)) +``` + + tensor([10, 20, 30]) + + +### Matrix multiplication (is all you need) +One of the most common operations in machine learning and deep learning algorithms (like neural networks) is matrix multiplication. + +PyTorch implements matrix multiplication functionality in the `torch.matmul()` method. + +The main two rules for matrix multiplication to remember are: + +The inner dimensions must match: +* (3, 2) @ (3, 2) won't work +* (2, 3) @ (3, 2) will work +* (3, 2) @ (2, 3) will work +The resulting matrix has the shape of the outer dimensions: +* (2, 3) @ (3, 2) -> (2, 2) +* (3, 2) @ (2, 3) -> (3, 3) + + +Note: "@" in Python is the symbol for matrix multiplication. + + +```python +# let's perform the matrix multiplication +tensor1 = torch.tensor([[[1,2,3], + [4,5,6], + [7,8,9]]]) +tensor2 = torch.tensor([[[1,1,1], + [2,2,2], + [3,3,3]]]) + +print(tensor1) , print(tensor2) +``` + + tensor([[[1, 2, 3], + [4, 5, 6], + [7, 8, 9]]]) + tensor([[[1, 1, 1], + [2, 2, 2], + [3, 3, 3]]]) + + + + + + (None, None) + + + + +```python +# let's check the shape +print(tensor1.shape) , print(tensor2.shape) +``` + + torch.Size([1, 3, 3]) + torch.Size([1, 3, 3]) + + + + + + (None, None) + + + + +```python + # Matrix multiplication +print(torch.matmul(tensor1, tensor2)) +``` + + tensor([[[14, 14, 14], + [32, 32, 32], + [50, 50, 50]]]) + + + +```python +# Can also use the "@" symbol for matrix multiplication, though not recommended +print(tensor1 @ tensor2) +``` + + tensor([[[14, 14, 14], + [32, 32, 32], + [50, 50, 50]]]) + + +Note: + +If shape is not perfect you can transpose the tensor and perform the matrix multiplication. From 03e9338a91c16d46dc36bc46d2c5b5ca223b81fc Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Sat, 8 Jun 2024 13:34:39 +0530 Subject: [PATCH 502/656] Update index.md --- contrib/machine-learning/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 0bf39f98..b52371a0 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -20,3 +20,4 @@ - [Grid Search](grid-search.md) - [Transformers](transformers.md) - [K-nearest neighbor (KNN)](knn.md) +- [PyTorch Fundamentals](PyTorch_Fundamentals.md) From b2643ad26d31b8e70e3bb41842745bb9f3129716 Mon Sep 17 00:00:00 2001 From: saiumasankar Date: Sat, 8 Jun 2024 15:03:43 +0530 Subject: [PATCH 503/656] deleting unnecessary --- contrib/web-scrapping/beautifulsoup.md | 206 ------------------------- contrib/web-scrapping/index.md | 1 - 2 files changed, 207 deletions(-) delete mode 100644 contrib/web-scrapping/beautifulsoup.md diff --git a/contrib/web-scrapping/beautifulsoup.md b/contrib/web-scrapping/beautifulsoup.md deleted file mode 100644 index 372b8f29..00000000 --- a/contrib/web-scrapping/beautifulsoup.md +++ /dev/null @@ -1,206 +0,0 @@ -# Beautiful Soup Library in Python - -## Table of Contents -1. Introduction -2. Prerequisites -3. Setting Up Environment -4. Beautiful Soup Objects -5. Tag object -6. Children, Parents and Siblings -7. Filter: Findall method -8. Web Scraping the Contents of a Web Page - -## 1. Introduction -Beautiful Soup is a Python library used for web scraping purposes to extract data from HTML and XML files. It creates a parse tree for parsed pages that can be used to extract data easily. - -## 2. Prerequisites -- Understanding of HTTP Requests and Responses. -- Understanding of HTTP methods (GET method). -- Basic Knowledge on HTML. -- Python installed on your machine (version 3.6 or higher). -- pip (Python package installer) installed. - -## 3. Setting Up Environment -1.**Install latest version of Python:** Go to Python.org and Download the latest version of Python. - -2.**Install a IDE:** Install any IDE of your choice to code. VS code is preferred. ->Note: You can use Google Colab without installing Python and IDE. - -3.**Install Beautiful Soup:** -```python -!pip install bs4 -``` - -## 4. Beautiful Soup Objects -Beautiful Soup is a Python library for pulling data out of HTML and XML files. This can be done by presenting the HTML as a set of objects. - -Take the below HTML page as input -``` - - - -Page Title - - -

    Title

    -

    This is the main context of the page

    - - -``` - -lets store the HTML code in a variable -``` -html= " - - - Page Title - - -

    Title

    -

    This is the main context of the page

    - - " -``` - -To parse the HTML document, pass the variable to the `BeautifulSoup` Constructor. -``` -soup = BeautifulSoup(html,'html.parser') -``` -Beautiful Soup transforms a complex HTML document into a complex tree of Python objects. - -## 5. Tags - -The Tag object corresponds to an HTML tag in the original document. - -``` -tag_obj = soup.title -print("tag object:", tag_obj) -print("tag object type:",type(tag_obj)) -``` -``` -Result: -tag object: Page Title - -tag object type: -``` - -## 6. Children, Parent and Siblings - -We can access the child of the tag or navigate down. - -``` -tag_obj = soup.h3 -child = tag_obj.b -print(child) -parent = child.parent -print(parent) -sib = tag_obj.next_sibling -print(sib); -``` -``` -Result: - Title -

    Title

    -

    This is the main context of the page

    -``` - -> We need to mention the child to which we want to navigate. It is because there can be more than one child to a tag but only one parent and next sibling. - -**Navigable String:** A string corresponds to a bit of text or content within a tag. Beautiful Soup uses the NavigableString class to contain this text. - -``` -tagstr = child.string -print(tagstr) -``` -``` -Result: -Title -``` - -## 7. Filter - -Filters allow you to find complex patterns, the simplest filter is a string. - -Consider the following HTML code: - -``` - - - - - - Sample Table - - -

    Sample Table

    - - - - - - - - - - - - - - - - - - - - - - - - - -
    NameAgeCity
    John Doe28New York
    Jane Smith34Los Angeles
    Emily Jones23Chicago
    - - - -``` -Store the above code in a variable. -``` -table = " - - - - - Sample Table -.............. - -table_bs = BeautifulSoup(table, 'html.parser') -" -``` -**Find All:** The find_all() method looks through a tag’s descendants and retrieves all descendants that match your filters. It places all the objects in a list. We can access by indexing. - -## 8. Web Scraping the Contents of a Web Page - -1. Get the URL of the webpage we need to scrape. - -2. Get the HTML content by using `get` method in HTTP request. - -3. Pass the HTML content to the BeautifulSoup Constructor to prepare an object. - -4. Access the HTML elements using Tag name. - -Below is the code to extract the img tags in a wikipedia page. - -``` -import requests -!pip install bs4 -from bs4 import BeautifulSoup - -url = "https://en.wikipedia.org/wiki/Encyclopedia" -data = requests.get(url).text -soup = BeautifulSoup(data,"html.parser") - -for link in soup.find_all('a'): - print(link) - print(link.get('src')) -``` -We can retrieve any data in the webpage by access through the tags. diff --git a/contrib/web-scrapping/index.md b/contrib/web-scrapping/index.md index 29f558aa..276014ea 100644 --- a/contrib/web-scrapping/index.md +++ b/contrib/web-scrapping/index.md @@ -2,4 +2,3 @@ - [Section title](filename.md) - [Introduction to Flask](flask.md) -- [Web Scrapping Using Beautiful Soup](beautifulsoup.md) From c2c84a09d21445382fc535a99d2745d8a8be0a29 Mon Sep 17 00:00:00 2001 From: Kosuri Indu Date: Sat, 8 Jun 2024 19:10:46 +0530 Subject: [PATCH 504/656] Added Splay Trees in DSA section --- contrib/ds-algorithms/index.md | 3 +- contrib/ds-algorithms/splay-trees.md | 162 +++++++++++++++++++++++++++ 2 files changed, 164 insertions(+), 1 deletion(-) create mode 100644 contrib/ds-algorithms/splay-trees.md diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 9c05b15e..e099f978 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -15,4 +15,5 @@ - [Trie](trie.md) - [Two Pointer Technique](two-pointer-technique.md) - [Hashing through Linear Probing](hashing-linear-probing.md) -- [Hashing through Chaining](hashing-chaining.md) \ No newline at end of file +- [Hashing through Chaining](hashing-chaining.md) +- [Splay Trees](splay-trees.md) \ No newline at end of file diff --git a/contrib/ds-algorithms/splay-trees.md b/contrib/ds-algorithms/splay-trees.md new file mode 100644 index 00000000..ee900ed8 --- /dev/null +++ b/contrib/ds-algorithms/splay-trees.md @@ -0,0 +1,162 @@ +# Splay Tree + +In Data Structures and Algorithms, a **Splay Tree** is a self-adjusting binary search tree with the additional property that recently accessed elements are quick to access again. It performs basic operations such as insertion, search, and deletion in O(log n) amortized time. This is achieved by a process called **splaying**, where the accessed node is moved to the root through a series of tree rotations. + +## Points to be Remembered + +- **Splaying**: Moving the accessed node to the root using rotations. +- **Rotations**: Tree rotations (left and right) are used to balance the tree during splaying. +- **Self-adjusting**: The tree adjusts itself with each access, keeping frequently accessed nodes near the root. + +## Real Life Examples of Splay Trees + +- **Cache Implementation**: Frequently accessed data is kept near the top of the tree, making repeated accesses faster. +- **Networking**: Routing tables in network switches can use splay trees to prioritize frequently accessed routes. + +## Applications of Splay Trees + +Splay trees are used in various applications in Computer Science: + +- **Cache Implementations** +- **Garbage Collection Algorithms** +- **Data Compression Algorithms (e.g., LZ78)** + +Understanding these applications is essential for Software Development. + +## Operations in Splay Tree + +Key operations include: + +- **INSERT**: Insert a new element into the splay tree. +- **SEARCH**: Find the position of an element in the splay tree. +- **DELETE**: Remove an element from the splay tree. + +## Implementing Splay Tree in Python + +```python +class SplayTreeNode: + def __init__(self, key): + self.key = key + self.left = None + self.right = None + +class SplayTree: + def __init__(self): + self.root = None + + def insert(self, key): + self.root = self.splay_insert(self.root, key) + + def search(self, key): + self.root = self.splay_search(self.root, key) + return self.root + + def splay(self, root, key): + if not root or root.key == key: + return root + + if root.key > key: + if not root.left: + return root + if root.left.key > key: + root.left.left = self.splay(root.left.left, key) + root = self.rotateRight(root) + elif root.left.key < key: + root.left.right = self.splay(root.left.right, key) + if root.left.right: + root.left = self.rotateLeft(root.left) + return root if not root.left else self.rotateRight(root) + + else: + if not root.right: + return root + if root.right.key > key: + root.right.left = self.splay(root.right.left, key) + if root.right.left: + root.right = self.rotateRight(root.right) + elif root.right.key < key: + root.right.right = self.splay(root.right.right, key) + root = self.rotateLeft(root) + return root if not root.right else self.rotateLeft(root) + + def splay_insert(self, root, key): + if not root: + return SplayTreeNode(key) + + root = self.splay(root, key) + + if root.key == key: + return root + + new_node = SplayTreeNode(key) + + if root.key > key: + new_node.right = root + new_node.left = root.left + root.left = None + else: + new_node.left = root + new_node.right = root.right + root.right = None + + return new_node + + def splay_search(self, root, key): + return self.splay(root, key) + + def rotateRight(self, node): + temp = node.left + node.left = temp.right + temp.right = node + return temp + + def rotateLeft(self, node): + temp = node.right + node.right = temp.left + temp.left = node + return temp + + def preOrder(self, root): + if root: + print(root.key, end=' ') + self.preOrder(root.left) + self.preOrder(root.right) + +#Example usage: +splay_tree = SplayTree() +splay_tree.insert(50) +splay_tree.insert(30) +splay_tree.insert(20) +splay_tree.insert(40) +splay_tree.insert(70) +splay_tree.insert(60) +splay_tree.insert(80) + +print("Preorder traversal of the Splay tree is:") +splay_tree.preOrder(splay_tree.root) + +splay_tree.search(60) + +print("\nSplay tree after search operation for key 60:") +splay_tree.preOrder(splay_tree.root) +``` + +## Output + +```markdown +Preorder traversal of the Splay tree is: +50 30 20 40 70 60 80 + +Splay tree after search operation for key 60: +60 50 30 20 40 70 80 +``` + +## Complexity Analysis + +The worst-case time complexities of the main operations in a Splay Tree are as follows: + +- **Insertion**: (O(n)). In the worst case, insertion may take linear time if the tree is highly unbalanced. +- **Search**: (O(n)). In the worst case, searching for a node may take linear time if the tree is highly unbalanced. +- **Deletion**: (O(n)). In the worst case, deleting a node may take linear time if the tree is highly unbalanced. + +While these operations can take linear time in the worst case, the splay operation ensures that the tree remains balanced over a sequence of operations, leading to better average-case performance. \ No newline at end of file From 0b301cb533e10345a68b98de1d8d2c4ee076102f Mon Sep 17 00:00:00 2001 From: Saimanjari777 Date: Sat, 8 Jun 2024 21:14:05 +0530 Subject: [PATCH 505/656] Added hash-tables, sets, and maps added --- contrib/ds-algorithms/hash-tables.md | 212 +++++++++++++++++++++++++++ contrib/ds-algorithms/index.md | 3 +- 2 files changed, 214 insertions(+), 1 deletion(-) create mode 100644 contrib/ds-algorithms/hash-tables.md diff --git a/contrib/ds-algorithms/hash-tables.md b/contrib/ds-algorithms/hash-tables.md new file mode 100644 index 00000000..f03b7c5e --- /dev/null +++ b/contrib/ds-algorithms/hash-tables.md @@ -0,0 +1,212 @@ +# Data Structures: Hash Tables, Hash Sets, and Hash Maps + +## Table of Contents +- [Introduction](#introduction) +- [Hash Tables](#hash-tables) + - [Overview](#overview) + - [Operations](#operations) +- [Hash Sets](#hash-sets) + - [Overview](#overview-1) + - [Operations](#operations-1) +- [Hash Maps](#hash-maps) + - [Overview](#overview-2) + - [Operations](#operations-2) +- [Conclusion](#conclusion) + +## Introduction +This document provides an overview of three fundamental data structures in computer science: hash tables, hash sets, and hash maps. These structures are widely used for efficient data storage and retrieval operations. + +## Hash Tables + +### Overview +A **hash table** is a data structure that stores key-value pairs. It uses a hash function to compute an index into an array of buckets or slots, from which the desired value can be found. + +### Operations +1. **Insertion**: Add a new key-value pair to the hash table. +2. **Deletion**: Remove a key-value pair from the hash table. +3. **Search**: Find the value associated with a given key. +4. **Update**: Modify the value associated with a given key. + +**Example Code (Python):** +```python +class Node: + def __init__(self, key, value): + self.key = key + self.value = value + self.next = None + + +class HashTable: + def __init__(self, capacity): + self.capacity = capacity + self.size = 0 + self.table = [None] * capacity + + def _hash(self, key): + return hash(key) % self.capacity + + def insert(self, key, value): + index = self._hash(key) + + if self.table[index] is None: + self.table[index] = Node(key, value) + self.size += 1 + else: + current = self.table[index] + while current: + if current.key == key: + current.value = value + return + current = current.next + new_node = Node(key, value) + new_node.next = self.table[index] + self.table[index] = new_node + self.size += 1 + + def search(self, key): + index = self._hash(key) + + current = self.table[index] + while current: + if current.key == key: + return current.value + current = current.next + + raise KeyError(key) + + def remove(self, key): + index = self._hash(key) + + previous = None + current = self.table[index] + + while current: + if current.key == key: + if previous: + previous.next = current.next + else: + self.table[index] = current.next + self.size -= 1 + return + previous = current + current = current.next + + raise KeyError(key) + + def __len__(self): + return self.size + + def __contains__(self, key): + try: + self.search(key) + return True + except KeyError: + return False + + +# Driver code +if __name__ == '__main__': + + ht = HashTable(5) + + ht.insert("apple", 3) + ht.insert("banana", 2) + ht.insert("cherry", 5) + + + print("apple" in ht) + print("durian" in ht) + + print(ht.search("banana")) + + ht.insert("banana", 4) + print(ht.search("banana")) # 4 + + ht.remove("apple") + + print(len(ht)) # 3 +``` + +# Insert elements +hash_table["key1"] = "value1" +hash_table["key2"] = "value2" + +# Search for an element +value = hash_table.get("key1") + +# Delete an element +del hash_table["key2"] + +# Update an element +hash_table["key1"] = "new_value1" + +## Hash Sets + +### Overview +A **hash set** is a collection of unique elements. It is implemented using a hash table where each bucket can store only one element. + +### Operations +1. **Insertion**: Add a new element to the set. +2. **Deletion**: Remove an element from the set. +3. **Search**: Check if an element exists in the set. +4. **Union**: Combine two sets to form a new set with elements from both. +5. **Intersection**: Find common elements between two sets. +6. **Difference**: Find elements present in one set but not in the other. + +**Example Code (Python):** +```python +# Create a hash set +hash_set = set() + +# Insert elements +hash_set.add("element1") +hash_set.add("element2") + +# Search for an element +exists = "element1" in hash_set + +# Delete an element +hash_set.remove("element2") + +# Union of sets +another_set = {"element3", "element4"} +union_set = hash_set.union(another_set) + +# Intersection of sets +intersection_set = hash_set.intersection(another_set) + +# Difference of sets +difference_set = hash_set.difference(another_set) +``` +## Hash Maps + +### Overview +A **hash map** is similar to a hash table but often provides additional functionalities and more user-friendly interfaces for developers. It is a collection of key-value pairs where each key is unique. + +### Operations +1. **Insertion**: Add a new key-value pair to the hash map. +2. **Deletion**: Remove a key-value pair from the hash map. +3. **Search**: Retrieve the value associated with a given key. +4. **Update**: Change the value associated with a given key. + +**Example Code (Python):** +```python +# Create a hash map +hash_map = {} + +# Insert elements +hash_map["key1"] = "value1" +hash_map["key2"] = "value2" + +# Search for an element +value = hash_map.get("key1") + +# Delete an element +del hash_map["key2"] + +# Update an element +hash_map["key1"] = "new_value1" + +``` +## Conclusion +Hash tables, hash sets, and hash maps are powerful data structures that provide efficient means of storing and retrieving data. Understanding these structures and their operations is crucial for developing optimized algorithms and applications. \ No newline at end of file diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 9c05b15e..9b8c192c 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -15,4 +15,5 @@ - [Trie](trie.md) - [Two Pointer Technique](two-pointer-technique.md) - [Hashing through Linear Probing](hashing-linear-probing.md) -- [Hashing through Chaining](hashing-chaining.md) \ No newline at end of file +- [Hashing through Chaining](hashing-chaining.md) +- [Hash Tables, Sets, Maps ](hash-tables.md) \ No newline at end of file From 4f096eb6a64c5e798f04a68740c7a8aae39b048c Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 8 Jun 2024 23:39:18 +0530 Subject: [PATCH 506/656] Update index.md --- contrib/advanced-python/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index 59ea9b50..b093e44e 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -12,7 +12,7 @@ - [Protocols](protocols.md) - [Exception Handling in Python](exception-handling.md) - [Generators](generators.md) -- [Match Case Statement](match_case_statement.md) +- [Match Case Statement](match-case.md) - [Closures](closures.md) - [Filter](filter-function.md) - [Reduce](reduce-function.md) From 7d18fe39d45bfdd2e4d432c8bc70b4c7496f7e2d Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 8 Jun 2024 23:39:52 +0530 Subject: [PATCH 507/656] Update and rename match_case_statement.md to match-case.md --- .../advanced-python/{match_case_statement.md => match-case.md} | 1 - 1 file changed, 1 deletion(-) rename contrib/advanced-python/{match_case_statement.md => match-case.md} (95%) diff --git a/contrib/advanced-python/match_case_statement.md b/contrib/advanced-python/match-case.md similarity index 95% rename from contrib/advanced-python/match_case_statement.md rename to contrib/advanced-python/match-case.md index b617f7a1..1b4f0171 100644 --- a/contrib/advanced-python/match_case_statement.md +++ b/contrib/advanced-python/match-case.md @@ -247,6 +247,5 @@ The match case statements provide an elegant and readible format to perform oper ## Further Reading This article provides a brief introduction to the match case statements and the overview on the pattern matching operations. To know more, the below articles can be used for in-depth understanding of the topic. -- [Python Match Case Statement - GeeksForGeeks](https://www.geeksforgeeks.org/python-match-case-statement/) - [PEP 634 – Structural Pattern Matching: Specification](https://peps.python.org/pep-0634/) - [PEP 636 – Structural Pattern Matching: Tutorial](https://peps.python.org/pep-0636/) From 10e40af9e97a57945f5a96ca1c2890ce17844cdb Mon Sep 17 00:00:00 2001 From: Ishita-Jena <167574361+Ishita-Jena@users.noreply.github.com> Date: Sun, 9 Jun 2024 04:01:25 +0530 Subject: [PATCH 508/656] Create binary_tree.md --- contrib/ds-algorithms/binary_tree.md | 71 ++++++++++++++++++++++++++++ 1 file changed, 71 insertions(+) create mode 100644 contrib/ds-algorithms/binary_tree.md diff --git a/contrib/ds-algorithms/binary_tree.md b/contrib/ds-algorithms/binary_tree.md new file mode 100644 index 00000000..fb112303 --- /dev/null +++ b/contrib/ds-algorithms/binary_tree.md @@ -0,0 +1,71 @@ +# Binary Tree + +A binary tree is a non-linear data structure in which each node can have atmost two children, known as the left and the right child. + +```python + A...................Level 0 + / \ + B C.................Level 1 + / \ \ + D E G...............Level 2 +``` + +## Basic Terminologies + +- **Root node:** The topmost node in a tree is the root node. The root node does not have any parent. In the example given above, **A** is the root node. +- **Parent node:** The predecessor of a node is called the parent of that node. **A** is the parent of **B** and **C**, **B** is the parent of **D** and **E** and **C** is the parent of **G**. +- **Child node:** The successor of a node is called the child of that node. **B** and **C** are children of **A**, **D** and **E** are children of **B** and **G** is the right child of **C**. +- **Leaf node:** Nodes without any children are called the leaf nodes. **D**, **E** and **G** are the leaf nodes. +- **Ancestor node:** Predecessor nodes on the path from the root to that node are called ancestor nodes. **A** and **B** are the ancestors of **E**. +- **Descendant node:** Successor nodes on the path from the root to that node are called descendant nodes. **B** and **E** are descendants of **A**. +- **Sibling node:** Nodes having the same parent are called sibling nodes. **B** and **C** are sibling nodes and so are **D** and **E**. +- **Level (Depth) of a node:** Number of edges in the path from the root to that node is the level of that node. The root node is always at level 0. The depth of root node is the depth of the tree. Nodes **D**, **E** and **G** are at level 2. +- **Height of a node:** Number of edges in the path from that node to the deepest leaf. The height of the root is the height of a tree. Height of nodes **B** and **C** are 1. + +## Types Of Binary Trees + +- **Full Binary Tree:** A binary tree where each node has 0 or 2 children is a full binary tree. +```python + A + / \ + B C + / \ + D E +``` +- **Complete Binary Tree:** A binary tree in which all levels are completely filled except the last level is a complete binary tree. Whenever new nodes are inserted, they are inserted from the left side. +```python + A + / \ + / \ + B C + / \ / + D E F +``` +- **Perfect Binary Tree:** A binary tree in which all nodes are completely filled, i.e., each node has two children is called a perfect binary tree. +```python + A + / \ + / \ + B C + / \ / \ + D E F G +``` +- **Skewed Binary Tree:** A binary tree in which each node has either 0 or 1 child is called a skewed binary tree. It is of two types - left skewed binary tree and right skewed binary tree. +```python + A A + \ / + B B + \ / + C C + Right skewed binary tree Left skewed binary tree +``` +- **Balanced Binary Tree:** A binary tree in which the height difference between the left and right subtree is not more than one and the subtrees are also balanced is a balanced binary tree. +```python + A + / \ + B C + / \ + D E +``` + +## Implementation of Binary Tree From ba59ef608ca96405abc6f9a9c14d2b1a26ed5ffe Mon Sep 17 00:00:00 2001 From: Ishita-Jena <167574361+Ishita-Jena@users.noreply.github.com> Date: Sun, 9 Jun 2024 04:02:49 +0530 Subject: [PATCH 509/656] Update index.md --- contrib/ds-algorithms/index.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 9c05b15e..ecc4a10e 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -15,4 +15,5 @@ - [Trie](trie.md) - [Two Pointer Technique](two-pointer-technique.md) - [Hashing through Linear Probing](hashing-linear-probing.md) -- [Hashing through Chaining](hashing-chaining.md) \ No newline at end of file +- [Hashing through Chaining](hashing-chaining.md) +- [Binary Tree](binary_tree.md) From 65e2aa42f851628e8aa1f5916236edc15aeae99b Mon Sep 17 00:00:00 2001 From: Kosuri Indu Date: Sun, 9 Jun 2024 09:33:07 +0530 Subject: [PATCH 510/656] Added AVL Trees in DSA section --- contrib/ds-algorithms/avl-trees.md | 185 +++++++++++++++++++++++++++++ contrib/ds-algorithms/index.md | 3 +- 2 files changed, 187 insertions(+), 1 deletion(-) create mode 100644 contrib/ds-algorithms/avl-trees.md diff --git a/contrib/ds-algorithms/avl-trees.md b/contrib/ds-algorithms/avl-trees.md new file mode 100644 index 00000000..b87e82cb --- /dev/null +++ b/contrib/ds-algorithms/avl-trees.md @@ -0,0 +1,185 @@ +# AVL Tree + +In Data Structures and Algorithms, an **AVL Tree** is a self-balancing binary search tree (BST) where the difference between heights of left and right subtrees cannot be more than one for all nodes. It ensures that the tree remains balanced, providing efficient search, insertion, and deletion operations. + +## Points to be Remembered + +- **Balance Factor**: The difference in heights between the left and right subtrees of a node. It should be -1, 0, or +1 for all nodes in an AVL tree. +- **Rotations**: Tree rotations (left, right, left-right, right-left) are used to maintain the balance factor within the allowed range. + +## Real Life Examples of AVL Trees + +- **Databases**: AVL trees can be used to maintain large indexes for database tables, ensuring quick data retrieval. +- **File Systems**: Some file systems use AVL trees to keep track of free and used memory blocks. + +## Applications of AVL Trees + +AVL trees are used in various applications in Computer Science: + +- **Database Indexing** +- **Memory Allocation** +- **Network Routing Algorithms** + +Understanding these applications is essential for Software Development. + +## Operations in AVL Tree + +Key operations include: + +- **INSERT**: Insert a new element into the AVL tree. +- **SEARCH**: Find the position of an element in the AVL tree. +- **DELETE**: Remove an element from the AVL tree. + +## Implementing AVL Tree in Python + +```python +class AVLTreeNode: + def __init__(self, key): + self.key = key + self.left = None + self.right = None + self.height = 1 + +class AVLTree: + def insert(self, root, key): + if not root: + return AVLTreeNode(key) + + if key < root.key: + root.left = self.insert(root.left, key) + else: + root.right = self.insert(root.right, key) + + root.height = 1 + max(self.getHeight(root.left), self.getHeight(root.right)) + balance = self.getBalance(root) + + if balance > 1 and key < root.left.key: + return self.rotateRight(root) + if balance < -1 and key > root.right.key: + return self.rotateLeft(root) + if balance > 1 and key > root.left.key: + root.left = self.rotateLeft(root.left) + return self.rotateRight(root) + if balance < -1 and key < root.right.key: + root.right = self.rotateRight(root.right) + return self.rotateLeft(root) + + return root + + def search(self, root, key): + if not root or root.key == key: + return root + + if key < root.key: + return self.search(root.left, key) + + return self.search(root.right, key) + + def delete(self, root, key): + if not root: + return root + + if key < root.key: + root.left = self.delete(root.left, key) + elif key > root.key: + root.right = self.delete(root.right, key) + else: + if root.left is None: + temp = root.right + root = None + return temp + elif root.right is None: + temp = root.left + root = None + return temp + + temp = self.getMinValueNode(root.right) + root.key = temp.key + root.right = self.delete(root.right, temp.key) + + if root is None: + return root + + root.height = 1 + max(self.getHeight(root.left), self.getHeight(root.right)) + balance = self.getBalance(root) + + if balance > 1 and self.getBalance(root.left) >= 0: + return self.rotateRight(root) + if balance < -1 and self.getBalance(root.right) <= 0: + return self.rotateLeft(root) + if balance > 1 and self.getBalance(root.left) < 0: + root.left = self.rotateLeft(root.left) + return self.rotateRight(root) + if balance < -1 and self.getBalance(root.right) > 0: + root.right = self.rotateRight(root.right) + return self.rotateLeft(root) + + return root + + def rotateLeft(self, z): + y = z.right + T2 = y.left + y.left = z + z.right = T2 + z.height = 1 + max(self.getHeight(z.left), self.getHeight(z.right)) + y.height = 1 + max(self.getHeight(y.left), self.getHeight(y.right)) + return y + + def rotateRight(self, z): + y = z.left + T3 = y.right + y.right = z + z.left = T3 + z.height = 1 + max(self.getHeight(z.left), self.getHeight(z.right)) + y.height = 1 + max(self.getHeight(y.left), self.getHeight(y.right)) + return y + + def getHeight(self, root): + if not root: + return 0 + return root.height + + def getBalance(self, root): + if not root: + return 0 + return self.getHeight(root.left) - self.getHeight(root.right) + + def getMinValueNode(self, root): + if root is None or root.left is None: + return root + return self.getMinValueNode(root.left) + + def preOrder(self, root): + if not root: + return + print(root.key, end=' ') + self.preOrder(root.left) + self.preOrder(root.right) + +#Example usage +avl_tree = AVLTree() +root = None + +root = avl_tree.insert(root, 10) +root = avl_tree.insert(root, 20) +root = avl_tree.insert(root, 30) +root = avl_tree.insert(root, 40) +root = avl_tree.insert(root, 50) +root = avl_tree.insert(root, 25) + +print("Preorder traversal of the AVL tree is:") +avl_tree.preOrder(root) +``` + +## Output + +```markdown +Preorder traversal of the AVL tree is: +30 20 10 25 40 50 +``` + +## Complexity Analysis + +- **Insertion**: O(logn). Inserting a node involves traversing the height of the tree, which is logarithmic due to the balancing property. +- **Search**: O(logn). Searching for a node involves traversing the height of the tree. +- **Deletion**: O(log⁡n). Deleting a node involves traversing and potentially rebalancing the tree, maintaining the logarithmic height. \ No newline at end of file diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 9c05b15e..d3dbe73e 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -15,4 +15,5 @@ - [Trie](trie.md) - [Two Pointer Technique](two-pointer-technique.md) - [Hashing through Linear Probing](hashing-linear-probing.md) -- [Hashing through Chaining](hashing-chaining.md) \ No newline at end of file +- [Hashing through Chaining](hashing-chaining.md) +- [AVL Trees](avl-trees.md) \ No newline at end of file From 6726d30e5590c5ed97ffbbc3b0d2c6d941656d3c Mon Sep 17 00:00:00 2001 From: Yogesh Vishwakarma <103316955+Yogeshkarma@users.noreply.github.com> Date: Sun, 9 Jun 2024 09:54:56 +0530 Subject: [PATCH 511/656] Create Universal-Functions.md Added Universal Functions file --- contrib/numpy/Universal-Functions.md | 130 +++++++++++++++++++++++++++ 1 file changed, 130 insertions(+) create mode 100644 contrib/numpy/Universal-Functions.md diff --git a/contrib/numpy/Universal-Functions.md b/contrib/numpy/Universal-Functions.md new file mode 100644 index 00000000..090f33c5 --- /dev/null +++ b/contrib/numpy/Universal-Functions.md @@ -0,0 +1,130 @@ +# Universal functions (ufunc) + +--- + +A `ufunc`, short for "`universal function`," is a fundamental concept in NumPy, a powerful library for numerical computing in Python. Universal functions are highly optimized, element-wise functions designed to perform operations on data stored in NumPy arrays. + + + +## Uses of Ufuncs in NumPy + +Universal functions (ufuncs) in NumPy provide a wide range of functionalities for efficient and powerful numerical computations. Below is a detailed explanation of their uses: + +### 1. **Element-wise Operations** +Ufuncs perform operations on each element of the arrays independently. + +```python +import numpy as np + +A = np.array([1, 2, 3, 4]) +B = np.array([5, 6, 7, 8]) + +# Element-wise addition +np.add(A, B) # Output: array([ 6, 8, 10, 12]) +``` + +### 2. **Broadcasting** +Ufuncs support broadcasting, allowing operations on arrays with different shapes, making it possible to perform operations without explicitly reshaping arrays. + +```python +C = np.array([1, 2, 3]) +D = np.array([[1], [2], [3]]) + +# Broadcasting addition +np.add(C, D) # Output: array([[2, 3, 4], [3, 4, 5], [4, 5, 6]]) +``` + +### 3. **Vectorization** +Ufuncs are vectorized, meaning they are implemented in low-level C code, allowing for fast execution and avoiding the overhead of Python loops. + +```python +# Vectorized square root +np.sqrt(A) # Output: array([1., 1.41421356, 1.73205081, 2.]) +``` + +### 4. **Type Flexibility** +Ufuncs handle various data types and perform automatic type casting as needed. + +```python +E = np.array([1.0, 2.0, 3.0]) +F = np.array([4, 5, 6]) + +# Addition with type casting +np.add(E, F) # Output: array([5., 7., 9.]) +``` + +### 5. **Reduction Operations** +Ufuncs support reduction operations, such as summing all elements of an array or finding the product of all elements. + +```python +# Summing all elements +np.add.reduce(A) # Output: 10 + +# Product of all elements +np.multiply.reduce(A) # Output: 24 +``` + +### 6. **Accumulation Operations** +Ufuncs can perform accumulation operations, which keep a running tally of the computation. + +```python +# Cumulative sum +np.add.accumulate(A) # Output: array([ 1, 3, 6, 10]) +``` + +### 7. **Reduceat Operations** +Ufuncs can perform segmented reductions using the `reduceat` method, which applies the ufunc at specified intervals. + +```python +G = np.array([0, 1, 2, 3, 4, 5, 6, 7]) +indices = [0, 2, 5] +np.add.reduceat(G, indices) # Output: array([ 1, 9, 18]) +``` + +### 8. **Outer Product** +Ufuncs can compute the outer product of two arrays, producing a matrix where each element is the result of applying the ufunc to each pair of elements from the input arrays. + +```python +# Outer product +np.multiply.outer([1, 2, 3], [4, 5, 6]) +# Output: array([[ 4, 5, 6], +# [ 8, 10, 12], +# [12, 15, 18]]) +``` + +### 9. **Out Parameter** +Ufuncs can use the `out` parameter to store results in a pre-allocated array, saving memory and improving performance. + +```python +result = np.empty_like(A) +np.multiply(A, B, out=result) # Output: array([ 5, 12, 21, 32]) +``` + +# Create Your Own Ufunc + +You can create custom ufuncs for specific needs using np.frompyfunc or np.vectorize, allowing Python functions to behave like ufuncs. + +Here, we are using `frompyfunc()` which takes three argument: + +1. function - the name of the function. +2. inputs - the number of input (arrays). +3. outputs - the number of output arrays. + +```python +def my_add(x, y): + return x + y + +my_add_ufunc = np.frompyfunc(my_add, 2, 1) +my_add_ufunc(A, B) # Output: array([ 6, 8, 10, 12], dtype=object) +``` +# Some Common Ufunc are + +Here are some commonly used ufuncs in NumPy: + +- **Arithmetic**: `np.add`, `np.subtract`, `np.multiply`, `np.divide` +- **Trigonometric**: `np.sin`, `np.cos`, `np.tan` +- **Exponential and Logarithmic**: `np.exp`, `np.log`, `np.log10` +- **Comparison**: `np.maximum`, `np.minimum`, `np.greater`, `np.less` +- **Logical**: `np.logical_and`, `np.logical_or`, `np.logical_not` + +For more such Ufunc, address to [Universal functions (ufunc) — NumPy](https://numpy.org/doc/stable/reference/ufuncs.html) From 9b4a621539a461ff0b4d283b4f07b8d5bb81b554 Mon Sep 17 00:00:00 2001 From: Yogesh Vishwakarma <103316955+Yogeshkarma@users.noreply.github.com> Date: Sun, 9 Jun 2024 09:56:16 +0530 Subject: [PATCH 512/656] Update index.md --- contrib/numpy/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 50e80460..2b0c357e 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -11,3 +11,4 @@ - [Sorting NumPy Arrays](sorting-array.md) - [NumPy Array Iteration](array-iteration.md) - [Concatenation of Arrays](concatenation-of-arrays.md) +- [Universal Functions (Ufunc)](Universal-Functions.md) From aefb52e5b3e6a81e55dce1a22f4870b3ef0e2c69 Mon Sep 17 00:00:00 2001 From: Revanth <109272714+revanth1718@users.noreply.github.com> Date: Sun, 9 Jun 2024 11:48:46 +0530 Subject: [PATCH 513/656] Update K-Means_Clustering.md --- contrib/machine-learning/K-Means_Clustering.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/contrib/machine-learning/K-Means_Clustering.md b/contrib/machine-learning/K-Means_Clustering.md index 859636bf..83fbd80a 100644 --- a/contrib/machine-learning/K-Means_Clustering.md +++ b/contrib/machine-learning/K-Means_Clustering.md @@ -76,6 +76,10 @@ The K-means algorithm follows an iterative approach to optimize cluster formatio Predicted cluster for new data: [0] ## Conclusion **K-Means** can be applied to data that has a smaller number of dimensions, is numeric, and is continuous or can be used to find groups that have not been explicitly labeled in the data. As an example, it can be used for Document Classification, Delivery Store Optimization, or Customer Segmentation. +## Reference +[[Survey of Machine Learning and Data Mining Techniques used in Multimedia System](https://www.researchgate.net/publication/333457161_Survey_of_Machine_Learning_and_Data_Mining_Techniques_used_in_Multimedia_System?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6Il9kaXJlY3QiLCJwYWdlIjoiX2RpcmVjdCJ9fQ)] + +[[A Clustering Approach for Outliers Detection in a Big Point-of-Sales Database](https://www.researchgate.net/publication/339267868_A_Clustering_Approach_for_Outliers_Detection_in_a_Big_Point-of-Sales_Database?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6Il9kaXJlY3QiLCJwYWdlIjoiX2RpcmVjdCJ9fQ)] From 4e33c752407656390fd2b89398c837c67959bfcb Mon Sep 17 00:00:00 2001 From: Ishita-Jena <167574361+Ishita-Jena@users.noreply.github.com> Date: Sun, 9 Jun 2024 12:08:02 +0530 Subject: [PATCH 514/656] Update binary_tree.md --- contrib/ds-algorithms/binary_tree.md | 168 ++++++++++++++++++++++++++- 1 file changed, 164 insertions(+), 4 deletions(-) diff --git a/contrib/ds-algorithms/binary_tree.md b/contrib/ds-algorithms/binary_tree.md index fb112303..4891204c 100644 --- a/contrib/ds-algorithms/binary_tree.md +++ b/contrib/ds-algorithms/binary_tree.md @@ -1,6 +1,6 @@ # Binary Tree -A binary tree is a non-linear data structure in which each node can have atmost two children, known as the left and the right child. +A binary tree is a non-linear data structure in which each node can have atmost two children, known as the left and the right child. It is a heirarchial data structure represented in the following way: ```python A...................Level 0 @@ -12,15 +12,15 @@ A binary tree is a non-linear data structure in which each node can have atmost ## Basic Terminologies -- **Root node:** The topmost node in a tree is the root node. The root node does not have any parent. In the example given above, **A** is the root node. +- **Root node:** The topmost node in a tree is the root node. The root node does not have any parent. In the above example, **A** is the root node. - **Parent node:** The predecessor of a node is called the parent of that node. **A** is the parent of **B** and **C**, **B** is the parent of **D** and **E** and **C** is the parent of **G**. - **Child node:** The successor of a node is called the child of that node. **B** and **C** are children of **A**, **D** and **E** are children of **B** and **G** is the right child of **C**. - **Leaf node:** Nodes without any children are called the leaf nodes. **D**, **E** and **G** are the leaf nodes. - **Ancestor node:** Predecessor nodes on the path from the root to that node are called ancestor nodes. **A** and **B** are the ancestors of **E**. - **Descendant node:** Successor nodes on the path from the root to that node are called descendant nodes. **B** and **E** are descendants of **A**. - **Sibling node:** Nodes having the same parent are called sibling nodes. **B** and **C** are sibling nodes and so are **D** and **E**. -- **Level (Depth) of a node:** Number of edges in the path from the root to that node is the level of that node. The root node is always at level 0. The depth of root node is the depth of the tree. Nodes **D**, **E** and **G** are at level 2. -- **Height of a node:** Number of edges in the path from that node to the deepest leaf. The height of the root is the height of a tree. Height of nodes **B** and **C** are 1. +- **Level (Depth) of a node:** Number of edges in the path from the root to that node is the level of that node. The root node is always at level 0. The depth of root node is the depth of the tree. +- **Height of a node:** Number of edges in the path from that node to the deepest leaf is the height of that node. The height of the root is the height of a tree. Height of node **A** is 2, nodes **B** and **C** is 1 and nodes **D**, **E** and **G** is 0. ## Types Of Binary Trees @@ -68,4 +68,164 @@ A binary tree is a non-linear data structure in which each node can have atmost D E ``` +## Real Life Applications Of Binary Tree + +- **File Systems:** File systems employ binary trees to organize the folders and files, facilitating efficient search and access of files. +- **Decision Trees:** Decision tree, a supervised learning algorithm, utilizes binary trees, with each node representing a decision and its edges showing the possible outcomes. +- **Routing Algorithms:** In routing algorithms, binary trees are used to efficiently transfer data packets from the source to destination through a network of nodes. +- **Searching and sorting Algorithms:** Searching algorithms like binary search and sorting algorithms like heapsort rely heavily on binary trees. + ## Implementation of Binary Tree + +```python +from collections import deque + +class Node: + def __init__(self, data): + self.data = data + self.left = None + self.right = None + +class Binary_tree: + @staticmethod + def insert(root, data): + if root is None: + return Node(data) + q = deque() + q.append(root) + while q: + temp = q.popleft() + if temp.left is None: + temp.left = Node(data) + break + else: + q.append(temp.left) + if temp.right is None: + temp.right = Node(data) + break + else: + q.append(temp.right) + return root + + @staticmethod + def inorder(root): + if not root: + return + b.inorder(root.left) + print(root.data, end=" ") + b.inorder(root.right) + + @staticmethod + def preorder(root): + if not root: + return + print(root.data, end=" ") + b.preorder(root.left) + b.preorder(root.right) + + @staticmethod + def postorder(root): + if not root: + return + b.postorder(root.left) + b.postorder(root.right) + print(root.data, end=" ") + + @staticmethod + def levelorder(root): + if not root: + return + q = deque() + q.append(root) + while q: + temp = q.popleft() + print(temp.data, end=" ") + if temp.left is not None: + q.append(temp.left) + if temp.right is not None: + q.append(temp.right) + + @staticmethod + def delete(root, value): + q = deque() + q.append(root) + while q: + temp = q.popleft() + if temp is value: + temp = None + return + if temp.right: + if temp.right is value: + temp.right = None + return + else: + q.append(temp.right) + if temp.left: + if temp.left is value: + temp.left = None + return + else: + q.append(temp.left) + + @staticmethod + def delete_value(root, value): + if root is None: + return None + if root.left is None and root.right is None: + if root.data == value: + return None + else: + return root + x = None + q = deque() + q.append(root) + temp = None + while q: + temp = q.popleft() + if temp.data == value: + x = temp + if temp.left: + q.append(temp.left) + if temp.right: + q.append(temp.right) + if x: + y = temp.data + x.data = y + b.delete(root, temp) + return root + +b = Binary_tree() +root = None +root = b.insert(root, 10) +root = b.insert(root, 20) +root = b.insert(root, 30) +root = b.insert(root, 40) +root = b.insert(root, 50) +root = b.insert(root, 60) + +print("Preorder traversal:", end=" ") +b.preorder(root) + +print("\nInorder traversal:", end=" ") +b.inorder(root) + +print("\nPostorder traversal:", end=" ") +b.postorder(root) + +print("\nLevel order traversal:", end=" ") +b.levelorder(root) + +root = b.delete_value(root, 20) +print("\nLevel order traversal after deletion:", end=" ") +b.levelorder(root) + + +''' +OUTPUT: +Preorder traversal: 10 20 40 50 30 60 +Inorder traversal: 40 20 50 10 60 30 +Postorder traversal: 40 50 20 60 30 10 +Level order traversal: 10 20 30 40 50 60 +Level order traversal after deletion: 10 60 30 40 50 +''' +``` From eee9f149f251c50fffcaede4c02a3a02610b60cc Mon Sep 17 00:00:00 2001 From: Ishita-Jena <167574361+Ishita-Jena@users.noreply.github.com> Date: Sun, 9 Jun 2024 12:26:28 +0530 Subject: [PATCH 515/656] Update binary_tree.md --- contrib/ds-algorithms/binary_tree.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/ds-algorithms/binary_tree.md b/contrib/ds-algorithms/binary_tree.md index 4891204c..867bca07 100644 --- a/contrib/ds-algorithms/binary_tree.md +++ b/contrib/ds-algorithms/binary_tree.md @@ -73,7 +73,7 @@ A binary tree is a non-linear data structure in which each node can have atmost - **File Systems:** File systems employ binary trees to organize the folders and files, facilitating efficient search and access of files. - **Decision Trees:** Decision tree, a supervised learning algorithm, utilizes binary trees, with each node representing a decision and its edges showing the possible outcomes. - **Routing Algorithms:** In routing algorithms, binary trees are used to efficiently transfer data packets from the source to destination through a network of nodes. -- **Searching and sorting Algorithms:** Searching algorithms like binary search and sorting algorithms like heapsort rely heavily on binary trees. +- **Searching and sorting Algorithms:** Searching algorithms like binary search and sorting algorithms like heapsort heavily rely on binary trees. ## Implementation of Binary Tree From 23fdbccff1a0ab84f8785391d774a35cc69f668f Mon Sep 17 00:00:00 2001 From: Ojaswi Chopra Date: Sun, 9 Jun 2024 13:12:13 +0530 Subject: [PATCH 516/656] Added Reinforcement Learning --- .../machine-learning/assets/rl-components.png | Bin 0 -> 62544 bytes contrib/machine-learning/index.md | 1 + .../reinforcement-learning.md | 327 ++++++++++++++++++ 3 files changed, 328 insertions(+) create mode 100644 contrib/machine-learning/assets/rl-components.png create mode 100644 contrib/machine-learning/reinforcement-learning.md diff --git a/contrib/machine-learning/assets/rl-components.png b/contrib/machine-learning/assets/rl-components.png new file mode 100644 index 0000000000000000000000000000000000000000..cb359cf455ebd9fdf83221d1b97c6bbb05810543 GIT binary patch literal 62544 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z8cGfp-Y#b$tDC@L)~~Wf!8!wukNGHFW2(QgCy*4(@Y}w|EAFo}l88YWkD^X{rraE3f1hMl>N-^okab;_6882OdyU)3R^7Iuo&;f^^vjWRGWq&{3OAiOJOSHxi|KPI>yhLSIsj6&EE(rmDs7#~S6fk`5@V+;N#lKpp8 zK-e%DySQOgrttr*nvcZa0rdYOp!^?20iYlbVAcEoxB7zQ0EdWfnWD!3k3tPl0=5vB z{+9>_`u}uUAh(PQ$mMg6fn74n4=77S@Coq(tNCve752H1CXd=>`Zo~?AO`%UP-o>3{*U!YNdVTv6n9AZA4T{t z1zUa!^*?5WjztGpI5eMI=6{sjzm)&KO;7hLJb-LhBJzQs0}A*^ewGuh6xQ|oe*kI_ Bzg7SM literal 0 HcmV?d00001 diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 0bf39f98..f6d988f8 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -20,3 +20,4 @@ - [Grid Search](grid-search.md) - [Transformers](transformers.md) - [K-nearest neighbor (KNN)](knn.md) +- [Reinforcement Learning](reinforcement-learning.md) diff --git a/contrib/machine-learning/reinforcement-learning.md b/contrib/machine-learning/reinforcement-learning.md new file mode 100644 index 00000000..b470ed3d --- /dev/null +++ b/contrib/machine-learning/reinforcement-learning.md @@ -0,0 +1,327 @@ +# Reinforcement Learning: A Comprehensive Guide + +Reinforcement Learning (RL) is a field of Machine Learing which focuses on goal-directed learning from interaction with the environment. In RL, an agent learns to make decisions by performing actions in an environment to maximize cumulative numerical reward signal. This README aims to provide a thorough understanding of RL, covering key concepts, algorithms, applications, and resources. + +## Table of Contents + +
    +Click to expand + +1. [What is Reinforcement Learning?](#what-is-reinforcement-learning) +2. [Key Concepts and Terminology](#key-concepts-and-terminology) + - [Agent](#agent) + - [Environment](#environment) + - [State](#state) + - [Action](#action) + - [Reward](#reward) + - [Policy](#policy) + - [Value Function](#value-function) + - [Model](#model) + - [Exploration vs. Exploitation](#exploration-vs-exploitation) +3. [Types of Reinforcement Learning](#types-of-reinforcement-learning) + - [Model-Based vs Model-Free](#model-based-vs-model-free) + - [Value-Based Methods](#value-based-methods) + - [Policy-Based Methods](#policy-based-methods) + - [Actor-Critic Methods](#actor-critic-methods) +4. [Important Algorithms](#important-algorithms) + - [Q-Learning](#q-learning) + - [Deep Q-Networks](#deep-q-networks-dqn) + - [SARSA](#sarsa) + - [REINFORCE Algorithm](#reinforce-algorithm) + - [Proximal Policy Optimization (PPO)](#proximal-policy-optimization-ppo) + - [A2C/A3C](#a2ca3c) +5. [Mathematical Background](#mathematical-background) + - [Markov Decision Processes (MDP)](#markov-decision-processes-mdps) + - [Bellman Equations](#bellman-equations) +6. [Applications of Reinforcement Learning](#applications-of-reinforcement-learning) + - [Gaming](#gaming) + - [Robotics](#robotics) + - [Finance](#finance) + - [Healthcare](#healthcare) +7. [Tools and Libraries](#tools-and-libraries) +8. [Resources for Further Learning](#resources-for-further-learning) + - [Books](#books) + - [Online Courses](#online-courses) + - [Research Papers](#research-papers) + - [Blogs and Websites](#blogs-and-websites) +9. [How to Start with Reinforcement Learning](#how-to-start-with-reinforcement-learning) + - [Prerequisites](#prerequisites) + - [Beginner Project](#beginner-project) +10. [Conclusion](#conclusion) + +
    + +## What is Reinforcement Learning? + +Reinforcement learning involves determining the best actions to take in various situations to maximize a numerical reward signal. Instead of being instructed on which actions to take, the learner must explore and identify the actions that lead to the highest rewards through trial and error. After each action performed in its environment, a trainer may give feedback in the form of rewards or penalties to indicate the desirability of the resulting state. Unlike supervised learning, reinforcement learning does not depend on labeled data but instead learns from the outcomes of its actions. + +## Key Concepts and Terminology + +![image](assets/rl-components.png) + +### Agent +Agent is a system or entity that learns to make decisions by interacting with an environment. The agent improves its performance by trial and error, receiving feedback from the environment in the form of rewards or punishments. + +### Environment +Environment is the setting or world in which the agent operates and interacts with. It provides the agent with states and feedback based on the agent's actions. + +### State +State represents the current situation of the environment, encapsulating all the relevant information needed for decision-making. + +### Action +Action represents a move that can be taken by the agent, which would affect the state of the environment. The set of all possible actions is called the action space. + +### Reward +Reward is the feedback from the environment in response to the agent’s action, thereby defining what are good and bad actions. Agent aims to maximize the total reward over time. + +### Policy +Policy is a strategy used by the agent to determine its actions based on the current state. In some cases the policy may be a simple function or lookup table, whereas in others it may involve extensive computation such as a search process. + +### Value Function +The value function of a state is the expected total amount of reward an agent can expect to accumulate over the future, starting from that state. There are two main types of value functions: + - **State Value Function (V)**: The expected reward starting from a state and following a certain policy thereafter. + - **Action Value Function (Q)**: The expected reward starting from a state, taking a specific action, and following a certain policy thereafter. + +### Model +Model mimics the behavior of the environment, or more generally, that allows inferences to be made about how the environment will behave. + +### Exploration vs. Exploitation +To accumulate substantial rewards, a reinforcement learning agent needs to favor actions that have previously yielded high rewards. However, to identify these effective actions, the agent must also attempt actions it hasn't tried before. This means the agent must *exploit* its past experiences to gain rewards, while also *exploring* new actions to improve its future decision-making. + +## Types of Reinforcement Learning + +### Model-Based vs Model-Free + +**Model-Based Reinforcement Learning:** Model-based methods involve creating a model of the environment to predict future states and rewards, allowing the agent to plan its actions by simulating various scenarios. These methods often involve two main components: + +**Model-Free Reinforcement Learning:** Model-free methods do not explicitly learn a model of the environment. Instead, they learn a policy or value function directly from the interactions with the environment. These methods can be further divided into two categories: value-based and policy-based methods. + +### Value-Based Methods: +Value-based methods focus on estimating the value function, and the policy is indirectly derived from the value function. + +### Policy-Based Methods: +Policy-based methods directly optimize the policy by maximizing the expected cumulative rewardto find the optimal parameters. + +### Actor-Critic Methods: +Actor-Critic methods combine the strengths of both value-based and policy-based methods. Actor learns the policy that maps states to actions and Critic learns the value function that evaluates the action chosen by the actor. + +## Important Algorithms + +### Q-Learning +Q-Learning is a model-free algorithm used in reinforcement learning to learn the value of an action in a particular state. It aims to find the optimal policy by iteratively updating the Q-values, which represent the expected cumulative reward of taking a particular action in a given state and following the optimal policy thereafter. + +#### Algorithm: +1. Initialize Q-values arbitrarily for all state-action pairs. +2. Repeat for each episode: + - Choose an action using an exploration strategy (e.g., epsilon-greedy). + - Take the action, observe the reward and the next state. + - Update the Q-value of the current state-action pair using the Bellman equation: + \[ Q(s, a) \leftarrow Q(s, a) + \alpha \left( r + \gamma \max_{a'} Q(s', a') - Q(s, a) \right) \] + where: + - \( Q(s, a) \) is the Q-value of state \( s \) and action \( a \). + - \( r \) is the observed reward. + - \( s' \) is the next state. + - \( \alpha \) is the learning rate. + - \( \gamma \) is the discount factor. +3. Until convergence or a maximum number of episodes. + +### Deep Q-Networks (DQN) +Deep Q-Networks (DQN) extend Q-learning to high-dimensional state spaces using deep neural networks to approximate the Q-function. It uses experience replay and target networks to improve stability and convergence. + +#### Algorithm: +1. Initialize the Q-network with random weights. +2. Initialize a target network with the same weights as the Q-network. +3. Repeat for each episode: + - Initialize the environment state \( s \). + - Repeat for each timestep: + - With probability \( \epsilon \), choose a random action. Otherwise, select the action with the highest Q-value according to the Q-network. + - Take the chosen action, observe the reward \( r \) and the next state \( s' \). + - Store the transition \( (s, a, r, s') \) in the replay memory. + - Sample a minibatch of transitions from the replay memory. + - Compute the target Q-value for each transition: + \[ y_j = \begin{cases} r_j & \text{if episode terminates at step } j+1 \\ + r_j + \gamma \max_{a'} Q(s', a'; \theta^-) & \text{otherwise} \end{cases} \] + where: + - \( \theta^- \) represents the parameters of the target network. + - \( y_j \) is the target Q-value for the \( j \)th transition. + - Update the Q-network parameters by minimizing the temporal difference loss: + \[ \mathcal{L}(\theta) = \frac{1}{N} \sum_{j} (y_j - Q(s_j, a_j; \theta))^2 \] +4. Until convergence or a maximum number of episodes. + +### SARSA +SARSA (State-Action-Reward-State-Action) is an on-policy temporal difference algorithm used for learning the Q-function. Unlike Q-learning, SARSA directly updates the Q-values based on the current policy. + +#### Algorithm: +1. Initialize Q-values arbitrarily for all state-action pairs. +2. Repeat for each episode: + - Initialize the environment state \( s \). + - Choose an action \( a \) using the current policy (e.g., epsilon-greedy). + - Repeat for each timestep: + - Take action \( a \), observe the reward \( r \) and the next state \( s' \). + - Choose the next action \( a' \) using the current policy. + - Update the Q-value of the current state-action pair using the SARSA update rule: + \[ Q(s, a) \leftarrow Q(s, a) + \alpha \left( r + \gamma Q(s', a') - Q(s, a) \right) \] +3. Until convergence or a maximum number of episodes. + +### REINFORCE Algorithm: +REINFORCE (Monte Carlo policy gradient) is a simple policy gradient method that updates the policy parameters in the direction of the gradient of expected rewards. + +### Proximal Policy Optimization (PPO): +PPO is an advanced policy gradient method that improves stability by limiting the policy updates within a certain trust region. + +### A2C/A3C: +Advantage Actor-Critic (A2C) and Asynchronous Advantage Actor-Critic (A3C) are variants of actor-critic methods that utilize multiple parallel agents to improve sample efficiency. + +## Mathematical Background + +### Markov Decision Processes (MDPs) +A Markov Decision Process (MDP) is a mathematical framework used to model decision-making problems. It consists of states, actions, rewards and transition probabilities. + +### Bellman Equations +Bellman equations are fundamental recursive equations in dynamic programming and reinforcement learning. They express the value of a decision at one point in time in terms of the expected value of the subsequent decisions. + +## Applications of Reinforcement Learning + +### Gaming +Reinforcement learning is extensively used in gaming for developing AI agents capable of playing complex games like AlphaGo, Chess, and video games. RL algorithms enable these agents to learn optimal strategies by interacting with the game environment and receiving feedback in the form of rewards. + +### Robotics +In robotics, reinforcement learning is employed to teach robots various tasks such as navigation, manipulation, and control. RL algorithms allow robots to learn from their interactions with the environment, enabling them to adapt and improve their behavior over time without explicit programming. + +### Finance +Reinforcement learning plays a crucial role in finance, particularly in algorithmic trading and portfolio management. RL algorithms are utilized to optimize trading strategies, automate decision-making processes, and manage investment portfolios dynamically based on changing market conditions and objectives. + +### Healthcare +In healthcare, reinforcement learning is utilized for various applications such as personalized treatment, drug discovery, and optimizing healthcare operations. RL algorithms can assist in developing personalized treatment plans for patients, identifying effective drug candidates, and optimizing resource allocation in hospitals to improve patient care and outcomes. + +## Tools and Libraries +- **OpenAI Gym:** A toolkit for developing and comparing RL algorithms. +- **TensorFlow/TF-Agents:** A library for RL in TensorFlow. +- **PyTorch:** Popular machine learning library with RL capabilities. +- **Stable Baselines3:** A set of reliable implementations of RL algorithms in PyTorch. + +## Resources for Further Learning +### Books +- "Reinforcement Learning: An Introduction" by Sutton and Barto +- "Deep Reinforcement Learning Hands-On" by Maxim Lapan + +### Online Courses +- "Reinforcement Learning" by Udacity +- "Fundamentals of Reinforcement Learning" by Coursera +- "Master Reinforcement Learning and Deep RL with Python" by Udemy + +### Research Papers +- [Reinforcement learning model, algorithms and its application](https://ieeexplore.ieee.org/abstract/document/6025669) +- [A Concise Introduction to Reinforcement Learning](https://www.researchgate.net/publication/323178749_A_Concise_Introduction_to_Reinforcement_Learning) + +### Blogs and Websites +- [OpenAI Blog](https://spinningup.openai.com/en/latest/) +- [Geeks for Geeks](https://www.geeksforgeeks.org/what-is-reinforcement-learning/) +- [Medium Blog](https://gordicaleksa.medium.com/how-to-get-started-with-reinforcement-learning-rl-4922fafeaf8c) + +## How to Start with Reinforcement Learning + +### Prerequisites +- Basic knowledge of machine learning and neural networks. +- Proficiency in Python. + +### Beginner Project +The provided Python code implements the Q-learning algorithm for a basic grid world environment. It defines the grid world, actions, and parameters such as discount factor and learning rate. The algorithm iteratively learns the optimal action-value function (Q-values) by updating them based on rewards obtained from actions taken in each state. Finally, the learned Q-values are printed for each state-action pair. + +```python +import numpy as np + +# Define the grid world environment +# 'S' represents the start state +# 'G' represents the goal state +# 'H' represents the hole (negative reward) +# '.' represents empty cells (neutral reward) +# 'W' represents walls (impassable) +grid_world = np.array([ + ['S', '.', '.', '.', '.'], + ['.', 'W', '.', 'H', '.'], + ['.', '.', '.', 'W', '.'], + ['.', 'W', '.', '.', 'G'] +]) + +# Define the actions (up, down, left, right) +actions = ['UP', 'DOWN', 'LEFT', 'RIGHT'] + +# Define parameters +gamma = 0.9 # discount factor +alpha = 0.1 # learning rate +epsilon = 0.1 # exploration rate + +# Initialize Q-values +num_rows, num_cols = grid_world.shape +num_actions = len(actions) +Q = np.zeros((num_rows, num_cols, num_actions)) + +# Define helper function to get possible actions in a state +def possible_actions(state): + row, col = state + possible_actions = [] + for i, action in enumerate(actions): + if action == 'UP' and row > 0 and grid_world[row - 1, col] != 'W': + possible_actions.append(i) + elif action == 'DOWN' and row < num_rows - 1 and grid_world[row + 1, col] != 'W': + possible_actions.append(i) + elif action == 'LEFT' and col > 0 and grid_world[row, col - 1] != 'W': + possible_actions.append(i) + elif action == 'RIGHT' and col < num_cols - 1 and grid_world[row, col + 1] != 'W': + possible_actions.append(i) + return possible_actions + +# Q-learning algorithm +num_episodes = 1000 +for episode in range(num_episodes): + # Initialize the starting state + state = (0, 0) # start state + while True: + # Choose an action using epsilon-greedy policy + if np.random.uniform(0, 1) < epsilon: + action = np.random.choice(possible_actions(state)) + else: + action = np.argmax(Q[state[0], state[1]]) + + # Perform the action and observe the next state and reward + if actions[action] == 'UP': + next_state = (state[0] - 1, state[1]) + elif actions[action] == 'DOWN': + next_state = (state[0] + 1, state[1]) + elif actions[action] == 'LEFT': + next_state = (state[0], state[1] - 1) + elif actions[action] == 'RIGHT': + next_state = (state[0], state[1] + 1) + + # Get the reward + if grid_world[next_state] == 'G': + reward = 1 # goal state + elif grid_world[next_state] == 'H': + reward = -1 # hole + else: + reward = 0 + + # Update Q-value using the Bellman equation + best_next_action = np.argmax(Q[next_state[0], next_state[1]]) + Q[state[0], state[1], action] += alpha * ( + reward + gamma * Q[next_state[0], next_state[1], best_next_action] - Q[state[0], state[1], action]) + + # Move to the next state + state = next_state + + # Check if the episode is terminated + if grid_world[state] in ['G', 'H']: + break + +# Print the learned Q-values +print("Learned Q-values:") +for i in range(num_rows): + for j in range(num_cols): + print(f"State ({i}, {j}):", Q[i, j]) +``` + +### Conclusion +Congratulations on completing your journey through this comprehensive guide to reinforcement learning! Armed with this knowledge, you are well-equipped to dive deeper into the exciting world of RL, whether it's for gaming, robotics, finance, healthcare, or any other domain. Keep exploring, experimenting, and learning, and remember, the only limit to what you can achieve with reinforcement learning is your imagination. + +*Happy coding, and may your RL adventures be rewarding!* \ No newline at end of file From da09e2332256a373a44ce980a55d7be171a96811 Mon Sep 17 00:00:00 2001 From: Ojaswi Chopra Date: Sun, 9 Jun 2024 13:17:23 +0530 Subject: [PATCH 517/656] Minor Changes --- .../reinforcement-learning.md | 20 +++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/contrib/machine-learning/reinforcement-learning.md b/contrib/machine-learning/reinforcement-learning.md index b470ed3d..25dc4420 100644 --- a/contrib/machine-learning/reinforcement-learning.md +++ b/contrib/machine-learning/reinforcement-learning.md @@ -116,7 +116,9 @@ Q-Learning is a model-free algorithm used in reinforcement learning to learn the - Choose an action using an exploration strategy (e.g., epsilon-greedy). - Take the action, observe the reward and the next state. - Update the Q-value of the current state-action pair using the Bellman equation: - \[ Q(s, a) \leftarrow Q(s, a) + \alpha \left( r + \gamma \max_{a'} Q(s', a') - Q(s, a) \right) \] + ```latex + Q(s, a) \leftarrow Q(s, a) + \alpha \left( r + \gamma \max_{a'} Q(s', a') - Q(s, a) \right) + ``` where: - \( Q(s, a) \) is the Q-value of state \( s \) and action \( a \). - \( r \) is the observed reward. @@ -139,13 +141,17 @@ Deep Q-Networks (DQN) extend Q-learning to high-dimensional state spaces using d - Store the transition \( (s, a, r, s') \) in the replay memory. - Sample a minibatch of transitions from the replay memory. - Compute the target Q-value for each transition: - \[ y_j = \begin{cases} r_j & \text{if episode terminates at step } j+1 \\ - r_j + \gamma \max_{a'} Q(s', a'; \theta^-) & \text{otherwise} \end{cases} \] + ```latex + y_j = \begin{cases} r_j & \text{if episode terminates at step } j+1 \\ + r_j + \gamma \max_{a'} Q(s', a'; \theta^-) & \text{otherwise} \end{cases} + ``` where: - \( \theta^- \) represents the parameters of the target network. - \( y_j \) is the target Q-value for the \( j \)th transition. - Update the Q-network parameters by minimizing the temporal difference loss: - \[ \mathcal{L}(\theta) = \frac{1}{N} \sum_{j} (y_j - Q(s_j, a_j; \theta))^2 \] + ```latex + \mathcal{L}(\theta) = \frac{1}{N} \sum_{j} (y_j - Q(s_j, a_j; \theta))^2 + ``` 4. Until convergence or a maximum number of episodes. ### SARSA @@ -160,7 +166,9 @@ SARSA (State-Action-Reward-State-Action) is an on-policy temporal difference alg - Take action \( a \), observe the reward \( r \) and the next state \( s' \). - Choose the next action \( a' \) using the current policy. - Update the Q-value of the current state-action pair using the SARSA update rule: - \[ Q(s, a) \leftarrow Q(s, a) + \alpha \left( r + \gamma Q(s', a') - Q(s, a) \right) \] + ```latex + Q(s, a) \leftarrow Q(s, a) + \alpha \left( r + \gamma Q(s', a') - Q(s, a) \right) + ``` 3. Until convergence or a maximum number of episodes. ### REINFORCE Algorithm: @@ -321,7 +329,7 @@ for i in range(num_rows): print(f"State ({i}, {j}):", Q[i, j]) ``` -### Conclusion +## Conclusion Congratulations on completing your journey through this comprehensive guide to reinforcement learning! Armed with this knowledge, you are well-equipped to dive deeper into the exciting world of RL, whether it's for gaming, robotics, finance, healthcare, or any other domain. Keep exploring, experimenting, and learning, and remember, the only limit to what you can achieve with reinforcement learning is your imagination. *Happy coding, and may your RL adventures be rewarding!* \ No newline at end of file From 69257a59103c8627eb1011653b7acdb1566d729c Mon Sep 17 00:00:00 2001 From: Ojaswi Chopra Date: Sun, 9 Jun 2024 13:19:31 +0530 Subject: [PATCH 518/656] Minor Changes Again --- .../machine-learning/reinforcement-learning.md | 17 ++++------------- 1 file changed, 4 insertions(+), 13 deletions(-) diff --git a/contrib/machine-learning/reinforcement-learning.md b/contrib/machine-learning/reinforcement-learning.md index 25dc4420..e55881a6 100644 --- a/contrib/machine-learning/reinforcement-learning.md +++ b/contrib/machine-learning/reinforcement-learning.md @@ -116,9 +116,7 @@ Q-Learning is a model-free algorithm used in reinforcement learning to learn the - Choose an action using an exploration strategy (e.g., epsilon-greedy). - Take the action, observe the reward and the next state. - Update the Q-value of the current state-action pair using the Bellman equation: - ```latex - Q(s, a) \leftarrow Q(s, a) + \alpha \left( r + \gamma \max_{a'} Q(s', a') - Q(s, a) \right) - ``` + where: - \( Q(s, a) \) is the Q-value of state \( s \) and action \( a \). - \( r \) is the observed reward. @@ -141,17 +139,12 @@ Deep Q-Networks (DQN) extend Q-learning to high-dimensional state spaces using d - Store the transition \( (s, a, r, s') \) in the replay memory. - Sample a minibatch of transitions from the replay memory. - Compute the target Q-value for each transition: - ```latex - y_j = \begin{cases} r_j & \text{if episode terminates at step } j+1 \\ - r_j + \gamma \max_{a'} Q(s', a'; \theta^-) & \text{otherwise} \end{cases} - ``` + where: - \( \theta^- \) represents the parameters of the target network. - \( y_j \) is the target Q-value for the \( j \)th transition. - Update the Q-network parameters by minimizing the temporal difference loss: - ```latex - \mathcal{L}(\theta) = \frac{1}{N} \sum_{j} (y_j - Q(s_j, a_j; \theta))^2 - ``` + 4. Until convergence or a maximum number of episodes. ### SARSA @@ -166,9 +159,7 @@ SARSA (State-Action-Reward-State-Action) is an on-policy temporal difference alg - Take action \( a \), observe the reward \( r \) and the next state \( s' \). - Choose the next action \( a' \) using the current policy. - Update the Q-value of the current state-action pair using the SARSA update rule: - ```latex - Q(s, a) \leftarrow Q(s, a) + \alpha \left( r + \gamma Q(s', a') - Q(s, a) \right) - ``` + 3. Until convergence or a maximum number of episodes. ### REINFORCE Algorithm: From ff4f5f9c4ccd2a1cc8db5edd848c25a2b915feee Mon Sep 17 00:00:00 2001 From: Ojaswi Chopra Date: Sun, 9 Jun 2024 13:29:24 +0530 Subject: [PATCH 519/656] Minor Changes in mathematical equation --- .../machine-learning/reinforcement-learning.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/contrib/machine-learning/reinforcement-learning.md b/contrib/machine-learning/reinforcement-learning.md index e55881a6..bab38a09 100644 --- a/contrib/machine-learning/reinforcement-learning.md +++ b/contrib/machine-learning/reinforcement-learning.md @@ -116,13 +116,13 @@ Q-Learning is a model-free algorithm used in reinforcement learning to learn the - Choose an action using an exploration strategy (e.g., epsilon-greedy). - Take the action, observe the reward and the next state. - Update the Q-value of the current state-action pair using the Bellman equation: - + where: - - \( Q(s, a) \) is the Q-value of state \( s \) and action \( a \). - - \( r \) is the observed reward. - - \( s' \) is the next state. - - \( \alpha \) is the learning rate. - - \( \gamma \) is the discount factor. + - is the Q-value of state and action. + - is the observed reward. + - is the next state. + - is the learning rate. + - is the discount factor. 3. Until convergence or a maximum number of episodes. ### Deep Q-Networks (DQN) @@ -132,9 +132,9 @@ Deep Q-Networks (DQN) extend Q-learning to high-dimensional state spaces using d 1. Initialize the Q-network with random weights. 2. Initialize a target network with the same weights as the Q-network. 3. Repeat for each episode: - - Initialize the environment state \( s \). + - Initialize the environment state . - Repeat for each timestep: - - With probability \( \epsilon \), choose a random action. Otherwise, select the action with the highest Q-value according to the Q-network. + - With probability , choose a random action. Otherwise, select the action with the highest Q-value according to the Q-network. - Take the chosen action, observe the reward \( r \) and the next state \( s' \). - Store the transition \( (s, a, r, s') \) in the replay memory. - Sample a minibatch of transitions from the replay memory. 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z8cGfp-Y#b$tDC@L)~~Wf!8!wukNGHFW2(QgCy*4(@Y}w|EAFo}l88YWkD^X{rraE3f1hMl>N-^okab;_6882OdyU)3R^7Iuo&;f^^vjWRGWq&{3OAiOJOSHxi|KPI>yhLSIsj6&EE(rmDs7#~S6fk`5@V+;N#lKpp8 zK-e%DySQOgrttr*nvcZa0rdYOp!^?20iYlbVAcEoxB7zQ0EdWfnWD!3k3tPl0=5vB z{+9>_`u}uUAh(PQ$mMg6fn74n4=77S@Coq(tNCve752H1CXd=>`Zo~?AO`%UP-o>3{*U!YNdVTv6n9AZA4T{t z1zUa!^*?5WjztGpI5eMI=6{sjzm)&KO;7hLJb-LhBJzQs0}A*^ewGuh6xQ|oe*kI_ Bzg7SM diff --git a/contrib/machine-learning/reinforcement-learning.md b/contrib/machine-learning/reinforcement-learning.md index bab38a09..0046ae42 100644 --- a/contrib/machine-learning/reinforcement-learning.md +++ b/contrib/machine-learning/reinforcement-learning.md @@ -57,8 +57,6 @@ Reinforcement learning involves determining the best actions to take in various ## Key Concepts and Terminology -![image](assets/rl-components.png) - ### Agent Agent is a system or entity that learns to make decisions by interacting with an environment. The agent improves its performance by trial and error, receiving feedback from the environment in the form of rewards or punishments. @@ -118,46 +116,24 @@ Q-Learning is a model-free algorithm used in reinforcement learning to learn the - Update the Q-value of the current state-action pair using the Bellman equation: where: - - is the Q-value of state and action. + - is the Q-value of state and action . - is the observed reward. - is the next state. - is the learning rate. - is the discount factor. 3. Until convergence or a maximum number of episodes. -### Deep Q-Networks (DQN) -Deep Q-Networks (DQN) extend Q-learning to high-dimensional state spaces using deep neural networks to approximate the Q-function. It uses experience replay and target networks to improve stability and convergence. - -#### Algorithm: -1. Initialize the Q-network with random weights. -2. Initialize a target network with the same weights as the Q-network. -3. Repeat for each episode: - - Initialize the environment state . - - Repeat for each timestep: - - With probability , choose a random action. Otherwise, select the action with the highest Q-value according to the Q-network. - - Take the chosen action, observe the reward \( r \) and the next state \( s' \). - - Store the transition \( (s, a, r, s') \) in the replay memory. - - Sample a minibatch of transitions from the replay memory. - - Compute the target Q-value for each transition: - - where: - - \( \theta^- \) represents the parameters of the target network. - - \( y_j \) is the target Q-value for the \( j \)th transition. - - Update the Q-network parameters by minimizing the temporal difference loss: - -4. Until convergence or a maximum number of episodes. - ### SARSA SARSA (State-Action-Reward-State-Action) is an on-policy temporal difference algorithm used for learning the Q-function. Unlike Q-learning, SARSA directly updates the Q-values based on the current policy. #### Algorithm: 1. Initialize Q-values arbitrarily for all state-action pairs. 2. Repeat for each episode: - - Initialize the environment state \( s \). - - Choose an action \( a \) using the current policy (e.g., epsilon-greedy). + - Initialize the environment state . + - Choose an action using the current policy (e.g., epsilon-greedy). - Repeat for each timestep: - - Take action \( a \), observe the reward \( r \) and the next state \( s' \). - - Choose the next action \( a' \) using the current policy. + - Take action , observe the reward and the next state . + - Choose the next action using the current policy. - Update the Q-value of the current state-action pair using the SARSA update rule: 3. Until convergence or a maximum number of episodes. From 709011baa2db795b45efda459dd954aefb609bb1 Mon Sep 17 00:00:00 2001 From: Ojaswi Chopra Date: Sun, 9 Jun 2024 13:36:20 +0530 Subject: [PATCH 521/656] Final Changes-1 --- contrib/machine-learning/reinforcement-learning.md | 1 - 1 file changed, 1 deletion(-) diff --git a/contrib/machine-learning/reinforcement-learning.md b/contrib/machine-learning/reinforcement-learning.md index 0046ae42..2c227429 100644 --- a/contrib/machine-learning/reinforcement-learning.md +++ b/contrib/machine-learning/reinforcement-learning.md @@ -25,7 +25,6 @@ Reinforcement Learning (RL) is a field of Machine Learing which focuses on goal- - [Actor-Critic Methods](#actor-critic-methods) 4. [Important Algorithms](#important-algorithms) - [Q-Learning](#q-learning) - - [Deep Q-Networks](#deep-q-networks-dqn) - [SARSA](#sarsa) - [REINFORCE Algorithm](#reinforce-algorithm) - [Proximal Policy Optimization (PPO)](#proximal-policy-optimization-ppo) From 4afd4a98f263a411c0eb80c223e1a72886f34cff Mon Sep 17 00:00:00 2001 From: anamika123 Date: Sun, 9 Jun 2024 16:12:58 +0530 Subject: [PATCH 522/656] Pandas Series' --- contrib/pandas/index.md | 1 + contrib/pandas/pandas_series.md | 283 ++++++++++++++++++++++++++++++++ 2 files changed, 284 insertions(+) create mode 100644 contrib/pandas/pandas_series.md diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index e5a83533..2a159291 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -9,3 +9,4 @@ - [Working with Date & Time in Pandas](datetime.md) - [Importing and Exporting Data in Pandas](import-export.md) - [Handling Missing Values in Pandas](handling-missing-values.md) +- [Pandas Series](pandas_series.md) \ No newline at end of file diff --git a/contrib/pandas/pandas_series.md b/contrib/pandas/pandas_series.md new file mode 100644 index 00000000..8cc12c89 --- /dev/null +++ b/contrib/pandas/pandas_series.md @@ -0,0 +1,283 @@ +# Pandas Series + + + A series is a Panda data structures that represents a one dimensional array-like object containing an array of data and an associated array of data type labels, called index. + +## Creating a Series object: + +### Basic Series +To create a basic Series, you can pass a list or array of data to the `pd.Series()` function. + +```python +import pandas as pd + +s1 = pd.Series([4, 5, 2, 3]) +print(s1) + +#Output: +#0 4 +#1 5 +#2 2 +#3 3 +#dtype: int64 + +``` + +### Series from a Dictionary + +If you pass a dictionary to `pd.Series()`, the keys become the index and the values become the data of the Series. +```python +import pandas as pd + +s2 = pd.Series({'A': 1, 'B': 2, 'C': 3}) +print(s2) + +#Output: +#A 1 +#B 2 +#C 3 +#dtype: int64 +``` + + +## Additional Functionality + + +### Specifying Data Type and Index +You can specify the data type and index while creating a Series. +```python +import pandas as pd + +s4 = pd.Series([1, 2, 3], index=['a', 'b', 'c'], dtype='float64') +print(s4) + +#output +#a 1.0 +#b 2.0 +#c 3.0 +#dtype: float64 +``` + +### Specifying NaN Values: +* Sometimes you need to create a series object of a certain size but you do not have complete data available so in such cases you can fill missing data with a NaN(Not a Number) value. +* When you store NaN value in series object, the data type must be floating pont type. Even if you specify an integer type , pandas will promote it to floating point type automatically because NaN is not supported by integer type. + +```python +import pandas as pd +s3=pd.Series([1,np.Nan,2]) +print(s3) + +#output: + +#0 1.0 +#1 NaN +#2 2.0 +#dtype: float64 +``` + + +### Creating Data from Expressions +You can create a Series using an expression or function. + +``=np.Series(data=,index=None) + +```python +import pandas as pd +a=np.arange(1,5) # [1,2,3,4] +s5=pd.Series(data=a**2,index=a) +print(s5) + +#output: +#1 1 +#2 4 +#3 9 +#4 16 +#dtype: int64 +``` + + +## Series Object Attributes + +| **Attribute** | **Description** | +|--------------------------|---------------------------------------------------| +| `.index` | Array of index of the Series | +| `.values` | Array of values of the Series | +| `.dtype` | Return the dtype of the data | +| `.shape` | Return a tuple representing the shape of the data | +| `.ndim` | Return the number of dimensions of the data | +| `.size` | Return the number of elements in the data | +| `.hasnans` | Return True if there is any NaN in the data | +| `.empty` | Return True if the Series object is empty | + + + +- If you use len() on a series object then it return total number of elements in the series object whereas .count() return only the number of non NaN elements. + +## Accessing a Series object and its elements + +### Accessing Individual Elements +You can access individual elements using their index. +'legal' indexes arte used to access individual element. +```python +import pandas as pd + +s7 = pd.Series(data=[13, 45, 67, 89], index=['A', 'B', 'C', 'D']) +print(s7['A']) + +#output +#13 +``` + + +### Slicing a Series + +- Slices are extracted based on their positional index, regardless of the custom index labels. +- Each element in the Series has a positional index starting from 0 (i.e., 0 for the first element, 1 for the second element, and so on). +- `[:]` will return the values of the elements between the start and end positions (excluding the end position). + +#### Example + +```python +import pandas as pd + +s = pd.Series(data=[13, 45, 67, 89], index=['A', 'B', 'C', 'D']) +print(s[:2]) + +#Output +#A 13 +#B 45 +#dtype: int64 +#This example demonstrates that the first two elements (positions 0 and 1) are returned, regardless of their custom index labels. +``` + + +## Operation on series object + +### Modifying elements and indexes +* [indexes]=< new data value > +* [start : end]=< new data value > +* .index=[new indexes] + +```python +import pandas as pd + +s8 = pd.Series([10, 20, 30], index=['a', 'b', 'c']) +s8['a'] = 100 +s8.index = ['x', 'y', 'z'] +print(s8) + +#output +#x 100 +#y 20 +#z 30 +#dtype: int64 +``` + +**Note: Series object are value-mutable but size immutable objects.** + +### vector operations +We can perform vector operations such as `+`,`-`,`/`,`%` etc. +```python +import pandas as pd + +s9 = pd.Series([1, 2, 3]) +print("addition:", s9 + 5) +print("subtraction:", s9 - 2) + +#output: +#addition: +#0 6 +#1 7 +#2 8 +#dtype: int64 +#subtraction: +#0 -1 +#1 0 +#2 1 +#dtype: int64 +``` + +### Arthmetic on series object +```python +import pandas as pd + +s10 = pd.Series([1, 2, 3]) +s11 = pd.Series([4, 5, 6]) +print("addition:", s10 + s11) + +print("multiplication:", s10 * s11) + +#output: +#addition: +#0 5 +#1 7 +#2 9 +#dtype: int64 +#multiplication: +#0 4 +#1 10 +#2 18 +#dtype: int64 +``` + +Here one thing we should keep in mind that both the series object should have same indexes otherwise it will return NaN value to all the indexes of two series object . + + +### Head and Tail Functions + +| **Functions** | **Description** | +|--------------------------|---------------------------------------------------| +| `.head(n)` | return the first n elements of the series | +| `.tail(n)` | return the last n elements of the series | + +```python +import pandas as pd + +s12 = pd.Series([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]) +print(s12.head(3)) +print(s12.tail(3)) + +#output +#0 10 +#1 20 +#2 30 +#dtype: int64 + +#7 80 +#8 90 +#9 100 +#dtype: int64 +``` +If you dont provide any value to n the by default it give results for `n=5`. + +### Few extra functions: +| **Function** | **Description** | +|----------------------------------------|------------------------------------------------------------------------| +| `.sort_values()` | Return the Series object in ascending order based on its values. | +| `.sort_index()` | Return the Series object in ascending order based on its index. | +| `.sort_drop()` | Return the Series with the deleted index and its corresponding value. | +```python +import pandas as pd + +s13 = pd.Series([3, 1, 2], index=['c', 'a', 'b']) +print(s13.sort_values()) +print(s13.sort_index()) +print(s13.drop('a')) +#Output +#a 1 +#b 2 +#c 3 +#dtype: int64 + +#a 1 +#b 2 +#c 3 +#dtype: int64 + +#c 3 +#b 2 +#dtype: int64 +``` + +## Conclusion +In short, Pandas Series is a fundamental data structure in Python for handling one-dimensional data. It combines an array of values with an index, offering efficient methods for data manipulation and analysis. With its ease of use and powerful functionality, Pandas Series is widely used in data science and analytics for tasks such as data cleaning, exploration, and visualization. \ No newline at end of file From b2db560bad79ac5b389cd97e25de22ee26dfaa7b Mon Sep 17 00:00:00 2001 From: rohit Date: Mon, 10 Jun 2024 01:17:11 +0530 Subject: [PATCH 523/656] Added Neural network regression --- .../Neural network regression.md | 64 ++++++++----------- 1 file changed, 25 insertions(+), 39 deletions(-) diff --git a/contrib/machine-learning/Neural network regression.md b/contrib/machine-learning/Neural network regression.md index 36c3b9a9..43301209 100644 --- a/contrib/machine-learning/Neural network regression.md +++ b/contrib/machine-learning/Neural network regression.md @@ -1,26 +1,24 @@ -# Neural Network Regression in Python +# Neural Network Regression in Python using Scikit-learn ## Overview -Neural Network Regression is a type of machine learning algorithm used to predict continuous values. Unlike classification, where the goal is to predict a category or class, regression aims to predict a specific numerical value. Neural networks are particularly powerful for regression tasks when dealing with complex, non-linear relationships in data. +Neural Network Regression is used to predict continuous values based on input features. Scikit-learn provides an easy-to-use interface for implementing neural network models, specifically through the `MLPRegressor` class, which stands for Multi-Layer Perceptron Regressor. ## When to Use Neural Network Regression ### Suitable Scenarios -1. **Complex Relationships**: When the relationship between input features and the target variable is non-linear and complex. -2. **Large Datasets**: When you have a large dataset that can support the training of a neural network. -3. **Feature Engineering**: When you can leverage the feature extraction capabilities of neural networks, especially in domains like image or text data. +1. **Complex Relationships**: Ideal when the relationship between features and the target variable is complex and non-linear. +2. **Sufficient Data**: Works well with large datasets that can support training deep learning models. +3. **Feature Extraction**: Useful in cases where the neural network's feature extraction capabilities can be leveraged, such as with image or text data. ### Unsuitable Scenarios -1. **Small Datasets**: Neural networks require substantial amounts of data to train effectively. For small datasets, simpler models like linear regression or decision trees might perform better. -2. **Real-time Predictions**: If low-latency predictions are required and computational resources are limited, simpler models might be more efficient. -3. **Interpretability**: If model interpretability is crucial, neural networks might not be the best choice due to their "black-box" nature. +1. **Small Datasets**: Less effective with small datasets due to overfitting and inability to learn complex patterns. +2. **Low-latency Predictions**: Might not be suitable for real-time applications with strict latency requirements. +3. **Interpretability**: Not ideal when model interpretability is crucial, as neural networks are often seen as "black-box" models. -## Implementing Neural Network Regression in Python - -We'll use TensorFlow and Keras, popular libraries for building and training neural networks in Python. +## Implementing Neural Network Regression in Python with Scikit-learn ### Step-by-Step Implementation @@ -31,9 +29,8 @@ import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler -from tensorflow.keras.models import Sequential -from tensorflow.keras.layers import Dense -from tensorflow.keras.optimizers import Adam +from sklearn.neural_network import MLPRegressor +from sklearn.metrics import mean_absolute_error ``` 2. **Load and Prepare Data** @@ -55,44 +52,33 @@ X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) ``` -3. **Build the Neural Network Model** +3. **Build and Train the Neural Network Model** ```python -model = Sequential() -model.add(Dense(64, input_dim=X_train.shape[1], activation='relu')) -model.add(Dense(64, activation='relu')) -model.add(Dense(1)) # Output layer for regression +# Create the MLPRegressor model +mlp = MLPRegressor(hidden_layer_sizes=(64, 64), activation='relu', solver='adam', max_iter=500, random_state=42) -model.compile(optimizer=Adam(learning_rate=0.001), loss='mse', metrics=['mae']) +# Train the model +mlp.fit(X_train, y_train) ``` -4. **Train the Model** +4. **Evaluate the Model** ```python -history = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.2) -``` +# Make predictions +y_pred = mlp.predict(X_test) -5. **Evaluate the Model** - -```python -loss, mae = model.evaluate(X_test, y_test) +# Calculate the Mean Absolute Error +mae = mean_absolute_error(y_test, y_pred) print(f"Test Mean Absolute Error: {mae}") ``` -6. **Make Predictions** - -```python -predictions = model.predict(X_test) -``` - ### Explanation -- **Data Generation and Preparation**: Synthetic data is created and split into training and test sets. The data is standardized to improve the neural network's training efficiency. -- **Model Construction**: A simple feedforward neural network is built using Keras. It consists of two hidden layers with 64 neurons each and ReLU activation functions. The output layer has a single neuron for regression. -- **Training**: The model is trained for 100 epochs with a batch size of 32. The Adam optimizer is used to adjust the weights. -- **Evaluation**: The model's performance is evaluated on the test set, using Mean Absolute Error (MAE) as a metric. -- **Prediction**: Predictions are made on the test data. +- **Data Generation and Preparation**: Synthetic data is created, split into training and test sets, and standardized to improve the efficiency of the neural network training process. +- **Model Construction and Training**: An `MLPRegressor` is created with two hidden layers, each containing 64 neurons and ReLU activation functions. The model is trained using the Adam optimizer for a maximum of 500 iterations. +- **Evaluation**: The model's performance is evaluated on the test set using Mean Absolute Error (MAE) as the performance metric. ## Conclusion -Neural Network Regression is a powerful tool for predicting continuous values, particularly in cases involving complex, non-linear relationships. However, they require large datasets and significant computational resources. For smaller datasets or scenarios requiring model interpretability, simpler models might be preferable. By following the steps outlined, you can build, train, and evaluate a neural network for regression tasks in Python using TensorFlow and Keras. \ No newline at end of file +Neural Network Regression with Scikit-learn's `MLPRegressor` is a powerful method for predicting continuous values in complex, non-linear scenarios. However, it's essential to ensure that you have enough data to train the model effectively and consider the computational resources required. Simpler models may be more appropriate for small datasets or when model interpretability is necessary. By following the steps outlined, you can build, train, and evaluate a neural network for regression tasks in Python using Scikit-learn. \ No newline at end of file From 518ffd2149049b79435f26887334937f370ed84c Mon Sep 17 00:00:00 2001 From: Apoorva-Atre Date: Mon, 10 Jun 2024 11:21:22 +0530 Subject: [PATCH 524/656] Added content on naive bayes in machine-learning --- contrib/machine-learning/naive-bayes.md | 369 ++++++++++++++++++++++++ 1 file changed, 369 insertions(+) create mode 100644 contrib/machine-learning/naive-bayes.md diff --git a/contrib/machine-learning/naive-bayes.md b/contrib/machine-learning/naive-bayes.md new file mode 100644 index 00000000..3f171663 --- /dev/null +++ b/contrib/machine-learning/naive-bayes.md @@ -0,0 +1,369 @@ +# Naive Bayes + +## Introduction + +The Naive Bayes model uses probabilities to predict an outcome.It is a supervised machine learning technique, i.e. it reqires labelled data for training. It is used for classification and is based on the Bayes' Theorem. The basic assumption of this model is the independence among the features, i.e. a feature is unaffected by any other feture. + +## Bayes' Theorem + +Bayes' theorem is given by: + +$$ +P(a|b) = \frac{P(b|a)*P(a)}{P(b)} +$$ + +where: +- $P(a|b)$ is the posterior probability, i.e. probability of 'a' given that 'b' is true, +- $P(b|a)$ is the likelihood probability i.e. probability of 'b' given that 'a' is true, +- $P(a)$ and $P(b)$ are the probabilities of 'a' and 'b' respectively, independent of each other. + + +## Applications + +Naive Bayes classifier has numerous applications including : + 1. Text classification. + 2. Sentiment analysis. + 3. Spam filtering. + 4. Multiclass classification (eg. Weather prediction). + 5. Recommendation Systems. + 6. Healthcare sector. + 7. Document categorization. + + +## Advantages + + 1. Easy to implement. + 2. Useful even if training dataset is limited (where a decision tree would not be recommended). + 3. Supports multiclass classification which is not supported by some machine learning algorithms like SVM and logistic regression. + 4. Scalable, fast and efficient. + +## Disadvantages + + 1. Assumes features to be independent, which may not be true in certain scenarios. + 2. Zero probability error. + 3. Sensitive to noise. + +## Zero Probability Error + + Zero probability error is said to occur if in some case the number of occurances of an event given another event is zero. + To handle zero probability error, Laplace's correction is used by adding a small constant . + +**Example:** + + +Given the data below, find whether tennis can be played if ( outlook=overcast, wind=weak ). + +**Data** + +--- +| SNo | Outlook (A) | Wind (B) | PlayTennis (R) | +|-----|--------------|------------|-------------------| +| 1 | Rain | Weak | No | +| 2 | Rain | Strong | No | +| 3 | Overcast | Weak | Yes | +| 4 | Rain | Weak | Yes | +| 5 | Overcast | Weak | Yes | +| 6 | Rain | Strong | No | +| 7 | Overcast | Strong | Yes | +| 8 | Rain | Weak | No | +| 9 | Overcast | Weak | Yes | +| 10 | Rain | Weak | Yes | +--- + +- **Calculate prior probabilities** + +$$ + P(Yes) = \frac{6}{10} = 0.6 +$$ +$$ + P(No) = \frac{4}{10} = 0.4 +$$ + +- **Calculate likelihoods** + + 1.**Outlook (A):** + + --- + | A\R | Yes | No | + |-----------|-------|-----| + | Rain | 2 | 4 | + | Overcast | 4 | 0 | + | Total | 6 | 4 | + --- + +- Rain: + +$$ +P(Rain|Yes) = \frac{2}{6} +$$ + +$$ +P(Rain|No) = \frac{4}{4} +$$ + +- Overcast: + +$$ + P(Overcast|Yes) = \frac{4}{6} +$$ +$$ + P(Overcast|No) = \frac{0}{4} +$$ + + +Here, we can see that + $$ + P(Overcast|No) = 0 + $$ +This is a zero probability error! + +Since probability is 0, naive bayes model fails to predict. + + **Applying Laplace's correction:** + + In Laplace's correction, we scale the values for 1000 instances. + - **Calculate prior probabilities** + + $$ + P(Yes) = \frac{600}{1002} + $$ + + $$ + P(No) = \frac{402}{1002} + $$ + +- **Calculate likelihoods** + + 1. **Outlook (A):** + + + ( Converted to 1000 instances ) + + We will add 1 instance each to the (PlayTennis|No) column {Laplace's correction} + + --- + | A\R | Yes | No | + |-----------|-------|---------------| + | Rain | 200 | (400+1)=401 | + | Overcast | 400 | (0+1)=1 | + | Total | 600 | 402 | + --- + + - **Rain:** + + $$ + P(Rain|Yes) = \frac{200}{600} + $$ + $$ + P(Rain|No) = \frac{401}{402} + $$ + + - **Overcast:** + + $$ + P(Overcast|Yes) = \frac{400}{600} + $$ + $$ + P(Overcast|No) = \frac{1}{402} + $$ + + + 2. **Wind (B):** + + + --- + | B\R | Yes | No | + |-----------|---------|-------| + | Weak | 500 | 200 | + | Strong | 100 | 200 | + | Total | 600 | 400 | + --- + + - **Weak:** + + $$ + P(Weak|Yes) = \frac{500}{600} + $$ + $$ + P(Weak|No) = \frac{200}{400} + $$ + + - **Strong:** + + $$ + P(Strong|Yes) = \frac{100}{600} + $$ + $$ + P(Strong|No) = \frac{200}{400} + $$ + + - **Calculting probabilities:** + + $$ + P(PlayTennis|Yes) = P(Yes) * P(Overcast|Yes) * P(Weak|Yes) + $$ + $$ + = \frac{600}{1002} * \frac{400}{600} * \frac{500}{600} + $$ + $$ + = 0.3326 + $$ + + $$ + P(PlayTennis|No) = P(No) * P(Overcast|No) * P(Weak|No) + $$ + $$ + = \frac{402}{1002} * \frac{1}{402} * \frac{200}{400} + $$ + $$ + = 0.000499 = 0.0005 + $$ + + +Since , +$$ + P(PlayTennis|Yes) > P(PlayTennis|No) +$$ +we can conclude that tennis can be played if outlook is overcast and wind is weak. + + +# Types of Naive Bayes classifier + + +## Guassian Naive Bayes + + It is used when the dataset has **continuous data**. It assumes that the data is distributed normally (also known as guassian distribution). + A guassian distribution can be characterized by a bell-shaped curve. + + **Continuous data features :** Features which can take any real values within a certain range. These features have an infinite number of possible values.They are generally measured, not counted. + eg. weight, height, temperature, etc. + + **Code** + + ```python + +#import libraries +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.naive_bayes import GaussianNB +from sklearn import metrics +from sklearn.metrics import confusion_matrix + +#read data +d=pd.read_csv("data.csv") +df=pd.DataFrame(d) + +X = df.iloc[:,1:7:1] +y = df.iloc[:,7:8:1] + +# splitting X and y into training and testing sets +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42) + + +# training the model on training set +obj = GaussianNB() +obj.fit(X_train, y_train) + +#making predictions on the testing set +y_pred = obj.predict(X_train) + +#comparing y_test and y_pred +print("Gaussian Naive Bayes model accuracy:", metrics.accuracy_score(y_train, y_pred)) +print("Confusion matrix: \n",confusion_matrix(y_train,y_pred)) + + ``` + + +## Multinomial Naive Bayes + + Appropriate when the features are categorical or countable. It models the likelihood of each feature as a multinomial distribution. + Multinomial distribution is used to find probabilities of each category, given multiple categories (eg. Text classification). + + **Code** + + ```python + +#import libraries +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.naive_bayes import MultinomialNB +from sklearn import metrics +from sklearn.metrics import confusion_matrix + +#read data +d=pd.read_csv("data.csv") +df=pd.DataFrame(d) + +X = df.iloc[:,1:7:1] +y = df.iloc[:,7:8:1] + +# splitting X and y into training and testing sets +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42) + + +# training the model on training set +obj = MultinomialNB() +obj.fit(X_train, y_train) + +#making predictions on the testing set +y_pred = obj.predict(X_train) + +#comparing y_test and y_pred +print("Gaussian Naive Bayes model accuracy:", metrics.accuracy_score(y_train, y_pred)) +print("Confusion matrix: \n",confusion_matrix(y_train,y_pred)) + + + ``` + +## Bernoulli Naive Bayes + + It is specifically designed for binary features (eg. Yes or No). It models the likelihood of each feature as a Bernoulli distribution. + Bernoulli distribution is used when there are only two possible outcomes (eg. success or failure of an event). + + **Code** + + ```python + +#import libraries +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.naive_bayes import BernoulliNB +from sklearn import metrics +from sklearn.metrics import confusion_matrix + +#read data +d=pd.read_csv("data.csv") +df=pd.DataFrame(d) + +X = df.iloc[:,1:7:1] +y = df.iloc[:,7:8:1] + +# splitting X and y into training and testing sets +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42) + + +# training the model on training set +obj = BernoulliNB() +obj.fit(X_train, y_train) + +#making predictions on the testing set +y_pred = obj.predict(X_train) + +#comparing y_test and y_pred +print("Gaussian Naive Bayes model accuracy:", metrics.accuracy_score(y_train, y_pred)) +print("Confusion matrix: \n",confusion_matrix(y_train,y_pred)) + + ``` + + +## Evaluation + + 1. Confusion matrix. + 2. Accuracy. + 3. ROC curve. + + +## Conclusion + + Although the assumption that the features are independent of each other may limit the naive bayes model in some cases, the simplicity and efficiency of this classifier makes it a valuable and reliable model in the domain of machine learning. Naive Bayes classifiers are suitable for a wide range of classification problems. + \ No newline at end of file From 684344bdccdbb480bec116ec1d649f67c7535ce1 Mon Sep 17 00:00:00 2001 From: Apoorva-Atre Date: Mon, 10 Jun 2024 11:35:45 +0530 Subject: [PATCH 525/656] Added naive bayes in machine-learning index --- contrib/machine-learning/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 0bf39f98..0369c43b 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -20,3 +20,4 @@ - [Grid Search](grid-search.md) - [Transformers](transformers.md) - [K-nearest neighbor (KNN)](knn.md) +- [Naive Bayes](naive-bayes.md) From 6a03b12726c158ca77414cfa586f8e40c962da56 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Mon, 10 Jun 2024 11:57:56 +0530 Subject: [PATCH 526/656] Create magic-methods.md Added intro Added Commonly used methods Added examples Added Output Added Conclusion --- contrib/advanced-python/magic-methods.md | 151 +++++++++++++++++++++++ 1 file changed, 151 insertions(+) create mode 100644 contrib/advanced-python/magic-methods.md diff --git a/contrib/advanced-python/magic-methods.md b/contrib/advanced-python/magic-methods.md new file mode 100644 index 00000000..447e36b5 --- /dev/null +++ b/contrib/advanced-python/magic-methods.md @@ -0,0 +1,151 @@ +# Magic Methods + +Magic methods, also known as dunder (double underscore) methods, are special methods in Python that start and end with double underscores (`__`). +These methods allow you to define the behavior of objects for built-in operations and functions, enabling you to customize how your objects interact with the +language's syntax and built-in features. Magic methods make your custom classes integrate seamlessly with Python’s built-in data types and operations. + +**Commonly Used Magic Methods** + +1. **Initialization and Representation** + - `__init__(self, ...)`: Called when an instance of the class is created. Used for initializing the object's attributes. + - `__repr__(self)`: Returns a string representation of the object, useful for debugging and logging. + - `__str__(self)`: Returns a human-readable string representation of the object. + +**Example** : + + ```python + class Person: + def __init__(self, name, age): + self.name = name + self.age = age + + def __repr__(self): + return f"Person({self.name}, {self.age})" + + def __str__(self): + return f"{self.name}, {self.age} years old" + + p = Person("Alice", 30) + print(repr(p)) + print(str(p)) + ``` + +**Output** : +```python +Person("Alice",30) +Alice, 30 years old +``` + +2. **Arithmetic Operations** + - `__add__(self, other)`: Defines behavior for the `+` operator. + - `__sub__(self, other)`: Defines behavior for the `-` operator. + - `__mul__(self, other)`: Defines behavior for the `*` operator. + - `__truediv__(self, other)`: Defines behavior for the `/` operator. + + +**Example** : + + ```python + class Vector: + def __init__(self, x, y): + self.x = x + self.y = y + + def __add__(self, other): + return Vector(self.x + other.x, self.y + other.y) + + def __repr__(self): + return f"Vector({self.x}, {self.y})" + + v1 = Vector(2, 3) + v2 = Vector(1, 1) + v3 = v1 + v2 + print(v3) + ``` + +**Output** : + +```python +Vector(3, 4) +``` + +3. **Comparison Operations** + - `__eq__(self, other)`: Defines behavior for the `==` operator. + - `__lt__(self, other)`: Defines behavior for the `<` operator. + - `__le__(self, other)`: Defines behavior for the `<=` operator. + +**Example** : + + ```python + class Person: + def __init__(self, name, age): + self.name = name + self.age = age + + def __eq__(self, other): + return self.age == other.age + + def __lt__(self, other): + return self.age < other.age + + p1 = Person("Alice", 30) + p2 = Person("Bob", 25) + print(p1 == p2) + print(p1 < p2) + ``` + + **Output** : + + ```python + False + False + ``` + +5. **Container and Sequence Methods** + + - `__len__(self)`: Defines behavior for the `len()` function. + - `__getitem__(self, key)`: Defines behavior for indexing (`self[key]`). + - `__setitem__(self, key, value)`: Defines behavior for item assignment (`self[key] = value`). + - `__delitem__(self, key)`: Defines behavior for item deletion (`del self[key]`). + +**Example** : + + ```python + class CustomList: + def __init__(self, *args): + self.items = list(args) + + def __len__(self): + return len(self.items) + + def __getitem__(self, index): + return self.items[index] + + def __setitem__(self, index, value): + self.items[index] = value + + def __delitem__(self, index): + del self.items[index] + + def __repr__(self): + return f"CustomList({self.items})" + + cl = CustomList(1, 2, 3) + print(len(cl)) + print(cl[1]) + cl[1] = 5 + print(cl) + del cl[1] + print(cl) + ``` + +**Output** : +```python +3 +2 +CustomList([1, 5, 3]) +CustomList([1, 3]) +``` + +Magic methods provide powerful ways to customize the behavior of your objects and make them work seamlessly with Python's syntax and built-in functions. +Use them judiciously to enhance the functionality and readability of your classes. From 120f3eb9e6618508cb9b4244aff94a32e9f02348 Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Mon, 10 Jun 2024 11:59:05 +0530 Subject: [PATCH 527/656] Update index.md Added Magic Methods section --- contrib/advanced-python/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index b093e44e..387ffd33 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -18,3 +18,4 @@ - [Reduce](reduce-function.md) - [List Comprehension](list-comprehension.md) - [Eval Function](eval_function.md) +- [Magic Methods](magic-methods.md) From a0e9936ce91dcbb26e2a3230e5f4d9c0c1d3f976 Mon Sep 17 00:00:00 2001 From: Niyonika Gaur <83643952+niyonikagaur@users.noreply.github.com> Date: Mon, 10 Jun 2024 12:09:17 +0530 Subject: [PATCH 528/656] SciPy_Graphs.md --- contrib/scipy/SciPy_Graphs.md | 137 ++++++++++++++++++++++++++++++++++ 1 file changed, 137 insertions(+) create mode 100644 contrib/scipy/SciPy_Graphs.md diff --git a/contrib/scipy/SciPy_Graphs.md b/contrib/scipy/SciPy_Graphs.md new file mode 100644 index 00000000..747ede73 --- /dev/null +++ b/contrib/scipy/SciPy_Graphs.md @@ -0,0 +1,137 @@ +# SciPy Graphs +Graphs are also a type of data structure, SciPy provides a module called scipy.sparse.csgraph for working with graphs. +## Adjacency Matrix +An adjacency matrix is a way of representing a graph using a square matrix. In the matrix, the element at the i-th row and j-th column indicates whether there is an edge from vertex +i to vertex j. +``` +import numpy as np +from scipy.sparse import csr_matrix + +adj_matrix = np.array([ + [0, 1, 0, 0], + [1, 0, 1, 0], + [0, 1, 0, 1], + [0, 0, 1, 0] +]) + + +sparse_matrix = csr_matrix(adj_matrix) + +print(sparse_matrix) +``` +In this example: + +1. The graph has 4 nodes. +2. is an edge between node 0 and node 1, node 1 and node 2, and node 2 and node 3. +3. The csr_matrix function converts the dense adjacency matrix into a compressed sparse row (CSR) format, which is efficient for storing large, sparse matrices. +## Floyd Warshall +The Floyd-Warshall algorithm is a classic algorithm used to find the shortest paths between all pairs of nodes in a weighted graph. +``` +import numpy as np +from scipy.sparse.csgraph import floyd_warshall +from scipy.sparse import csr_matrix + +arr = np.array([ + [0, 1, 2], + [1, 0, 0], + [2, 0, 0] +]) + +newarr = csr_matrix(arr) + +print(floyd_warshall(newarr, return_predecessors=True)) +``` +### Output +``` +(array([[0., 1., 2.], + [1., 0., 3.], + [2., 3., 0.]]), array([[-9999, 0, 0], + [ 1, -9999, 0], + [ 2, 0, -9999]], dtype=int32)) +``` +## Dijkstra +Dijkstra's algorithm is used to find the shortest path from a source node to all other nodes in a graph with non-negative edge weights. +``` +import numpy as np +from scipy.sparse.csgraph import dijkstra +from scipy.sparse import csr_matrix + +arr = np.array([ + [0, 1, 2], + [1, 0, 0], + [2, 0, 0] +]) + +newarr = csr_matrix(arr) + +print(dijkstra(newarr, return_predecessors=True, indices=0)) +``` +### Output +``` +(array([ 0., 1., 2.]), array([-9999, 0, 0], dtype=int32)) +``` +## Bellman Ford +The Bellman-Ford algorithm is used to find the shortest path from a single source vertex to all other vertices in a weighted graph. It can handle graphs with negative weights, and it also detects negative weight cycles. +``` +import numpy as np +from scipy.sparse.csgraph import bellman_ford +from scipy.sparse import csr_matrix + +arr = np.array([ + [0, -1, 2], + [1, 0, 0], + [2, 0, 0] +]) + +newarr = csr_matrix(arr) + +print(bellman_ford(newarr, return_predecessors=True, indices=0)) +``` +### Output +``` +(array([ 0., -1., 2.]), array([-9999, 0, 0], dtype=int32)) +``` +## Depth First Order +Depth-First Search (DFS) is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root and explores as far as possible along each branch before backtracking. +``` +import numpy as np +from scipy.sparse.csgraph import depth_first_order +from scipy.sparse import csr_matrix + +arr = np.array([ + [0, 1, 0, 1], + [1, 1, 1, 1], + [2, 1, 1, 0], + [0, 1, 0, 1] +]) + +newarr = csr_matrix(arr) + +print(depth_first_order(newarr, 1)) +``` +### Output +``` +(array([1, 0, 3, 2], dtype=int32), array([ 1, -9999, 1, 0], dtype=int32)) +``` +## Breadth First Order +Breadth-First Search (BFS) is an algorithm for traversing or searching tree or graph data structures. It starts at the root present depth level before moving on to nodes at the next depth level. +``` +import numpy as np +from scipy.sparse.csgraph import breadth_first_order +from scipy.sparse import csr_matrix + +arr = np.array([ + [0, 1, 0, 1], + [1, 1, 1, 1], + [2, 1, 1, 0], + [0, 1, 0, 1] +]) + +newarr = csr_matrix(arr) + +print(breadth_first_order(newarr, 1)) +``` +### Output +``` +(array([1, 0, 2, 3], dtype=int32), array([ 1, -9999, 1, 1], dtype=int32)) +``` From 8dfaed116e75ea44d56b47dbf851d0c20b8c10e8 Mon Sep 17 00:00:00 2001 From: Niyonika Gaur <83643952+niyonikagaur@users.noreply.github.com> Date: Mon, 10 Jun 2024 12:11:06 +0530 Subject: [PATCH 529/656] Delete contrib/plotting-visualization/Visualizing_nifti_files.md --- .../Visualizing_nifti_files.md | 121 ------------------ 1 file changed, 121 deletions(-) delete mode 100644 contrib/plotting-visualization/Visualizing_nifti_files.md diff --git a/contrib/plotting-visualization/Visualizing_nifti_files.md b/contrib/plotting-visualization/Visualizing_nifti_files.md deleted file mode 100644 index bcd26196..00000000 --- a/contrib/plotting-visualization/Visualizing_nifti_files.md +++ /dev/null @@ -1,121 +0,0 @@ -## NIfTI Files -Neuroimaging Informatics Technology Initiative is a data format for storage. Functional Magnetic Resonance Imaging (fMRI) and other medical images. -## In this, we'll be using different packages: -1. Matplotlib -2. Nibabel: It gives access to a variety of imaging formats, it provides a common interface for various formats produced by different scanners. - -## Various Methods for viewing a NIfTI file -### 1. Load NIfTI file -``` -brain_vol = nib.load("") #Enter the path to NIfTI file here -type(brain_vol) -``` -### 2. View Metadata -``` -print(brain_vol.header) -``` -### Output -``` - object, endian='<' -sizeof_hdr : 348 -data_type : b'' -db_name : b'' -extents : 0 -session_error : 0 -regular : b'r' -dim_info : 54 -dim : [ 3 192 256 256 1 1 1 1] -intent_p1 : 0.0 -intent_p2 : 0.0 -intent_p3 : 0.0 -intent_code : none -datatype : int16 -bitpix : 16 -slice_start : 0 -pixdim : [1. 1. 1. 1. 2.3 0. 0. 0. ] -vox_offset : 0.0 -scl_slope : nan -scl_inter : nan -slice_end : 0 -slice_code : unknown -xyzt_units : 10 -cal_max : 0.0 -cal_min : 0.0 -slice_duration : 0.0 -toffset : 0.0 -glmax : 0 -glmin : 0 -descrip : b'TE=2.7;Time=150734.828;phase=1' -aux_file : b'' -qform_code : scanner -sform_code : scanner -quatern_b : 0.00042526424 -quatern_c : -0.027039066 -quatern_d : 0.01571919 -qoffset_x : -81.5222 -qoffset_y : -130.72974 -qoffset_z : -144.47054 -srow_x : [ 9.9804670e-01 -3.1445995e-02 -5.4038301e-02 -8.1522202e+01] -srow_y : [ 3.1400096e-02 9.9950546e-01 -1.7001767e-03 -1.3072974e+02] -srow_z : [ 5.4065209e-02 4.7863640e-08 9.9853742e-01 -1.4447054e+02] -intent_name : b'' -magic : b'n+1' -``` -### 3. Access data in the NIfTI object -``` -brain_vol_data = brain_vol.get_fdata() -type(brain_vol_data) -``` -### Output -``` -numpy.ndarray -``` -### Dimensions of the image -``` -brain_vol_data.shape -``` -### Output -``` -(192, 256, 256) -``` -### 4. Visualize a Slice -``` -plt.imshow(brain_vol_data[96], cmap='bone') -plt.axis('off') -plt.show() -``` -The image can be rotated -``` -plt.imshow(ndi.rotate(brain_vol_data[96], 90), cmap='bone') -plt.axis('off') -plt.show() -``` -### Plot a series of slices -``` -fig_rows = 4 -fig_cols = 4 -n_subplots = fig_rows * fig_cols -n_slice = brain_vol_data.shape[0] -step_size = n_slice // n_subplots -plot_range = n_subplots * step_size -start_stop = int((n_slice - plot_range) / 2) - -fig, axs = plt.subplots(fig_rows, fig_cols, figsize=[10, 10]) - -for idx, img in enumerate(range(start_stop, plot_range, step_size)): - axs.flat[idx].imshow(ndi.rotate(brain_vol_data[img, :, :], 90), cmap='gray') - axs.flat[idx].axis('off') - -plt.tight_layout() -plt.show() -``` -### Multiples slices at once -``` -plotting.plot_img(brain_vol, display_mode='mosaic', cmap='gray') -plt.show() -``` - - - - - From 35ca73c13ab69728a317ee32859a770e45d8a948 Mon Sep 17 00:00:00 2001 From: Niyonika Gaur <83643952+niyonikagaur@users.noreply.github.com> Date: Mon, 10 Jun 2024 12:13:15 +0530 Subject: [PATCH 530/656] index.md --- contrib/scipy/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/scipy/index.md b/contrib/scipy/index.md index 57371141..4916bfff 100644 --- a/contrib/scipy/index.md +++ b/contrib/scipy/index.md @@ -1,4 +1,5 @@ # List of sections - [Installation of Scipy and its key uses](installation_features.md) +- [SciPy Graphs](SciPy_Graphs.md) From 23e146c14508dbec01ed1f3fcc2d72fa1c7320f9 Mon Sep 17 00:00:00 2001 From: Niyonika Gaur <83643952+niyonikagaur@users.noreply.github.com> Date: Mon, 10 Jun 2024 12:13:53 +0530 Subject: [PATCH 531/656] index.md --- contrib/plotting-visualization/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 03994ec4..7a33cc14 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -1,4 +1,4 @@ # List of sections - [Installing Matplotlib](matplotlib_installation.md) -- [Visualizing NIfTI files](Visualizing_nifti_files.md) + From d886f800a8731d8f445e8126ffc699e80240ad32 Mon Sep 17 00:00:00 2001 From: Apoorva-Atre Date: Mon, 10 Jun 2024 12:32:40 +0530 Subject: [PATCH 532/656] modified naive bayes file --- contrib/machine-learning/naive-bayes.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/machine-learning/naive-bayes.md b/contrib/machine-learning/naive-bayes.md index 3f171663..82525997 100644 --- a/contrib/machine-learning/naive-bayes.md +++ b/contrib/machine-learning/naive-bayes.md @@ -365,5 +365,5 @@ print("Confusion matrix: \n",confusion_matrix(y_train,y_pred)) ## Conclusion - Although the assumption that the features are independent of each other may limit the naive bayes model in some cases, the simplicity and efficiency of this classifier makes it a valuable and reliable model in the domain of machine learning. Naive Bayes classifiers are suitable for a wide range of classification problems. + We can conclude that naive bayes may limit in some cases due to the assumption that the features are independent of each other but still reliable in many cases. Naive Bayes is an efficient classifier and works even on small datasets. \ No newline at end of file From 8d4d11464f486ca16f1d5cfa7f5501f09466672d Mon Sep 17 00:00:00 2001 From: mankalavaishnavi Date: Mon, 10 Jun 2024 16:39:15 +0530 Subject: [PATCH 533/656] added heaps --- contrib/ds-algorithms/heaps.md | 164 +++++++++++++++++++++++++++++++++ 1 file changed, 164 insertions(+) create mode 100644 contrib/ds-algorithms/heaps.md diff --git a/contrib/ds-algorithms/heaps.md b/contrib/ds-algorithms/heaps.md new file mode 100644 index 00000000..396186c7 --- /dev/null +++ b/contrib/ds-algorithms/heaps.md @@ -0,0 +1,164 @@ +# Heaps in Python3 + +## Definition: +Heaps are a crucial data structure that support efficient priority queue operations. They come in two main types: min heaps and max heaps. Python's heapq module provides a robust implementation for min heaps, and with some minor adjustments, it can also be used to implement max heaps. + +## Overview: +A heap is a specialized binary tree-based data structure that satisfies the heap property: + +- **Min Heap:** The key at the root must be the minimum among all keys present in the Binary Heap. This property must be recursively true for all nodes in the Binary Tree. + +- **Max Heap:** The key at the root must be the maximum among all keys present in the Binary Heap. This property must be recursively true for all nodes in the Binary Tree. + +## Python heapq Module: +The heapq module provides an implementation of the heap queue algorithm, also known as the priority queue algorithm. + +- **Min Heap:** In a min heap, the smallest element is always at the root. Here's how to use heapq to create and manipulate a min heap: + + ```python + import heapq + +# Create an empty heap +min_heap = [] + +# Adding elements to the heap + +heapq.heappush(min_heap, 10) +heapq.heappush(min_heap, 5) +heapq.heappush(min_heap, 3) +heapq.heappush(min_heap, 12) +print("Min Heap:", min_heap) + +# Pop the smallest element +smallest = heapq.heappop(min_heap) +print("Smallest element:", smallest) +print("Min Heap after pop:", min_heap) +``` +**Output:** + ```python +Min Heap: [3, 5, 10, 12] +Smallest element: 3 +Min Heap after pop: [5, 12, 10] +``` + +- **Max Heap:** To create a max heap, we can store negative values. +```python +import heapq + +# Create an empty heap +max_heap = [] + +# Adding elements to the heap by pushing negative values +heapq.heappush(max_heap, -10) +heapq.heappush(max_heap, -5) +heapq.heappush(max_heap, -3) +heapq.heappush(max_heap, -12) + +# Convert back to positive values for display +print("Max Heap:", [-x for x in max_heap]) + +# Pop the largest element +largest = -heapq.heappop(max_heap) +print("Largest element:", largest) +print("Max Heap after pop:", [-x for x in max_heap]) + +``` +**Output:** +```python +Max Heap: [12, 10, 3, 5] +Largest element: 12 +Max Heap after pop: [10, 5, 3] +``` + +## Heap Operations: +1. **Push Operation:** Adds an element to the heap, maintaining the heap property.
    +```python +heapq.heappush(heap, item) +``` +2. **Pop Operation:** Removes and returns the smallest element from the heap.
    +```python +smallest = heapq.heappop(heap) +``` +3. **Heapify Operation:** Converts a list into a heap in-place.
    +```python +heapq.heapify(list) +``` +4. **Peek Operation:** To get the smallest element without popping it (not directly available, but can be done by accessing the first element). +```python +smallest = heap[0] +``` + +## Example: +```python +# importing "heapq" to implement heap queue +import heapq + +# initializing list +li = [15, 77, 90, 1, 3] + +# using heapify to convert list into heap +heapq.heapify(li) + +# printing created heap +print("The created heap is : ", end="") +print(list(li)) + +# using heappush() to push elements into heap +# pushes 4 +heapq.heappush(li, 4) + +# printing modified heap +print("The modified heap after push is : ", end="") +print(list(li)) + +# using heappop() to pop smallest element +print("The popped and smallest element is : ", end="") +print(heapq.heappop(li)) + +``` + +Output: +```python +The created heap is : [1, 3, 15, 77, 90] +The modified heap after push is : [1, 3, 4, 15, 77, 90] +The popped and smallest element is : 1 +``` + +## Advantages and Disadvantages of Heaps: + +## Advantages: + +**Efficient:** Heap queues, implemented in Python's heapq module, offer remarkable efficiency in managing priority queues and heaps. With logarithmic time complexity for key operations, they are widely favored in various applications for their performance. + +**Space-efficient:** Leveraging an array-based representation, heap queues optimize memory usage compared to node-based structures like linked lists. This design minimizes overhead, enhancing efficiency in memory management. + +**Ease of Use:** Python's heap queues boast a user-friendly API, simplifying fundamental operations such as insertion, deletion, and retrieval. This simplicity contributes to rapid development and code maintenance. + +**Flexibility:** Beyond their primary use in priority queues and heaps, Python's heap queues lend themselves to diverse applications. They can be adapted to implement various data structures, including binary trees, showcasing their versatility and broad utility across different domains. + +## Disadvantages: + +**Limited functionality:** Heap queues are primarily designed for managing priority queues and heaps, and may not be suitable for more complex data structures and algorithms. + +**No random access:** Heap queues do not support random access to elements, making it difficult to access elements in the middle of the heap or modify elements that are not at the top of the heap. + +**No sorting:** Heap queues do not support sorting, so if you need to sort elements in a specific order, you will need to use a different data structure or algorithm. + +**Not thread-safe:** Heap queues are not thread-safe, meaning that they may not be suitable for use in multi-threaded applications where data synchronization is critical. + +## Real-Life Examples of Heaps: + +1. **Priority Queues:** +Heaps are commonly used to implement priority queues, which are used in various algorithms like Dijkstra's shortest path algorithm and Prim's minimum spanning tree algorithm. + +2. **Scheduling Algorithms:** +Heaps are used in job scheduling algorithms where tasks with the highest priority need to be processed first. + +3. **Merge K Sorted Lists:** +Heaps can be used to efficiently merge multiple sorted lists into a single sorted list. + +4. **Real-Time Event Simulation:** +Heaps are used in event-driven simulators to manage events scheduled to occur at future times. + +5. **Median Finding Algorithm:** +Heaps can be used to maintain a dynamic set of numbers to find the median efficiently. \ No newline at end of file From 89fe7785d17059c94e7de4c53b799086a3ed7d39 Mon Sep 17 00:00:00 2001 From: mankalavaishnavi Date: Mon, 10 Jun 2024 16:45:31 +0530 Subject: [PATCH 534/656] updated index.md --- contrib/ds-algorithms/index.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 9c05b15e..aa99f4f0 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -15,4 +15,5 @@ - [Trie](trie.md) - [Two Pointer Technique](two-pointer-technique.md) - [Hashing through Linear Probing](hashing-linear-probing.md) -- [Hashing through Chaining](hashing-chaining.md) \ No newline at end of file +- [Hashing through Chaining](hashing-chaining.md) +- [Heaps in Python](heaps.md) \ No newline at end of file From 3364f1d353ec2ad096a4d603688c1c7f482dcb28 Mon Sep 17 00:00:00 2001 From: Ritesh Date: Mon, 10 Jun 2024 18:39:49 +0530 Subject: [PATCH 535/656] Create polynomial-regression.md --- .../machine-learning/polynomial-regression.md | 114 ++++++++++++++++++ 1 file changed, 114 insertions(+) create mode 100644 contrib/machine-learning/polynomial-regression.md diff --git a/contrib/machine-learning/polynomial-regression.md b/contrib/machine-learning/polynomial-regression.md new file mode 100644 index 00000000..3f1287e0 --- /dev/null +++ b/contrib/machine-learning/polynomial-regression.md @@ -0,0 +1,114 @@ +# Polynomial Regression + +Polynomial Regression is a form of regression analysis in which the relationship between the independent variable \( x \) and the dependent variable \( y \) is modeled as an \( n \)th degree polynomial. This README provides an overview of polynomial regression, including its fundamental concepts, assumptions, and how to implement it using Python. + +## Table of Contents + +1. [Introduction](#introduction) +2. [Concepts](#concepts) +3. [Assumptions](#assumptions) +4. [Implementation](#implementation) + - [Using Scikit-learn](#using-scikit-learn) + - [Code Example](#code-example) +5. [Evaluation Metrics](#evaluation-metrics) +6. [Conclusion](#conclusion) +7. [References](#references) + +## Introduction + +Polynomial Regression is used when the data shows a non-linear relationship between the independent variable \( x \) and the dependent variable \( y \). It extends the simple linear regression model by considering polynomial terms of the independent variable, allowing for a more flexible fit to the data. + +## Concepts + +### Polynomial Equation + +The polynomial regression model is based on the following polynomial equation: + +$$ +\[ y = \beta_0 + \beta_1 x + \beta_2 x^2 + \beta_3 x^3 + \cdots + \beta_n x^n + \epsilon \] +$$ + +Where: +- \( y \) is the dependent variable. +- \( x \) is the independent variable. +- \( \beta_0, \beta_1, \ldots, \beta_n \) are the coefficients of the polynomial. +- \( \epsilon \) is the error term. + +### Degree of Polynomial + +The degree of the polynomial (n) determines the flexibility of the model. A higher degree allows the model to fit more complex, non-linear relationships, but it also increases the risk of overfitting. + +### Overfitting and Underfitting + +- **Overfitting**: When the model fits the noise in the training data too closely, resulting in poor generalization to new data. +- **Underfitting**: When the model is too simple to capture the underlying pattern in the data. + +## Assumptions + +1. **Independence**: Observations are independent of each other. +2. **Homoscedasticity**: The variance of the residuals (errors) is constant across all levels of the independent variable. +3. **Normality**: The residuals of the model are normally distributed. +4. **No Multicollinearity**: The predictor variables are not highly correlated with each other. + +## Implementation + +### Using Scikit-learn + +Scikit-learn is a popular machine learning library in Python that provides tools for polynomial regression. + +### Code Example + +```python +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from sklearn.preprocessing import PolynomialFeatures +from sklearn.linear_model import LinearRegression +from sklearn.metrics import mean_squared_error, r2_score + +# Load dataset +data = pd.read_csv('path/to/your/dataset.csv') + +# Define features and target variable +X = data[['feature']] +y = data['target'] + +# Transform features to polynomial features +poly = PolynomialFeatures(degree=3) +X_poly = poly.fit_transform(X) + +# Initialize and train polynomial regression model +model = LinearRegression() +model.fit(X_poly, y) + +# Make predictions +y_pred = model.predict(X_poly) + +# Evaluate the model +mse = mean_squared_error(y, y_pred) +r2 = r2_score(y, y_pred) +print("Mean Squared Error:", mse) +print("R^2 Score:", r2) + +# Visualize the results +plt.scatter(X, y, color='blue') +plt.plot(X, y_pred, color='red') +plt.xlabel('Feature') +plt.ylabel('Target') +plt.title('Polynomial Regression') +plt.show() +``` + +## Evaluation Metrics + +- **Mean Squared Error (MSE)**: The average of the squared differences between actual and predicted values. +- **R-squared (R²) Score**: A statistical measure that represents the proportion of the variance for the dependent variable that is explained by the independent variables in the model. + +## Conclusion + +Polynomial Regression is a powerful tool for modeling non-linear relationships between variables. It is important to choose the degree of the polynomial carefully to balance between underfitting and overfitting. Understanding and properly evaluating the model using appropriate metrics ensures its effectiveness. + +## References + +- [Scikit-learn Documentation](https://scikit-learn.org/stable/modules/linear_model.html#polynomial-regression) +- [Wikipedia: Polynomial Regression](https://en.wikipedia.org/wiki/Polynomial_reg From b4aaf5587d714e5b925cde85626ab970e858edf0 Mon Sep 17 00:00:00 2001 From: Ritesh Date: Mon, 10 Jun 2024 18:40:58 +0530 Subject: [PATCH 536/656] Update index.md --- contrib/machine-learning/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 0bf39f98..c0e73682 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -20,3 +20,4 @@ - [Grid Search](grid-search.md) - [Transformers](transformers.md) - [K-nearest neighbor (KNN)](knn.md) +- [Polynomial Regression](polynomial-regression.md) From d668b34829b6d005e3a903691e8d71f2142a9a48 Mon Sep 17 00:00:00 2001 From: Yodha Sudarsi <101962069+Yodha-Sudarsi@users.noreply.github.com> Date: Mon, 10 Jun 2024 21:27:23 +0530 Subject: [PATCH 537/656] Update index.md --- contrib/numpy/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 50e80460..3abfb277 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -11,3 +11,4 @@ - [Sorting NumPy Arrays](sorting-array.md) - [NumPy Array Iteration](array-iteration.md) - [Concatenation of Arrays](concatenation-of-arrays.md) +- [Statistical Operations on Arrays](statistical-operations-on-arrays) From 06bdae004b468a6d2e4d72a3fa3dc513958752cf Mon Sep 17 00:00:00 2001 From: Yodha Sudarsi <101962069+Yodha-Sudarsi@users.noreply.github.com> Date: Mon, 10 Jun 2024 21:28:01 +0530 Subject: [PATCH 538/656] Create statistical-operations-on-arrays.md --- contrib/numpy/statistical-operations-on-arrays.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 contrib/numpy/statistical-operations-on-arrays.md diff --git a/contrib/numpy/statistical-operations-on-arrays.md b/contrib/numpy/statistical-operations-on-arrays.md new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/contrib/numpy/statistical-operations-on-arrays.md @@ -0,0 +1 @@ + From f8699a52a752ac381190362594dd202a648837f3 Mon Sep 17 00:00:00 2001 From: Yodha Sudarsi <101962069+Yodha-Sudarsi@users.noreply.github.com> Date: Mon, 10 Jun 2024 21:28:42 +0530 Subject: [PATCH 539/656] Update index.md --- contrib/numpy/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 3abfb277..4c3edce3 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -11,4 +11,4 @@ - [Sorting NumPy Arrays](sorting-array.md) - [NumPy Array Iteration](array-iteration.md) - [Concatenation of Arrays](concatenation-of-arrays.md) -- [Statistical Operations on Arrays](statistical-operations-on-arrays) +- [Statistical Operations on Arrays](statistical-operations-on-arrays.md) From dc08f67d5603677a9b89baefda7236f787844c5d Mon Sep 17 00:00:00 2001 From: Yodha Sudarsi <101962069+Yodha-Sudarsi@users.noreply.github.com> Date: Mon, 10 Jun 2024 22:22:10 +0530 Subject: [PATCH 540/656] Update statistical-operations-on-arrays.md --- .../numpy/statistical-operations-on-arrays.md | 153 ++++++++++++++++++ 1 file changed, 153 insertions(+) diff --git a/contrib/numpy/statistical-operations-on-arrays.md b/contrib/numpy/statistical-operations-on-arrays.md index 8b137891..06fbae22 100644 --- a/contrib/numpy/statistical-operations-on-arrays.md +++ b/contrib/numpy/statistical-operations-on-arrays.md @@ -1 +1,154 @@ +# Statistical Operations on Arrays +Statistics involves collecting data, analyzing it, and drawing conclusions from the gathered information. + +NumPy provides powerful statistical functions to perform efficient data analysis on arrays, including `minimum`, `maximum`, `mean`, `median`, `variance`, `standard deviation`, and more. + +## Minimum + +In NumPy, the minimum value of an array is the smallest element present. + +The smallest element of an array is calculated using the `np.min()` function. + +**Code** +```python +import numpy as np +array = np.array([100,20,300,400]) +#Calculating the minimum +result = np.min(array) +print("Minimum :", result) +``` + +**Output** +``` +Minimum : 20 +``` + +## Maximum + +In NumPy, the maximum value of an array is the largest element present. + +The largest element of an array is calculated using the `np.max()` function. + +**Code** +```python +import numpy as np +array = np.array([100,20,300,400]) +#Calculating the maximum +result = np.max(array) +print("Maximum :", result) +``` + +**Output** +``` +Maximum : 400 +``` + +## Mean + +The mean value of a NumPy array is the average of all its elements. + +It is calculated by summing all the elements and then dividing by the total number of elements. + +The mean of an array is calculated using the `np.mean()` function. + +**Code** +```python +import numpy as np +array = np.array([10,20,30,40]) +#Calculating the mean +result = np.mean(array) +print("Mean :", result) +``` + +**Output** +``` +Mean : 25.0 +``` + +## Median + +The median value of a NumPy array is the middle value in a sorted array. + +It separates the higher half of the data from the lower half. + +The median of an array is calculated using the `np.median()` function. + +It is important to note that: + +- If the number of elements is `odd`, the median is the middle element. +- If the number of elements is `even`, the median is the average of the two middle elements. + +**Code** +```python +import numpy as np +#The number of elements is odd +array = np.array([5,6,7,8,9]) +#Calculating the median +result = np.median(array) +print("Median :", result) +``` + +**Output** +``` +Median : 7.0 +``` + +**Code** +```python +import numpy as np +#The number of elements is even +array = np.array([1,2,3,4,5,6]) +#Calculating the median +result = np.median(array) +print("Median :", result) +``` + +**Output** +``` +Median : 3.5 +``` + +## Variance + +Variance in a NumPy array measures the spread or dispersion of data points. + +Calculated as the average of the squared differences from the mean. + +The variance of an array is calculated using the `np.var()` function. + +**Code** +```python +import numpy as np +array = np.array([10,70,80,50,30]) +#Calculating the variance +result = np.var(array) +print("Variance :", result) +``` + +**Output** +``` +Variance : 656.0 +``` + +## Standard Deviation + +The standard deviation of a NumPy array measures the amount of variation or dispersion of the elements in the array. + +It is calculated as the square root of the average of the squared differences from the mean, providing insight into how spread out the values are around the mean. + +The standard deviation of an array is calculated using the `np.std()` function. + +**Code** +```python +import numpy as np +array = np.array([25,30,40,55,75,100]) +#Calculating the standard deviation +result = np.std(array) +print("Standard Deviation :", result) +``` + +**Output** +``` +Standard Deviation : 26.365486699260625 +``` From 259f028a731dffeae342f523f98e1146cf855a2c Mon Sep 17 00:00:00 2001 From: Yodha Sudarsi <101962069+Yodha-Sudarsi@users.noreply.github.com> Date: Mon, 10 Jun 2024 22:29:28 +0530 Subject: [PATCH 541/656] Create splitting-arrays.md --- contrib/numpy/splitting-arrays.md | 135 ++++++++++++++++++++++++++++++ 1 file changed, 135 insertions(+) create mode 100644 contrib/numpy/splitting-arrays.md diff --git a/contrib/numpy/splitting-arrays.md b/contrib/numpy/splitting-arrays.md new file mode 100644 index 00000000..3228cb71 --- /dev/null +++ b/contrib/numpy/splitting-arrays.md @@ -0,0 +1,135 @@ +# Splitting Arrays + +Splitting a NumPy array refers to dividing the array into smaller sub-arrays. This can be done in various ways, along specific rows, columns, or even based on conditions applied to the elements. + +There are several ways to split a NumPy array in Python using different functions. Some of these methods include: + +- Splitting a NumPy array using `numpy.split()` +- Splitting a NumPy array using `numpy.array_split()` +- Splitting a NumPy array using `numpy.vsplit()` +- Splitting a NumPy array using `numpy.hsplit()` +- Splitting a NumPy array using `numpy.dsplit()` + +## NumPy split() + +The `numpy.split()` function divides an array into equal parts along a specified axis. + +**Code** +```python +import numpy as np +array = np.array([1,2,3,4,5,6]) +#Splitting the array into 3 equal parts along axis=0 +result = np.split(array,3) +print(result) +``` + +**Output** +``` +[array([1, 2]), array([3, 4]), array([5, 6])] +``` + +## NumPy array_split() + +The `numpy.array_split()` function divides an array into equal or nearly equal sub-arrays. Unlike `numpy.split()`, it allows for uneven splitting, making it useful when the array cannot be evenly divided by the specified number of splits. + +**Code** +```python +import numpy as np +array = np.array([1,2,3,4,5,6,7,8]) +#Splitting the array into 3 unequal parts along axis=0 +result = np.array_split(array,3) +print(result) +``` + +**Output** +``` +[array([1, 2, 3]), array([4, 5, 6]), array([7, 8])] +``` + +## NumPy vsplit() + +The `numpy.vsplit()`, which is vertical splitting (row-wise), divides an array along the vertical axis (axis=0). + +**Code** +```python +import numpy as np +array = np.array([[1, 2, 3], + [4, 5, 6], + [7, 8, 9], + [10, 11, 12]]) +#Vertically Splitting the array into 2 subarrays along axis=0 +result = np.vsplit(array,2) +print(result) +``` + +**Output** +``` +[array([[1, 2, 3], + [4, 5, 6]]), array([[ 7, 8, 9], + [10, 11, 12]])] +``` + + +## NumPy hsplit() + +The `numpy.hsplit()`, which is horizontal splitting (column-wise), divides an array along the horizontal axis (axis=1). + +**Code** +```python +import numpy as np +array = np.array([[1, 2, 3, 4], + [5, 7, 8, 9], + [11,12,13,14]]) +#Horizontally Splitting the array into 4 subarrays along axis=1 +result = np.hsplit(array,4) +print(result) +``` + +**Output** +``` +[array([[ 1], + [ 5], + [11]]), array([[ 2], + [ 7], + [12]]), array([[ 3], + [ 8], + [13]]), array([[ 4], + [ 9], + [14]])] +``` + +## NumPy dsplit() + +The`numpy.dsplit()` is employed for splitting arrays along the third axis (axis=2), which is applicable for 3D arrays and beyond. + +**Code** +```python +import numpy as np +#3D array +array = np.array([[[ 1, 2, 3, 4,], + [ 5, 6, 7, 8,], + [ 9, 10, 11, 12]], + [[13, 14, 15, 16,], + [17, 18, 19, 20,], + [21, 22, 23, 24]]]) +#Splitting the array along axis=2 +result = np.dsplit(array,2) +print(result) +``` + +**Output** +``` +[array([[[ 1, 2], + [ 5, 6], + [ 9, 10]], + + [[13, 14], + [17, 18], + [21, 22]]]), array([[[ 3, 4], + [ 7, 8], + [11, 12]], + + [[15, 16], + [19, 20], + [23, 24]]])] +``` From 07c7601ce605bd9a493d99b327cee3ab665799a2 Mon Sep 17 00:00:00 2001 From: Yodha Sudarsi <101962069+Yodha-Sudarsi@users.noreply.github.com> Date: Mon, 10 Jun 2024 22:30:41 +0530 Subject: [PATCH 542/656] Update index.md --- contrib/numpy/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 50e80460..03a1175e 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -11,3 +11,4 @@ - [Sorting NumPy Arrays](sorting-array.md) - [NumPy Array Iteration](array-iteration.md) - [Concatenation of Arrays](concatenation-of-arrays.md) +- [Splitting of Arrays](splitting-arrays.md) From a894cc04238880e54609fed59d5eec16c84e2c2e Mon Sep 17 00:00:00 2001 From: Pradnya Gaitonde Date: Tue, 11 Jun 2024 21:03:47 +0530 Subject: [PATCH 543/656] Added Content for Asynchronous Context managers and generators Added content to asynchronous context managers and generators --- ...synchronous_context_managers_generators.md | 110 ++++++++++++++++++ contrib/advanced-python/index.md | 1 + 2 files changed, 111 insertions(+) create mode 100644 contrib/advanced-python/asynchronous_context_managers_generators.md diff --git a/contrib/advanced-python/asynchronous_context_managers_generators.md b/contrib/advanced-python/asynchronous_context_managers_generators.md new file mode 100644 index 00000000..a78db28f --- /dev/null +++ b/contrib/advanced-python/asynchronous_context_managers_generators.md @@ -0,0 +1,110 @@ +## Asynchronous Context Managers and Generators in Python +Asynchronous programming in Python allows for more efficient use of resources by enabling tasks to run concurrently. Python provides support for asynchronous +context managers and generators, which help manage resources and perform operations asynchronously. + +### Asynchronous Context Managers +Asynchronous context managers are similar to regular context managers but are designed to work with asynchronous code. They use the async with statement and +typically include the '__aenter__' and '__aexit__' methods. + +### Creating an Asynchronous Context Manager +Here's a simple example of an asynchronous context manager: + +```bash +import asyncio + +class AsyncContextManager: + async def __aenter__(self): + print("Entering context") + await asyncio.sleep(1) # Simulate an async operation + return self + + async def __aexit__(self, exc_type, exc, tb): + print("Exiting context") + await asyncio.sleep(1) # Simulate cleanup + +async def main(): + async with AsyncContextManager() as acm: + print("Inside context") + +asyncio.run(main()) +``` + +Output: + +```bash +Entering context +Inside context +Exiting context +``` + +### Asynchronous Generators +Asynchronous generators allow you to yield values within an asynchronous function. They use the async def syntax along with the yield statement and are +iterated using the async for loop. + +### Creating an Asynchronous Generator +Here's a basic example of an asynchronous generator: + +```bash +import asyncio + +async def async_generator(): + for i in range(5): + await asyncio.sleep(1) # Simulate an async operation + yield i + +async def main(): + async for value in async_generator(): + print(value) + +asyncio.run(main()) +``` +Output: +```bash +0 +1 +2 +3 +4 +``` +### Combining Asynchronous Context Managers and Generators +You can combine asynchronous context managers and generators to create more complex and efficient asynchronous workflows. +Example: Fetching Data with an Async Context Manager and Generator +Consider a scenario where you need to fetch data from an API asynchronously and manage the connection using an asynchronous context manager: +```bash +import aiohttp +import asyncio + +class AsyncHTTPClient: + def __init__(self, url): + self.url = url + + async def __aenter__(self): + self.session = aiohttp.ClientSession() + self.response = await self.session.get(self.url) + return self.response + + async def __aexit__(self, exc_type, exc, tb): + await self.response.release() + await self.session.close() + +async def async_fetch(urls): + for url in urls: + async with AsyncHTTPClient(url) as response: + data = await response.text() + yield data + +async def main(): + urls = ["http://example.com", "http://example.org", "http://example.net"] + async for data in async_fetch(urls): + print(data) + +asyncio.run(main()) +``` +### Benefits of Asynchronous Context Managers and Generators +1. Efficient Resource Management: They help manage resources like network connections or file handles more efficiently by releasing them as soon as they are no longer needed. +2. Concurrency: They enable concurrent operations, improving performance in I/O-bound tasks such as network requests or file I/O. +3. Readability and Maintainability: They provide a clear and structured way to handle asynchronous operations, making the code easier to read and maintain. +### Summary +Asynchronous context managers and generators are powerful tools in Python that enhance the efficiency and readability +of asynchronous code. By using 'async with' for resource management and 'async for' for iteration, you can write more performant and maintainable asynchronous +programs. \ No newline at end of file diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index b093e44e..a04bb97f 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -18,3 +18,4 @@ - [Reduce](reduce-function.md) - [List Comprehension](list-comprehension.md) - [Eval Function](eval_function.md) +- [Asynchronous Context Managers & Generators](asynchronous_context_managers_generators.md) From fc5f366e6a64eac6948054af5c51bfee1a82a792 Mon Sep 17 00:00:00 2001 From: Shraman Jain <60072287+Shraman-jain@users.noreply.github.com> Date: Wed, 12 Jun 2024 00:27:10 +0530 Subject: [PATCH 544/656] Update threading.md --- contrib/advanced-python/threading.md | 138 ++++++++++++++++++++++++++- 1 file changed, 137 insertions(+), 1 deletion(-) diff --git a/contrib/advanced-python/threading.md b/contrib/advanced-python/threading.md index 85b5f4bf..07bc4bfe 100644 --- a/contrib/advanced-python/threading.md +++ b/contrib/advanced-python/threading.md @@ -1 +1,137 @@ -#Threading +# Threading in Python +Threading is a sequence of instructions in a program that can be executed independently of the remaining process and +Threads are like lightweight processes that share the same memory space but can execute independently. +The process is an executable instance of a computer program. +This guide provides an overview of the threading module and its key functionalities. + +## Key Characteristics of Threads: +* Shared Memory: All threads within a process share the same memory space, which allows for efficient communication between threads. +* Independent Execution: Each thread can run independently and concurrently. +* Context Switching: The operating system can switch between threads, enabling concurrent execution. + +## Threading Module +This module will allows you to create and manage threads easily. This module includes several functions and classes to work with threads. + +**1. Creating Thread:** +To create a thread in Python, you can use the Thread class from the threading module. + +Example: +```python +import threading + +# Create a thread +thread = threading.Thread() + +# Start the thread +thread.start() + +# Wait for the thread to complete +thread.join() + +print("Thread has finished execution.") +``` +**2. Performing Task with Thread:** +We can also perform a specific task by thread by giving a function as target and its argument as arg ,as a parameter to Thread object. + +Example: + +```python +import threading + +# Define a function that will be executed by the thread +def print_numbers(arg): + for i in range(arg): + print(f"Thread: {i}") +# Create a thread +thread = threading.Thread(target=print_numbers,args=(5,)) + +# Start the thread +thread.start() + +# Wait for the thread to complete +thread.join() + +print("Thread has finished execution.") +``` +**3. Delaying a Task with Thread's Timer Function:** +We can set a time for which we want a thread to start. Timer function takes 4 arguments (interval,function,args,kwargs). + +Example: +```python +import threading + +# Define a function that will be executed by the thread +def print_numbers(arg): + for i in range(arg): + print(f"Thread: {i}") +# Create a thread after 3 seconds +thread = threading.Timer(3,print_numbers,args=(5,)) + +# Start the thread +thread.start() + +# Wait for the thread to complete +thread.join() + +print("Thread has finished execution.") +``` +**4. Creating Multiple Threads** +We can create and manage multiple threads to achieve concurrent execution. + +Example: +```python +import threading + +def print_numbers(thread_name): + for i in range(5): + print(f"{thread_name}: {i}") + +# Create multiple threads +thread1 = threading.Thread(target=print_numbers, args=("Thread 1",)) +thread2 = threading.Thread(target=print_numbers, args=("Thread 2",)) + +# Start the threads +thread1.start() +thread2.start() + +# Wait for both threads to complete +thread1.join() +thread2.join() + +print("Both threads have finished execution.") +``` + +**5. Thread Synchronization** +When we create multiple threads and they access shared resources, there is a risk of race conditions and data corruption. To prevent this, you can use synchronization primitives such as locks. +A lock is a synchronization primitive that ensures that only one thread can access a shared resource at a time. + +Example: +```Python +import threading + +lock = threading.Lock() + +def print_numbers(thread_name): + for i in range(10): + with lock: + print(f"{thread_name}: {i}") + +# Create multiple threads +thread1 = threading.Thread(target=print_numbers, args=("Thread 1",)) +thread2 = threading.Thread(target=print_numbers, args=("Thread 2",)) + +# Start the threads +thread1.start() +thread2.start() + +# Wait for both threads to complete +thread1.join() +thread2.join() + +print("Both threads have finished execution.") +``` + +A ```lock``` object is created using threading.Lock() and The ```with lock``` statement ensures that the lock is acquired before printing and released after printing. This prevents other threads from accessing the print statement simultaneously. + +## Conclusion +Threading in Python is a powerful tool for achieving concurrency and improving the performance of I/O-bound tasks. By understanding and implementing threads using the threading module, you can enhance the efficiency of your programs. To prevent race situations and maintain data integrity, keep in mind that thread synchronization must be properly managed. From bb38f8fb5a9418a359635dcbec275e77e364c6fa Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Wed, 12 Jun 2024 21:57:30 +0530 Subject: [PATCH 545/656] Update K-Means_Clustering.md --- .../machine-learning/K-Means_Clustering.md | 106 ++++++++++-------- 1 file changed, 58 insertions(+), 48 deletions(-) diff --git a/contrib/machine-learning/K-Means_Clustering.md b/contrib/machine-learning/K-Means_Clustering.md index 83fbd80a..ee615c34 100644 --- a/contrib/machine-learning/K-Means_Clustering.md +++ b/contrib/machine-learning/K-Means_Clustering.md @@ -1,19 +1,25 @@ # K-Means Clustering Unsupervised Learning Algorithm for Grouping Similar Data. + ## Introduction K-means clustering is a fundamental unsupervised machine learning algorithm that excels at grouping similar data points together. It's a popular choice due to its simplicity and efficiency in uncovering hidden patterns within unlabeled datasets. + ## Unsupervised Learning Unlike supervised learning algorithms that rely on labeled data for training, unsupervised algorithms, like K-means, operate solely on input data (without predefined categories). Their objective is to discover inherent structures or groupings within the data. + ## The K-Means Objective Organize similar data points into clusters to unveil underlying patterns. The main objective is to minimize total intra-cluster variance or the squared function. ![image](assets/knm.png) ## Clusters and Centroids A cluster represents a collection of data points that share similar characteristics. K-means identifies a pre-determined number (k) of clusters within the dataset. Each cluster is represented by a centroid, which acts as its central point (imaginary or real). + ## Minimizing In-Cluster Variation The K-means algorithm strategically assigns each data point to a cluster such that the total variation within each cluster (measured by the sum of squared distances between points and their centroid) is minimized. In simpler terms, K-means strives to create clusters where data points are close to their respective centroids. + ## The Meaning Behind "K-Means" The "means" in K-means refers to the averaging process used to compute the centroid, essentially finding the center of each cluster. + ## K-Means Algorithm in Action ![image](assets/km_.png) The K-means algorithm follows an iterative approach to optimize cluster formation: @@ -24,62 +30,66 @@ The K-means algorithm follows an iterative approach to optimize cluster formatio 4. **Iteration Until Convergence:** Steps 2 and 3 are repeated iteratively until a stopping criterion is met. This criterion can be either: - **Centroid Stability:** No significant change occurs in the centroids' positions, indicating successful clustering. - **Reaching Maximum Iterations:** A predefined number of iterations is completed. - ## Code - Following is a simple implementation of K-Means. + +## Code +Following is a simple implementation of K-Means. - - # Generate and Visualize Sample Data - # import the necessary Libraries - - import numpy as np - import matplotlib.pyplot as plt +```python +# Generate and Visualize Sample Data +# import the necessary Libraries - # Create data points for cluster 1 and cluster 2 - X = -2 * np.random.rand(100, 2) - X1 = 1 + 2 * np.random.rand(50, 2) - - # Combine data points from both clusters - X[50:100, :] = X1 - - # Plot data points and display the plot - plt.scatter(X[:, 0], X[:, 1], s=50, c='b') - plt.show() - - # K-Means Model Creation and Training - from sklearn.cluster import KMeans - - # Create KMeans object with 2 clusters - kmeans = KMeans(n_clusters=2) - kmeans.fit(X) # Train the model on the data - - # Visualize Data Points with Centroids - centroids = kmeans.cluster_centers_ # Get centroids (cluster centers) - - plt.scatter(X[:, 0], X[:, 1], s=50, c='b') # Plot data points again - plt.scatter(centroids[0, 0], centroids[0, 1], s=200, c='g', marker='s') # Plot centroid 1 - plt.scatter(centroids[1, 0], centroids[1, 1], s=200, c='r', marker='s') # Plot centroid 2 - plt.show() # Display the plot with centroids - - # Predict Cluster Label for New Data Point - new_data = np.array([-3.0, -3.0]) - new_data_reshaped = new_data.reshape(1, -1) - predicted_cluster = kmeans.predict(new_data_reshaped) - print("Predicted cluster for new data:", predicted_cluster) - - ### Output: - Before Implementing K-Means Clustering +import numpy as np +import matplotlib.pyplot as plt + +# Create data points for cluster 1 and cluster 2 +X = -2 * np.random.rand(100, 2) +X1 = 1 + 2 * np.random.rand(50, 2) + +# Combine data points from both clusters +X[50:100, :] = X1 + +# Plot data points and display the plot +plt.scatter(X[:, 0], X[:, 1], s=50, c='b') +plt.show() + +# K-Means Model Creation and Training +from sklearn.cluster import KMeans + +# Create KMeans object with 2 clusters +kmeans = KMeans(n_clusters=2) +kmeans.fit(X) # Train the model on the data + +# Visualize Data Points with Centroids +centroids = kmeans.cluster_centers_ # Get centroids (cluster centers) + +plt.scatter(X[:, 0], X[:, 1], s=50, c='b') # Plot data points again +plt.scatter(centroids[0, 0], centroids[0, 1], s=200, c='g', marker='s') # Plot centroid 1 +plt.scatter(centroids[1, 0], centroids[1, 1], s=200, c='r', marker='s') # Plot centroid 2 +plt.show() # Display the plot with centroids + +# Predict Cluster Label for New Data Point +new_data = np.array([-3.0, -3.0]) +new_data_reshaped = new_data.reshape(1, -1) +predicted_cluster = kmeans.predict(new_data_reshaped) +print("Predicted cluster for new data:", predicted_cluster) +``` + +### Output: +Before Implementing K-Means Clustering ![Before Implementing K-Means Clustering](assets/km_2.png) - After Implementing K-Means Clustering - ![After Implementing K-Means Clustering](assets/km_3.png) +After Implementing K-Means Clustering +![After Implementing K-Means Clustering](assets/km_3.png) + +Predicted cluster for new data: `[0]` - Predicted cluster for new data: [0] ## Conclusion **K-Means** can be applied to data that has a smaller number of dimensions, is numeric, and is continuous or can be used to find groups that have not been explicitly labeled in the data. As an example, it can be used for Document Classification, Delivery Store Optimization, or Customer Segmentation. -## Reference -[[Survey of Machine Learning and Data Mining Techniques used in Multimedia System](https://www.researchgate.net/publication/333457161_Survey_of_Machine_Learning_and_Data_Mining_Techniques_used_in_Multimedia_System?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6Il9kaXJlY3QiLCJwYWdlIjoiX2RpcmVjdCJ9fQ)] -[[A Clustering Approach for Outliers Detection in a Big Point-of-Sales Database](https://www.researchgate.net/publication/339267868_A_Clustering_Approach_for_Outliers_Detection_in_a_Big_Point-of-Sales_Database?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6Il9kaXJlY3QiLCJwYWdlIjoiX2RpcmVjdCJ9fQ)] +## References + +- [Survey of Machine Learning and Data Mining Techniques used in Multimedia System](https://www.researchgate.net/publication/333457161_Survey_of_Machine_Learning_and_Data_Mining_Techniques_used_in_Multimedia_System?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6Il9kaXJlY3QiLCJwYWdlIjoiX2RpcmVjdCJ9fQ) +- [A Clustering Approach for Outliers Detection in a Big Point-of-Sales Database](https://www.researchgate.net/publication/339267868_A_Clustering_Approach_for_Outliers_Detection_in_a_Big_Point-of-Sales_Database?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6Il9kaXJlY3QiLCJwYWdlIjoiX2RpcmVjdCJ9fQ) From cf01273be27a0d9f82c45a41e505fde46360b749 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Wed, 12 Jun 2024 21:58:13 +0530 Subject: [PATCH 546/656] Update and rename K-Means_Clustering.md to kmeans.md --- contrib/machine-learning/{K-Means_Clustering.md => kmeans.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/machine-learning/{K-Means_Clustering.md => kmeans.md} (100%) diff --git a/contrib/machine-learning/K-Means_Clustering.md b/contrib/machine-learning/kmeans.md similarity index 100% rename from contrib/machine-learning/K-Means_Clustering.md rename to contrib/machine-learning/kmeans.md From 8f0e9808356ae8a02937d490f8952f3de28a42e4 Mon Sep 17 00:00:00 2001 From: Revanth <109272714+revanth1718@users.noreply.github.com> Date: Wed, 12 Jun 2024 22:16:20 +0530 Subject: [PATCH 547/656] Update index.md --- contrib/machine-learning/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index b6945cd2..62275611 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -16,3 +16,4 @@ - [Types_of_Cost_Functions](cost-functions.md) - [Clustering](clustering.md) - [Grid Search](grid-search.md) +- [K-Means](kmeans.md) From 51011db02b624db7723f246984e3a2d9644c5d57 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Thu, 13 Jun 2024 00:38:06 +0530 Subject: [PATCH 548/656] Update kmeans.md --- contrib/machine-learning/kmeans.md | 8 -------- 1 file changed, 8 deletions(-) diff --git a/contrib/machine-learning/kmeans.md b/contrib/machine-learning/kmeans.md index ee615c34..52db92e5 100644 --- a/contrib/machine-learning/kmeans.md +++ b/contrib/machine-learning/kmeans.md @@ -90,11 +90,3 @@ Predicted cluster for new data: `[0]` - [Survey of Machine Learning and Data Mining Techniques used in Multimedia System](https://www.researchgate.net/publication/333457161_Survey_of_Machine_Learning_and_Data_Mining_Techniques_used_in_Multimedia_System?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6Il9kaXJlY3QiLCJwYWdlIjoiX2RpcmVjdCJ9fQ) - [A Clustering Approach for Outliers Detection in a Big Point-of-Sales Database](https://www.researchgate.net/publication/339267868_A_Clustering_Approach_for_Outliers_Detection_in_a_Big_Point-of-Sales_Database?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6Il9kaXJlY3QiLCJwYWdlIjoiX2RpcmVjdCJ9fQ) - - - - - - - - From 41e41d804d105358a512e7a6aaa0feb1e3db4522 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Thu, 13 Jun 2024 01:33:30 +0530 Subject: [PATCH 549/656] Update dynamic-programming.md --- contrib/ds-algorithms/dynamic-programming.md | 20 +++----------------- 1 file changed, 3 insertions(+), 17 deletions(-) diff --git a/contrib/ds-algorithms/dynamic-programming.md b/contrib/ds-algorithms/dynamic-programming.md index 54c6fcbf..0f16d5c9 100644 --- a/contrib/ds-algorithms/dynamic-programming.md +++ b/contrib/ds-algorithms/dynamic-programming.md @@ -51,10 +51,6 @@ print(f"The {n}th Fibonacci number is: {fibonacci(n)}.") - **Time Complexity**: O(n) for both approaches - **Space Complexity**: O(n) for the top-down approach (due to memoization), O(1) for the bottom-up approach -
    -
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    - # 2. Longest Common Subsequence The longest common subsequence (LCS) problem is to find the longest subsequence common to two sequences. A subsequence is a sequence that appears in the same relative order but not necessarily contiguous. @@ -112,10 +108,6 @@ print("Length of LCS is", longestCommonSubsequence(S1, S2, m, n)) - **Time Complexity**: O(m * n) for both approaches, where m and n are the lengths of the input sequences - **Space Complexity**: O(m * n) for the memoization table -
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    - # 3. 0-1 Knapsack Problem The 0-1 knapsack problem is a classic optimization problem where the goal is to maximize the total value of items selected while keeping the total weight within a specified limit. @@ -146,6 +138,7 @@ capacity = 50 n = len(weights) print("Maximum value that can be obtained:", knapsack(weights, values, capacity, n)) ``` + ## 0-1 Knapsack Problem Code in Python (Bottom-up Approach) ```python @@ -170,14 +163,11 @@ capacity = 50 n = len(weights) print(knapSack(capacity, weights, values, n)) ``` + ## Complexity Analysis - **Time Complexity**: O(n * W) for both approaches, where n is the number of items and W is the capacity of the knapsack - **Space Complexity**: O(n * W) for the memoization table -
    -
    -
    - # 4. Longest Increasing Subsequence The Longest Increasing Subsequence (LIS) is a task is to find the longest subsequence that is strictly increasing, meaning each element in the subsequence is greater than the one before it. This subsequence must maintain the order of elements as they appear in the original sequence but does not need to be contiguous. The goal is to identify the subsequence with the maximum possible length. @@ -190,7 +180,6 @@ The Longest Increasing Subsequence (LIS) is a task is to find the longest subseq ## Longest Increasing Subsequence Code in Python (Top-Down Approach using Memoization) ```python - import sys def f(idx, prev_idx, n, a, dp): @@ -241,10 +230,7 @@ def lis(arr): arr = [10, 22, 9, 33, 21, 50, 41, 60] print("Length of lis is", lis(arr)) ``` + ## Complexity Analysis - **Time Complexity**: O(n * n) for both approaches, where n is the length of the array. - **Space Complexity**: O(n * n) for the memoization table in Top-Down Approach, O(n) in Bottom-Up Approach. - -
    -
    -
    From 71bf180dd5d15b1f4af8a622d076364d477a35c8 Mon Sep 17 00:00:00 2001 From: Krishna Kaushik <131583096+kRiShNa-429407@users.noreply.github.com> Date: Thu, 13 Jun 2024 09:24:26 +0530 Subject: [PATCH 550/656] Update PyTorch_Fundamentals.md --- .../machine-learning/PyTorch_Fundamentals.md | 171 +++++++----------- 1 file changed, 64 insertions(+), 107 deletions(-) diff --git a/contrib/machine-learning/PyTorch_Fundamentals.md b/contrib/machine-learning/PyTorch_Fundamentals.md index a504ffc1..3248a084 100644 --- a/contrib/machine-learning/PyTorch_Fundamentals.md +++ b/contrib/machine-learning/PyTorch_Fundamentals.md @@ -5,9 +5,9 @@ # Import pytorch in our codespace import torch print(torch.__version__) -``` - 2.3.0+cu121 +output -> 2.3.0+cu121 +``` 2.3.0 is the pytorch version and 121 is the cuda version @@ -21,27 +21,26 @@ Now you have already seen how to create a tensor in pytorch. In this notebook i # Scalar tensor ( a zero dimension tensor) scalar = torch.tensor(7) print(scalar) -``` - tensor(7) +output -> tensor(7) +``` ```python # Check the dimension of the above tensor print(scalar.ndim) -``` - 0 - +output -> 0 +``` ```python # To retrieve the number from the tensor we use `item()` print(scalar.item()) -``` - 7 +output -> 7 +``` @@ -49,27 +48,25 @@ print(scalar.item()) # Vector (It is a single dimension tensor but contain many numbers) vector = torch.tensor([1,2]) print(vector) -``` - tensor([1, 2]) - +output -> tensor([1, 2]) +``` ```python # Check the dimensions print(vector.ndim) -``` - 1 - +output -> 1 +``` ```python # Check the shape of the vector print(vector.shape) -``` - torch.Size([2]) +output -> torch.Size([2]) +``` The above returns torch.Size([2]) which means our vector has a shape of [2]. This is because of the two elements we placed inside the square brackets ([1,2]) @@ -85,30 +82,27 @@ You can tell the number of dimensions a tensor in PyTorch has by the number of s MATRIX = torch.tensor([[1,2], [4,5]]) print(MATRIX) -``` - tensor([[1, 2], +output -> tensor([[1, 2], [4, 5]]) - +``` There are two brackets so it must be 2 dimensions , lets check ```python print(MATRIX.ndim) -``` - 2 - +output -> 2 +``` ```python # Shape print(MATRIX.shape) -``` - torch.Size([2, 2]) - +output -> torch.Size([2, 2]) +``` It means MATRIX has 2 rows and 2 columns. @@ -119,30 +113,27 @@ TENSOR = torch.tensor([[[1,2,3], [4,5,6], [7,8,9]]]) print(TENSOR) -``` - tensor([[[1, 2, 3], +output -> tensor([[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]) - +``` ```python # Let's check the dimensions print(TENSOR.ndim) -``` - 3 - +output -> 3 +``` ```python # shape? print(TENSOR.shape) -``` - torch.Size([1, 3, 3]) - +output -> torch.Size([1, 3, 3]) +``` The dimensions go outer to inner. @@ -164,39 +155,35 @@ We can create them using `torch.rand()` and passing in the `size` parameter. # creating a random tensor of size (3,4) rand_tensor = torch.rand(size = (3,4)) print(rand_tensor) -``` - tensor([[0.7462, 0.4950, 0.7851, 0.8277], +output -> tensor([[0.7462, 0.4950, 0.7851, 0.8277], [0.6112, 0.5159, 0.1728, 0.6847], [0.4472, 0.1612, 0.6481, 0.3236]]) - +``` ```python # Check the dimensions print(rand_tensor.ndim) -``` - 2 - +output -> 2 +``` ```python # Shape print(rand_tensor.shape) -``` - torch.Size([3, 4]) - +output -> torch.Size([3, 4]) +``` ```python # datatype print(rand_tensor.dtype) -``` - torch.float32 - +output -> torch.float32 +``` ### Zeros and ones @@ -207,24 +194,22 @@ Here we will create a tensor of any shape filled with zeros and ones # Create a tensor of all zeros zeros = torch.zeros(size = (3,4)) print(zeros) -``` - tensor([[0., 0., 0., 0.], +output -> tensor([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]]) - +``` ```python # create a tensor of ones ones = torch.ones(size = (3,4)) print(ones) -``` - tensor([[1., 1., 1., 1.], +output -> tensor([[1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.]]) - +``` ### Create a tensor having range of numbers @@ -244,10 +229,9 @@ zero_to_ten = torch.arange(start = 0, end = 10, step = 1) print(zero_to_ten) -``` - tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) - +output -> tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) +``` # 2. Manipulating tensors (tensor operations) @@ -265,10 +249,9 @@ The operations are : ```python tensor = torch.tensor([1,2,3]) print(tensor+10) -``` - tensor([11, 12, 13]) - +output -> tensor([11, 12, 13]) +``` We have add 10 to each tensor element. @@ -276,10 +259,9 @@ We have add 10 to each tensor element. ```python tensor1 = torch.tensor([4,5,6]) print(tensor+tensor1) -``` - tensor([5, 7, 9]) - +output -> tensor([5, 7, 9]) +``` We have added two tensors , remember that addition takes place element wise. @@ -288,20 +270,18 @@ We have added two tensors , remember that addition takes place element wise. ```python print(tensor-8) -``` - tensor([-7, -6, -5]) - +output -> tensor([-7, -6, -5]) +``` We've subtracted 8 from the above tensor. ```python print(tensor-tensor1) -``` - tensor([-3, -3, -3]) - +output -> tensor([-3, -3, -3]) +``` ### 3. Multiplication @@ -309,10 +289,9 @@ print(tensor-tensor1) ```python # Multiply the tensor with 10 (element wise) print(tensor*10) -``` - tensor([10, 20, 30]) - +output -> tensor([10, 20, 30]) +``` Each element of tensor gets multiplied by 10. @@ -324,18 +303,16 @@ PyTorch also has a bunch of built-in functions like `torch.mul()` (short for mul ```python # let's see them print(torch.add(tensor,10)) -``` - tensor([11, 12, 13]) - +output -> tensor([11, 12, 13]) +``` ```python print(torch.mul(tensor,10)) -``` - tensor([10, 20, 30]) - +output -> tensor([10, 20, 30]) +``` ### Matrix multiplication (is all you need) One of the most common operations in machine learning and deep learning algorithms (like neural networks) is matrix multiplication. @@ -366,61 +343,41 @@ tensor2 = torch.tensor([[[1,1,1], [3,3,3]]]) print(tensor1) , print(tensor2) -``` - tensor([[[1, 2, 3], +output1 -> tensor([[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]) - tensor([[[1, 1, 1], +output2 -> tensor([[[1, 1, 1], [2, 2, 2], [3, 3, 3]]]) - - - - - - (None, None) - - - +``` ```python # let's check the shape print(tensor1.shape) , print(tensor2.shape) -``` - - torch.Size([1, 3, 3]) - torch.Size([1, 3, 3]) - - - - - - (None, None) - - +output1 -> torch.Size([1, 3, 3]) +output2 ->torch.Size([1, 3, 3]) +``` ```python # Matrix multiplication print(torch.matmul(tensor1, tensor2)) -``` - tensor([[[14, 14, 14], +output -> tensor([[[14, 14, 14], [32, 32, 32], [50, 50, 50]]]) - +``` ```python # Can also use the "@" symbol for matrix multiplication, though not recommended print(tensor1 @ tensor2) -``` - tensor([[[14, 14, 14], +output -> tensor([[[14, 14, 14], [32, 32, 32], [50, 50, 50]]]) - +``` Note: From 9ac46ea0c122e6ec0d32de20fa5aedf9783fdc0a Mon Sep 17 00:00:00 2001 From: yashasvi232 <154299201+yashasvi232@users.noreply.github.com> Date: Thu, 13 Jun 2024 22:19:22 +0530 Subject: [PATCH 551/656] Add files via upload --- .../images/Dijkstra's_algorithm photo 1.png | Bin 0 -> 36497 bytes .../images/Dijkstra's_algorithm photo2.png | Bin 0 -> 32050 bytes .../images/Dijkstra's_algorithm photo3.png | Bin 0 -> 29460 bytes .../images/Dijkstra's_algorithm photo4.png | Bin 0 -> 34236 bytes .../images/Dijkstra's_algorithm photo5.png | Bin 0 -> 34352 bytes .../images/Dijkstra's_algorithm photo6.png | Bin 0 -> 27225 bytes .../images/Dijkstra's_algorithm photo7.png | Bin 0 -> 26334 bytes 7 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 contrib/ds-algorithms/images/Dijkstra's_algorithm photo 1.png create mode 100644 contrib/ds-algorithms/images/Dijkstra's_algorithm photo2.png create mode 100644 contrib/ds-algorithms/images/Dijkstra's_algorithm photo3.png create mode 100644 contrib/ds-algorithms/images/Dijkstra's_algorithm photo4.png create mode 100644 contrib/ds-algorithms/images/Dijkstra's_algorithm photo5.png create mode 100644 contrib/ds-algorithms/images/Dijkstra's_algorithm photo6.png create mode 100644 contrib/ds-algorithms/images/Dijkstra's_algorithm photo7.png diff --git a/contrib/ds-algorithms/images/Dijkstra's_algorithm photo 1.png b/contrib/ds-algorithms/images/Dijkstra's_algorithm photo 1.png new file mode 100644 index 0000000000000000000000000000000000000000..b937f046e0f4e7c97d3766ffc61c1dd9be2f2414 GIT binary patch literal 36497 zcmeEuUdI2z(-d*wO-i zLv(&8FNIJ#M7a(Af?_VIEQx?n5r=tej0*ma?(j<6836&`1^x?h@dLF70zxd~YZ*y( z4}<+oRM^w5>+Z)oe29TWSf8XE(eWES0v@5iErt>3%}T{fI5K^GmHRdjm60q!)U~}GiDC_c4{~?+iilpU0@$XYLNj6x_zYmLOi0A+PW4AgkrrrO(5{_E?-#3Ul1Lyu- zCk?}-&Gz5*lu#>{`Tx6I;Qzne{~NK3-)0n0O%s4WwZ5kvVK_M1A1|~$4x27l`OGT1 zh@-}nHFTI=b|=|~DAer!%ZxqjerHqO_Gsl#;+t4C$ZXVq z3Aq!S63n0Hm8i^*-sE*2iI}@U^{kQF_iE*+mp+crq~&qj$MfHsbE~$qA^-crqj}kj zS?e-~DSdY$lh2ZR_KnBg#y9)5ad{1tMt4*%T`aD~v+oHv)ym!0tW1=2%m3xJlD_{7 zZxEi?rWmx~i`+0O@<}a7jFgfY?CxUDkEXH0N&N3tzVUGv31=s%8u|Z%AflD%NQrKj!eaWKdu@VxJ z_Tk4xL8^fc|5U<7V6yk+f_rxRtSt$_I60N&82lxCfo?)U@=4uhP0VS*b?Uvc4YFwF zvO;AKl{cY@R8_OCLq^J#s@kf<4i|XPO3DG#a(Q|#qp~4sxI-^@i>Hk$HLk9Q{iq5b zZa2l(lA2rV>>dqrGj?$>5czjgUb%RNYr$*89gT81X`$F$`2NtvlpU*fiygVv#rAlQ z*a9?MUqQiPGt<4BxMybMr}^-fyoMFZ5BH^y0{0i6ori?hYCan{555RthZl-*;)$M` zZ*ptB7Y@_LVX~Se6~kgTGWtf*-XUwxjZ`Y_F!dFss?U-XVx>7HN4;u+K`;@e5@aAmdT4sW9#00x8rVPCI36U1y zF%?J2CwasfVH@n;6mHv!UzWR;?Xeo+(1uFV>CMv}D7-Rf--2c6cjsCmeOHj8!}3e? 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9961a0c9f80160a2b28db22b802b45c77ff22d1f Mon Sep 17 00:00:00 2001 From: yashasvi232 <154299201+yashasvi232@users.noreply.github.com> Date: Thu, 13 Jun 2024 22:30:27 +0530 Subject: [PATCH 553/656] Rename Dijkstra's_algorithm photo2.png to Dijkstra's_algorithm_photo2.png --- ...m photo2.png => Dijkstra's_algorithm_photo2.png} | Bin 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/ds-algorithms/images/{Dijkstra's_algorithm photo2.png => Dijkstra's_algorithm_photo2.png} (100%) diff --git a/contrib/ds-algorithms/images/Dijkstra's_algorithm photo2.png b/contrib/ds-algorithms/images/Dijkstra's_algorithm_photo2.png similarity index 100% rename from contrib/ds-algorithms/images/Dijkstra's_algorithm photo2.png rename to contrib/ds-algorithms/images/Dijkstra's_algorithm_photo2.png From 3e7e46969390235b3c12fbef8be0522f52dc4155 Mon Sep 17 00:00:00 2001 From: yashasvi232 <154299201+yashasvi232@users.noreply.github.com> Date: Thu, 13 Jun 2024 22:31:20 +0530 Subject: [PATCH 554/656] Rename Dijkstra's_algorithm photo3.png to Dijkstra's_algorithm_photo3.png --- ...m photo3.png => Dijkstra's_algorithm_photo3.png} | Bin 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/ds-algorithms/images/{Dijkstra's_algorithm photo3.png => Dijkstra's_algorithm_photo3.png} (100%) diff --git a/contrib/ds-algorithms/images/Dijkstra's_algorithm photo3.png b/contrib/ds-algorithms/images/Dijkstra's_algorithm_photo3.png similarity index 100% rename from contrib/ds-algorithms/images/Dijkstra's_algorithm photo3.png rename to contrib/ds-algorithms/images/Dijkstra's_algorithm_photo3.png From 77f20e9908d4be3e616e9ccefd4f0e5c9c672648 Mon Sep 17 00:00:00 2001 From: yashasvi232 <154299201+yashasvi232@users.noreply.github.com> Date: Thu, 13 Jun 2024 22:31:43 +0530 Subject: [PATCH 555/656] Rename Dijkstra's_algorithm photo4.png to Dijkstra's_algorithm_photo4.png --- ...m photo4.png => Dijkstra's_algorithm_photo4.png} | Bin 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/ds-algorithms/images/{Dijkstra's_algorithm photo4.png => Dijkstra's_algorithm_photo4.png} (100%) diff --git a/contrib/ds-algorithms/images/Dijkstra's_algorithm photo4.png b/contrib/ds-algorithms/images/Dijkstra's_algorithm_photo4.png similarity index 100% rename from contrib/ds-algorithms/images/Dijkstra's_algorithm photo4.png rename to contrib/ds-algorithms/images/Dijkstra's_algorithm_photo4.png From c32dc12b1a7ea1dd1bcf6e1576f308b01d4e3d56 Mon Sep 17 00:00:00 2001 From: yashasvi232 <154299201+yashasvi232@users.noreply.github.com> Date: Thu, 13 Jun 2024 22:32:03 +0530 Subject: [PATCH 556/656] Rename Dijkstra's_algorithm photo5.png to Dijkstra's_algorithm_photo5.png --- ...m photo5.png => Dijkstra's_algorithm_photo5.png} | Bin 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/ds-algorithms/images/{Dijkstra's_algorithm photo5.png => Dijkstra's_algorithm_photo5.png} (100%) diff --git a/contrib/ds-algorithms/images/Dijkstra's_algorithm photo5.png b/contrib/ds-algorithms/images/Dijkstra's_algorithm_photo5.png similarity index 100% rename from contrib/ds-algorithms/images/Dijkstra's_algorithm photo5.png rename to contrib/ds-algorithms/images/Dijkstra's_algorithm_photo5.png From 01207f441c592d8a00411aa41b89f533458da6ef Mon Sep 17 00:00:00 2001 From: yashasvi232 <154299201+yashasvi232@users.noreply.github.com> Date: Thu, 13 Jun 2024 22:32:22 +0530 Subject: [PATCH 557/656] Rename Dijkstra's_algorithm photo6.png to Dijkstra's_algorithm_photo6.png --- ...m photo6.png => Dijkstra's_algorithm_photo6.png} | Bin 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/ds-algorithms/images/{Dijkstra's_algorithm photo6.png => Dijkstra's_algorithm_photo6.png} (100%) diff --git a/contrib/ds-algorithms/images/Dijkstra's_algorithm photo6.png b/contrib/ds-algorithms/images/Dijkstra's_algorithm_photo6.png similarity index 100% rename from contrib/ds-algorithms/images/Dijkstra's_algorithm photo6.png rename to contrib/ds-algorithms/images/Dijkstra's_algorithm_photo6.png From d98376b7da3417e9b1b8bb60b00e0e0092c86267 Mon Sep 17 00:00:00 2001 From: yashasvi232 <154299201+yashasvi232@users.noreply.github.com> Date: Thu, 13 Jun 2024 22:32:40 +0530 Subject: [PATCH 558/656] Rename Dijkstra's_algorithm photo7.png to Dijkstra's_algorithm_photo7.png --- ...m photo7.png => Dijkstra's_algorithm_photo7.png} | Bin 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/ds-algorithms/images/{Dijkstra's_algorithm photo7.png => Dijkstra's_algorithm_photo7.png} (100%) diff --git a/contrib/ds-algorithms/images/Dijkstra's_algorithm photo7.png b/contrib/ds-algorithms/images/Dijkstra's_algorithm_photo7.png similarity index 100% rename from contrib/ds-algorithms/images/Dijkstra's_algorithm photo7.png rename to contrib/ds-algorithms/images/Dijkstra's_algorithm_photo7.png From 85ecb2cabcda3c16c72480e6268f1e946d36c76d Mon Sep 17 00:00:00 2001 From: yashasvi232 <154299201+yashasvi232@users.noreply.github.com> Date: Thu, 13 Jun 2024 22:33:49 +0530 Subject: [PATCH 559/656] Update Dijkstra's_algorithm.md --- contrib/ds-algorithms/Dijkstra's_algorithm.md | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/contrib/ds-algorithms/Dijkstra's_algorithm.md b/contrib/ds-algorithms/Dijkstra's_algorithm.md index 18845f6b..cea6da40 100644 --- a/contrib/ds-algorithms/Dijkstra's_algorithm.md +++ b/contrib/ds-algorithms/Dijkstra's_algorithm.md @@ -13,23 +13,25 @@ Dijkstra's algorithm is a graph algorithm that gives the shortest distance of ea ## Dry Run We will now do a manual simulation using an example graph given. First, (0, a) is pushed to the priority queue (pq). -![gssoc photo 1](https://github.com/animator/learn-python/assets/154299201/713cf7dd-fa8a-4f81-829f-00ac33eb12f4) +![Photo 1](images/Dijkstra's_algorithm_photo1.png) + - **Step1:** The lowest element is popped from the pq, which is (0, a), and all its neighboring nodes are added to the pq while simultaneously checking the distance list. Thus (3, b), (7, c), (1, d) are added to the pq. -![gssoc photo2](https://github.com/animator/learn-python/assets/154299201/9456b701-7877-44dc-854b-da416c5e2e9b) +![Photo 2](images/Dijkstra's_algorithm_photo2.png) + - **Step2:** Again, the lowest element is popped from the pq, which is (1, d). It has two neighboring nodes, a and e, from which (0 + 1, a) will not be added to the pq as dist[a] = 0 is less than 1. -![gssoc photo3](https://github.com/animator/learn-python/assets/154299201/83990a40-f3fc-4173-9e21-10eb9e2003cb) +![Photo 3](images/Dijkstra's_algorithm_photo3.png) - **Step3:** Now, the lowest element is popped from the pq, which is (3, b). It has two neighboring nodes, a and c, from which (0 + 1, a) will not be added to the pq. But the new distance to reach c is 5 (3 + 2), which is less than dist[c] = 7. So (5, c) is added to the pq. -![gssoc photo4](https://github.com/animator/learn-python/assets/154299201/b47eb063-8ce7-4e68-85a5-da98f5ac9a3d) +![Photo 4](images/Dijkstra's_algorithm_photo4.png) - **Step4:** The next smallest element is (5, c). It has neighbors a and e. The new distance to reach a will be 5 + 7 = 12, which is more than dist[a], so it will not be considered. Similarly, the new distance for e is 5 + 3 = 8, which again will not be considered. So, no new tuple has been added to the pq. -![gssoc photo5](https://github.com/animator/learn-python/assets/154299201/c0ef3c2a-1938-4c33-9e07-0929e8e5e631) +![Photo 5](images/Dijkstra's_algorithm_photo5.png) - **Step5:** Similarly, both the elements of the pq will be popped one by one without any new addition. -![gssoc photo6](https://github.com/animator/learn-python/assets/154299201/828739c6-413b-41f6-a09f-b702802b20e1) -![gssoc photo7](https://github.com/animator/learn-python/assets/154299201/488d4c7b-d7bf-4887-a9c5-04accc6c6c89) +![Photo 6](images/Dijkstra's_algorithm_photo6.png) +![Photo 7](images/Dijkstra's_algorithm_photo7.png) - The distance list we get at the end will be our answer. - `Output` `dist=[1, 3, 7, 1, 6]` From 848af819957763fa9a3231cd62265e2fb6081057 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 15 Jun 2024 23:34:33 +0530 Subject: [PATCH 560/656] Rename Dijkstra's_algorithm.md to dijkstra.md --- contrib/ds-algorithms/{Dijkstra's_algorithm.md => dijkstra.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/ds-algorithms/{Dijkstra's_algorithm.md => dijkstra.md} (100%) diff --git a/contrib/ds-algorithms/Dijkstra's_algorithm.md b/contrib/ds-algorithms/dijkstra.md similarity index 100% rename from contrib/ds-algorithms/Dijkstra's_algorithm.md rename to contrib/ds-algorithms/dijkstra.md From fbc528b00bb794b358064b0585cbc1da23bd0d93 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 15 Jun 2024 23:34:50 +0530 Subject: [PATCH 561/656] Update index.md --- contrib/ds-algorithms/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index e1d12ce8..047b3dd7 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -18,4 +18,4 @@ - [Hashing through Chaining](hashing-chaining.md) - [AVL Trees](avl-trees.md) - [Splay Trees](splay-trees.md) -- [Dijkstra's Algorithm](Dijkstra's_algorithm.md) \ No newline at end of file +- [Dijkstra's Algorithm](dijkstra.md) From 8be8ae0b6e3ed7eab44655b91ce5790b56d27038 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Tue, 18 Jun 2024 03:15:08 +0530 Subject: [PATCH 562/656] Rename Universal-Functions.md to universal-functions.md --- contrib/numpy/{Universal-Functions.md => universal-functions.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/numpy/{Universal-Functions.md => universal-functions.md} (100%) diff --git a/contrib/numpy/Universal-Functions.md b/contrib/numpy/universal-functions.md similarity index 100% rename from contrib/numpy/Universal-Functions.md rename to contrib/numpy/universal-functions.md From 7f43373d0e9844dbb366e23b559794c3d5301f1f Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Tue, 18 Jun 2024 03:15:29 +0530 Subject: [PATCH 563/656] Update index.md --- contrib/numpy/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 2b0c357e..a61195b8 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -11,4 +11,4 @@ - [Sorting NumPy Arrays](sorting-array.md) - [NumPy Array Iteration](array-iteration.md) - [Concatenation of Arrays](concatenation-of-arrays.md) -- [Universal Functions (Ufunc)](Universal-Functions.md) +- [Universal Functions (Ufunc)](universal-functions.md) From 696714e8422ad323097532659d813451f6076334 Mon Sep 17 00:00:00 2001 From: Ramya Korupolu <104009248+Ramya-korupolu@users.noreply.github.com> Date: Fri, 21 Jun 2024 15:19:29 +0530 Subject: [PATCH 564/656] Create Tree-Traversal.md --- contrib/ds-algorithms/Tree-Traversal.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 contrib/ds-algorithms/Tree-Traversal.md diff --git a/contrib/ds-algorithms/Tree-Traversal.md b/contrib/ds-algorithms/Tree-Traversal.md new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/contrib/ds-algorithms/Tree-Traversal.md @@ -0,0 +1 @@ + From 2d25b8ea41556a1a9ba66237dceecea0df91053d Mon Sep 17 00:00:00 2001 From: Ramya Korupolu <104009248+Ramya-korupolu@users.noreply.github.com> Date: Fri, 21 Jun 2024 16:33:57 +0530 Subject: [PATCH 565/656] Update Tree-Traversal.md --- contrib/ds-algorithms/Tree-Traversal.md | 181 ++++++++++++++++++++++++ 1 file changed, 181 insertions(+) diff --git a/contrib/ds-algorithms/Tree-Traversal.md b/contrib/ds-algorithms/Tree-Traversal.md index 8b137891..2df72027 100644 --- a/contrib/ds-algorithms/Tree-Traversal.md +++ b/contrib/ds-algorithms/Tree-Traversal.md @@ -1 +1,182 @@ +# Tree Traversal Algorithms +Tree Traversal refers to the process of visiting or accessing each node of the tree exactly once in a certain order. Tree traversal algorithms help us to visit and process all the nodes of the tree. Since tree is not a linear data structure, there are multiple nodes which we can visit after visiting a certain node. There are multiple tree traversal techniques which decide the order in which the nodes of the tree are to be visited. + + +A Tree Data Structure can be traversed in following ways: +- **Level Order Traversal or Breadth First Search or BFS** + - **Depth First Search or DFS** + - Inorder Traversal + - Preorder Traversal + - Postorder Traversal + +## Binary Tree Structure + Before diving into traversal techniques, let's define a simple binary tree node structure: + ```python +class Node: + def __init__(self, key): + self.leftChild = None + self.rightChild = None + self.data = key + +# Main class +if __name__ == "__main__": + root = Node(1) + root.leftChild = Node(2) + root.rightChild = Node(3) + root.leftChild.leftChild = Node(4) + root.leftChild.rightChild = Node(5) + root.rightChild.leftChild = Node(6) + root.rightChild.rightChild = Node(6) +``` + +## Level Order Traversal +When the nodes of the tree are wrapped in a level-wise mode from left to right, then it represents the level order traversal. We can use a queue data structure to execute a level order traversal. + +### Algorithm + - Create an empty queue Q + - Enqueue the root node of the tree to Q + - Loop while Q is not empty + - Dequeue a node from Q and visit it + - Enqueue the left child of the dequeued node if it exists + - Enqueue the right child of the dequeued node if it exists + +### code for level order traversal in python +```python +def printLevelOrder(root): + if root is None: + return + + # Create an empty queue + queue = [] + + # Enqueue Root and initialize height + queue.append(root) + + while(len(queue) > 0): + + # Print front of queue and + # remove it from queue + print(queue[0].data, end=" ") + node = queue.pop(0) + + # Enqueue left child + if node.left is not None: + queue.append(node.left) + + # Enqueue right child + if node.right is not None: + queue.append(node.right) +``` + +**output** + +` Inorder traversal of binary tree is : +1 2 3 4 5 6 7 ` + + + +## Depth First Search +When we do a depth-first traversal, we travel in one direction up to the bottom first, then turn around and go the other way. There are three kinds of depth-first traversals. + +## 1. Inorder Traversal + +In this traversal method, the left subtree is visited first, then the root and later the right sub-tree. We should always remember that every node may represent a subtree itself. + +`Note :` If a binary search tree is traversed in-order, the output will produce sorted key values in an ascending order. + +**The order:** Left -> Root -> Right + +### Algorithm + - Traverse the left subtree. + - Visit the root node. + - Traverse the right subtree. + +### code for inorder traversal in python +```python +def printInorder(root): + if root: + # First recur on left child + printInorder(root.left) + + # Then print the data of node + print(root.val, end=" "), + + # Now recur on right child + printInorder(root.right) +``` + +**output** + +` Inorder traversal of binary tree is : +4 2 5 1 6 3 7 ` + + +## 2. Preorder Traversal + +In this traversal method, the root node is visited first, then the left subtree and finally the right subtree. + +**The order:** Root -> Left -> Right + +### Algorithm + - Visit the root node. + - Traverse the left subtree. + - Traverse the right subtree. + +### code for preorder traversal in python +```python +def printPreorder(root): + if root: + # First print the data of node + print(root.val, end=" "), + + # Then recur on left child + printPreorder(root.left) + + # Finally recur on right child + printPreorder(root.right) +``` + +**output** + +` Inorder traversal of binary tree is : +1 2 4 5 3 6 7 ` + +## 3. Postorder Traversal + +In this traversal method, the root node is visited last, hence the name. First we traverse the left subtree, then the right subtree and finally the root node. + +**The order:** Left -> Right -> Root + +### Algorithm + - Traverse the left subtree. + - Traverse the right subtree. + - Visit the root node. + +### code for postorder traversal in python +```python +def printPostorder(root): + if root: + # First recur on left child + printPostorder(root.left) + + # The recur on right child + printPostorder(root.right) + + # Now print the data of node + print(root.val, end=" ") +``` + +**output** + +` Inorder traversal of binary tree is : +4 5 2 6 7 3 1 ` + + +## Complexity Analysis + - **Time Complexity:** All three tree traversal methods (Inorder, Preorder, and Postorder) have a time complexity of `𝑂(𝑛)`, where 𝑛 is the number of nodes in the tree. + - **Space Complexity:** The space complexity is influenced by the recursion stack. 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b/contrib/ds-algorithms/Tree-Traversal.md @@ -4,14 +4,21 @@ Tree Traversal refers to the process of visiting or accessing each node of the t A Tree Data Structure can be traversed in following ways: -- **Level Order Traversal or Breadth First Search or BFS** + - **Level Order Traversal or Breadth First Search or BFS** - **Depth First Search or DFS** - Inorder Traversal - Preorder Traversal - Postorder Traversal + + ![Tree Traversal](images/traversal.png) + + ## Binary Tree Structure Before diving into traversal techniques, let's define a simple binary tree node structure: + +![Binary Tree](images/binarytree.png) + ```python class Node: def __init__(self, key): @@ -85,6 +92,8 @@ In this traversal method, the left subtree is visited first, then the root and l `Note :` If a binary search tree is traversed in-order, the output will produce sorted key values in an ascending order. +![Inorder](images/inorder-traversal.png) + **The order:** Left -> Root -> Right ### Algorithm @@ -116,6 +125,8 @@ def printInorder(root): In this traversal method, the root node is visited first, then the left subtree and finally the right subtree. +![preorder](images/preorder-traversal.png) + **The order:** Root -> Left -> Right ### Algorithm @@ -146,6 +157,8 @@ def printPreorder(root): In this traversal method, the root node is visited last, hence the name. First we traverse the left subtree, then the right subtree and finally the root node. +![postorder](images/postorder-traversal.png) + **The order:** Left -> Right -> Root ### Algorithm From 86afd64a7bd7c9e265282e095b459f0e64928d6d Mon Sep 17 00:00:00 2001 From: Ramya Korupolu <104009248+Ramya-korupolu@users.noreply.github.com> Date: Fri, 21 Jun 2024 16:59:56 +0530 Subject: [PATCH 570/656] Update index.md --- contrib/ds-algorithms/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 047b3dd7..274c4be9 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -19,3 +19,4 @@ - [AVL Trees](avl-trees.md) - [Splay Trees](splay-trees.md) - [Dijkstra's Algorithm](dijkstra.md) +- [Tree Traversals](Tree-Traversal.md) From 8736789d297cd9300ca357668e6903c1dbff1ae4 Mon Sep 17 00:00:00 2001 From: piyush-poddar Date: Sat, 22 Jun 2024 04:36:40 +0530 Subject: [PATCH 571/656] Introduction to Bar Plots in Plotly --- .../images/plotly-bar-colors-basic.png | Bin 0 -> 25977 bytes .../images/plotly-bar-colors.png | Bin 0 -> 24663 bytes .../images/plotly-bar-labels-1.png | Bin 0 -> 26606 bytes .../images/plotly-bar-labels-2.png | Bin 0 -> 27233 bytes .../images/plotly-bar-labels-3.png | Bin 0 -> 26751 bytes .../images/plotly-bar-title.png | Bin 0 -> 23149 bytes .../images/plotly-basic-bar-plot.png | Bin 0 -> 21916 bytes .../images/plotly-horizontal-bar-plot.png | Bin 0 -> 29742 bytes .../images/plotly-long-format-bar-plot.png | Bin 0 -> 22016 bytes .../images/plotly-rounded-bars.png | Bin 0 -> 27343 bytes .../images/plotly-wide-format-bar-plot.png | Bin 0 -> 22469 bytes contrib/plotting-visualization/index.md | 1 + .../plotly-bar-plots.md | 348 ++++++++++++++++++ 13 files changed, 349 insertions(+) create mode 100644 contrib/plotting-visualization/images/plotly-bar-colors-basic.png create mode 100644 contrib/plotting-visualization/images/plotly-bar-colors.png create mode 100644 contrib/plotting-visualization/images/plotly-bar-labels-1.png create mode 100644 contrib/plotting-visualization/images/plotly-bar-labels-2.png create mode 100644 contrib/plotting-visualization/images/plotly-bar-labels-3.png create mode 100644 contrib/plotting-visualization/images/plotly-bar-title.png create mode 100644 contrib/plotting-visualization/images/plotly-basic-bar-plot.png create mode 100644 contrib/plotting-visualization/images/plotly-horizontal-bar-plot.png create mode 100644 contrib/plotting-visualization/images/plotly-long-format-bar-plot.png create mode 100644 contrib/plotting-visualization/images/plotly-rounded-bars.png create mode 100644 contrib/plotting-visualization/images/plotly-wide-format-bar-plot.png create mode 100644 contrib/plotting-visualization/plotly-bar-plots.md diff --git a/contrib/plotting-visualization/images/plotly-bar-colors-basic.png b/contrib/plotting-visualization/images/plotly-bar-colors-basic.png new file mode 100644 index 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b/contrib/plotting-visualization/index.md index 7e43d9b2..33f3a17a 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -8,3 +8,4 @@ - [Scatter Plots in Matplotlib](matplotlib-scatter-plot.md) - [Introduction to Seaborn and Installation](seaborn-intro.md) - [Getting started with Seaborn](seaborn-basics.md) +- [Bar Plots in Plotly](plotly-bar-plots.md) diff --git a/contrib/plotting-visualization/plotly-bar-plots.md b/contrib/plotting-visualization/plotly-bar-plots.md new file mode 100644 index 00000000..5f2159a8 --- /dev/null +++ b/contrib/plotting-visualization/plotly-bar-plots.md @@ -0,0 +1,348 @@ +# Bar Plots in Plotly + +A bar plot or a bar chart is a type of data visualisation that represents data in the form of rectangular bars, with lengths or heights proportional to the values and data which they represent. The bar plots can be plotted both vertically and horizontally. + +It is one of the most widely used type of data visualisation as it is easy to interpret and is pleasing to the eyes. + +Plotly is a very powerful library for creating modern visualizations and it provides a very easy and intuitive method to create highly customized bar plots. + +## Prerequisites + +Before creating bar plots in Plotly you must ensure that you have Python, Plotly and Pandas installed on your system. + +## Introduction + +There are various ways to create bar plots in `plotly`. One of the prominent and easiest one is using `plotly.express`. Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures. On the other hand you can also use `plotly.graph_objects` to create various plots. + +Here, we'll be using `plotly.express` to create the bar plots. Also we'll be converting our datasets into pandas DataFrames which makes it extremely convenient to create plots. + +Also, note that when you execute the codes in a simple python file, the output plot will be shown in your **browser**, rather than a pop-up window like in matplotlib. If you do not want that, it is **recommended to create the plots in a notebook (like jupyter)**. For this, install an additional library `nbformat`. This way you can see the output on the notebook itself, and can also render its format to png, jpg, etc. + +## Creating a simple bar plot using `plotly.express.bar` + +With `plotly.express.bar`, each row of the DataFrame is represented as a rectangular mark. + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] + +# Converting dataset to pandas DataFrame +dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold} +df = pd.DataFrame(dataset) + +# Creating bar plot +fig = px.bar(df, x='Years', y='Number of Cars sold') + +# Showing plot +fig.show() +``` +![Basic Bar Plot](images/plotly-basic-bar-plot.png) + +Here, we are first creating the dataset and converting it into Pandas DataFrames using dictionaries, with its keys being DataFrame columns. Next, we are plotting the bar chart by using `px.bar`. In the `x` and `y` parameters, we have to specify a column name in the DataFrame. + +**Note:** When you generate the image using above code, it will show you an **interactive plot**, if you want image, you can download it from their itself. + +## Customizing Bar Plots + +### Adding title to the graph + +Let us create an imaginary graph of number of cars sold in a various years. Simply pass the title of your graph as a parameter in `px.bar`. + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] + +# Converting dataset to pandas DataFrame +dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold} +df = pd.DataFrame(dataset) + +# Creating bar plot +fig = px.bar(df, x='Years', y='Number of Cars sold', + title='Number of cars sold in various years') + +# Showing plot +fig.show() +``` +![Bar Plot Title](images/plotly-bar-title.png) + +### Adding bar colors and legends + +To add different colors to different bars, simply pass the column name of the x-axis or a custom column which groups different bars in `color` parameter. + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] + +# Converting dataset to pandas DataFrame +dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold} +df = pd.DataFrame(dataset) + +# Creating bar plot +fig = px.bar(df, x='Years', y='Number of Cars sold', + title='Number of cars sold in various years', + color='Years') + +# Showing plot +fig.show() +``` +![Bar Colors Basic](images/plotly-bar-colors-basic.png) + +Now, let us consider our previous example of number of cars sold in various years and suppose that we want to add different colors to the bars from different centuries and respective legends for better interpretation. + +The easiest way to achieve this is to add a new column to the dataframe and then pass it to the `color` parameter. + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] +# Creating the relevant colors dataset +colors = ['1900s','1900s','2000s','2000s','2000s'] + +# Converting dataset to pandas DataFrame +dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors} +df = pd.DataFrame(dataset) + +# Creating bar plot +fig = px.bar(df, x='Years', y='Number of Cars sold', + title='Number of cars sold in various years', + color='Century') + +# Showing plot +fig.show() +``` +![Bar Colors](images/plotly-bar-colors.png) + +### Adding labels to bars + +We may want to add labels to bars representing their absolute (or truncated) values for instant and accurate reading. This can be achieved by setting `text_auto` parameter to `True`. If you want custom text then you can pass a column name to the `text` parameter. + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] +colors = ['1900s','1900s','2000s','2000s','2000s'] + +# Converting dataset to pandas DataFrame +dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors} +df = pd.DataFrame(dataset) + +# Creating bar plot +fig = px.bar(df, x='Years', y='Number of Cars sold', + title='Number of cars sold in various years', + color='Century', + text_auto=True) + +# Showing plot +fig.show() +``` +![Bar Labels-1](images/plotly-bar-labels-1.png) + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] +colors = ['1900s','1900s','2000s','2000s','2000s'] + +# Converting dataset to pandas DataFrame +dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors} +df = pd.DataFrame(dataset) + +# Creating bar plot +fig = px.bar(df, x='Years', y='Number of Cars sold', + title='Number of cars sold in various years', + color='Century', + text='Century') + +# Showing plot +fig.show() +``` +![Bar Labels-2](images/plotly-bar-labels-2.png) + +You can also change the features of text (or any other element of your plot) using `fig.update_traces`. + +Here, we are changing the position of text to position it outside the bars. + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] +colors = ['1900s','1900s','2000s','2000s','2000s'] + +# Converting dataset to pandas DataFrame +dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors} +df = pd.DataFrame(dataset) + +# Creating bar plot +fig = px.bar(df, x='Years', y='Number of Cars sold', + title='Number of cars sold in various years', + color='Century', + text_auto=True) + +# Updating bar text properties +fig.update_traces(textposition="outside", cliponaxis=False) + +# Showing plot +fig.show() +``` +![Bar Labels-3](images/plotly-bar-labels-3.png) + +### Rounded Bars + +You can create rounded by specifying the radius value to `barcornerradius` in `fig.update_layout`. + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] +colors = ['1900s','1900s','2000s','2000s','2000s'] + +# Converting dataset to pandas DataFrame +dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors} +df = pd.DataFrame(dataset) + +# Creating bar plot +fig = px.bar(df, x='Years', y='Number of Cars sold', + title='Number of cars sold in various years', + color='Century', + text_auto=True) + +# Updating bar text properties +fig.update_traces(textposition="outside", cliponaxis=False) + +# Updating figure layout +fig.update_layout({ +'plot_bgcolor': 'rgba(255, 255, 255, 1)', +'paper_bgcolor': 'rgba(255, 255, 255, 1)', +'barcornerradius': 15 +}) + +# Showing plot +fig.show() +``` + +![Rounded Bars](images/plotly-rounded-bars.png) + +## Horizontal Bar Plot + +To create a horizontal bar plot, you just have to interchange your `x` and `y` DataFrame columns. Plotly takes care of the rest! + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] +colors = ['1900s','1900s','2000s','2000s','2000s'] + +# Converting dataset to pandas DataFrame +dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors} +df = pd.DataFrame(dataset) + +# Creating bar plot +fig = px.bar(df, x='Number of Cars sold', y='Years', + title='Number of cars sold in various years', + color='Century', + text_auto=True) + +# Updating bar text properties +fig.update_traces(textposition="outside", cliponaxis=False) + +# Updating figure layout +fig.update_layout({ +'barcornerradius': 30 +}) + +# Showing plot +fig.show() +``` +![Horizontal Bar Plot](images/plotly-horizontal-bar-plot.png) + +## Plotting Long Format and Wide Format Data + +Long-form data has one row per observation, and one column per variable. This is suitable for storing and displaying multivariate data i.e. with dimension greater than 2. + +```Python +# Plotting long format data + +import plotly.express as px + +# Long format dataset +long_df = px.data.medals_long() + +# Creating Bar Plot +fig = px.bar(long_df, x="nation", y="count", color="medal", title="Long-Form Input") + +# Showing Plot +fig.show() +``` +![Long format bar plot](images/plotly-long-format-bar-plot.png) + +```Python +print(long_df) + +# Output + nation medal count +0 South Korea gold 24 +1 China gold 10 +2 Canada gold 9 +3 South Korea silver 13 +4 China silver 15 +5 Canada silver 12 +6 South Korea bronze 11 +7 China bronze 8 +8 Canada bronze 12 +``` + +Wide-form data has one row per value of one of the first variable, and one column per value of the second variable. This is suitable for storing and displaying 2-dimensional data. + +```Python +# Plotting wide format data +import plotly.express as px + +# Wide format dataset +wide_df = px.data.medals_wide() + +# Creating Bar Plot +fig = px.bar(wide_df, x="nation", y=["gold", "silver", "bronze"], title="Wide-Form Input") + +# Showing Plot +fig.show() +``` +![Wide format bar plot](images/plotly-wide-format-bar-plot.png) + +```Python +print(wide_df) + +# Output + nation gold silver bronze +0 South Korea 24 13 11 +1 China 10 15 8 +2 Canada 9 12 12 \ No newline at end of file From 6822f4238aa09e266d340bab9d82950181721d4f Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 15:44:24 +0530 Subject: [PATCH 572/656] Update binary_tree.md --- contrib/ds-algorithms/binary_tree.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/contrib/ds-algorithms/binary_tree.md b/contrib/ds-algorithms/binary_tree.md index 867bca07..03da2cf8 100644 --- a/contrib/ds-algorithms/binary_tree.md +++ b/contrib/ds-algorithms/binary_tree.md @@ -2,7 +2,7 @@ A binary tree is a non-linear data structure in which each node can have atmost two children, known as the left and the right child. It is a heirarchial data structure represented in the following way: -```python +``` A...................Level 0 / \ B C.................Level 1 @@ -25,7 +25,7 @@ A binary tree is a non-linear data structure in which each node can have atmost ## Types Of Binary Trees - **Full Binary Tree:** A binary tree where each node has 0 or 2 children is a full binary tree. -```python +``` A / \ B C @@ -33,7 +33,7 @@ A binary tree is a non-linear data structure in which each node can have atmost D E ``` - **Complete Binary Tree:** A binary tree in which all levels are completely filled except the last level is a complete binary tree. Whenever new nodes are inserted, they are inserted from the left side. -```python +``` A / \ / \ @@ -42,7 +42,7 @@ A binary tree is a non-linear data structure in which each node can have atmost D E F ``` - **Perfect Binary Tree:** A binary tree in which all nodes are completely filled, i.e., each node has two children is called a perfect binary tree. -```python +``` A / \ / \ @@ -51,7 +51,7 @@ A binary tree is a non-linear data structure in which each node can have atmost D E F G ``` - **Skewed Binary Tree:** A binary tree in which each node has either 0 or 1 child is called a skewed binary tree. It is of two types - left skewed binary tree and right skewed binary tree. -```python +``` A A \ / B B @@ -60,7 +60,7 @@ A binary tree is a non-linear data structure in which each node can have atmost Right skewed binary tree Left skewed binary tree ``` - **Balanced Binary Tree:** A binary tree in which the height difference between the left and right subtree is not more than one and the subtrees are also balanced is a balanced binary tree. -```python +``` A / \ B C @@ -218,14 +218,14 @@ b.levelorder(root) root = b.delete_value(root, 20) print("\nLevel order traversal after deletion:", end=" ") b.levelorder(root) +``` +#### OUTPUT -''' -OUTPUT: +``` Preorder traversal: 10 20 40 50 30 60 Inorder traversal: 40 20 50 10 60 30 Postorder traversal: 40 50 20 60 30 10 Level order traversal: 10 20 30 40 50 60 Level order traversal after deletion: 10 60 30 40 50 -''' ``` From 00949d96ec517120f7eac1adc946bc1bd9aeaffa Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 15:44:45 +0530 Subject: [PATCH 573/656] Rename binary_tree.md to binary-tree.md --- contrib/ds-algorithms/{binary_tree.md => binary-tree.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/ds-algorithms/{binary_tree.md => binary-tree.md} (100%) diff --git a/contrib/ds-algorithms/binary_tree.md b/contrib/ds-algorithms/binary-tree.md similarity index 100% rename from contrib/ds-algorithms/binary_tree.md rename to contrib/ds-algorithms/binary-tree.md From 1c0d60b5d67b9155c473eb65549fd7850d2369dc Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 15:45:35 +0530 Subject: [PATCH 574/656] Update index.md --- contrib/ds-algorithms/index.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index dda67135..f6249d1d 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -16,8 +16,7 @@ - [Two Pointer Technique](two-pointer-technique.md) - [Hashing through Linear Probing](hashing-linear-probing.md) - [Hashing through Chaining](hashing-chaining.md) -- [Binary Tree](binary_tree.md) +- [Binary Tree](binary-tree.md) - [AVL Trees](avl-trees.md) - [Splay Trees](splay-trees.md) -- [Splay Trees](splay-trees.md) - [Dijkstra's Algorithm](dijkstra.md) From d977cd8d2e683db43181157df219336ab05ea926 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 15:57:59 +0530 Subject: [PATCH 575/656] Rename Neural network regression.md to neural-network-regression.md --- ...eural network regression.md => neural-network-regression.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/machine-learning/{Neural network regression.md => neural-network-regression.md} (99%) diff --git a/contrib/machine-learning/Neural network regression.md b/contrib/machine-learning/neural-network-regression.md similarity index 99% rename from contrib/machine-learning/Neural network regression.md rename to contrib/machine-learning/neural-network-regression.md index 43301209..aa16bc6c 100644 --- a/contrib/machine-learning/Neural network regression.md +++ b/contrib/machine-learning/neural-network-regression.md @@ -81,4 +81,4 @@ print(f"Test Mean Absolute Error: {mae}") ## Conclusion -Neural Network Regression with Scikit-learn's `MLPRegressor` is a powerful method for predicting continuous values in complex, non-linear scenarios. However, it's essential to ensure that you have enough data to train the model effectively and consider the computational resources required. Simpler models may be more appropriate for small datasets or when model interpretability is necessary. By following the steps outlined, you can build, train, and evaluate a neural network for regression tasks in Python using Scikit-learn. \ No newline at end of file +Neural Network Regression with Scikit-learn's `MLPRegressor` is a powerful method for predicting continuous values in complex, non-linear scenarios. However, it's essential to ensure that you have enough data to train the model effectively and consider the computational resources required. Simpler models may be more appropriate for small datasets or when model interpretability is necessary. By following the steps outlined, you can build, train, and evaluate a neural network for regression tasks in Python using Scikit-learn. From 61e8ed73707194f7c49aef6b399a3b9dd85ddee7 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 15:58:20 +0530 Subject: [PATCH 576/656] Update index.md --- contrib/machine-learning/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 82fbbcc9..d3ae1099 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -20,4 +20,4 @@ - [Grid Search](grid-search.md) - [Transformers](transformers.md) - [K-nearest neighbor (KNN)](knn.md) -- [Neural network regression](Neural network regression.md) +- [Neural network regression](neural-network-regression.md) From f86cdffb92bec774a65f726d218f6fcc222c8e21 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 16:02:57 +0530 Subject: [PATCH 577/656] Update index.md --- contrib/plotting-visualization/index.md | 1 - 1 file changed, 1 deletion(-) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 7a33cc14..67bee6dc 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -1,4 +1,3 @@ # List of sections - [Installing Matplotlib](matplotlib_installation.md) - From 4a6f2692cbe50d0e778682edbcd1a795cad1e2f2 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 16:03:21 +0530 Subject: [PATCH 578/656] Rename SciPy_Graphs.md to scipy-graphs.md --- contrib/scipy/{SciPy_Graphs.md => scipy-graphs.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/scipy/{SciPy_Graphs.md => scipy-graphs.md} (100%) diff --git a/contrib/scipy/SciPy_Graphs.md b/contrib/scipy/scipy-graphs.md similarity index 100% rename from contrib/scipy/SciPy_Graphs.md rename to contrib/scipy/scipy-graphs.md From 915f810b0080d64fb1c651656abe7e3b5b95ad16 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 16:03:38 +0530 Subject: [PATCH 579/656] Update index.md --- contrib/scipy/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/scipy/index.md b/contrib/scipy/index.md index 4916bfff..5425c4ac 100644 --- a/contrib/scipy/index.md +++ b/contrib/scipy/index.md @@ -1,5 +1,5 @@ # List of sections - [Installation of Scipy and its key uses](installation_features.md) -- [SciPy Graphs](SciPy_Graphs.md) +- [SciPy Graphs](scipy-graphs.md) From eacf86cec5b3a8089f7697339a7b7bb5e475a3a9 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 16:06:16 +0530 Subject: [PATCH 580/656] Update scipy-graphs.md --- contrib/scipy/scipy-graphs.md | 48 +++++++++++++++++++++++++++-------- 1 file changed, 38 insertions(+), 10 deletions(-) diff --git a/contrib/scipy/scipy-graphs.md b/contrib/scipy/scipy-graphs.md index 747ede73..60bb5659 100644 --- a/contrib/scipy/scipy-graphs.md +++ b/contrib/scipy/scipy-graphs.md @@ -1,9 +1,11 @@ # SciPy Graphs Graphs are also a type of data structure, SciPy provides a module called scipy.sparse.csgraph for working with graphs. + ## Adjacency Matrix An adjacency matrix is a way of representing a graph using a square matrix. In the matrix, the element at the i-th row and j-th column indicates whether there is an edge from vertex i to vertex j. -``` + +```python import numpy as np from scipy.sparse import csr_matrix @@ -19,14 +21,18 @@ sparse_matrix = csr_matrix(adj_matrix) print(sparse_matrix) ``` + In this example: 1. The graph has 4 nodes. 2. is an edge between node 0 and node 1, node 1 and node 2, and node 2 and node 3. 3. The csr_matrix function converts the dense adjacency matrix into a compressed sparse row (CSR) format, which is efficient for storing large, sparse matrices. + ## Floyd Warshall + The Floyd-Warshall algorithm is a classic algorithm used to find the shortest paths between all pairs of nodes in a weighted graph. -``` + +```python import numpy as np from scipy.sparse.csgraph import floyd_warshall from scipy.sparse import csr_matrix @@ -41,7 +47,9 @@ newarr = csr_matrix(arr) print(floyd_warshall(newarr, return_predecessors=True)) ``` -### Output + +#### Output + ``` (array([[0., 1., 2.], [1., 0., 3.], @@ -49,9 +57,12 @@ print(floyd_warshall(newarr, return_predecessors=True)) [ 1, -9999, 0], [ 2, 0, -9999]], dtype=int32)) ``` + ## Dijkstra + Dijkstra's algorithm is used to find the shortest path from a source node to all other nodes in a graph with non-negative edge weights. -``` + +```python import numpy as np from scipy.sparse.csgraph import dijkstra from scipy.sparse import csr_matrix @@ -66,13 +77,18 @@ newarr = csr_matrix(arr) print(dijkstra(newarr, return_predecessors=True, indices=0)) ``` -### Output + +#### Output + ``` (array([ 0., 1., 2.]), array([-9999, 0, 0], dtype=int32)) ``` + ## Bellman Ford + The Bellman-Ford algorithm is used to find the shortest path from a single source vertex to all other vertices in a weighted graph. It can handle graphs with negative weights, and it also detects negative weight cycles. -``` + +```python import numpy as np from scipy.sparse.csgraph import bellman_ford from scipy.sparse import csr_matrix @@ -87,13 +103,18 @@ newarr = csr_matrix(arr) print(bellman_ford(newarr, return_predecessors=True, indices=0)) ``` -### Output + +#### Output + ``` (array([ 0., -1., 2.]), array([-9999, 0, 0], dtype=int32)) ``` + ## Depth First Order + Depth-First Search (DFS) is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root and explores as far as possible along each branch before backtracking. -``` + +```python import numpy as np from scipy.sparse.csgraph import depth_first_order from scipy.sparse import csr_matrix @@ -109,13 +130,18 @@ newarr = csr_matrix(arr) print(depth_first_order(newarr, 1)) ``` -### Output + +#### Output + ``` (array([1, 0, 3, 2], dtype=int32), array([ 1, -9999, 1, 0], dtype=int32)) ``` + ## Breadth First Order + Breadth-First Search (BFS) is an algorithm for traversing or searching tree or graph data structures. It starts at the root present depth level before moving on to nodes at the next depth level. -``` + +```python import numpy as np from scipy.sparse.csgraph import breadth_first_order from scipy.sparse import csr_matrix @@ -131,7 +157,9 @@ newarr = csr_matrix(arr) print(breadth_first_order(newarr, 1)) ``` + ### Output + ``` (array([1, 0, 2, 3], dtype=int32), array([ 1, -9999, 1, 1], dtype=int32)) ``` From 65094669ca29e0b33674d07e4895f99cce9a435f Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 16:08:45 +0530 Subject: [PATCH 581/656] Update index.md --- contrib/ds-algorithms/index.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index dc3ccf86..0fae2b97 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -16,8 +16,6 @@ - [Two Pointer Technique](two-pointer-technique.md) - [Hashing through Linear Probing](hashing-linear-probing.md) - [Hashing through Chaining](hashing-chaining.md) -- [Heaps in Python](heaps.md) +- [Heaps](heaps.md) - [AVL Trees](avl-trees.md) - [Splay Trees](splay-trees.md) -- [Splay Trees](splay-trees.md) -- [Dijkstra's Algorithm](dijkstra.md) \ No newline at end of file From 095ca582058409ca91cd1639e8ccdbb85f252dae Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 16:09:13 +0530 Subject: [PATCH 582/656] Update index.md --- contrib/ds-algorithms/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 0fae2b97..7f26bc70 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -19,3 +19,4 @@ - [Heaps](heaps.md) - [AVL Trees](avl-trees.md) - [Splay Trees](splay-trees.md) +- [Dijkstra's Algorithm](dijkstra.md) From ca64d125b03ed28891de9f62dbbe8c25efc03c73 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 16:10:55 +0530 Subject: [PATCH 583/656] Update heaps.md --- contrib/ds-algorithms/heaps.md | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/contrib/ds-algorithms/heaps.md b/contrib/ds-algorithms/heaps.md index 396186c7..6a9ba71a 100644 --- a/contrib/ds-algorithms/heaps.md +++ b/contrib/ds-algorithms/heaps.md @@ -1,4 +1,4 @@ -# Heaps in Python3 +# Heaps ## Definition: Heaps are a crucial data structure that support efficient priority queue operations. They come in two main types: min heaps and max heaps. Python's heapq module provides a robust implementation for min heaps, and with some minor adjustments, it can also be used to implement max heaps. @@ -34,14 +34,17 @@ smallest = heapq.heappop(min_heap) print("Smallest element:", smallest) print("Min Heap after pop:", min_heap) ``` + **Output:** - ```python + + ``` Min Heap: [3, 5, 10, 12] Smallest element: 3 Min Heap after pop: [5, 12, 10] ``` - **Max Heap:** To create a max heap, we can store negative values. + ```python import heapq @@ -63,23 +66,25 @@ print("Largest element:", largest) print("Max Heap after pop:", [-x for x in max_heap]) ``` + **Output:** -```python + +``` Max Heap: [12, 10, 3, 5] Largest element: 12 Max Heap after pop: [10, 5, 3] ``` ## Heap Operations: -1. **Push Operation:** Adds an element to the heap, maintaining the heap property.
    +1. **Push Operation:** Adds an element to the heap, maintaining the heap property. ```python heapq.heappush(heap, item) ``` -2. **Pop Operation:** Removes and returns the smallest element from the heap.
    +2. **Pop Operation:** Removes and returns the smallest element from the heap. ```python smallest = heapq.heappop(heap) ``` -3. **Heapify Operation:** Converts a list into a heap in-place.
    +3. **Heapify Operation:** Converts a list into a heap in-place. ```python heapq.heapify(list) ``` @@ -118,7 +123,7 @@ print(heapq.heappop(li)) ``` Output: -```python +``` The created heap is : [1, 3, 15, 77, 90] The modified heap after push is : [1, 3, 4, 15, 77, 90] The popped and smallest element is : 1 @@ -161,4 +166,4 @@ Heaps can be used to efficiently merge multiple sorted lists into a single sorte Heaps are used in event-driven simulators to manage events scheduled to occur at future times. 5. **Median Finding Algorithm:** -Heaps can be used to maintain a dynamic set of numbers to find the median efficiently. \ No newline at end of file +Heaps can be used to maintain a dynamic set of numbers to find the median efficiently. From c8dffec853ce55b690c56d95fd3a008b371c7553 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 16:17:30 +0530 Subject: [PATCH 584/656] Update polynomial-regression.md --- .../machine-learning/polynomial-regression.md | 26 +++++-------------- 1 file changed, 7 insertions(+), 19 deletions(-) diff --git a/contrib/machine-learning/polynomial-regression.md b/contrib/machine-learning/polynomial-regression.md index 3f1287e0..d00ede3b 100644 --- a/contrib/machine-learning/polynomial-regression.md +++ b/contrib/machine-learning/polynomial-regression.md @@ -1,22 +1,10 @@ # Polynomial Regression -Polynomial Regression is a form of regression analysis in which the relationship between the independent variable \( x \) and the dependent variable \( y \) is modeled as an \( n \)th degree polynomial. This README provides an overview of polynomial regression, including its fundamental concepts, assumptions, and how to implement it using Python. - -## Table of Contents - -1. [Introduction](#introduction) -2. [Concepts](#concepts) -3. [Assumptions](#assumptions) -4. [Implementation](#implementation) - - [Using Scikit-learn](#using-scikit-learn) - - [Code Example](#code-example) -5. [Evaluation Metrics](#evaluation-metrics) -6. [Conclusion](#conclusion) -7. [References](#references) +Polynomial Regression is a form of regression analysis in which the relationship between the independent variable $x$ and the dependent variable $y$ is modeled as an $nth$ degree polynomial. This guide provides an overview of polynomial regression, including its fundamental concepts, assumptions, and how to implement it using Python. ## Introduction -Polynomial Regression is used when the data shows a non-linear relationship between the independent variable \( x \) and the dependent variable \( y \). It extends the simple linear regression model by considering polynomial terms of the independent variable, allowing for a more flexible fit to the data. +Polynomial Regression is used when the data shows a non-linear relationship between the independent variable $x$ and the dependent variable $y$ is modeled as an $nth$ degree polynomial. It extends the simple linear regression model by considering polynomial terms of the independent variable, allowing for a more flexible fit to the data. ## Concepts @@ -29,10 +17,10 @@ $$ $$ Where: -- \( y \) is the dependent variable. -- \( x \) is the independent variable. -- \( \beta_0, \beta_1, \ldots, \beta_n \) are the coefficients of the polynomial. -- \( \epsilon \) is the error term. +- $y$ is the dependent variable. +- $x$ is the independent variable. +- $\beta_0, \beta_1, \ldots, \beta_n$ are the coefficients of the polynomial. +- $\epsilon$ is the error term. ### Degree of Polynomial @@ -111,4 +99,4 @@ Polynomial Regression is a powerful tool for modeling non-linear relationships b ## References - [Scikit-learn Documentation](https://scikit-learn.org/stable/modules/linear_model.html#polynomial-regression) -- [Wikipedia: Polynomial Regression](https://en.wikipedia.org/wiki/Polynomial_reg +- [Wikipedia: Polynomial Regression](https://en.wikipedia.org/wiki/Polynomial_reg) From e6359caf84689a4d6503b78123ca2b15e7551dd7 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 16:33:01 +0530 Subject: [PATCH 585/656] Rename statistical-operations-on-arrays.md to statistical-functions.md --- ...atistical-operations-on-arrays.md => statistical-functions.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/numpy/{statistical-operations-on-arrays.md => statistical-functions.md} (100%) diff --git a/contrib/numpy/statistical-operations-on-arrays.md b/contrib/numpy/statistical-functions.md similarity index 100% rename from contrib/numpy/statistical-operations-on-arrays.md rename to contrib/numpy/statistical-functions.md From 41e7c65b297279380d1729a0acfee31d3ceb94c6 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 16:33:26 +0530 Subject: [PATCH 586/656] Update index.md --- contrib/numpy/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index 4c3edce3..3f673753 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -11,4 +11,4 @@ - [Sorting NumPy Arrays](sorting-array.md) - [NumPy Array Iteration](array-iteration.md) - [Concatenation of Arrays](concatenation-of-arrays.md) -- [Statistical Operations on Arrays](statistical-operations-on-arrays.md) +- [Statistical Functions on Arrays](statistical-functions.md) From 9286f219d911975deb3323c33ceaad0a74d77d6b Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 17:52:33 +0530 Subject: [PATCH 587/656] Rename asynchronous_context_managers_generators.md to asynchronous-context-managers-generators.md --- ...enerators.md => asynchronous-context-managers-generators.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/advanced-python/{asynchronous_context_managers_generators.md => asynchronous-context-managers-generators.md} (99%) diff --git a/contrib/advanced-python/asynchronous_context_managers_generators.md b/contrib/advanced-python/asynchronous-context-managers-generators.md similarity index 99% rename from contrib/advanced-python/asynchronous_context_managers_generators.md rename to contrib/advanced-python/asynchronous-context-managers-generators.md index a78db28f..00516495 100644 --- a/contrib/advanced-python/asynchronous_context_managers_generators.md +++ b/contrib/advanced-python/asynchronous-context-managers-generators.md @@ -107,4 +107,4 @@ asyncio.run(main()) ### Summary Asynchronous context managers and generators are powerful tools in Python that enhance the efficiency and readability of asynchronous code. By using 'async with' for resource management and 'async for' for iteration, you can write more performant and maintainable asynchronous -programs. \ No newline at end of file +programs. From e51a92e1662cb89006a1e7c4fa7feadeffe5d90f Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 17:52:54 +0530 Subject: [PATCH 588/656] Update index.md --- contrib/advanced-python/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md index a04bb97f..021dd8bd 100644 --- a/contrib/advanced-python/index.md +++ b/contrib/advanced-python/index.md @@ -18,4 +18,4 @@ - [Reduce](reduce-function.md) - [List Comprehension](list-comprehension.md) - [Eval Function](eval_function.md) -- [Asynchronous Context Managers & Generators](asynchronous_context_managers_generators.md) +- [Asynchronous Context Managers & Generators](asynchronous-context-managers-generators.md) From 4916b99a3ad28b1d464894e50b09f9868d640856 Mon Sep 17 00:00:00 2001 From: anamika123 Date: Sat, 22 Jun 2024 18:19:33 +0530 Subject: [PATCH 589/656] Update pandas_series.md --- contrib/pandas/pandas_series.md | 211 +++++++++++++++++--------------- 1 file changed, 113 insertions(+), 98 deletions(-) diff --git a/contrib/pandas/pandas_series.md b/contrib/pandas/pandas_series.md index 8cc12c89..cbb9a0ff 100644 --- a/contrib/pandas/pandas_series.md +++ b/contrib/pandas/pandas_series.md @@ -13,14 +13,14 @@ import pandas as pd s1 = pd.Series([4, 5, 2, 3]) print(s1) - -#Output: -#0 4 -#1 5 -#2 2 -#3 3 -#dtype: int64 - +``` +``` +Output: +0 4 +1 5 +2 2 +3 3 +dtype: int64 ``` ### Series from a Dictionary @@ -31,12 +31,13 @@ import pandas as pd s2 = pd.Series({'A': 1, 'B': 2, 'C': 3}) print(s2) - -#Output: -#A 1 -#B 2 -#C 3 -#dtype: int64 +``` +``` +Output: +A 1 +B 2 +C 3 +dtype: int64 ``` @@ -50,12 +51,13 @@ import pandas as pd s4 = pd.Series([1, 2, 3], index=['a', 'b', 'c'], dtype='float64') print(s4) - -#output -#a 1.0 -#b 2.0 -#c 3.0 -#dtype: float64 +``` +``` +Output: +a 1.0 +b 2.0 +c 3.0 +dtype: float64 ``` ### Specifying NaN Values: @@ -66,13 +68,13 @@ print(s4) import pandas as pd s3=pd.Series([1,np.Nan,2]) print(s3) - -#output: - -#0 1.0 -#1 NaN -#2 2.0 -#dtype: float64 +``` +``` +Output: +0 1.0 +1 NaN +2 2.0 +dtype: float64 ``` @@ -86,15 +88,15 @@ import pandas as pd a=np.arange(1,5) # [1,2,3,4] s5=pd.Series(data=a**2,index=a) print(s5) - -#output: -#1 1 -#2 4 -#3 9 -#4 16 -#dtype: int64 ``` - +``` +Output: +1 1 +2 4 +3 9 +4 16 +dtype: int64 +``` ## Series Object Attributes @@ -123,9 +125,10 @@ import pandas as pd s7 = pd.Series(data=[13, 45, 67, 89], index=['A', 'B', 'C', 'D']) print(s7['A']) - -#output -#13 +``` +``` +Output: +13 ``` @@ -142,12 +145,14 @@ import pandas as pd s = pd.Series(data=[13, 45, 67, 89], index=['A', 'B', 'C', 'D']) print(s[:2]) +``` +``` +Output: +A 13 +B 45 +dtype: int64 -#Output -#A 13 -#B 45 -#dtype: int64 -#This example demonstrates that the first two elements (positions 0 and 1) are returned, regardless of their custom index labels. +This example demonstrates that the first two elements (positions 0 and 1) are returned, regardless of their custom index labels. ``` @@ -165,12 +170,13 @@ s8 = pd.Series([10, 20, 30], index=['a', 'b', 'c']) s8['a'] = 100 s8.index = ['x', 'y', 'z'] print(s8) - -#output -#x 100 -#y 20 -#z 30 -#dtype: int64 +``` +``` +Output: +x 100 +y 20 +z 30 +dtype: int64 ``` **Note: Series object are value-mutable but size immutable objects.** @@ -183,18 +189,21 @@ import pandas as pd s9 = pd.Series([1, 2, 3]) print("addition:", s9 + 5) print("subtraction:", s9 - 2) - -#output: -#addition: -#0 6 -#1 7 -#2 8 -#dtype: int64 -#subtraction: -#0 -1 -#1 0 -#2 1 -#dtype: int64 +``` +``` +output: + +addition: +0 6 +1 7 +2 8 +dtype: int64 + +subtraction: +0 -1 +1 0 +2 1 +dtype: int64 ``` ### Arthmetic on series object @@ -206,18 +215,21 @@ s11 = pd.Series([4, 5, 6]) print("addition:", s10 + s11) print("multiplication:", s10 * s11) - -#output: -#addition: -#0 5 -#1 7 -#2 9 -#dtype: int64 -#multiplication: -#0 4 -#1 10 -#2 18 -#dtype: int64 +``` +``` +output: + +addition: +0 5 +1 7 +2 9 +dtype: int64 + +multiplication: +0 4 +1 10 +2 18 +dtype: int64 ``` Here one thing we should keep in mind that both the series object should have same indexes otherwise it will return NaN value to all the indexes of two series object . @@ -236,17 +248,18 @@ import pandas as pd s12 = pd.Series([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]) print(s12.head(3)) print(s12.tail(3)) - -#output -#0 10 -#1 20 -#2 30 -#dtype: int64 - -#7 80 -#8 90 -#9 100 -#dtype: int64 +``` +``` +Output: +0 10 +1 20 +2 30 +dtype: int64 + +7 80 +8 90 +9 100 +dtype: int64 ``` If you dont provide any value to n the by default it give results for `n=5`. @@ -263,20 +276,22 @@ s13 = pd.Series([3, 1, 2], index=['c', 'a', 'b']) print(s13.sort_values()) print(s13.sort_index()) print(s13.drop('a')) -#Output -#a 1 -#b 2 -#c 3 -#dtype: int64 - -#a 1 -#b 2 -#c 3 -#dtype: int64 - -#c 3 -#b 2 -#dtype: int64 +``` +``` +Output: +a 1 +b 2 +c 3 +dtype: int64 + +a 1 +b 2 +c 3 +dtype: int64 + +c 3 +b 2 +dtype: int64 ``` ## Conclusion From a6bc70dad9f70cfe0e8b66cc42184e63c195a566 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 18:52:17 +0530 Subject: [PATCH 590/656] Update PyTorch_Fundamentals.md --- .../machine-learning/PyTorch_Fundamentals.md | 213 ++++++++++++------ 1 file changed, 149 insertions(+), 64 deletions(-) diff --git a/contrib/machine-learning/PyTorch_Fundamentals.md b/contrib/machine-learning/PyTorch_Fundamentals.md index 3248a084..b244ec1f 100644 --- a/contrib/machine-learning/PyTorch_Fundamentals.md +++ b/contrib/machine-learning/PyTorch_Fundamentals.md @@ -5,8 +5,11 @@ # Import pytorch in our codespace import torch print(torch.__version__) +``` -output -> 2.3.0+cu121 +#### Output +``` +2.3.0+cu121 ``` @@ -16,56 +19,72 @@ Now you have already seen how to create a tensor in pytorch. In this notebook i ### 1. Creating tensors +Scalar tensor ( a zero dimension tensor) ```python -# Scalar tensor ( a zero dimension tensor) scalar = torch.tensor(7) print(scalar) +``` -output -> tensor(7) +#### Output +``` +tensor(7) ``` - +Check the dimension of the above tensor ```python -# Check the dimension of the above tensor print(scalar.ndim) +``` -output -> 0 -``` - +#### Output +``` +0 +``` + +To retrieve the number from the tensor we use `item()` ```python -# To retrieve the number from the tensor we use `item()` print(scalar.item()) +``` -output -> 7 +#### Output +``` +7 ``` - +Vector (It is a single dimension tensor but contain many numbers) ```python -# Vector (It is a single dimension tensor but contain many numbers) vector = torch.tensor([1,2]) print(vector) +``` -output -> tensor([1, 2]) +#### Output +``` +tensor([1, 2]) ``` +Check the dimensions ```python -# Check the dimensions print(vector.ndim) +``` -output -> 1 +#### Output +``` +1 ``` +Check the shape of the vector ```python -# Check the shape of the vector print(vector.shape) +``` -output -> torch.Size([2]) +#### Output +``` +torch.Size([2]) ``` @@ -82,8 +101,11 @@ You can tell the number of dimensions a tensor in PyTorch has by the number of s MATRIX = torch.tensor([[1,2], [4,5]]) print(MATRIX) +``` -output -> tensor([[1, 2], +#### Output +``` +tensor([[1, 2], [4, 5]]) ``` @@ -92,47 +114,60 @@ There are two brackets so it must be 2 dimensions , lets check ```python print(MATRIX.ndim) +``` -output -> 2 +#### Output +``` +2 ``` ```python # Shape print(MATRIX.shape) +``` -output -> torch.Size([2, 2]) +#### Output +``` +torch.Size([2, 2]) ``` It means MATRIX has 2 rows and 2 columns. +Let's create a TENSOR ```python -# Let's create a TENSOR TENSOR = torch.tensor([[[1,2,3], [4,5,6], [7,8,9]]]) print(TENSOR) +``` -output -> tensor([[[1, 2, 3], +#### Output +``` +tensor([[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]) ``` - +Let's check the dimensions ```python -# Let's check the dimensions print(TENSOR.ndim) +``` -output -> 3 +#### Output +``` +3 ``` - +shape ```python -# shape? print(TENSOR.shape) +``` -output -> torch.Size([1, 3, 3]) +#### Output +``` +torch.Size([1, 3, 3]) ``` The dimensions go outer to inner. @@ -150,39 +185,48 @@ That means there's 1 dimension of 3 by 3. We can create them using `torch.rand()` and passing in the `size` parameter. - -```python -# creating a random tensor of size (3,4) +Creating a random tensor of size (3,4) +```python rand_tensor = torch.rand(size = (3,4)) print(rand_tensor) +``` -output -> tensor([[0.7462, 0.4950, 0.7851, 0.8277], +#### Output +``` +tensor([[0.7462, 0.4950, 0.7851, 0.8277], [0.6112, 0.5159, 0.1728, 0.6847], [0.4472, 0.1612, 0.6481, 0.3236]]) ``` +Check the dimensions ```python -# Check the dimensions print(rand_tensor.ndim) +``` -output -> 2 +#### Output +``` +2 ``` - +Shape ```python -# Shape print(rand_tensor.shape) +``` -output -> torch.Size([3, 4]) +#### Output +``` +torch.Size([3, 4]) ``` - +Datatype ```python -# datatype print(rand_tensor.dtype) +``` -output -> torch.float32 +#### Output +``` +torch.float32 ``` ### Zeros and ones @@ -194,19 +238,24 @@ Here we will create a tensor of any shape filled with zeros and ones # Create a tensor of all zeros zeros = torch.zeros(size = (3,4)) print(zeros) +``` -output -> tensor([[0., 0., 0., 0.], +#### Output +``` +tensor([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]]) ``` - -```python -# create a tensor of ones +Create a tensor of ones +```python ones = torch.ones(size = (3,4)) print(ones) +``` -output -> tensor([[1., 1., 1., 1.], +#### Output +``` +tensor([[1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.]]) ``` @@ -229,8 +278,11 @@ zero_to_ten = torch.arange(start = 0, end = 10, step = 1) print(zero_to_ten) +``` -output -> tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) +#### Output +``` +tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) ``` # 2. Manipulating tensors (tensor operations) @@ -249,8 +301,11 @@ The operations are : ```python tensor = torch.tensor([1,2,3]) print(tensor+10) +``` -output -> tensor([11, 12, 13]) +#### Output +``` +tensor([11, 12, 13]) ``` We have add 10 to each tensor element. @@ -259,8 +314,11 @@ We have add 10 to each tensor element. ```python tensor1 = torch.tensor([4,5,6]) print(tensor+tensor1) +``` -output -> tensor([5, 7, 9]) +#### Output +``` +tensor([5, 7, 9]) ``` We have added two tensors , remember that addition takes place element wise. @@ -270,8 +328,11 @@ We have added two tensors , remember that addition takes place element wise. ```python print(tensor-8) +``` -output -> tensor([-7, -6, -5]) +#### Output +``` +tensor([-7, -6, -5]) ``` We've subtracted 8 from the above tensor. @@ -279,8 +340,11 @@ We've subtracted 8 from the above tensor. ```python print(tensor-tensor1) +``` -output -> tensor([-3, -3, -3]) +#### Output +``` +tensor([-3, -3, -3]) ``` ### 3. Multiplication @@ -289,8 +353,11 @@ output -> tensor([-3, -3, -3]) ```python # Multiply the tensor with 10 (element wise) print(tensor*10) +``` -output -> tensor([10, 20, 30]) +#### Output +``` +tensor([10, 20, 30]) ``` Each element of tensor gets multiplied by 10. @@ -303,15 +370,21 @@ PyTorch also has a bunch of built-in functions like `torch.mul()` (short for mul ```python # let's see them print(torch.add(tensor,10)) +``` -output -> tensor([11, 12, 13]) +#### Output +``` +tensor([11, 12, 13]) ``` ```python print(torch.mul(tensor,10)) +``` -output -> tensor([10, 20, 30]) +#### Output +``` +tensor([10, 20, 30]) ``` ### Matrix multiplication (is all you need) @@ -344,37 +417,49 @@ tensor2 = torch.tensor([[[1,1,1], print(tensor1) , print(tensor2) -output1 -> tensor([[[1, 2, 3], +``` + +#### Output +``` +tensor([[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]) -output2 -> tensor([[[1, 1, 1], +tensor([[[1, 1, 1], [2, 2, 2], [3, 3, 3]]]) ``` +Let's check the shape ```python -# let's check the shape print(tensor1.shape) , print(tensor2.shape) +``` -output1 -> torch.Size([1, 3, 3]) -output2 ->torch.Size([1, 3, 3]) +#### Output +``` +torch.Size([1, 3, 3]) +torch.Size([1, 3, 3]) ``` -```python - # Matrix multiplication +Matrix multiplication +```python print(torch.matmul(tensor1, tensor2)) +``` -output -> tensor([[[14, 14, 14], +#### Output +``` +tensor([[[14, 14, 14], [32, 32, 32], [50, 50, 50]]]) ``` - +Can also use the "@" symbol for matrix multiplication, though not recommended ```python -# Can also use the "@" symbol for matrix multiplication, though not recommended print(tensor1 @ tensor2) +``` -output -> tensor([[[14, 14, 14], +#### Output +``` +tensor([[[14, 14, 14], [32, 32, 32], [50, 50, 50]]]) ``` From 2770cd082aadcbde21e7bdb94ebdec51d01e9b19 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 18:52:37 +0530 Subject: [PATCH 591/656] Rename PyTorch_Fundamentals.md to pytorch-fundamentals.md --- .../{PyTorch_Fundamentals.md => pytorch-fundamentals.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/machine-learning/{PyTorch_Fundamentals.md => pytorch-fundamentals.md} (100%) diff --git a/contrib/machine-learning/PyTorch_Fundamentals.md b/contrib/machine-learning/pytorch-fundamentals.md similarity index 100% rename from contrib/machine-learning/PyTorch_Fundamentals.md rename to contrib/machine-learning/pytorch-fundamentals.md From d09dd856613fa0c680407db69aa5d763c4113317 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 18:52:59 +0530 Subject: [PATCH 592/656] Update index.md --- contrib/machine-learning/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index b52371a0..df47be87 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -20,4 +20,4 @@ - [Grid Search](grid-search.md) - [Transformers](transformers.md) - [K-nearest neighbor (KNN)](knn.md) -- [PyTorch Fundamentals](PyTorch_Fundamentals.md) +- [PyTorch Fundamentals](pytorch-fundamentals.md) From e1c0e243e2d8c75fa9bb517f018e02ea5695b264 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 19:54:46 +0530 Subject: [PATCH 593/656] Rename SeabornPlotting .md to seaborn-plotting.md --- .../{SeabornPlotting .md => seaborn-plotting.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/plotting-visualization/{SeabornPlotting .md => seaborn-plotting.md} (99%) diff --git a/contrib/plotting-visualization/SeabornPlotting .md b/contrib/plotting-visualization/seaborn-plotting.md similarity index 99% rename from contrib/plotting-visualization/SeabornPlotting .md rename to contrib/plotting-visualization/seaborn-plotting.md index 0e219c49..0a3d0050 100644 --- a/contrib/plotting-visualization/SeabornPlotting .md +++ b/contrib/plotting-visualization/seaborn-plotting.md @@ -235,4 +235,4 @@ plt.show() ``` ![Alt text](/images/plot_images/image16.png) -Seaborn is a versatile library that simplifies the creation of complex visualizations. By using Seaborn's plotting functions, you can create a wide range of statistical graphics with minimal effort. Whether you're working with relational data, categorical data, or distributions, Seaborn provides the tools you need to visualize your data effectively. \ No newline at end of file +Seaborn is a versatile library that simplifies the creation of complex visualizations. By using Seaborn's plotting functions, you can create a wide range of statistical graphics with minimal effort. Whether you're working with relational data, categorical data, or distributions, Seaborn provides the tools you need to visualize your data effectively. 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from images/plot_images/image7.png rename to contrib/plotting-visualization/images/seaborn-plotting/image7.png diff --git a/images/plot_images/image8.png b/contrib/plotting-visualization/images/seaborn-plotting/image8.png similarity index 100% rename from images/plot_images/image8.png rename to contrib/plotting-visualization/images/seaborn-plotting/image8.png diff --git a/images/plot_images/image9.png b/contrib/plotting-visualization/images/seaborn-plotting/image9.png similarity index 100% rename from images/plot_images/image9.png rename to contrib/plotting-visualization/images/seaborn-plotting/image9.png From 469e6c4d35b0fbb04406e8b7227523708ad0d7a7 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sat, 22 Jun 2024 20:10:13 +0530 Subject: [PATCH 596/656] Update seaborn-plotting.md --- .../seaborn-plotting.md | 159 ++++++++++-------- 1 file changed, 90 insertions(+), 69 deletions(-) diff --git a/contrib/plotting-visualization/seaborn-plotting.md b/contrib/plotting-visualization/seaborn-plotting.md index 0a3d0050..655f625e 100644 --- a/contrib/plotting-visualization/seaborn-plotting.md +++ b/contrib/plotting-visualization/seaborn-plotting.md @@ -1,12 +1,16 @@ -### Seaborn Plotting Functions: +# Seaborn + Seaborn is a powerful and easy-to-use data visualization library in Python built on top of Matplotlib.It provides a high-level interface for drawing attractive and informative statistical graphics.Now we will cover various functions covered by Seaborn, along with examples to illustrate their usage. Seaborn simplifies the process of creating complex visualizations with a few lines of code and it integrates closely with pandas data structure , making it an excellent choice for data analysis and exploration. -### Setting up Seaborn: + +## Setting up Seaborn + Make sure seaborn library is installed in your system. If not use command `pip install seaborn` + After installing you are all set to experiment with plotting functions. -``` +```python #import necessary libraries import seaborn as sns @@ -14,26 +18,29 @@ import matplotlib.pyplot as plt import pandas as pd ``` -Seaborn includes several built-in datasets that you can use for practice +Seaborn includes several built-in datasets that you can use for practice You can list all available datasets using below command -`sns.get_dataset_names()` +```python +sns.get_dataset_names() +``` + Here we are using 'tips' dataset -``` +```python # loading an example dataset tips=sns.load_dataset('tips') ``` Before delving into plotting, make yourself comfortable with the dataset. To do that, use the pandas library to understand what information the dataset contains and preprocess the data. If you get stuck, feel free to refer to the pandas documentation. -### Relational Plots: +## Relational Plots + Relational plots are used to visualize the relationship between two or more variables -##### Scatter Plot: + +### Scatter Plot A scatter plot displays data points based on two numerical variables.Seaborn `scatterplot` function allows you to create scatter plots with ease -##### Line Plot: -A line plot connects data points in the order they appear in the dataset.This is useful for time series data.`lineplot` function allows you to create lineplots. -``` +```python # scatter plot using Seaborn plt.figure(figsize=(5,5)) @@ -41,9 +48,12 @@ sns.scatterplot(data=tips,x='total_bill',y='tip',hue='day',style='time') plt.title('Scatter Plot of Total Bill vs Tip') plt.show() ``` -![Alt text](/images/plot_images/image1.png) +![scatter plot](images/seaborn-plotting/image1.png) -``` +### Line Plot +A line plot connects data points in the order they appear in the dataset.This is useful for time series data.`lineplot` function allows you to create lineplots. + +```python # lineplot using seaborn plt.figure(figsize=(5,5)) @@ -51,20 +61,17 @@ sns.lineplot(data=tips,x='size',y='total_bill',hue='day') plt.title('Line Plot of Total Bill by Size and Day') plt.show() ``` -![Alt text](images/plot_images/image2.png) -### Distribution Plots: +![lineplot](images/seaborn-plotting/image2.png) + +## Distribution Plots + Distribution Plots visualize the distribution of a single numerical variable -##### HistPlot: + +### HistPlot A histplot displays the distribution of a numerical variable by dividing the data into bins. -##### KDE Plot: -A Kernel Density Estimate (KDE) plot represents the distribution of a variable as a smooth curve. -##### ECDF Plot: -An Empirical Cumulative Distribution Function (ECDF) plot shows the proportion of data points below each value. -##### Rug Plot: -A rug plot in Seaborn is a simple way to show the distribution of a variable by drawing small vertical lines (or "rugs") at each data point along the x-axis. -``` +```python # Histplot using Seaborn plt.figure(figsize=(5,5)) @@ -72,9 +79,12 @@ sns.histplot(data=tips, x='total_bill', kde=True) plt.title('Histplot of Total Bill') plt.show() ``` -![Alt text](/images/plot_images/image3.png) +![Histplot](images/seaborn-plotting/image3.png) -``` +### KDE Plot +A Kernel Density Estimate (KDE) plot represents the distribution of a variable as a smooth curve. + +```python # KDE Plot using Seaborn plt.figure(figsize=(5,5)) @@ -82,9 +92,12 @@ sns.kdeplot(data=tips, x='total_bill', hue='sex', fill=True) plt.title('KDE Plot of Total Bill by Sex') plt.show() ``` -![Alt text](/images/plot_images/image4.png) +![KDE](images/seaborn-plotting/image4.png) -``` +### ECDF Plot +An Empirical Cumulative Distribution Function (ECDF) plot shows the proportion of data points below each value. + +```python # ECDF Plot using Seaborn plt.figure(figsize=(5,5)) @@ -92,9 +105,12 @@ sns.ecdfplot(data=tips, x='total_bill', hue='sex') plt.title('ECDF Plot of Total Bill by Sex') plt.show() ``` -![Alt text](/images/plot_images/image5.png) +![ECDF](images/seaborn-plotting/image5.png) -``` +### Rug Plot +A rug plot in Seaborn is a simple way to show the distribution of a variable by drawing small vertical lines (or "rugs") at each data point along the x-axis. + +```python # Rug Plot using Seaborn plt.figure(figsize=(3,3)) @@ -102,30 +118,28 @@ sns.rugplot(x='total_bill', data=tips) plt.title('Rug Plot of Total Bill Amounts') plt.show() ``` -![Alt text](/images/plot_images/image6.png) +![Rug](images/seaborn-plotting/image6.png) - ### Categorical Plots: - Categorical plots are used to visualize data where one or more variables are categorical. - ##### Bar Plot: - A bar plot shows the relationship between a categorical variable and a numerical variable. - ##### Point Plot: - A point plot in Seaborn is used to show the relationship between two categorical variables, with the size of the points representing the values of third variable. - ##### Box Plot: - A box plot displays the distribution of a numerical variable across different categories. - ##### Violin Plot: - A violin plot combines aspects of a box plot and a KDE plot to show the distribution of data +## Categorical Plots +Categorical plots are used to visualize data where one or more variables are categorical. - ``` - # Bar Plot using Seaborn +### Bar Plot + +A bar plot shows the relationship between a categorical variable and a numerical variable. +```python +# Bar Plot using Seaborn plt.figure(figsize=(5,5)) sns.barplot(data=tips,x='day',y='total_bill',hue='sex') plt.title('Bar Plot of Total Bill by Day and Sex') plt.show() ``` -![Alt text](/images/plot_images/image7.png) +![Bar](images/seaborn-plotting/image7.png) -``` +### Point Plot +A point plot in Seaborn is used to show the relationship between two categorical variables, with the size of the points representing the values of third variable. + +```python # Point Plot using Seaborn plt.figure(figsize=(5,5)) @@ -133,9 +147,12 @@ sns.pointplot(x='day',y='total_bill',hue='sex',data=tips) plt.title('Average Total Bill by Day and Sex') plt.show() ``` -![Alt text](/images/plot_images/image8.png) +![Point](images/seaborn-plotting/image8.png) + +### Box Plot +A box plot displays the distribution of a numerical variable across different categories. -``` +```python # Box Plot using Seaborn plt.figure(figsize=(5,5)) @@ -143,9 +160,12 @@ sns.boxplot(data=tips, x='day', y='total_bill', hue='sex') plt.title('Box Plot of Total Bill by Day and Sex') plt.show() ``` -![Alt text](/images/plot_images/image9.png) +![Box](images/seaborn-plotting/image9.png) -``` +### Violin Plot +A violin plot combines aspects of a box plot and a KDE plot to show the distribution of data + +```python # Violin Plot using Seaborn plt.figure(figsize=(5,5)) @@ -153,14 +173,15 @@ sns.violinplot(data=tips,x='day',y='total_bill',hue='sex',split=True) plt.title('Violin Plot of Total Bill by Day and Sex') plt.show() ``` -![Alt text](/images/plot_images/image10.png) +![Violin](images/seaborn-plotting/image10.png) -### Matrix Plots: +## Matrix Plots Matrix plots are useful for visualizing data in a matrix format. -##### Heatmap: + +### Heatmap A heatmap displays data in a matrix where values are represented by color. -``` +```python # Heatmap using Seaborn plt.figure(figsize=(10,8)) @@ -170,12 +191,12 @@ sns.heatmap(flights_pivot, annot=True, fmt='d', cmap='YlGnBu') plt.title('Heatmap of Flight Passengers') plt.show() ``` -![Alt text](/images/plot_images/image11.png) +![Heatmap](images/seaborn-plotting/image11.png) -### Pair Plot: +## Pair Plot A pair plot shows the pairwise relationships between multiple variables in a dataset. -``` +```python #Pairplot using Seaborn plt.figure(figsize=(5,5)) @@ -183,12 +204,12 @@ sns.pairplot(tips, hue='sex') plt.title('Pair Plot of Tips Dataset') plt.show() ``` -![Alt text](/images/plot_images/image12.png) +![Pair](images/seaborn-plotting/image12.png) -### FacetGrid: +## FacetGrid FacetGrid allows you to create a grid of plots based on the values of one or more categorical variables. -``` +```python #Facetgrid using Seaborn plt.figure(figsize=(5,5)) @@ -196,43 +217,43 @@ g = sns.FacetGrid(tips, col='sex', row='time', margin_titles=True) g.map(sns.scatterplot, 'total_bill', 'tip') plt.show() ``` -![Alt text](/images/plot_images/image13.png) +![Facetgrid](images/seaborn-plotting/image13.png) -### Customizing Seaborn Plots: +## Customizing Seaborn Plots Seaborn plots can be customized to improve their appearance and convey more information. -##### Changing the Aesthetic Style: +### Changing the Aesthetic Style Seaborn comes with several built-in themes. -``` +```python sns.set_style('whitegrid') sns.scatterplot(data=tips, x='total_bill', y='tip') plt.title('Scatter Plot with Whitegrid Style') plt.show() ``` -![Alt text](/images/plot_images/image14.png) +![Aesthetic](images/seaborn-plotting/image14.png) -##### Customizing Colors: +### Customizing Colors You can use color palettes to customize the colors in your plots. -``` +```python sns.set_palette('pastel') sns.barplot(data=tips, x='day', y='total_bill', hue='sex') plt.title('Bar Plot with Pastel Palette') plt.show() ``` -![Alt text](/images/plot_images/image15.png) +![Colors](images/seaborn-plotting/image15.png) -##### Adding Titles and Labels: +### Adding Titles and Labels Titles and labels can be added to make plots more informative. -``` +```python plot = sns.scatterplot(data=tips, x='total_bill', y='tip') plot.set_title('Scatter Plot of Total Bill vs Tip') plot.set_xlabel('Total Bill ($)') plot.set_ylabel('Tip ($)') plt.show() ``` -![Alt text](/images/plot_images/image16.png) +![Titles](images/seaborn-plotting/image16.png) Seaborn is a versatile library that simplifies the creation of complex visualizations. By using Seaborn's plotting functions, you can create a wide range of statistical graphics with minimal effort. Whether you're working with relational data, categorical data, or distributions, Seaborn provides the tools you need to visualize your data effectively. From 8ee6007bbfb34dda9d6785dcd041622b4a14201f Mon Sep 17 00:00:00 2001 From: mankalavaishnavi Date: Sat, 22 Jun 2024 21:17:42 +0530 Subject: [PATCH 597/656] added deque and updated index.md --- contrib/ds-algorithms/deque.md | 108 +++++++++++++++++++++++++++++++++ contrib/ds-algorithms/index.md | 1 + 2 files changed, 109 insertions(+) create mode 100644 contrib/ds-algorithms/deque.md diff --git a/contrib/ds-algorithms/deque.md b/contrib/ds-algorithms/deque.md new file mode 100644 index 00000000..459ac04b --- /dev/null +++ b/contrib/ds-algorithms/deque.md @@ -0,0 +1,108 @@ +## Double-Ended Queue (Deque) + +A double-ended queue (deque) supports insertion and deletion from both ends, making it a more versatile queue implementation. + +### Input-Restricted Deque + +In an input-restricted deque, elements can only be inserted from one end while deletions can occur from both ends. + +### Output-Restricted Deque + +An output-restricted deque allows elements to be deleted from one end only, while insertions can be made from both ends. + +## Real Life Examples of Deques + +### Task Scheduling + +Deques are useful in task scheduling algorithms where tasks can be added to either end and processed accordingly based on priority or other scheduling criteria. + +### Sliding Window Problems + +Algorithms solving sliding window problems often use deques to efficiently manage and query elements in the current window. + +### Implementations in Python + +Python provides a built-in `collections.deque` which supports efficient append and pop operations from both ends. It's ideal for scenarios requiring a simple and efficient double-ended queue. + +## Operations on a Deque + +- **isEmpty**: Checks if the deque is empty. +- **appendLeft**: Adds an element to the left end of the deque. +- **appendRight**: Adds an element to the right end of the deque. +- **popLeft**: Removes and returns the element from the left end of the deque. +- **popRight**: Removes and returns the element from the right end of the deque. +- **peekLeft**: Returns the element from the left end without removing it. +- **peekRight**: Returns the element from the right end without removing it. +- **clear**: Removes all elements from the deque. + +## Implementation of Deque in Python + +Python's `collections.deque` provides an efficient implementation of a deque. + +```python +from collections import deque + +# Creating a deque +dq = deque() + +# Adding elements to the deque +dq.append(1) +dq.append(2) +dq.append(3) + +# Removing elements from the deque +dq.popleft() # Removes and returns 1 +dq.pop() # Removes and returns 3 + +# Peeking elements +print("Left end peek:", dq[0]) +print("Right end peek:", dq[-1]) + +# Displaying elements in the deque +print("Deque:", list(dq)) +``` + +## Example: Sliding Window Maximum + +In this example, we'll use a deque to efficiently find the maximum element in sliding windows of a list. + +```python +from collections import deque + +def sliding_window_maximum(nums, k): + if not nums: + return [] + + n = len(nums) + result = [] + dq = deque() + + for i in range(n): + # Remove elements from the deque that are out of the current window + while dq and dq[0] <= i - k: + dq.popleft() + + # Remove elements from the deque that are less than the current element + while dq and nums[dq[-1]] <= nums[i]: + dq.pop() + + # Add the current element index to the deque + dq.append(i) + + # Add the maximum of the current window to the result + if i >= k - 1: + result.append(nums[dq[0]]) + + return result + +# Example usage: +nums = [1, 3, -1, -3, 5, 3, 6, 7] +k = 3 +print("Sliding Window Maximum:", sliding_window_maximum(nums, k)) +``` + +## Conclusion + +Queues and deques are fundamental data structures that facilitate efficient data processing and +management in various applications. Understanding their principles and implementations is crucial +for developing robust software solutions. \ No newline at end of file diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 07fd98a7..b156468f 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -22,3 +22,4 @@ - [AVL Trees](avl-trees.md) - [Splay Trees](splay-trees.md) - [Dijkstra's Algorithm](dijkstra.md) +- [Deque](deque.md) \ No newline at end of file From d1a2bd56a3f1c0f4d2ba8e9e891ad26238e7d311 Mon Sep 17 00:00:00 2001 From: Ojaswi Chopra Date: Sat, 22 Jun 2024 22:56:59 +0530 Subject: [PATCH 598/656] Trial --- contrib/machine-learning/reinforcement-learning.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/contrib/machine-learning/reinforcement-learning.md b/contrib/machine-learning/reinforcement-learning.md index 2c227429..47c350b9 100644 --- a/contrib/machine-learning/reinforcement-learning.md +++ b/contrib/machine-learning/reinforcement-learning.md @@ -298,4 +298,13 @@ for i in range(num_rows): ## Conclusion Congratulations on completing your journey through this comprehensive guide to reinforcement learning! Armed with this knowledge, you are well-equipped to dive deeper into the exciting world of RL, whether it's for gaming, robotics, finance, healthcare, or any other domain. Keep exploring, experimenting, and learning, and remember, the only limit to what you can achieve with reinforcement learning is your imagination. -*Happy coding, and may your RL adventures be rewarding!* \ No newline at end of file +*Happy coding, and may your RL adventures be rewarding!* + +\( Q(s, a) \leftarrow Q(s, a) + \alpha \left( r + \gamma \max_{a'} Q(s', a') - Q(s, a) \right) \) + +where: +- \( Q(s, a) \) is the Q-value of state \( s \) and action \( a \). +- \( r \) is the observed reward. +- \( s' \) is the next state. +- \( \alpha \) is the learning rate. +- \( \gamma \) is the discount factor. From 5cf4b9b5cbd88c094427cffff5a4881d3cc48118 Mon Sep 17 00:00:00 2001 From: Ojaswi Chopra Date: Sat, 22 Jun 2024 23:01:52 +0530 Subject: [PATCH 599/656] Trial-2 --- contrib/machine-learning/reinforcement-learning.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/contrib/machine-learning/reinforcement-learning.md b/contrib/machine-learning/reinforcement-learning.md index 47c350b9..760d5304 100644 --- a/contrib/machine-learning/reinforcement-learning.md +++ b/contrib/machine-learning/reinforcement-learning.md @@ -300,11 +300,11 @@ Congratulations on completing your journey through this comprehensive guide to r *Happy coding, and may your RL adventures be rewarding!* -\( Q(s, a) \leftarrow Q(s, a) + \alpha \left( r + \gamma \max_{a'} Q(s', a') - Q(s, a) \right) \) +$$ Q(s, a) \leftarrow Q(s, a) + \alpha \left( r + \gamma \max_{a'} Q(s', a') - Q(s, a) \right) $$ where: -- \( Q(s, a) \) is the Q-value of state \( s \) and action \( a \). -- \( r \) is the observed reward. -- \( s' \) is the next state. -- \( \alpha \) is the learning rate. -- \( \gamma \) is the discount factor. +- $Q(s, a)$ is the Q-value of state $s$ and action $a$. +- $r$ is the observed reward. +- $s'$ is the next state. +- $\alpha$ is the learning rate. +- $\gamma$ is the discount factor. From d6f24246c75c1e5f7cfbfa51f8b39db5a2f811a3 Mon Sep 17 00:00:00 2001 From: Ojaswi Chopra Date: Sat, 22 Jun 2024 23:05:57 +0530 Subject: [PATCH 600/656] Final Commit --- .../reinforcement-learning.md | 34 +++++++------------ 1 file changed, 13 insertions(+), 21 deletions(-) diff --git a/contrib/machine-learning/reinforcement-learning.md b/contrib/machine-learning/reinforcement-learning.md index 760d5304..fcc26d6c 100644 --- a/contrib/machine-learning/reinforcement-learning.md +++ b/contrib/machine-learning/reinforcement-learning.md @@ -113,13 +113,14 @@ Q-Learning is a model-free algorithm used in reinforcement learning to learn the - Choose an action using an exploration strategy (e.g., epsilon-greedy). - Take the action, observe the reward and the next state. - Update the Q-value of the current state-action pair using the Bellman equation: - - where: - - is the Q-value of state and action . - - is the observed reward. - - is the next state. - - is the learning rate. - - is the discount factor. + $$ Q(s, a) \leftarrow Q(s, a) + \alpha \left( r + \gamma \max_{a'} Q(s', a') - Q(s, a) \right) $$ + + where: + - $Q(s, a)$ is the Q-value of state $s$ and action $a$. + - $r$ is the observed reward. + - $s'$ is the next state. + - $\alpha$ is the learning rate. + - $\gamma$ is the discount factor. 3. Until convergence or a maximum number of episodes. ### SARSA @@ -128,13 +129,13 @@ SARSA (State-Action-Reward-State-Action) is an on-policy temporal difference alg #### Algorithm: 1. Initialize Q-values arbitrarily for all state-action pairs. 2. Repeat for each episode: - - Initialize the environment state . - - Choose an action using the current policy (e.g., epsilon-greedy). + - Initialize the environment state $s$. + - Choose an action $a$ using the current policy (e.g., epsilon-greedy). - Repeat for each timestep: - - Take action , observe the reward and the next state . - - Choose the next action using the current policy. + - Take action $a$, observe the reward $r$ and the next state $s'$. + - Choose the next action $a'$ using the current policy. - Update the Q-value of the current state-action pair using the SARSA update rule: - + $$Q(s, a) \leftarrow Q(s, a) + \alpha \left( r + \gamma Q(s', a') - Q(s, a) \right)$$ 3. Until convergence or a maximum number of episodes. ### REINFORCE Algorithm: @@ -299,12 +300,3 @@ for i in range(num_rows): Congratulations on completing your journey through this comprehensive guide to reinforcement learning! Armed with this knowledge, you are well-equipped to dive deeper into the exciting world of RL, whether it's for gaming, robotics, finance, healthcare, or any other domain. Keep exploring, experimenting, and learning, and remember, the only limit to what you can achieve with reinforcement learning is your imagination. *Happy coding, and may your RL adventures be rewarding!* - -$$ Q(s, a) \leftarrow Q(s, a) + \alpha \left( r + \gamma \max_{a'} Q(s', a') - Q(s, a) \right) $$ - -where: -- $Q(s, a)$ is the Q-value of state $s$ and action $a$. -- $r$ is the observed reward. -- $s'$ is the next state. -- $\alpha$ is the learning rate. -- $\gamma$ is the discount factor. From 194fc52150204544790c525234ad7f21387c4b59 Mon Sep 17 00:00:00 2001 From: Ojaswi Chopra <99067527+ojaswichopra@users.noreply.github.com> Date: Sat, 22 Jun 2024 23:06:47 +0530 Subject: [PATCH 601/656] Update reinforcement-learning.md --- contrib/machine-learning/reinforcement-learning.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/machine-learning/reinforcement-learning.md b/contrib/machine-learning/reinforcement-learning.md index fcc26d6c..027b6822 100644 --- a/contrib/machine-learning/reinforcement-learning.md +++ b/contrib/machine-learning/reinforcement-learning.md @@ -113,7 +113,7 @@ Q-Learning is a model-free algorithm used in reinforcement learning to learn the - Choose an action using an exploration strategy (e.g., epsilon-greedy). - Take the action, observe the reward and the next state. - Update the Q-value of the current state-action pair using the Bellman equation: - $$ Q(s, a) \leftarrow Q(s, a) + \alpha \left( r + \gamma \max_{a'} Q(s', a') - Q(s, a) \right) $$ + $$Q(s, a) \leftarrow Q(s, a) + \alpha \left( r + \gamma \max_{a'} Q(s', a') - Q(s, a) \right)$$ where: - $Q(s, a)$ is the Q-value of state $s$ and action $a$. From 212fbb2f19aa65ae308e989e21b8929f93a4289c Mon Sep 17 00:00:00 2001 From: srilekha279 <158455553+srilekha279@users.noreply.github.com> Date: Sat, 22 Jun 2024 23:16:31 +0530 Subject: [PATCH 602/656] Update index.md I have made changes as mentioned in the issue. --- contrib/machine-learning/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index df7a3e58..72ef6092 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -10,8 +10,8 @@ - [Support Vector Machine Algorithm](support-vector-machine.md) - [Artificial Neural Network from the Ground Up](ann.md) - [Introduction To Convolutional Neural Networks (CNNs)](intro-to-cnn.md) -- [TensorFlow.md](tensorflow.md) -- [PyTorch.md](pytorch.md) +- [TensorFlow](tensorflow.md) +- [PyTorch](pytorch.md) - [Ensemble Learning](ensemble-learning.md) - [Types of optimizers](types-of-optimizers.md) - [Logistic Regression](logistic-regression.md) From 5174c9dc1f8f93ab717ea5c0d0bb52176ebeac3f Mon Sep 17 00:00:00 2001 From: Shraman Jain <60072287+Shraman-jain@users.noreply.github.com> Date: Sat, 22 Jun 2024 23:30:23 +0530 Subject: [PATCH 603/656] Update threading.md --- contrib/advanced-python/threading.md | 61 ++++++++++++++++++++++++++++ 1 file changed, 61 insertions(+) diff --git a/contrib/advanced-python/threading.md b/contrib/advanced-python/threading.md index 07bc4bfe..fa315335 100644 --- a/contrib/advanced-python/threading.md +++ b/contrib/advanced-python/threading.md @@ -30,6 +30,10 @@ thread.join() print("Thread has finished execution.") ``` +Output : +``` +Thread has finished execution. +``` **2. Performing Task with Thread:** We can also perform a specific task by thread by giving a function as target and its argument as arg ,as a parameter to Thread object. @@ -53,6 +57,15 @@ thread.join() print("Thread has finished execution.") ``` +Output : +``` +Thread: 0 +Thread: 1 +Thread: 2 +Thread: 3 +Thread: 4 +Thread has finished execution. +``` **3. Delaying a Task with Thread's Timer Function:** We can set a time for which we want a thread to start. Timer function takes 4 arguments (interval,function,args,kwargs). @@ -75,6 +88,16 @@ thread.join() print("Thread has finished execution.") ``` +Output : +``` +# after three second output will be generated +Thread: 0 +Thread: 1 +Thread: 2 +Thread: 3 +Thread: 4 +Thread has finished execution. +``` **4. Creating Multiple Threads** We can create and manage multiple threads to achieve concurrent execution. @@ -100,6 +123,20 @@ thread2.join() print("Both threads have finished execution.") ``` +Output : +``` +Thread 1: 0 +Thread 1: 1 +Thread 2: 0 +Thread 1: 2 +Thread 1: 3 +Thread 2: 1 +Thread 2: 2 +Thread 2: 3 +Thread 2: 4 +Thread 1: 4 +Both threads have finished execution. +``` **5. Thread Synchronization** When we create multiple threads and they access shared resources, there is a risk of race conditions and data corruption. To prevent this, you can use synchronization primitives such as locks. @@ -130,6 +167,30 @@ thread2.join() print("Both threads have finished execution.") ``` +Output : +``` +Thread 1: 0 +Thread 1: 1 +Thread 1: 2 +Thread 1: 3 +Thread 1: 4 +Thread 1: 5 +Thread 1: 6 +Thread 1: 7 +Thread 1: 8 +Thread 1: 9 +Thread 2: 0 +Thread 2: 1 +Thread 2: 2 +Thread 2: 3 +Thread 2: 4 +Thread 2: 5 +Thread 2: 6 +Thread 2: 7 +Thread 2: 8 +Thread 2: 9 +Both threads have finished execution. +``` A ```lock``` object is created using threading.Lock() and The ```with lock``` statement ensures that the lock is acquired before printing and released after printing. This prevents other threads from accessing the print statement simultaneously. 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zg~Ho}Iez3SLtd5+x$(!@TI%I;A`@1#km&0WMLNd!t^R}jf>;Q}Q@P7sh4alcWm+xF z3q{USAf7r)VO(?xBp|f5r>QZ3zU~9ina%`VwqNjom#Ur^WeJ&82nOu`sRApV1}Pt| zPK|=Fmp1bGs2N3#iBoRI%nwf94^Sz%158ip_hJ3!x77ZFAI4{w=v|)ufzeu{3$t~c zDoU>k58+fD#V8>AT7oCAJX-zUrIyMsBINF2b~*gxm8~yr!*t=43rsCc0Eukx7tBxY z4gs4)76;LNDR1|TmZ;&TtTf!TI#ixTA>{%NbDmR2uKul`v6`%XL^d|MS3)k;;^xJ6 zW^xgcW7w0h0 z>`Ap#CVgQWeJa7Y;QENajqr@}^B5ok(79`8+RZsL;CAO9n$*bDU(P>*NP%G^QK5&k z8Ta?v{($5WP7*hFhu?I*J9yC(c|B)LvTeK>u9S01gIA;oV1*WiASmH$0M;FP|LialS`3RyE=4!k~tjiBOT;OvbHp4r`omOZdx3-~B`sJ=j$~LEQV_r_H&Q@$ff16M4TFd5B)3;uJi3YARc?hlOgW zsPq8a+|rXtx505J_I@y`E9cS|z>KuU`CzZLF~08|h#0LVZ6e>yG*#DAsQ zKaBT3toIL7|IY^WA4UWH|LrZq!2fIikMO@u^56I$p8rom0s!$)0Puer<{vD~APN76 nLH;oS^8cr2VP*!&|35|`5C8zp|3Ai(e_Mh55BvRx@?ZOZ$>WS_ literal 0 HcmV?d00001 From 995c7c6581a7c44cc81c184876f8d55a901b63fa Mon Sep 17 00:00:00 2001 From: Revanth <109272714+revanth1718@users.noreply.github.com> Date: Sat, 22 Jun 2024 23:45:44 +0530 Subject: [PATCH 606/656] Add Xgboost --- contrib/machine-learning/xgboost.md | 96 +++++++++++++++++++++++++++++ 1 file changed, 96 insertions(+) create mode 100644 contrib/machine-learning/xgboost.md diff --git a/contrib/machine-learning/xgboost.md b/contrib/machine-learning/xgboost.md new file mode 100644 index 00000000..f7044638 --- /dev/null +++ b/contrib/machine-learning/xgboost.md @@ -0,0 +1,96 @@ +# XGBoost +XGBoost is an implementation of gradient boosted decision trees designed for speed and performance. +## Introduction to Gradient Boosting +Gradient boosting is a powerful technique for building predictive models that has seen widespread success in various applications. + +- **Boosting Concept**: Boosting originated from the idea of modifying weak learners to improve their predictive capability. +- **AdaBoost**: The first successful boosting algorithm was Adaptive Boosting (AdaBoost), which utilizes decision stumps as weak learners. +- **Gradient Boosting Machines (GBM)**: AdaBoost and related algorithms were later reformulated as Gradient Boosting Machines, casting boosting as a numerical optimization problem. +- **Algorithm Elements**: + - _Loss function_: Determines the objective to minimize (e.g., cross-entropy for classification, mean squared error for regression). + - _Weak learner_: Typically, decision trees are used as weak learners. + - _Additive model_: New weak learners are added iteratively to minimize the loss function, correcting the errors of previous models. +## Introduction to XGBoost +- eXtreme Gradient Boosting (XBGoost): a more **regularized form** of Gradient Boosting, as it uses **advanced regularization (L1&L2)**, improving the model’s **generalization capabilities.** +- It’s suitable when there is **a large number of training samples and a small number of features**; or when there is **a mixture of categorical and numerical features**. + + +- **Development**: Created by Tianqi Chen, XGBoost is designed for computational speed and model performance. +- **Key Features**: + - _Speed_: Achieved through careful engineering, including parallelization of tree construction, distributed computing, and cache optimization. + - _Support for Variations_: XGBoost supports various techniques and optimizations. + - _Out-of-Core Computing_: Can handle very large datasets that don't fit into memory. +- **Advantages**: + - _Sparse Optimization_: Suitable for datasets with many zero values. + - _Regularization_: Implements advanced regularization techniques (L1 and L2), enhancing generalization capabilities. + - _Parallel Training_: Utilizes all CPU cores during training for faster processing. + - _Multiple Loss Functions_: Supports different loss functions based on the problem type. + - _Bagging and Early Stopping_: Additional techniques for improving performance and efficiency. +- **Pre-Sorted Decision Tree Algorithm**: + 1. Features are pre-sorted by their values. + 2. Traversing segmentation points involves finding the best split point on a feature with a cost of O(#data). + 3. Data is split into left and right child nodes after finding the split point. + 4. Pre-sorting allows for accurate split point determination. + - **Limitations**: + + 1. Iterative Traversal: Each iteration requires traversing the entire training data multiple times. + 2. Memory Consumption: Loading the entire training data into memory limits size, while not loading it leads to time-consuming read/write operations. + 3. Space Consumption: Pre-sorting consumes space, storing feature sorting results and split gain calculations. + + XGBoosting:![enter image description here](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*8Y_e29rVdBZ4pC3DFDEZDQ.png) +## Develop Your First XGBoost Model +This code uses the XGBoost library to train a model on the Iris dataset, splitting the data, setting hyperparameters, training the model, making predictions, and evaluating accuracy, achieving an accuracy score of X on the testing set. + + +```python +# XGBoost with Iris Dataset +# Importing necessary libraries +import numpy as np +import xgboost as xgb +from sklearn.datasets import load_iris +from sklearn.model_selection import train_test_split +from sklearn.metrics import accuracy_score + +# Loading a sample dataset (Iris dataset) +data = load_iris() +X = data.data +y = data.target + +# Splitting the dataset into training and testing sets +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + +# Converting the dataset into DMatrix format +dtrain = xgb.DMatrix(X_train, label=y_train) +dtest = xgb.DMatrix(X_test, label=y_test) + +# Setting hyperparameters for XGBoost +params = { + 'max_depth': 3, + 'eta': 0.1, + 'objective': 'multi:softmax', + 'num_class': 3 +} + +# Training the XGBoost model +num_round = 50 +model = xgb.train(params, dtrain, num_round) + +# Making predictions on the testing set +y_pred = model.predict(dtest) + +# Evaluating the model +accuracy = accuracy_score(y_test, y_pred) +print("Accuracy:", accuracy) +``` + +### Output + + Accuracy: 1.0 + +## **Conclusion** + +XGBoost's focus on speed, performance, and scalability has made it one of the most widely used and powerful predictive modeling algorithms available. Its ability to handle large datasets efficiently, along with its advanced features and optimizations, makes it a valuable tool in machine learning and data science. + + +## Reference +- [Machine Learning Prediction of Turning Precision Using Optimized XGBoost Model](https://www.mdpi.com/2076-3417/12/15/7739) \ No newline at end of file From bc20d105b779532363b05ce104c5a519742c0cd3 Mon Sep 17 00:00:00 2001 From: Revanth <109272714+revanth1718@users.noreply.github.com> Date: Sat, 22 Jun 2024 23:49:10 +0530 Subject: [PATCH 607/656] Update xgboost.md --- contrib/machine-learning/xgboost.md | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/contrib/machine-learning/xgboost.md b/contrib/machine-learning/xgboost.md index f7044638..1eb7f09a 100644 --- a/contrib/machine-learning/xgboost.md +++ b/contrib/machine-learning/xgboost.md @@ -1,8 +1,8 @@ # XGBoost XGBoost is an implementation of gradient boosted decision trees designed for speed and performance. + ## Introduction to Gradient Boosting Gradient boosting is a powerful technique for building predictive models that has seen widespread success in various applications. - - **Boosting Concept**: Boosting originated from the idea of modifying weak learners to improve their predictive capability. - **AdaBoost**: The first successful boosting algorithm was Adaptive Boosting (AdaBoost), which utilizes decision stumps as weak learners. - **Gradient Boosting Machines (GBM)**: AdaBoost and related algorithms were later reformulated as Gradient Boosting Machines, casting boosting as a numerical optimization problem. @@ -10,11 +10,10 @@ Gradient boosting is a powerful technique for building predictive models that ha - _Loss function_: Determines the objective to minimize (e.g., cross-entropy for classification, mean squared error for regression). - _Weak learner_: Typically, decision trees are used as weak learners. - _Additive model_: New weak learners are added iteratively to minimize the loss function, correcting the errors of previous models. + ## Introduction to XGBoost - eXtreme Gradient Boosting (XBGoost): a more **regularized form** of Gradient Boosting, as it uses **advanced regularization (L1&L2)**, improving the model’s **generalization capabilities.** - It’s suitable when there is **a large number of training samples and a small number of features**; or when there is **a mixture of categorical and numerical features**. - - - **Development**: Created by Tianqi Chen, XGBoost is designed for computational speed and model performance. - **Key Features**: - _Speed_: Achieved through careful engineering, including parallelization of tree construction, distributed computing, and cache optimization. @@ -32,16 +31,15 @@ Gradient boosting is a powerful technique for building predictive models that ha 3. Data is split into left and right child nodes after finding the split point. 4. Pre-sorting allows for accurate split point determination. - **Limitations**: - 1. Iterative Traversal: Each iteration requires traversing the entire training data multiple times. 2. Memory Consumption: Loading the entire training data into memory limits size, while not loading it leads to time-consuming read/write operations. - 3. Space Consumption: Pre-sorting consumes space, storing feature sorting results and split gain calculations. - - XGBoosting:![enter image description here](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*8Y_e29rVdBZ4pC3DFDEZDQ.png) + 3. Space Consumption: Pre-sorting consumes space, storing feature sorting results and split gain calculations. + XGBoosting: + ![image](assets/XG_1.webp) + ## Develop Your First XGBoost Model This code uses the XGBoost library to train a model on the Iris dataset, splitting the data, setting hyperparameters, training the model, making predictions, and evaluating accuracy, achieving an accuracy score of X on the testing set. - ```python # XGBoost with Iris Dataset # Importing necessary libraries @@ -88,9 +86,7 @@ print("Accuracy:", accuracy) Accuracy: 1.0 ## **Conclusion** - XGBoost's focus on speed, performance, and scalability has made it one of the most widely used and powerful predictive modeling algorithms available. Its ability to handle large datasets efficiently, along with its advanced features and optimizations, makes it a valuable tool in machine learning and data science. - ## Reference -- [Machine Learning Prediction of Turning Precision Using Optimized XGBoost Model](https://www.mdpi.com/2076-3417/12/15/7739) \ No newline at end of file +- [Machine Learning Prediction of Turning Precision Using Optimized XGBoost Model](https://www.mdpi.com/2076-3417/12/15/7739) From da0d7f9beb36e76f3e47ed4025f98d82b13381e0 Mon Sep 17 00:00:00 2001 From: Revanth <109272714+revanth1718@users.noreply.github.com> Date: Sat, 22 Jun 2024 23:50:33 +0530 Subject: [PATCH 608/656] Update index.md --- contrib/machine-learning/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index df7a3e58..caea81e4 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -25,3 +25,4 @@ - [Naive Bayes](naive-bayes.md) - [Neural network regression](neural-network-regression.md) - [PyTorch Fundamentals](pytorch-fundamentals.md) +- [Xgboost](xgboost.md) From 417e3c81a3ab4a2bb286e9f252eddb0b67e512a9 Mon Sep 17 00:00:00 2001 From: Damini2004 Date: Sun, 23 Jun 2024 00:04:05 +0530 Subject: [PATCH 609/656] Commit 1 --- .../advanced-python/regular_expressions.md | 110 +++++++++++++++++- 1 file changed, 106 insertions(+), 4 deletions(-) diff --git a/contrib/advanced-python/regular_expressions.md b/contrib/advanced-python/regular_expressions.md index 65ff2c2b..a8679812 100644 --- a/contrib/advanced-python/regular_expressions.md +++ b/contrib/advanced-python/regular_expressions.md @@ -1,6 +1,7 @@ ## Regular Expressions in Python Regular expressions (regex) are a powerful tool for pattern matching and text manipulation. Python's re module provides comprehensive support for regular expressions, enabling efficient text processing and validation. +Regular expressions (regex) are a versitile tool for matching patterns in strings. In Python, the `re` module provides support for working with regular expressions. ## 1. Introduction to Regular Expressions A regular expression is a sequence of characters defining a search pattern. Common use cases include validating input, searching within text, and extracting @@ -20,7 +21,81 @@ Metacharacters: Special characters like ., *, ?, +, ^, $, [ ], and | used to bui * ?: 0 or 1 repetition. * []: Any one character inside brackets (e.g., [a-z]). * |: Either the pattern before or after. - +* \ : Used to drop the special meaning of character following it +* {} : Indicate the number of occurrences of a preceding regex to match. +* () : Enclose a group of Regex + +Examples: +```bash +1. `.` +import re +pattern = r'c.t' +text = 'cat cot cut cit' +matches = re.findall(pattern, text) +print(matches) # Output: ['cat', 'cot', 'cut', 'cit'] + +2. `^` +pattern = r'^Hello' +text = 'Hello, world!' +match = re.search(pattern, text) +print(match.group() if match else 'No match') # Output: 'Hello' + +3. `$` +pattern = r'world!$' +text = 'Hello, world!' +match = re.search(pattern, text) +print(match.group() if match else 'No match') # Output: 'world!' + +4. `*` +pattern = r'ab*' +text = 'a ab abb abbb' +matches = re.findall(pattern, text) +print(matches) # Output: ['a', 'ab', 'abb', 'abbb'] + +5. `+` +pattern = r'ab+' +text = 'a ab abb abbb' +matches = re.findall(pattern, text) +print(matches) # Output: ['ab', 'abb', 'abbb'] + +6. `?` +pattern = r'ab?' +text = 'a ab abb abbb' +matches = re.findall(pattern, text) +print(matches) # Output: ['a', 'ab', 'ab', 'ab'] + +7. `[]` +pattern = r'[aeiou]' +text = 'hello world' +matches = re.findall(pattern, text) +print(matches) # Output: ['e', 'o', 'o'] + +8. `|` +pattern = r'cat|dog' +text = 'I have a cat and a dog.' +matches = re.findall(pattern, text) +print(matches) # Output: ['cat', 'dog'] + +9. `\`` +pattern = r'\$100' +text = 'The price is $100.' +match = re.search(pattern, text) +print(match.group() if match else 'No match') # Output: '$100' + +10. `{}` +pattern = r'\d{3}' +text = 'My number is 123456' +matches = re.findall(pattern, text) +print(matches) # Output: ['123', '456'] + +11. `()` +pattern = r'(cat|dog)' +text = 'I have a cat and a dog.' +matches = re.findall(pattern, text) +print(matches) # Output: ['cat', 'dog'] + +``` + ## 3. Using the re Module **Key functions in the re module:** @@ -29,7 +104,8 @@ Metacharacters: Special characters like ., *, ?, +, ^, $, [ ], and | used to bui * re.search(): Searches for a match anywhere in the string. * re.findall(): Returns a list of all matches. * re.sub(): Replaces matches with a specified string. - +* re.split(): Returns a list where the string has been split at each match. +* re.escape(): Escapes special character Examples: ```bash import re @@ -45,6 +121,12 @@ print(re.findall(r'\d+', 'abc123def456')) # Output: ['123', '456'] # Substitute matches print(re.sub(r'\d+', '#', 'abc123def456')) # Output: abc#def# + +#Return a list where it get matched +print(re.split("\s", txt)) #['The', 'Donkey', 'in', 'the','Town'] + +# Escape special character +print(re.escape("We are good to go")) #We\ are\ good\ to\ go ``` ## 4. Compiling Regular Expressions @@ -58,6 +140,7 @@ pattern = re.compile(r'\d+') print(pattern.match('123abc').group()) # Output: 123 print(pattern.search('abc123').group()) # Output: 123 print(pattern.findall('abc123def456')) # Output: ['123', '456'] + ``` ## 5. Groups and Capturing @@ -78,19 +161,38 @@ if match: ## 6. Special Sequences Special sequences are shortcuts for common patterns: +* \A:Returns a match if the specified characters are at the beginning of the string. +* \b:Returns a match where the specified characters are at the beginning or at the end of a word. +* \B:Returns a match where the specified characters are present, but NOT at the beginning (or at the end) of a word. * \d: Any digit. * \D: Any non-digit. * \w: Any alphanumeric character. * \W: Any non-alphanumeric character. * \s: Any whitespace character. * \S: Any non-whitespace character. +* \Z:Returns a match if the specified characters are at the end of the string. + Example: ```bash import re print(re.search(r'\w+@\w+\.\w+', 'Contact: support@example.com').group()) # Output: support@example.com ``` +## 7.Sets +A set is a set of characters inside a pair of square brackets [] with a special meaning: + +* [arn] : Returns a match where one of the specified characters (a, r, or n) is present. +* [a-n] : Returns a match for any lower case character, alphabetically between a and n. +* [^arn] : Returns a match for any character EXCEPT a, r, and n. +* [0123] : Returns a match where any of the specified digits (0, 1, 2, or 3) are present. +* [0-9] : Returns a match for any digit between 0 and 9. +* [0-5][0-9] : Returns a match for any two-digit numbers from 00 and 59. +* [a-zA-Z] : Returns a match for any character alphabetically between a and z, lower case OR upper case. +* [+] : In sets, +, *, ., |, (), $,{} has no special meaning, so [+] means: return a match for any + character in the string. ## Summary -Regular expressions are a versatile tool for text processing in Python. The re module offers powerful functions and metacharacters for pattern matching, -searching, and manipulation, making it an essential skill for handling complex text processing tasks. +Regular expressions (regex) are a powerful tool for text processing in Python, offering a flexible way to match, search, and manipulate text patterns. The re module provides a comprehensive set of functions and metacharacters to tackle complex text processing tasks. +With regex, you can: +1.Match patterns: Use metacharacters like ., *, ?, and {} to match specific patterns in text. +2.Search text: Employ functions like re.search() and re.match() to find occurrences of patterns in text. +3.Manipulate text: Utilize functions like re.sub() to replace patterns with new text. 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-17,7 +17,7 @@ A Tree Data Structure can be traversed in following ways: ## Binary Tree Structure Before diving into traversal techniques, let's define a simple binary tree node structure: -![Binary Tree](images/binarytree.png) +![Binary Tree](images/binarytree.png)) ```python class Node: From b6632c6d7fe9f8212a79d846174c3dff9f19b087 Mon Sep 17 00:00:00 2001 From: Ramya Korupolu <104009248+Ramya-korupolu@users.noreply.github.com> Date: Sun, 23 Jun 2024 12:23:02 +0530 Subject: [PATCH 617/656] Add files via upload --- .../ds-algorithms/images/Preorder traversal.png | Bin 0 -> 53953 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 contrib/ds-algorithms/images/Preorder traversal.png diff --git a/contrib/ds-algorithms/images/Preorder traversal.png b/contrib/ds-algorithms/images/Preorder traversal.png new file mode 100644 index 0000000000000000000000000000000000000000..b6f999e385c1b0104adaefe1cc42d5439e4b8210 GIT binary patch literal 53953 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Date: Sun, 23 Jun 2024 12:24:08 +0530 Subject: [PATCH 618/656] Update Tree-Traversal.md --- contrib/ds-algorithms/Tree-Traversal.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/ds-algorithms/Tree-Traversal.md b/contrib/ds-algorithms/Tree-Traversal.md index cd19515b..1fa6fe57 100644 --- a/contrib/ds-algorithms/Tree-Traversal.md +++ b/contrib/ds-algorithms/Tree-Traversal.md @@ -125,7 +125,7 @@ def printInorder(root): In this traversal method, the root node is visited first, then the left subtree and finally the right subtree. -![preorder](images/preorder-traversal.png) +![preorder](images/Preorder%20traversal.png)) **The order:** Root -> Left -> Right From bd26f9766b8751aa859a04bc4569ebd74049714b Mon Sep 17 00:00:00 2001 From: Ramya Korupolu <104009248+Ramya-korupolu@users.noreply.github.com> Date: Sun, 23 Jun 2024 12:25:01 +0530 Subject: [PATCH 619/656] Delete contrib/ds-algorithms/images/Preorder traversal.png --- .../ds-algorithms/images/Preorder 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Tree-Traversal.md --- contrib/ds-algorithms/Tree-Traversal.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/ds-algorithms/Tree-Traversal.md b/contrib/ds-algorithms/Tree-Traversal.md index 1fa6fe57..4ec72ee8 100644 --- a/contrib/ds-algorithms/Tree-Traversal.md +++ b/contrib/ds-algorithms/Tree-Traversal.md @@ -125,7 +125,7 @@ def printInorder(root): In this traversal method, the root node is visited first, then the left subtree and finally the right subtree. -![preorder](images/Preorder%20traversal.png)) +![preorder](images/preorder-traversal.png)) **The order:** Root -> Left -> Right From f1c00b3443b3c1b0188d28424581c128c012d256 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sun, 23 Jun 2024 12:50:58 +0530 Subject: [PATCH 622/656] Update and rename pandas_series.md to pandas-series.md --- .../{pandas_series.md => pandas-series.md} | 90 +++++++++++-------- 1 file changed, 54 insertions(+), 36 deletions(-) rename contrib/pandas/{pandas_series.md => pandas-series.md} (91%) diff --git a/contrib/pandas/pandas_series.md b/contrib/pandas/pandas-series.md similarity index 91% rename from contrib/pandas/pandas_series.md rename to contrib/pandas/pandas-series.md index cbb9a0ff..a6fe042f 100644 --- a/contrib/pandas/pandas_series.md +++ b/contrib/pandas/pandas-series.md @@ -1,7 +1,6 @@ # Pandas Series - - A series is a Panda data structures that represents a one dimensional array-like object containing an array of data and an associated array of data type labels, called index. +A series is a Panda data structures that represents a one dimensional array-like object containing an array of data and an associated array of data type labels, called index. ## Creating a Series object: @@ -14,8 +13,9 @@ import pandas as pd s1 = pd.Series([4, 5, 2, 3]) print(s1) ``` + +#### Output ``` -Output: 0 4 1 5 2 2 @@ -32,8 +32,9 @@ import pandas as pd s2 = pd.Series({'A': 1, 'B': 2, 'C': 3}) print(s2) ``` + +#### Output ``` -Output: A 1 B 2 C 3 @@ -52,8 +53,9 @@ import pandas as pd s4 = pd.Series([1, 2, 3], index=['a', 'b', 'c'], dtype='float64') print(s4) ``` + +#### Output ``` -Output: a 1.0 b 2.0 c 3.0 @@ -69,8 +71,9 @@ import pandas as pd s3=pd.Series([1,np.Nan,2]) print(s3) ``` + +#### Output ``` -Output: 0 1.0 1 NaN 2 2.0 @@ -89,8 +92,9 @@ a=np.arange(1,5) # [1,2,3,4] s5=pd.Series(data=a**2,index=a) print(s5) ``` + +#### Output ``` -Output: 1 1 2 4 3 9 @@ -111,8 +115,6 @@ dtype: int64 | `.hasnans` | Return True if there is any NaN in the data | | `.empty` | Return True if the Series object is empty | - - - If you use len() on a series object then it return total number of elements in the series object whereas .count() return only the number of non NaN elements. ## Accessing a Series object and its elements @@ -126,12 +128,12 @@ import pandas as pd s7 = pd.Series(data=[13, 45, 67, 89], index=['A', 'B', 'C', 'D']) print(s7['A']) ``` + +#### Output ``` -Output: 13 ``` - ### Slicing a Series - Slices are extracted based on their positional index, regardless of the custom index labels. @@ -146,15 +148,15 @@ import pandas as pd s = pd.Series(data=[13, 45, 67, 89], index=['A', 'B', 'C', 'D']) print(s[:2]) ``` + +#### Output ``` -Output: A 13 B 45 dtype: int64 - -This example demonstrates that the first two elements (positions 0 and 1) are returned, regardless of their custom index labels. ``` +This example demonstrates that the first two elements (positions 0 and 1) are returned, regardless of their custom index labels. ## Operation on series object @@ -171,8 +173,9 @@ s8['a'] = 100 s8.index = ['x', 'y', 'z'] print(s8) ``` + +#### Output ``` -Output: x 100 y 20 z 30 @@ -181,25 +184,32 @@ dtype: int64 **Note: Series object are value-mutable but size immutable objects.** -### vector operations +### Vector operations We can perform vector operations such as `+`,`-`,`/`,`%` etc. + +#### Addition ```python import pandas as pd s9 = pd.Series([1, 2, 3]) -print("addition:", s9 + 5) -print("subtraction:", s9 - 2) -``` +print(s9 + 5) ``` -output: -addition: +#### Output +``` 0 6 1 7 2 8 dtype: int64 +``` + +#### Subtraction +```python +print(s9 - 2) +``` -subtraction: +#### Output +``` 0 -1 1 0 2 1 @@ -207,25 +217,32 @@ dtype: int64 ``` ### Arthmetic on series object + +#### Addition ```python import pandas as pd s10 = pd.Series([1, 2, 3]) s11 = pd.Series([4, 5, 6]) -print("addition:", s10 + s11) - -print("multiplication:", s10 * s11) +print(s10 + s11) ``` -``` -output: -addition: +#### Output +``` 0 5 1 7 2 9 dtype: int64 +``` + +#### Multiplication + +```python +print("s10 * s11) +``` -multiplication: +#### Output +``` 0 4 1 10 2 18 @@ -249,26 +266,28 @@ s12 = pd.Series([10, 20, 30, 40, 50, 60, 70, 80, 90, 100]) print(s12.head(3)) print(s12.tail(3)) ``` + +#### Output ``` -Output: 0 10 1 20 2 30 dtype: int64 - 7 80 8 90 9 100 dtype: int64 ``` + If you dont provide any value to n the by default it give results for `n=5`. -### Few extra functions: +### Few extra functions | **Function** | **Description** | |----------------------------------------|------------------------------------------------------------------------| | `.sort_values()` | Return the Series object in ascending order based on its values. | | `.sort_index()` | Return the Series object in ascending order based on its index. | | `.sort_drop()` | Return the Series with the deleted index and its corresponding value. | + ```python import pandas as pd @@ -277,22 +296,21 @@ print(s13.sort_values()) print(s13.sort_index()) print(s13.drop('a')) ``` + +#### Output ``` -Output: a 1 b 2 c 3 dtype: int64 - a 1 b 2 c 3 dtype: int64 - c 3 b 2 dtype: int64 ``` ## Conclusion -In short, Pandas Series is a fundamental data structure in Python for handling one-dimensional data. It combines an array of values with an index, offering efficient methods for data manipulation and analysis. With its ease of use and powerful functionality, Pandas Series is widely used in data science and analytics for tasks such as data cleaning, exploration, and visualization. \ No newline at end of file +In short, Pandas Series is a fundamental data structure in Python for handling one-dimensional data. It combines an array of values with an index, offering efficient methods for data manipulation and analysis. With its ease of use and powerful functionality, Pandas Series is widely used in data science and analytics for tasks such as data cleaning, exploration, and visualization. From 372f5ff634d6f4b66aafe0440da2943ef0274724 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sun, 23 Jun 2024 12:51:40 +0530 Subject: [PATCH 623/656] Update index.md --- contrib/pandas/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 2a159291..db008e2f 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -9,4 +9,4 @@ - [Working with Date & Time in Pandas](datetime.md) - [Importing and Exporting Data in Pandas](import-export.md) - [Handling Missing Values in Pandas](handling-missing-values.md) -- [Pandas Series](pandas_series.md) \ No newline at end of file +- [Pandas Series](pandas-series.md) From 33be2407c8ef074649604f7ea743088195476df1 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Sun, 23 Jun 2024 12:52:35 +0530 Subject: [PATCH 624/656] Update pandas-series.md --- contrib/pandas/pandas-series.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/contrib/pandas/pandas-series.md b/contrib/pandas/pandas-series.md index a6fe042f..88b12351 100644 --- a/contrib/pandas/pandas-series.md +++ b/contrib/pandas/pandas-series.md @@ -256,8 +256,8 @@ Here one thing we should keep in mind that both the series object should have sa | **Functions** | **Description** | |--------------------------|---------------------------------------------------| -| `.head(n)` | return the first n elements of the series | -| `.tail(n)` | return the last n elements of the series | +| `.head(n)` | return the first n elements of the series | +| `.tail(n)` | return the last n elements of the series | ```python import pandas as pd @@ -282,6 +282,7 @@ dtype: int64 If you dont provide any value to n the by default it give results for `n=5`. ### Few extra functions + | **Function** | **Description** | |----------------------------------------|------------------------------------------------------------------------| | `.sort_values()` | Return the Series object in 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It is a combination of a box plot and a kernel density plot, providing a richer visualization of the distribution of the data. In a violin plot, each data point is represented by a kernel density plot, mirrored and joined together to form a symmetrical shape resembling a violin, hence the name. + +Violin plots are particularly useful when comparing distributions across different categories or groups. They provide insights into the shape, spread, and central tendency of the data, allowing for a more comprehensive understanding than traditional box plots. + +Violin plots offer a more detailed distribution representation, combining summary statistics and kernel density plots, handle unequal sample sizes effectively, allow easy comparison across groups, and facilitate identification of multiple modes compared to box plots. + +![Violen plot 1](images/violen-plots1.webp) + +## Prerequisites + +Before creating violin charts in matplotlib you must ensure that you have Python as well as Matplotlib installed on your system. + +## Creating a simple Violin Plot with `violinplot()` method + +A basic violin plot can be created with `violinplot()` method in `matplotlib.pyplot`. + +```Python +import matplotlib.pyplot as plt +import numpy as np + +# Creating dataset +data = [np.random.normal(0, std, 100) for std in range(1, 5)] + +# Creating Plot +plt.violinplot(data) + +# Show plot +plt.show() + +``` + +When executed, this would show the following pie chart: + + +![Basic violin plot](images\violinplotnocolor.png) + + +The `Violinplot` function in matplotlib.pyplot creates a violin plot, which is a graphical representation of the distribution of data across different levels of a categorical variable. Here's a breakdown of its usage: + +```Python +plt.violinplot(data, showmeans=False, showextrema=False) +``` + +- `data`: This parameter represents the dataset used to create the violin plot. It can be a single array or a sequence of arrays. + +- `showmeans`: This optional parameter, if set to True, displays the mean value as a point on the violin plot. Default is False. + +- `showextrema`: This optional parameter, if set to True, displays the minimum and maximum values as points on the violin plot. Default is False. + +Additional parameters can be used to further customize the appearance of the violin plot, such as setting custom colors, adding labels, and adjusting the orientation. For instance: + +```Python +plt.violinplot(data, showmedians=True, showmeans=True, showextrema=True, vert=False, widths=0.9, bw_method=0.5) +``` +- showmedians: Setting this parameter to True displays the median value as a line on the violin plot. + +- `vert`: This parameter determines the orientation of the violin plot. Setting it to False creates a horizontal violin plot. Default is True. + +- `widths`: This parameter sets the width of the violins. Default is 0.5. + +- `bw_method`: This parameter determines the method used to calculate the kernel bandwidth for the kernel density estimation. Default is 0.5. + +Using these parameters, you can customize the violin plot according to your requirements, enhancing its readability and visual appeal. + + +## Customizing Violin Plots in Matplotlib + +When customizing violin plots in Matplotlib, using `matplotlib.pyplot.subplots()` provides greater flexibility for applying customizations. + +### Coloring Violin Plots + +You can assign custom colors to the `violins` by passing an array of colors to the color parameter in `violinplot()` method. + +```Python +import matplotlib.pyplot as plt +import numpy as np + +# Creating dataset +data = [np.random.normal(0, std, 100) for std in range(1, 5)] +colors = ['tab:red', 'tab:blue', 'tab:green', 'tab:orange'] + +# Creating plot using matplotlib.pyplot.subplots() +fig, ax = plt.subplots() + +# Customizing colors of violins +for i in range(len(data)): + parts = ax.violinplot(data[i], positions=[i], vert=False, showmeans=False, showextrema=False, showmedians=True, widths=0.9, bw_method=0.5) + for pc in parts['bodies']: + pc.set_facecolor(colors[i]) + +# Show plot +plt.show() +``` +This code snippet creates a violin plot with custom colors assigned to each violin, enhancing the visual appeal and clarity of the plot. + + +![Coloring violin](images\violenplotnormal.png) + + +When customizing violin plots using `matplotlib.pyplot.subplots()`, you obtain a `Figure` object `fig` and an `Axes` object `ax`, allowing for extensive customization. Each `violin plot` consists of various components, including the `violin body`, `lines representing median and quartiles`, and `potential markers for mean and outliers`. You can customize these components using the appropriate methods and attributes of the Axes object. + +- Here's an example of how to customize violin plots: + +```Python +import matplotlib.pyplot as plt +import numpy as np + +# Creating dataset +data = [np.random.normal(0, std, 100) for std in range(1, 5)] +colors = ['tab:red', 'tab:blue', 'tab:green', 'tab:orange'] + +# Creating plot using matplotlib.pyplot.subplots() +fig, ax = plt.subplots() + +# Creating violin plots +parts = ax.violinplot(data, showmeans=False, showextrema=False, showmedians=True, widths=0.9, bw_method=0.5) + +# Customizing colors of violins +for i, pc in enumerate(parts['bodies']): + pc.set_facecolor(colors[i]) + +# Customizing median lines +for line in parts['cmedians'].get_segments(): + ax.plot(line[:, 0], line[:, 1], color='black') + +# Customizing quartile lines +for line in parts['cmedians'].get_segments(): + ax.plot(line[:, 0], line[:, 1], linestyle='--', color='black', linewidth=2) + +# Adding mean markers +for line in parts['cmedians'].get_segments(): + ax.scatter(np.mean(line[:, 0]), np.mean(line[:, 1]), marker='o', color='black') + +# Customizing axes labels +ax.set_xlabel('X Label') +ax.set_ylabel('Y Label') + +# Adding title +ax.set_title('Customized Violin Plot') + +# Show plot +plt.show() +``` + +![Customizing violin](images/violin-plot4.png) + +In this example, we customize various components of the violin plot, such as colors, line styles, and markers, to enhance its visual appeal and clarity. Additionally, we modify the axes labels and add a title to provide context to the plot. + +### Adding Hatching to Violin Plots + +You can add hatching patterns to the violin plots to enhance their visual distinction. This can be achieved by setting the `hatch` parameter in the `violinplot()` function. + +```Python +import matplotlib.pyplot as plt +import numpy as np + +# Creating dataset +data = [np.random.normal(0, std, 100) for std in range(1, 5)] +colors = ['tab:red', 'tab:blue', 'tab:green', 'tab:orange'] +hatches = ['/', '\\', '|', '-'] + +# Creating plot using matplotlib.pyplot.subplots() +fig, ax = plt.subplots() + +# Creating violin plots with hatching +parts = ax.violinplot(data, showmeans=False, showextrema=False, showmedians=True, widths=0.9, bw_method=0.5) + +for i, pc in enumerate(parts['bodies']): + pc.set_facecolor(colors[i]) + pc.set_hatch(hatches[i]) + +# Show plot +plt.show() +``` + +![violin_hatching](images/violin-hatching.png) + + + +### Labeling Violin Plots + +You can add `labels` to violin plots to provide additional information about the data. This can be achieved by setting the label parameter in the `violinplot()` function. + +An example in shown here: + +```Python +import matplotlib.pyplot as plt +import numpy as np + +# Creating dataset +data = [np.random.normal(0, std, 100) for std in range(1, 5)] +labels = ['Group {}'.format(i) for i in range(1, 5)] + +# Creating plot using matplotlib.pyplot.subplots() +fig, ax = plt.subplots() + +# Creating violin plots +parts = ax.violinplot(data, showmeans=False, showextrema=False, showmedians=True, widths=0.9, bw_method=0.5) + +# Adding labels to violin plots +for i, label in enumerate(labels): + parts['bodies'][i].set_label(label) + +# Show plot +plt.legend() +plt.show() +``` +![violin_labeling](images/violin-labelling.png) + +In this example, each violin plot is labeled according to its group, providing context to the viewer. +These customizations can be combined and further refined to create violin plots that effectively convey the underlying data distributions. + +### Stacked Violin Plots + +`Stacked violin plots` are useful when you want to compare the distribution of a `single` variable across different categories or groups. In a stacked violin plot, violins for each category or group are `stacked` on top of each other, allowing for easy visual comparison. + +```Python +import matplotlib.pyplot as plt +import numpy as np + +# Generating sample data +np.random.seed(0) +data1 = np.random.normal(0, 1, 100) +data2 = np.random.normal(2, 1, 100) +data3 = np.random.normal(1, 1, 100) + +# Creating a stacked violin plot +plt.violinplot([data1, data2, data3], showmedians=True) + +# Adding labels to x-axis ticks +plt.xticks([1, 2, 3], ['Group 1', 'Group 2', 'Group 3']) + +# Adding title and labels +plt.title('Stacked Violin Plot') +plt.xlabel('Groups') +plt.ylabel('Values') + +# Displaying the plot +plt.show() +``` +![stacked violin plots](images/stacked_violin_plots.png) + + +### Split Violin Plots + +`Split violin plots` are effective for comparing the distribution of a `single variable` across `two` different categories or groups. In a split violin plot, each violin is split into two parts representing the distributions of the variable for each category. + +```Python +import matplotlib.pyplot as plt +import numpy as np + +# Generating sample data +np.random.seed(0) +data_male = np.random.normal(0, 1, 100) +data_female = np.random.normal(2, 1, 100) + +# Creating a split violin plot +plt.violinplot([data_male, data_female], showmedians=True) + +# Adding labels to x-axis ticks +plt.xticks([1, 2], ['Male', 'Female']) + +# Adding title and labels +plt.title('Split Violin Plot') +plt.xlabel('Gender') +plt.ylabel('Values') + +# Displaying the plot +plt.show() +``` + +![Shadow](images/split-violin-plot.png) + +In both examples, we use Matplotlib's `violinplot()` function to create the violin plots. These unique features provide additional flexibility and insights when analyzing data distributions across different groups or categories. + From 7c7752dd94a7a4a6584d167bb722c1bf28199650 Mon Sep 17 00:00:00 2001 From: RajKhanke <137288727+RajKhanke@users.noreply.github.com> Date: Sun, 23 Jun 2024 18:37:43 +0530 Subject: [PATCH 627/656] Update matplotlib-violin_plots.md From e4ff080e31f3bebe2e6a776c1d8f92aeea008dff Mon Sep 17 00:00:00 2001 From: RajKhanke <137288727+RajKhanke@users.noreply.github.com> Date: Sun, 23 Jun 2024 18:55:53 +0530 Subject: [PATCH 628/656] Update matplotlib-violin_plots.md --- contrib/plotting-visualization/matplotlib-violin_plots.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/contrib/plotting-visualization/matplotlib-violin_plots.md b/contrib/plotting-visualization/matplotlib-violin_plots.md index bc12f68d..ef2ec42c 100644 --- a/contrib/plotting-visualization/matplotlib-violin_plots.md +++ b/contrib/plotting-visualization/matplotlib-violin_plots.md @@ -34,7 +34,7 @@ plt.show() When executed, this would show the following pie chart: -![Basic violin plot](images\violinplotnocolor.png) +![Basic violin plot](images/violinplotnocolor.png) The `Violinplot` function in matplotlib.pyplot creates a violin plot, which is a graphical representation of the distribution of data across different levels of a categorical variable. Here's a breakdown of its usage: @@ -96,7 +96,7 @@ plt.show() This code snippet creates a violin plot with custom colors assigned to each violin, enhancing the visual appeal and clarity of the plot. -![Coloring violin](images\violenplotnormal.png) +![Coloring violin](images/violenplotnormal.png) When customizing violin plots using `matplotlib.pyplot.subplots()`, you obtain a `Figure` object `fig` and an `Axes` object `ax`, allowing for extensive customization. Each `violin plot` consists of various components, including the `violin body`, `lines representing median and quartiles`, and `potential markers for mean and outliers`. You can customize these components using the appropriate methods and attributes of the Axes object. 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ztT^b(Tp`a8y`9CzKsW`;u;%3%siUwXiU-5M5~oK@&BO&rSU_l@E-2~+rt8CZ0u1l` z1~u9~f~lbeA^L*D<;xMP;Zkh=9o62v&3 z*V$-9e}bRftU|H_$o?Rtc9PX_JWk32vA!nW+;(c+qaeMa0}zFY7WkkBp+hQ|A*y2{ zDLf)!KE*4al}?~pe9v0uMpo>nHA_>(va+1da8`8;hRpLD`!b5{*qO&DvSQw@c1Mdjxq!BS{`#t-GiMe z`H`JVEcV2Aw&V$5LINPy08=m~d3ZMfwcXTNeToY;{V(# zAR}(wC?Eyw)<4%ewA5b0nkr(rhyUE#0D+w3J_P=EodoFqgK;F;Kko-V*&NW3u2qT? xq(6`RZwWZU{f`R&qr(3*;{Q9l0Egjk$KK@`%PTzre$IB~zN*%}%DZNP{|7-@C>Q_$ literal 0 HcmV?d00001 diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 644704b0..02324f22 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -10,3 +10,4 @@ - [Seaborn Plotting Functions](seaborn-plotting.md) - [Getting started with Seaborn](seaborn-basics.md) - [Bar Plots in Plotly](plotly-bar-plots.md) +- [Pie Charts in Plotly](plotly-pie-charts.md) \ No newline at end of file diff --git a/contrib/plotting-visualization/plotly-pie-charts.md b/contrib/plotting-visualization/plotly-pie-charts.md new file mode 100644 index 00000000..2f788096 --- /dev/null +++ b/contrib/plotting-visualization/plotly-pie-charts.md @@ -0,0 +1,221 @@ +# Pie Charts in Plotly + +A pie chart is a type of graph that represents the data in the circular graph. The slices of pie show the relative size of the data, and it is a type of pictorial representation of data. A pie chart requires a list of categorical variables and numerical variables. Here, the term "pie" represents the whole, and the "slices" represent the parts of the whole. + +Pie charts are commonly used in business presentations like sales, operations, survey results, resources, etc. as they are pleasing to the eye and provide a quick summary. + +Plotly is a very powerful library for creating modern visualizations and it provides a very easy and intuitive method to create highly customized pie charts. + +## Prerequisites + +Before creating bar plots in Plotly you must ensure that you have Python, Plotly and Pandas installed on your system. + +## Introduction + +There are various ways to create pie charts in `plotly`. One of the prominent and easiest one is using `plotly.express`. Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures. On the other hand you can also use `plotly.graph_objects` to create various plots. + +Here, we'll be using `plotly.express` to create the pie charts. Also we'll be converting our datasets into pandas DataFrames which makes it extremely convenient and easy to create charts. + +Also, note that when you execute the codes in a simple python file, the output plot will be shown in your **browser**, rather than a pop-up window like in matplotlib. If you do not want that, it is **recommended to create the plots in a notebook (like jupyter)**. For this, install an additional library `nbformat`. This way you can see the output on the notebook itself, and can also render its format to png, jpg, etc. + +## Creating a simple pie chart using `plotly.express.pie` + +In `plotly.express.pie`, data visualized by the sectors of the pie is set in values. The sector labels are set in names. + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +flowers = ['Rose','Tulip','Marigold','Sunflower','Daffodil'] +petals = [11,9,17,4,7] + +# Converting dataset to pandas DataFrame +dataset = {'flowers':flowers, 'petals':petals} +df = pd.DataFrame(dataset) + +# Creating pie chart +fig = px.pie(df, values='petals', names='flowers') + +# Showing plot +fig.show() +``` +![Basic Pie Chart](images/plotly-basic-pie-chart.png) + +Here, we are first creating the dataset and converting it into Pandas DataFrames using dictionaries, with its keys being DataFrame columns. Next, we are plotting the pie chart by using `px.pie`. In the `values` and `names` parameters, we have to specify a column name in the DataFrame. + +`px.pie(df, values='Petals', names='Flowers')` is used to specify that the pie chart is to be plotted by taking the values from column `Petals` and the fractional area of each slice is represented by **petal/sum(petals)**. The column `flowers` represents the labels of slices corresponding to each value in `petals`. + +**Note:** When you generate the image using above code, it will show you an **interactive plot**, if you want image, you can download it from their itself. + +## Customizing Pie Charts + +### Adding title to the chart + +Simply pass the title of your chart as a parameter in `px.pie`. + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +flowers = ['Rose','Tulip','Marigold','Sunflower','Daffodil'] +petals = [11,9,17,4,7] + +# Converting dataset to pandas DataFrame +dataset = {'flowers':flowers, 'petals':petals} +df = pd.DataFrame(dataset) + +# Creating pie chart +fig = px.pie(df, values='petals', names='flowers', + title='Number of Petals in Flowers') + +# Showing plot +fig.show() +``` +![Title in Pie Chart](images/plotly-pie-title.png) + +### Coloring Slices + +There are a lot of beautiful color scales available in plotly and can be found here [plotly color scales](https://plotly.com/python/builtin-colorscales/). Choose your favourite colorscale apply it like this: + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +flowers = ['Rose','Tulip','Marigold','Sunflower','Daffodil'] +petals = [11,9,17,4,7] + +# Converting dataset to pandas DataFrame +dataset = {'flowers':flowers, 'petals':petals} +df = pd.DataFrame(dataset) + +# Creating pie chart +fig = px.pie(df, values='petals', names='flowers', + title='Number of Petals in Flowers', + color_discrete_sequence=px.colors.sequential.Agsunset) + +# Showing plot +fig.show() +``` +![Pie Chart Colors-1](images/plotly-pie-color-1.png) + +You can also set custom colors for each label by passing it as a dictionary(map) in `color_discrete_map`, like this: + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +flowers = ['Rose','Tulip','Marigold','Sunflower','Daffodil'] +petals = [11,9,17,4,7] + +# Converting dataset to pandas DataFrame +dataset = {'flowers':flowers, 'petals':petals} +df = pd.DataFrame(dataset) + +# Creating pie chart +fig = px.pie(df, values='petals', names='flowers', + title='Number of Petals in Flowers', + color='flowers', + color_discrete_map={'Rose':'red', + 'Tulip':'magenta', + 'Marigold':'green', + 'Sunflower':'yellow', + 'Daffodil':'royalblue'}) + +# Showing plot +fig.show() +``` +![Pie Chart Colors-1](images/plotly-pie-color-2.png) + +### Labeling Slices + +You can use `fig.update_traces` to effectively control the properties of text being displayed on your figure, for example if we want both flower name , petal count and percentage in our slices, we can do it like this: + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +flowers = ['Rose','Tulip','Marigold','Sunflower','Daffodil'] +petals = [11,9,17,4,7] + +# Converting dataset to pandas DataFrame +dataset = {'flowers':flowers, 'petals':petals} +df = pd.DataFrame(dataset) + +# Creating pie chart +fig = px.pie(df, values='petals', names='flowers', + title='Number of Petals in Flowers') + +# Updating text properties +fig.update_traces(textposition='inside', textinfo='label+value+percent') + +# Showing plot +fig.show() +``` +![Pie Labels](images/plotly-pie-labels.png) + +### Pulling out a slice + +To pull out a slice pass an array to parameter `pull` in `fig.update_traces` corresponding to the slices and amount to be pulled. + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +flowers = ['Rose','Tulip','Marigold','Sunflower','Daffodil'] +petals = [11,9,17,4,7] + +# Converting dataset to pandas DataFrame +dataset = {'flowers':flowers, 'petals':petals} +df = pd.DataFrame(dataset) + +# Creating pie chart +fig = px.pie(df, values='petals', names='flowers', + title='Number of Petals in Flowers') + +# Updating text properties +fig.update_traces(textposition='inside', textinfo='label+value') + +# Pulling out slice +fig.update_traces(pull=[0,0,0,0.2,0]) + +# Showing plot +fig.show() +``` +![Slice Pull](images/plotly-pie-pull.png) + +### Pattern Fills + +You can also add patterns (hatches), in addition to colors in pie charts. + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +flowers = ['Rose','Tulip','Marigold','Sunflower','Daffodil'] +petals = [11,9,17,4,7] + +# Converting dataset to pandas DataFrame +dataset = {'flowers':flowers, 'petals':petals} +df = pd.DataFrame(dataset) + +# Creating pie chart +fig = px.pie(df, values='petals', names='flowers', + title='Number of Petals in Flowers') + +# Updating text properties +fig.update_traces(textposition='outside', textinfo='label+value') + +# Adding pattern fills +fig.update_traces(marker=dict(pattern=dict(shape=[".", "/", "+", "-","+"]))) + +# Showing plot +fig.show() +``` +![Pattern Fills Pie Chart](images/plotly-pie-patterns.png) \ No newline at end of file From fd466af7e0544ed31f8fc5d6d0c2529863ec0d32 Mon Sep 17 00:00:00 2001 From: mankalavaishnavi Date: Mon, 24 Jun 2024 13:27:00 +0530 Subject: [PATCH 630/656] added deque --- contrib/ds-algorithms/deque.md | 198 +++++++++++++++++++++------------ 1 file changed, 129 insertions(+), 69 deletions(-) diff --git a/contrib/ds-algorithms/deque.md b/contrib/ds-algorithms/deque.md index 459ac04b..db489a82 100644 --- a/contrib/ds-algorithms/deque.md +++ b/contrib/ds-algorithms/deque.md @@ -1,108 +1,168 @@ -## Double-Ended Queue (Deque) +# Deque in Python -A double-ended queue (deque) supports insertion and deletion from both ends, making it a more versatile queue implementation. +## Definition +A deque, short for double-ended queue, is an ordered collection of items that allows rapid insertion and deletion at both ends. -### Input-Restricted Deque +## Syntax +In Python, deques are implemented in the collections module: -In an input-restricted deque, elements can only be inserted from one end while deletions can occur from both ends. +```py +from collections import deque -### Output-Restricted Deque +# Creating a deque +d = deque(iterable) # Create deque from iterable (optional) +``` -An output-restricted deque allows elements to be deleted from one end only, while insertions can be made from both ends. +## Operations +1. **Appending Elements**: -## Real Life Examples of Deques + - append(x): Adds element x to the right end of the deque. + - appendleft(x): Adds element x to the left end of the deque. -### Task Scheduling +2. **Removing Elements**: -Deques are useful in task scheduling algorithms where tasks can be added to either end and processed accordingly based on priority or other scheduling criteria. + - pop(): Removes and returns the rightmost element. + - popleft(): Removes and returns the leftmost element. -### Sliding Window Problems +3. **Accessing Elements**: -Algorithms solving sliding window problems often use deques to efficiently manage and query elements in the current window. + - deque[index]: Accesses element at index. -### Implementations in Python +4. **Other Operations**: -Python provides a built-in `collections.deque` which supports efficient append and pop operations from both ends. It's ideal for scenarios requiring a simple and efficient double-ended queue. + - extend(iterable): Extends deque by appending elements from iterable. + - extendleft(iterable): Extends deque by appending elements from iterable to the left. + - rotate(n): Rotates deque n steps to the right (negative n rotates left). -## Operations on a Deque +## Example -- **isEmpty**: Checks if the deque is empty. -- **appendLeft**: Adds an element to the left end of the deque. -- **appendRight**: Adds an element to the right end of the deque. -- **popLeft**: Removes and returns the element from the left end of the deque. -- **popRight**: Removes and returns the element from the right end of the deque. -- **peekLeft**: Returns the element from the left end without removing it. -- **peekRight**: Returns the element from the right end without removing it. -- **clear**: Removes all elements from the deque. +### 1. showing all the operations +```py +from collections import deque -## Implementation of Deque in Python +# Initialize a deque +d = deque([1, 2, 3, 4, 5]) +print("Initial deque:", d) -Python's `collections.deque` provides an efficient implementation of a deque. +# Append elements +d.append(6) +print("After append(6):", d) -```python -from collections import deque +# Append left +d.appendleft(0) +print("After appendleft(0):", d) -# Creating a deque -dq = deque() +# Pop from the right end +rightmost = d.pop() +print("Popped from right end:", rightmost) +print("Deque after pop():", d) -# Adding elements to the deque -dq.append(1) -dq.append(2) -dq.append(3) +# Pop from the left end +leftmost = d.popleft() +print("Popped from left end:", leftmost) +print("Deque after popleft():", d) -# Removing elements from the deque -dq.popleft() # Removes and returns 1 -dq.pop() # Removes and returns 3 +# Accessing elements +print("Element at index 2:", d[2]) -# Peeking elements -print("Left end peek:", dq[0]) -print("Right end peek:", dq[-1]) +# Extend deque +d.extend([6, 7, 8]) +print("After extend([6, 7, 8]):", d) -# Displaying elements in the deque -print("Deque:", list(dq)) -``` +# Extend left +d.extendleft([-1, 0]) +print("After extendleft([-1, 0]):", d) -## Example: Sliding Window Maximum +# Rotate deque +d.rotate(2) +print("After rotate(2):", d) -In this example, we'll use a deque to efficiently find the maximum element in sliding windows of a list. +# Rotate left +d.rotate(-3) +print("After rotate(-3):", d) +``` +Output +```py +Initial deque: deque([1, 2, 3, 4, 5]) +After append(6): deque([1, 2, 3, 4, 5, 6]) +After appendleft(0): deque([0, 1, 2, 3, 4, 5, 6]) +Popped from right end: 6 +Deque after pop(): deque([0, 1, 2, 3, 4, 5]) +Popped from left end: 0 +Deque after popleft(): deque([1, 2, 3, 4, 5]) +Element at index 2: 3 +After extend([6, 7, 8]): deque([1, 2, 3, 4, 5, 6, 7, 8]) +After extendleft([-1, 0]): deque([0, -1, 1, 2, 3, 4, 5, 6, 7, 8]) +After rotate(2): deque([7, 8, 0, -1, 1, 2, 3, 4, 5, 6]) +After rotate(-3): deque([1, 2, 3, 4, 5, 6, 7, 8, 0, -1]) + +``` -```python +### 2. Finding Maximum in Sliding Window +```py from collections import deque -def sliding_window_maximum(nums, k): +def max_sliding_window(nums, k): if not nums: return [] - - n = len(nums) + + d = deque() result = [] - dq = deque() - - for i in range(n): - # Remove elements from the deque that are out of the current window - while dq and dq[0] <= i - k: - dq.popleft() - - # Remove elements from the deque that are less than the current element - while dq and nums[dq[-1]] <= nums[i]: - dq.pop() - - # Add the current element index to the deque - dq.append(i) - - # Add the maximum of the current window to the result + + for i, num in enumerate(nums): + # Remove elements from deque that are out of the current window + if d and d[0] <= i - k: + d.popleft() + + # Remove elements from deque smaller than the current element + while d and nums[d[-1]] <= num: + d.pop() + + d.append(i) + + # Add maximum for current window if i >= k - 1: - result.append(nums[dq[0]]) - + result.append(nums[d[0]]) + return result # Example usage: nums = [1, 3, -1, -3, 5, 3, 6, 7] k = 3 -print("Sliding Window Maximum:", sliding_window_maximum(nums, k)) +print("Maximums in sliding window of size", k, "are:", max_sliding_window(nums, k)) + +``` + +Output +```py +Maximums in sliding window of size 3 are: [3, 3, 5, 5, 6, 7] ``` -## Conclusion -Queues and deques are fundamental data structures that facilitate efficient data processing and -management in various applications. Understanding their principles and implementations is crucial -for developing robust software solutions. \ No newline at end of file +## Applications +- **Efficient Queues and Stacks**: Deques allow fast O(1) append and pop operations from both ends, +making them ideal for implementing queues and stacks. +- **Sliding Window Maximum/Minimum**: Used in algorithms that require efficient windowed +computations. + + +## Advantages +- Efficiency: O(1) time complexity for append and pop operations from both ends. +- Versatility: Can function both as a queue and as a stack. +- Flexible: Supports rotation and slicing operations efficiently. + + +## Disadvantages +- Memory Usage: Requires more memory compared to simple lists due to overhead in managing linked +nodes. + +## Conclusion +- Deques in Python, provided by the collections.deque module, offer efficient double-ended queue +operations with O(1) time complexity for append and pop operations on both ends. They are versatile +data structures suitable for implementing queues, stacks, and more complex algorithms requiring +efficient manipulation of elements at both ends. + +- While deques excel in scenarios requiring fast append and pop operations from either end, they do +consume more memory compared to simple lists due to their implementation using doubly-linked lists. +However, their flexibility and efficiency make them invaluable for various programming tasks and +algorithmic solutions. \ No newline at end of file From afec460fbd8b3031d7e1bdcd44389135573b4121 Mon Sep 17 00:00:00 2001 From: Vedant Zope Date: Mon, 24 Jun 2024 21:23:20 +0530 Subject: [PATCH 631/656] Added Cyclic Sort algorithm --- contrib/ds-algorithms/sorting-algorithms.md | 55 +++++++++++++++++++++ 1 file changed, 55 insertions(+) diff --git a/contrib/ds-algorithms/sorting-algorithms.md b/contrib/ds-algorithms/sorting-algorithms.md index 55367b82..a3cd72e2 100644 --- a/contrib/ds-algorithms/sorting-algorithms.md +++ b/contrib/ds-algorithms/sorting-algorithms.md @@ -561,3 +561,58 @@ print("Sorted string:", sorted_str) ### Complexity Analysis - **Time Complexity:** O(n+k) for all cases.No matter how the elements are placed in the array, the algorithm goes through n+k times - **Space Complexity:** O(max). Larger the range of elements, larger is the space complexity. + + +## 9. Cyclic Sort + +### Theory +Cyclic Sort is an in-place sorting algorithm that is useful for sorting arrays where the elements are in a known range (e.g., 1 to N). The key idea behind the algorithm is that each number should be placed at its correct index. If we find a number that is not at its correct index, we swap it with the number at its correct index. This process is repeated until every number is at its correct index. + +### Algorithm +- Iterate over the array from the start to the end. +- For each element, check if it is at its correct index. +- If it is not at its correct index, swap it with the element at its correct index. +- Continue this process until the element at the current index is in its correct position. Move to the next index and repeat the process until the end of the array is reached. + +### Steps +- Start with the first element. +- Check if it is at the correct index (i.e., if arr[i] == i + 1). +- If it is not, swap it with the element at the index arr[i] - 1. +- Repeat step 2 for the current element until it is at the correct index. +- Move to the next element and repeat the process. + +### Code + +```python +def cyclic_sort(nums): + i = 0 + while i < len(nums): + correct_index = nums[i] - 1 + if nums[i] != nums[correct_index]: + nums[i], nums[correct_index] = nums[correct_index], nums[i] # Swap + else: + i += 1 + return nums +``` + +### Example +``` +arr = [3, 1, 5, 4, 2] +sorted_arr = cyclic_sort(arr) +print(sorted_arr) + ``` +### Output +``` +[1, 2, 3, 4, 5] +``` + +### Complexity Analysis +**Time Complexity:** + +The time complexity of Cyclic Sort is **O(n)**. +This is because in each cycle, each element is either placed in its correct position or a swap is made. Since each element is swapped at most once, the total number of swaps (and hence the total number of operations) is linear in the number of elements. + +**Space Complexity:** + +The space complexity of Cyclic Sort is **O(1)**. +This is because the algorithm only requires a constant amount of additional space beyond the input array. \ No newline at end of file From 7b123e505fad404a79ac3a000850d241101529ad Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Tue, 25 Jun 2024 04:18:43 +0530 Subject: [PATCH 632/656] Update CONTRIBUTING.md --- CONTRIBUTING.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 86880091..0a046b41 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -23,7 +23,7 @@ The list of topics for which we are looking for content are provided below along - Interacting with Databases - [Link](https://github.com/animator/learn-python/tree/main/contrib/database) - Web Scrapping - [Link](https://github.com/animator/learn-python/tree/main/contrib/web-scrapping) - API Development - [Link](https://github.com/animator/learn-python/tree/main/contrib/api-development) -- Data Structures & Algorithms - [Link](https://github.com/animator/learn-python/tree/main/contrib/ds-algorithms) +- Data Structures & Algorithms - [Link](https://github.com/animator/learn-python/tree/main/contrib/ds-algorithms) **(Not accepting)** - Python Mini Projects - [Link](https://github.com/animator/learn-python/tree/main/contrib/mini-projects) **(Not accepting)** - Python Question Bank - [Link](https://github.com/animator/learn-python/tree/main/contrib/question-bank) **(Not accepting)** From bd2635bc6e2106c36e94f868f40b0e0114471fe9 Mon Sep 17 00:00:00 2001 From: StutiTiwari <116055334+s-tuti@users.noreply.github.com> Date: Tue, 25 Jun 2024 19:40:46 +0530 Subject: [PATCH 633/656] Update dynamic-programming.md --- contrib/ds-algorithms/dynamic-programming.md | 189 +++++++++++++++++++ 1 file changed, 189 insertions(+) diff --git a/contrib/ds-algorithms/dynamic-programming.md b/contrib/ds-algorithms/dynamic-programming.md index 0f16d5c9..986ecd37 100644 --- a/contrib/ds-algorithms/dynamic-programming.md +++ b/contrib/ds-algorithms/dynamic-programming.md @@ -234,3 +234,192 @@ print("Length of lis is", lis(arr)) ## Complexity Analysis - **Time Complexity**: O(n * n) for both approaches, where n is the length of the array. - **Space Complexity**: O(n * n) for the memoization table in Top-Down Approach, O(n) in Bottom-Up Approach. + +# 5. String Edit Distance + +The String Edit Distance algorithm calculates the minimum number of operations (insertions, deletions, or substitutions) required to convert one string into another. + +**Algorithm Overview:** +- **Base Cases:** If one string is empty, the edit distance is the length of the other string. +- **Memoization:** Store the results of previously computed edit distances to avoid redundant computations. +- **Recurrence Relation:** Compute the edit distance by considering insertion, deletion, and substitution operations. + +## String Edit Distance Code in Python (Top-Down Approach with Memoization) +```python +def edit_distance(str1, str2, memo={}): + m, n = len(str1), len(str2) + if (m, n) in memo: + return memo[(m, n)] + if m == 0: + return n + if n == 0: + return m + if str1[m - 1] == str2[n - 1]: + memo[(m, n)] = edit_distance(str1[:m-1], str2[:n-1], memo) + else: + memo[(m, n)] = 1 + min(edit_distance(str1, str2[:n-1], memo), # Insert + edit_distance(str1[:m-1], str2, memo), # Remove + edit_distance(str1[:m-1], str2[:n-1], memo)) # Replace + return memo[(m, n)] + +str1 = "sunday" +str2 = "saturday" +print(f"Edit Distance between '{str1}' and '{str2}' is {edit_distance(str1, str2)}.") +# Output: Edit Distance between 'sunday' and 'saturday' is 3. +``` +## String Edit Distance Code in Python (Bottom-Up Approach) +```python +def edit_distance(str1, str2): + m, n = len(str1), len(str2) + dp = [[0 for _ in range(n + 1)] for _ in range(m + 1)] + + for i in range(m + 1): + for j in range(n + 1): + if i == 0: + dp[i][j] = j + elif j == 0: + dp[i][j] = i + elif str1[i - 1] == str2[j - 1]: + dp[i][j] = dp[i - 1][j - 1] + else: + dp[i][j] = 1 + min(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1]) + + return dp[m][n] + +str1 = "sunday" +str2 = "saturday" +print(f"Edit Distance between '{str1}' and '{str2}' is {edit_distance(str1, str2)}.") +# Output: Edit Distance between 'sunday' and 'saturday' is 3. +``` +## **Complexity Analysis:** +- **Time Complexity:** O(m * n) where m and n are the lengths of string 1 and string 2 respectively +- **Space Complexity:** O(m * n) for both top-down and bottom-up approaches + + +# 6. Matrix Chain Multiplication + +The Matrix Chain Multiplication finds the optimal way to multiply a sequence of matrices to minimize the number of scalar multiplications. + +**Algorithm Overview:** +- **Base Cases:** The cost of multiplying one matrix is zero. +- **Memoization:** Store the results of previously computed matrix chain orders to avoid redundant computations. +- **Recurrence Relation:** Compute the optimal cost by splitting the product at different points and choosing the minimum cost. + +## Matrix Chain Multiplication Code in Python (Top-Down Approach with Memoization) +```python +def matrix_chain_order(p, memo={}): + n = len(p) - 1 + def compute_cost(i, j): + if (i, j) in memo: + return memo[(i, j)] + if i == j: + return 0 + memo[(i, j)] = float('inf') + for k in range(i, j): + q = compute_cost(i, k) + compute_cost(k + 1, j) + p[i - 1] * p[k] * p[j] + if q < memo[(i, j)]: + memo[(i, j)] = q + return memo[(i, j)] + return compute_cost(1, n) + +p = [1, 2, 3, 4] +print(f"Minimum number of multiplications is {matrix_chain_order(p)}.") +# Output: Minimum number of multiplications is 18. +``` +## Matrix Chain Multiplication Code in Python (Bottom-Up Approach) +```python +def matrix_chain_order(p): + n = len(p) - 1 + m = [[0 for _ in range(n)] for _ in range(n)] + + for L in range(2, n + 1): + for i in range(n - L + 1): + j = i + L - 1 + m[i][j] = float('inf') + for k in range(i, j): + q = m[i][k] + m[k + 1][j] + p[i] * p[k + 1] * p[j + 1] + if q < m[i][j]: + m[i][j] = q + + return m[0][n - 1] + +p = [1, 2, 3, 4] +print(f"Minimum number of multiplications is {matrix_chain_order(p)}.") +# Output: Minimum number of multiplications is 18. +``` +## **Complexity Analysis:** +- **Time Complexity:** O(n^3) where n is the number of matrices in the chain. For an `array p` of dimensions representing the matrices such that the `i-th matrix` has dimensions `p[i-1] x p[i]`, n is `len(p) - 1` +- **Space Complexity:** O(n^2) for both top-down and bottom-up approaches + + +# 7. Optimal Binary Search Tree + +The Matrix Chain Multiplication finds the optimal way to multiply a sequence of matrices to minimize the number of scalar multiplications. + +**Algorithm Overview:** +- **Base Cases:** The cost of a single key is its frequency. +- **Memoization:** Store the results of previously computed subproblems to avoid redundant computations. +- **Recurrence Relation:** Compute the optimal cost by trying each key as the root and choosing the minimum cost. + +## Optimal Binary Search Tree Code in Python (Top-Down Approach with Memoization) +```python +def optimal_bst(keys, freq, memo={}): + n = len(keys) + def compute_cost(i, j): + if (i, j) in memo: + return memo[(i, j)] + if i > j: + return 0 + if i == j: + return freq[i] + memo[(i, j)] = float('inf') + total_freq = sum(freq[i:j+1]) + for r in range(i, j + 1): + cost = (compute_cost(i, r - 1) + + compute_cost(r + 1, j) + + total_freq) + if cost < memo[(i, j)]: + memo[(i, j)] = cost + return memo[(i, j)] + return compute_cost(0, n - 1) + +keys = [10, 12, 20] +freq = [34, 8, 50] +print(f"Cost of Optimal BST is {optimal_bst(keys, freq)}.") +# Output: Cost of Optimal BST is 142. +``` +## Optimal Binary Search Tree Code in Python (Bottom-Up Approach) +```python +def optimal_bst(keys, freq): + n = len(keys) + cost = [[0 for x in range(n)] for y in range(n)] + + for i in range(n): + cost[i][i] = freq[i] + + for L in range(2, n + 1): + for i in range(n - L + 1): + j = i + L - 1 + cost[i][j] = float('inf') + total_freq = sum(freq[i:j+1]) + for r in range(i, j + 1): + c = (cost[i][r - 1] if r > i else 0) + \ + (cost[r + 1][j] if r < j else 0) + \ + total_freq + if c < cost[i][j]: + cost[i][j] = c + + return cost[0][n - 1] + +keys = [10, 12, 20] +freq = [34, 8, 50] +print(f"Cost of Optimal BST is {optimal_bst(keys, freq)}.") +# Output: Cost of Optimal BST is 142. +``` +## **Complexity Analysis:** +- **Time Complexity:** O(n^3) where n is the number of keys in the binary search tree. +- **Space Complexity:** O(n^2) for both top-down and bottom-up approaches + +
    +

    +
    From b4b295e2a0102d7eb5b53e21aa16ebd3da989f3a Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Tue, 25 Jun 2024 22:14:58 +0530 Subject: [PATCH 634/656] Update index.md --- contrib/machine-learning/index.md | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 3a2cccf9..95ee61dc 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -2,16 +2,13 @@ - [Introduction to scikit-learn](sklearn-introduction.md) - [Binomial Distribution](binomial-distribution.md) +- [Naive Bayes](naive-bayes.md) - [Regression in Machine Learning](regression.md) - [Polynomial Regression](polynomial-regression.md) - [Confusion Matrix](confusion-matrix.md) - [Decision Tree Learning](decision-tree.md) - [Random Forest](random-forest.md) - [Support Vector Machine Algorithm](support-vector-machine.md) -- [Artificial Neural Network from the Ground Up](ann.md) -- [Introduction To Convolutional Neural Networks (CNNs)](intro-to-cnn.md) -- [TensorFlow](tensorflow.md) -- [PyTorch](pytorch.md) - [Ensemble Learning](ensemble-learning.md) - [Types of optimizers](types-of-optimizers.md) - [Logistic Regression](logistic-regression.md) @@ -19,14 +16,14 @@ - [Clustering](clustering.md) - [Hierarchical Clustering](hierarchical-clustering.md) - [Grid Search](grid-search.md) -- [Transformers](transformers.md) - [K-Means](kmeans.md) - [K-nearest neighbor (KNN)](knn.md) -<<<<<<< RL +- [Xgboost](xgboost.md) +- [Artificial Neural Network from the Ground Up](ann.md) +- [Introduction To Convolutional Neural Networks (CNNs)](intro-to-cnn.md) +- [TensorFlow](tensorflow.md) +- [PyTorch](pytorch.md) +- [PyTorch Fundamentals](pytorch-fundamentals.md) +- [Transformers](transformers.md) - [Reinforcement Learning](reinforcement-learning.md) -======= -- [Naive Bayes](naive-bayes.md) - [Neural network regression](neural-network-regression.md) -- [PyTorch Fundamentals](pytorch-fundamentals.md) -- [Xgboost](xgboost.md) ->>>>>>> main From 3275592f71770951206aa5487d3a1369d608dd5e Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Tue, 25 Jun 2024 22:17:47 +0530 Subject: [PATCH 635/656] Update reinforcement-learning.md --- .../reinforcement-learning.md | 69 ------------------- 1 file changed, 69 deletions(-) diff --git a/contrib/machine-learning/reinforcement-learning.md b/contrib/machine-learning/reinforcement-learning.md index 027b6822..c5529fc9 100644 --- a/contrib/machine-learning/reinforcement-learning.md +++ b/contrib/machine-learning/reinforcement-learning.md @@ -2,54 +2,6 @@ Reinforcement Learning (RL) is a field of Machine Learing which focuses on goal-directed learning from interaction with the environment. In RL, an agent learns to make decisions by performing actions in an environment to maximize cumulative numerical reward signal. This README aims to provide a thorough understanding of RL, covering key concepts, algorithms, applications, and resources. -## Table of Contents - -
    -Click to expand - -1. [What is Reinforcement Learning?](#what-is-reinforcement-learning) -2. [Key Concepts and Terminology](#key-concepts-and-terminology) - - [Agent](#agent) - - [Environment](#environment) - - [State](#state) - - [Action](#action) - - [Reward](#reward) - - [Policy](#policy) - - [Value Function](#value-function) - - [Model](#model) - - [Exploration vs. Exploitation](#exploration-vs-exploitation) -3. [Types of Reinforcement Learning](#types-of-reinforcement-learning) - - [Model-Based vs Model-Free](#model-based-vs-model-free) - - [Value-Based Methods](#value-based-methods) - - [Policy-Based Methods](#policy-based-methods) - - [Actor-Critic Methods](#actor-critic-methods) -4. [Important Algorithms](#important-algorithms) - - [Q-Learning](#q-learning) - - [SARSA](#sarsa) - - [REINFORCE Algorithm](#reinforce-algorithm) - - [Proximal Policy Optimization (PPO)](#proximal-policy-optimization-ppo) - - [A2C/A3C](#a2ca3c) -5. [Mathematical Background](#mathematical-background) - - [Markov Decision Processes (MDP)](#markov-decision-processes-mdps) - - [Bellman Equations](#bellman-equations) -6. [Applications of Reinforcement Learning](#applications-of-reinforcement-learning) - - [Gaming](#gaming) - - [Robotics](#robotics) - - [Finance](#finance) - - [Healthcare](#healthcare) -7. [Tools and Libraries](#tools-and-libraries) -8. [Resources for Further Learning](#resources-for-further-learning) - - [Books](#books) - - [Online Courses](#online-courses) - - [Research Papers](#research-papers) - - [Blogs and Websites](#blogs-and-websites) -9. [How to Start with Reinforcement Learning](#how-to-start-with-reinforcement-learning) - - [Prerequisites](#prerequisites) - - [Beginner Project](#beginner-project) -10. [Conclusion](#conclusion) - -
    - ## What is Reinforcement Learning? Reinforcement learning involves determining the best actions to take in various situations to maximize a numerical reward signal. Instead of being instructed on which actions to take, the learner must explore and identify the actions that lead to the highest rewards through trial and error. After each action performed in its environment, a trainer may give feedback in the form of rewards or penalties to indicate the desirability of the resulting state. Unlike supervised learning, reinforcement learning does not depend on labeled data but instead learns from the outcomes of its actions. @@ -175,25 +127,6 @@ In healthcare, reinforcement learning is utilized for various applications such - **PyTorch:** Popular machine learning library with RL capabilities. - **Stable Baselines3:** A set of reliable implementations of RL algorithms in PyTorch. -## Resources for Further Learning -### Books -- "Reinforcement Learning: An Introduction" by Sutton and Barto -- "Deep Reinforcement Learning Hands-On" by Maxim Lapan - -### Online Courses -- "Reinforcement Learning" by Udacity -- "Fundamentals of Reinforcement Learning" by Coursera -- "Master Reinforcement Learning and Deep RL with Python" by Udemy - -### Research Papers -- [Reinforcement learning model, algorithms and its application](https://ieeexplore.ieee.org/abstract/document/6025669) -- [A Concise Introduction to Reinforcement Learning](https://www.researchgate.net/publication/323178749_A_Concise_Introduction_to_Reinforcement_Learning) - -### Blogs and Websites -- [OpenAI Blog](https://spinningup.openai.com/en/latest/) -- [Geeks for Geeks](https://www.geeksforgeeks.org/what-is-reinforcement-learning/) -- [Medium Blog](https://gordicaleksa.medium.com/how-to-get-started-with-reinforcement-learning-rl-4922fafeaf8c) - ## How to Start with Reinforcement Learning ### Prerequisites @@ -298,5 +231,3 @@ for i in range(num_rows): ## Conclusion Congratulations on completing your journey through this comprehensive guide to reinforcement learning! Armed with this knowledge, you are well-equipped to dive deeper into the exciting world of RL, whether it's for gaming, robotics, finance, healthcare, or any other domain. Keep exploring, experimenting, and learning, and remember, the only limit to what you can achieve with reinforcement learning is your imagination. - -*Happy coding, and may your RL adventures be rewarding!* From 5d41cfd48175673f4c6fcd29254f5fe8bd53d78b Mon Sep 17 00:00:00 2001 From: Ganesh Date: Wed, 26 Jun 2024 10:31:03 +0530 Subject: [PATCH 636/656] resolved conflicts --- .../assets/eda/bi-variate-analysis.png | Bin 0 -> 23786 bytes .../assets/eda/correlation-analysis.png | Bin 0 -> 25512 bytes .../assets/eda/multi-variate-analysis.png | Bin 0 -> 278287 bytes .../assets/eda/uni-variate-analysis1.png | Bin 0 -> 57396 bytes .../assets/eda/uni-variate-analysis2.png | Bin 0 -> 15235 bytes .../exploratory_dataAnalysis.md | 184 ++++++++++++++++++ contrib/machine-learning/index.md | 1 + 7 files changed, 185 insertions(+) create mode 100644 contrib/machine-learning/assets/eda/bi-variate-analysis.png create mode 100644 contrib/machine-learning/assets/eda/correlation-analysis.png create mode 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zcRd`ula_XWX{-rUT<@;K!NE0%AgCgNs;{)PaTdnSPlCOMH(Xz4t9;IysC90)A{B3{|aY<=u>l;Dt z2+Sk4*2(2G^Q`xz+C&6N!0}~dm6Od_o@*mEmqBX>VpWe+?`CBrT-#mg`WQ;Dw<)PF zM1Ob7N9qX33`edGUKjRxncV^sjMKLuj`NS|0qrql17OU~I(lDxXh3Q`ACQ;~hg^EV zJw7R>>)@l8j!n>W%}tO_azxvaP&$zVkR22OtAL*0UkB*~F?m}UN+%ZqIY_IM4SL4c z1JC*MVl)0wI$;B-aUD4ZJ%8l_>7+Z}d$*ha}_$rcFa}i5sVc>Etp0d2UT)xa5 G-~R;?rlCjx literal 0 HcmV?d00001 diff --git a/contrib/machine-learning/exploratory_dataAnalysis.md b/contrib/machine-learning/exploratory_dataAnalysis.md new file mode 100644 index 00000000..1559a099 --- /dev/null +++ b/contrib/machine-learning/exploratory_dataAnalysis.md @@ -0,0 +1,184 @@ +# Exploratory Data Analysis + +Exploratory Data Analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. EDA is used to understand the data, get a sense of the data, and to identify relationships between variables. EDA is a crucial step in the data analysis process and should be done before building a model. + +## Why is EDA important? + +1. **Understand the data**: EDA helps to understand the data, its structure, and its characteristics. + +2. **Identify patterns and relationships**: EDA helps to identify patterns and relationships between variables. + +3. **Detect outliers and anomalies**: EDA helps to detect outliers and anomalies in the data. + +4. **Prepare data for modeling**: EDA helps to prepare the data for modeling by identifying missing values, handling missing values, and transforming variables. + +## Steps in EDA + +1. **Data Collection**: Collect the data from various sources. + +2. **Data Cleaning**: Clean the data by handling missing values, removing duplicates, and transforming variables. + +3. **Data Exploration**: Explore the data by visualizing the data, summarizing the data, and identifying patterns and relationships. + +4. **Data Analysis**: Analyze the data by performing statistical analysis, hypothesis testing, and building models. + +5. **Data Visualization**: Visualize the data using various plots and charts to understand the data better. + +## Tools for EDA + +1. **Python**: Python is a popular programming language for data analysis and has many libraries for EDA, such as Pandas, NumPy, Matplotlib, Seaborn, and Plotly. + +2. **Jupiter Notebook**: Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. + +## Techniques for EDA + +1. **Descriptive Statistics**: Descriptive statistics summarize the main characteristics of a data set, such as mean, median, mode, standard deviation, and variance. + +2. **Data Visualization**: Data visualization is the graphical representation of data to understand the data better, such as histograms, scatter plots, box plots, and heat maps. + +3. **Correlation Analysis**: Correlation analysis is used to measure the strength and direction of the relationship between two variables. + +4. **Hypothesis Testing**: Hypothesis testing is used to test a hypothesis about a population parameter based on sample data. + +5. **Dimensionality Reduction**: Dimensionality reduction is the process of reducing the number of variables in a data set while retaining as much information as possible. + +6. **Clustering Analysis**: Clustering analysis is used to group similar data points together based on their characteristics. + +## Commonly Used Techniques in EDA + +1. **Uni-variate Analysis**: Uni-variate analysis is the simplest form of data analysis that involves analyzing a single variable at a time. + +2. **Bi-variate Analysis**: Bi-variate analysis involves analyzing two variables at a time to understand the relationship between them. + +3. **Multi-variate Analysis**: Multi-variate analysis involves analyzing more than two variables at a time to understand the relationship between them. + +## Understand with an Example + +Let's understand EDA with an example. Here we use a famous dataset called Iris dataset. + +The dataset consists of 150 samples of iris flowers, where each sample represents measurements of four features (variables) for three species of iris flowers. + +The four features measured are : +Sepal length (in cm) Sepal width (in cm) Petal length (in cm) Petal width (in cm). + +The three species of iris flowers included in the dataset are : +**Setosa**, **Versicolor**, **Virginica** + +```python +# Import libraries +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +from sklearn import datasets + +# Load the Iris dataset +iris = datasets.load_iris() +df = pd.DataFrame(iris.data, columns=iris.feature_names) +df.head() +``` + +| Sepal Length (cm) | Sepal Width (cm) | Petal Length (cm) | Petal Width (cm) | +|-------------------|------------------|-------------------|------------------| +| 5.1 | 3.5 | 1.4 | 0.2 | +| 4.9 | 3.0 | 1.4 | 0.2 | +| 4.7 | 3.2 | 1.3 | 0.2 | +| 4.6 | 3.1 | 1.5 | 0.2 | +| 5.0 | 3.6 | 1.4 | 0.2 | + + +### Uni-variate Analysis + +```python +# Uni-variate Analysis +df_setosa=df.loc[df['species']=='setosa'] +df_virginica=df.loc[df['species']=='virginica'] +df_versicolor=df.loc[df['species']=='versicolor'] + +plt.plot(df_setosa['sepal_length']) +plt.plot(df_virginica['sepal_length']) +plt.plot(df_versicolor['sepal_length']) +plt.xlabel('sepal length') +plt.show() +``` +![Uni-variate Analysis](assets/eda/uni-variate-analysis1.png) + +```python +plt.hist(df_setosa['petal_length']) +plt.hist(df_virginica['petal_length']) +plt.hist(df_versicolor['petal_length']) +plt.xlabel('petal length') +plt.show() +``` +![Uni-variate Analysis](assets/eda/uni-variate-analysis2.png) + +### Bi-variate Analysis + +```python +# Bi-variate Analysis +sns.FacetGrid(df,hue="species",height=5).map(plt.scatter,"petal_length","sepal_width").add_legen() +plt.show() +``` +![Bi-variate Analysis](assets/eda/bi-variate-analysis.png) + +### Multi-variate Analysis + +```python +# Multi-variate Analysis +sns.pairplot(df,hue="species",height=3) +``` +![Multi-variate Analysis](assets/eda/multi-variate-analysis.png) + +### Correlation Analysis + +```python +# Correlation Analysis +corr_matrix = df.corr() +sns.heatmap(corr_matrix) +``` +| | sepal_length | sepal_width | petal_length | petal_width | +|-------------|--------------|-------------|--------------|-------------| +| sepal_length| 1.000000 | -0.109369 | 0.871754 | 0.817954 | +| sepal_width | -0.109369 | 1.000000 | -0.420516 | -0.356544 | +| petal_length| 0.871754 | -0.420516 | 1.000000 | 0.962757 | +| petal_width | 0.817954 | -0.356544 | 0.962757 | 1.000000 | + +![Correlation Analysis](assets/eda/correlation-analysis.png) + +## Exploratory Data Analysis (EDA) Report on Iris Dataset + +### Introduction +The Iris dataset consists of 150 samples of iris flowers, each characterized by four features: Sepal Length, Sepal Width, Petal Length, and Petal Width. These samples belong to three species of iris flowers: Setosa, Versicolor, and Virginica. In this EDA report, we explore the dataset to gain insights into the characteristics and relationships among the features and species. + +### Uni-variate Analysis +Uni-variate analysis examines each variable individually. +- Sepal Length: The distribution of Sepal Length varies among the different species, with Setosa generally having shorter sepals compared to Versicolor and Virginica. +- Petal Length: Setosa tends to have shorter petal lengths, while Versicolor and Virginica have relatively longer petal lengths. + +### Bi-variate Analysis +Bi-variate analysis explores the relationship between two variables. +- Petal Length vs. Sepal Width: There is a noticeable separation between species, especially Setosa, which typically has shorter and wider sepals compared to Versicolor and Virginica. +- This analysis suggests potential patterns distinguishing the species based on these two features. + +### Multi-variate Analysis +Multi-variate analysis considers interactions among multiple variables simultaneously. +- Pairplot: The pairplot reveals distinctive clusters for each species, particularly in the combinations of Petal Length and Petal Width, indicating clear separation among species based on these features. + +### Correlation Analysis +Correlation analysis examines the relationship between variables. +- Correlation Heatmap: There are strong positive correlations between Petal Length and Petal Width, as well as between Petal Length and Sepal Length. Sepal Width shows a weaker negative correlation with Petal Length and Petal Width. + +### Insights +1. Petal dimensions (length and width) exhibit strong correlations, suggesting that they may collectively contribute more significantly to distinguishing between iris species. +2. Setosa tends to have shorter and wider sepals compared to Versicolor and Virginica. +3. The combination of Petal Length and Petal Width appears to be a more effective discriminator among iris species, as indicated by the distinct clusters observed in multi-variate analysis. + +### Conclusion +Through comprehensive exploratory data analysis, we have gained valuable insights into the Iris dataset, highlighting key characteristics and relationships among features and species. Further analysis and modeling could leverage these insights to develop robust classification models for predicting iris species based on their measurements. + +## Conclusion + +Exploratory Data Analysis (EDA) is a critical step in the data analysis process that helps to understand the data, identify patterns and relationships, detect outliers, and prepare the data for modeling. By using various techniques and tools, such as descriptive statistics, data visualization, correlation analysis, and hypothesis testing, EDA provides valuable insights into the data, enabling data scientists to make informed decisions and build accurate models. + + + diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 95ee61dc..37df2a8e 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -27,3 +27,4 @@ - [Transformers](transformers.md) - [Reinforcement Learning](reinforcement-learning.md) - [Neural network regression](neural-network-regression.md) +- [exploratory data analysis](exploratory_dataAnalysis.md) \ No newline at end of file From b2ef489f44925b489c9634dfd9f727153283b436 Mon Sep 17 00:00:00 2001 From: Damini2004 Date: Wed, 26 Jun 2024 14:31:54 +0530 Subject: [PATCH 637/656] Commit -2 --- .../advanced-python/regular_expressions.md | 131 ++++++++++++------ 1 file changed, 88 insertions(+), 43 deletions(-) diff --git a/contrib/advanced-python/regular_expressions.md b/contrib/advanced-python/regular_expressions.md index a8679812..040a8c78 100644 --- a/contrib/advanced-python/regular_expressions.md +++ b/contrib/advanced-python/regular_expressions.md @@ -1,112 +1,147 @@ ## Regular Expressions in Python -Regular expressions (regex) are a powerful tool for pattern matching and text manipulation. + +Regular expressions (regex) are a powerful tool for pattern matching and text manipulation. Python's re module provides comprehensive support for regular expressions, enabling efficient text processing and validation. Regular expressions (regex) are a versitile tool for matching patterns in strings. In Python, the `re` module provides support for working with regular expressions. ## 1. Introduction to Regular Expressions -A regular expression is a sequence of characters defining a search pattern. Common use cases include validating input, searching within text, and extracting + +A regular expression is a sequence of characters defining a search pattern. Common use cases include validating input, searching within text, and extracting specific patterns. ## 2. Basic Syntax + Literal Characters: Match exact characters (e.g., abc matches "abc"). -Metacharacters: Special characters like ., *, ?, +, ^, $, [ ], and | used to build patterns. +Metacharacters: Special characters like ., \*, ?, +, ^, $, [ ], and | used to build patterns. **Common Metacharacters:** -* .: Any character except newline. -* ^: Start of the string. -* $: End of the string. -* *: 0 or more repetitions. -* +: 1 or more repetitions. -* ?: 0 or 1 repetition. -* []: Any one character inside brackets (e.g., [a-z]). -* |: Either the pattern before or after. -* \ : Used to drop the special meaning of character following it -* {} : Indicate the number of occurrences of a preceding regex to match. -* () : Enclose a group of Regex +- .: Any character except newline. +- ^: Start of the string. +- $: End of the string. +- \*: 0 or more repetitions. +- +: 1 or more repetitions. +- ?: 0 or 1 repetition. +- []: Any one character inside brackets (e.g., [a-z]). +- |: Either the pattern before or after. +- \ : Used to drop the special meaning of character following it +- {} : Indicate the number of occurrences of a preceding regex to match. +- () : Enclose a group of Regex Examples: -```bash + 1. `.` + +```bash import re pattern = r'c.t' text = 'cat cot cut cit' matches = re.findall(pattern, text) print(matches) # Output: ['cat', 'cot', 'cut', 'cit'] +``` 2. `^` + +```bash pattern = r'^Hello' text = 'Hello, world!' match = re.search(pattern, text) print(match.group() if match else 'No match') # Output: 'Hello' +``` 3. `$` + +```bash pattern = r'world!$' text = 'Hello, world!' match = re.search(pattern, text) print(match.group() if match else 'No match') # Output: 'world!' +``` 4. `*` + +```bash pattern = r'ab*' text = 'a ab abb abbb' matches = re.findall(pattern, text) print(matches) # Output: ['a', 'ab', 'abb', 'abbb'] +``` 5. `+` + +```bash pattern = r'ab+' text = 'a ab abb abbb' matches = re.findall(pattern, text) print(matches) # Output: ['ab', 'abb', 'abbb'] +``` 6. `?` + +```bash pattern = r'ab?' text = 'a ab abb abbb' matches = re.findall(pattern, text) print(matches) # Output: ['a', 'ab', 'ab', 'ab'] +``` 7. `[]` + +```bash pattern = r'[aeiou]' text = 'hello world' matches = re.findall(pattern, text) print(matches) # Output: ['e', 'o', 'o'] +``` 8. `|` + +```bash pattern = r'cat|dog' text = 'I have a cat and a dog.' matches = re.findall(pattern, text) print(matches) # Output: ['cat', 'dog'] +``` 9. `\`` + +```bash pattern = r'\$100' text = 'The price is $100.' match = re.search(pattern, text) print(match.group() if match else 'No match') # Output: '$100' +``` 10. `{}` + +```bash pattern = r'\d{3}' text = 'My number is 123456' matches = re.findall(pattern, text) print(matches) # Output: ['123', '456'] +``` 11. `()` + +```bash pattern = r'(cat|dog)' text = 'I have a cat and a dog.' matches = re.findall(pattern, text) print(matches) # Output: ['cat', 'dog'] - ``` ## 3. Using the re Module **Key functions in the re module:** -* re.match(): Checks for a match at the beginning of the string. -* re.search(): Searches for a match anywhere in the string. -* re.findall(): Returns a list of all matches. -* re.sub(): Replaces matches with a specified string. -* re.split(): Returns a list where the string has been split at each match. -* re.escape(): Escapes special character -Examples: +- re.match(): Checks for a match at the beginning of the string. +- re.search(): Searches for a match anywhere in the string. +- re.findall(): Returns a list of all matches. +- re.sub(): Replaces matches with a specified string. +- re.split(): Returns a list where the string has been split at each match. +- re.escape(): Escapes special character + Examples: + ```bash import re @@ -122,7 +157,7 @@ print(re.findall(r'\d+', 'abc123def456')) # Output: ['123', '456'] # Substitute matches print(re.sub(r'\d+', '#', 'abc123def456')) # Output: abc#def# -#Return a list where it get matched +#Return a list where it get matched print(re.split("\s", txt)) #['The', 'Donkey', 'in', 'the','Town'] # Escape special character @@ -130,9 +165,11 @@ print(re.escape("We are good to go")) #We\ are\ good\ to\ go ``` ## 4. Compiling Regular Expressions + Compiling regular expressions improves performance for repeated use. Example: + ```bash import re @@ -144,9 +181,11 @@ print(pattern.findall('abc123def456')) # Output: ['123', '456'] ``` ## 5. Groups and Capturing + Parentheses () group and capture parts of the match. Example: + ```bash import re @@ -159,40 +198,46 @@ if match: ``` ## 6. Special Sequences + Special sequences are shortcuts for common patterns: -* \A:Returns a match if the specified characters are at the beginning of the string. -* \b:Returns a match where the specified characters are at the beginning or at the end of a word. -* \B:Returns a match where the specified characters are present, but NOT at the beginning (or at the end) of a word. -* \d: Any digit. -* \D: Any non-digit. -* \w: Any alphanumeric character. -* \W: Any non-alphanumeric character. -* \s: Any whitespace character. -* \S: Any non-whitespace character. -* \Z:Returns a match if the specified characters are at the end of the string. +- \A:Returns a match if the specified characters are at the beginning of the string. +- \b:Returns a match where the specified characters are at the beginning or at the end of a word. +- \B:Returns a match where the specified characters are present, but NOT at the beginning (or at the end) of a word. +- \d: Any digit. +- \D: Any non-digit. +- \w: Any alphanumeric character. +- \W: Any non-alphanumeric character. +- \s: Any whitespace character. +- \S: Any non-whitespace character. +- \Z:Returns a match if the specified characters are at the end of the string. Example: + ```bash import re print(re.search(r'\w+@\w+\.\w+', 'Contact: support@example.com').group()) # Output: support@example.com ``` + ## 7.Sets + A set is a set of characters inside a pair of square brackets [] with a special meaning: -* [arn] : Returns a match where one of the specified characters (a, r, or n) is present. -* [a-n] : Returns a match for any lower case character, alphabetically between a and n. -* [^arn] : Returns a match for any character EXCEPT a, r, and n. -* [0123] : Returns a match where any of the specified digits (0, 1, 2, or 3) are present. -* [0-9] : Returns a match for any digit between 0 and 9. -* [0-5][0-9] : Returns a match for any two-digit numbers from 00 and 59. -* [a-zA-Z] : Returns a match for any character alphabetically between a and z, lower case OR upper case. -* [+] : In sets, +, *, ., |, (), $,{} has no special meaning, so [+] means: return a match for any + character in the string. +- [arn] : Returns a match where one of the specified characters (a, r, or n) is present. +- [a-n] : Returns a match for any lower case character, alphabetically between a and n. +- [^arn] : Returns a match for any character EXCEPT a, r, and n. +- [0123] : Returns a match where any of the specified digits (0, 1, 2, or 3) are present. +- [0-9] : Returns a match for any digit between 0 and 9. +- [0-5][0-9] : Returns a match for any two-digit numbers from 00 and 59. +- [a-zA-Z] : Returns a match for any character alphabetically between a and z, lower case OR upper case. +- [+] : In sets, +, \*, ., |, (), $,{} has no special meaning +- [+] means: return a match for any + character in the string. ## Summary + Regular expressions (regex) are a powerful tool for text processing in Python, offering a flexible way to match, search, and manipulate text patterns. The re module provides a comprehensive set of functions and metacharacters to tackle complex text processing tasks. With regex, you can: -1.Match patterns: Use metacharacters like ., *, ?, and {} to match specific patterns in text. +1.Match patterns: Use metacharacters like ., \*, ?, and {} to match specific patterns in text. 2.Search text: Employ functions like re.search() and re.match() to find occurrences of patterns in text. 3.Manipulate text: Utilize functions like re.sub() to replace patterns with new text. From 65c78e90aa80b0057825535bc65612209ad9588e Mon Sep 17 00:00:00 2001 From: Damini2004 Date: Thu, 27 Jun 2024 01:12:55 +0530 Subject: [PATCH 638/656] Commit -2 --- contrib/advanced-python/regular_expressions.md | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/contrib/advanced-python/regular_expressions.md b/contrib/advanced-python/regular_expressions.md index 040a8c78..81c883ec 100644 --- a/contrib/advanced-python/regular_expressions.md +++ b/contrib/advanced-python/regular_expressions.md @@ -1,16 +1,13 @@ ## Regular Expressions in Python - Regular expressions (regex) are a powerful tool for pattern matching and text manipulation. Python's re module provides comprehensive support for regular expressions, enabling efficient text processing and validation. Regular expressions (regex) are a versitile tool for matching patterns in strings. In Python, the `re` module provides support for working with regular expressions. ## 1. Introduction to Regular Expressions - A regular expression is a sequence of characters defining a search pattern. Common use cases include validating input, searching within text, and extracting specific patterns. ## 2. Basic Syntax - Literal Characters: Match exact characters (e.g., abc matches "abc"). Metacharacters: Special characters like ., \*, ?, +, ^, $, [ ], and | used to build patterns. @@ -19,7 +16,7 @@ Metacharacters: Special characters like ., \*, ?, +, ^, $, [ ], and | used to bu - .: Any character except newline. - ^: Start of the string. - $: End of the string. -- \*: 0 or more repetitions. +- *: 0 or more repetitions. - +: 1 or more repetitions. - ?: 0 or 1 repetition. - []: Any one character inside brackets (e.g., [a-z]). From b03352ee2352648cd6c168c1f4091521dae5c8d8 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Thu, 27 Jun 2024 19:04:00 +0530 Subject: [PATCH 639/656] Rename Tree-Traversal.md to tree-traversal.md --- contrib/ds-algorithms/{Tree-Traversal.md => tree-traversal.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/ds-algorithms/{Tree-Traversal.md => tree-traversal.md} (100%) diff --git a/contrib/ds-algorithms/Tree-Traversal.md b/contrib/ds-algorithms/tree-traversal.md similarity index 100% rename from contrib/ds-algorithms/Tree-Traversal.md rename to contrib/ds-algorithms/tree-traversal.md From 7159c1c0473689519146f3c89bec1dffe72b36c9 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Thu, 27 Jun 2024 19:04:24 +0530 Subject: [PATCH 640/656] Update index.md --- contrib/ds-algorithms/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 25e797fa..71a9cb52 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -22,4 +22,4 @@ - [AVL Trees](avl-trees.md) - [Splay Trees](splay-trees.md) - [Dijkstra's Algorithm](dijkstra.md) -- [Tree Traversals](Tree-Traversal.md) +- [Tree Traversals](tree-traversal.md) From 59e2353bc94d2d5dd6840002e82eb4e81d140501 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Thu, 27 Jun 2024 19:37:55 +0530 Subject: [PATCH 641/656] Update dynamic-programming.md --- contrib/ds-algorithms/dynamic-programming.md | 54 +++++++++++++++----- 1 file changed, 41 insertions(+), 13 deletions(-) diff --git a/contrib/ds-algorithms/dynamic-programming.md b/contrib/ds-algorithms/dynamic-programming.md index 986ecd37..f4958689 100644 --- a/contrib/ds-algorithms/dynamic-programming.md +++ b/contrib/ds-algorithms/dynamic-programming.md @@ -265,8 +265,13 @@ def edit_distance(str1, str2, memo={}): str1 = "sunday" str2 = "saturday" print(f"Edit Distance between '{str1}' and '{str2}' is {edit_distance(str1, str2)}.") -# Output: Edit Distance between 'sunday' and 'saturday' is 3. ``` + +#### Output +``` +Edit Distance between 'sunday' and 'saturday' is 3. +``` + ## String Edit Distance Code in Python (Bottom-Up Approach) ```python def edit_distance(str1, str2): @@ -289,8 +294,13 @@ def edit_distance(str1, str2): str1 = "sunday" str2 = "saturday" print(f"Edit Distance between '{str1}' and '{str2}' is {edit_distance(str1, str2)}.") -# Output: Edit Distance between 'sunday' and 'saturday' is 3. ``` + +#### Output +``` +Edit Distance between 'sunday' and 'saturday' is 3. +``` + ## **Complexity Analysis:** - **Time Complexity:** O(m * n) where m and n are the lengths of string 1 and string 2 respectively - **Space Complexity:** O(m * n) for both top-down and bottom-up approaches @@ -324,8 +334,14 @@ def matrix_chain_order(p, memo={}): p = [1, 2, 3, 4] print(f"Minimum number of multiplications is {matrix_chain_order(p)}.") -# Output: Minimum number of multiplications is 18. ``` + +#### Output +``` +Minimum number of multiplications is 18. +``` + + ## Matrix Chain Multiplication Code in Python (Bottom-Up Approach) ```python def matrix_chain_order(p): @@ -345,13 +361,17 @@ def matrix_chain_order(p): p = [1, 2, 3, 4] print(f"Minimum number of multiplications is {matrix_chain_order(p)}.") -# Output: Minimum number of multiplications is 18. ``` + +#### Output +``` +Minimum number of multiplications is 18. +``` + ## **Complexity Analysis:** - **Time Complexity:** O(n^3) where n is the number of matrices in the chain. For an `array p` of dimensions representing the matrices such that the `i-th matrix` has dimensions `p[i-1] x p[i]`, n is `len(p) - 1` - **Space Complexity:** O(n^2) for both top-down and bottom-up approaches - # 7. Optimal Binary Search Tree The Matrix Chain Multiplication finds the optimal way to multiply a sequence of matrices to minimize the number of scalar multiplications. @@ -362,6 +382,7 @@ The Matrix Chain Multiplication finds the optimal way to multiply a sequence of - **Recurrence Relation:** Compute the optimal cost by trying each key as the root and choosing the minimum cost. ## Optimal Binary Search Tree Code in Python (Top-Down Approach with Memoization) + ```python def optimal_bst(keys, freq, memo={}): n = len(keys) @@ -386,9 +407,15 @@ def optimal_bst(keys, freq, memo={}): keys = [10, 12, 20] freq = [34, 8, 50] print(f"Cost of Optimal BST is {optimal_bst(keys, freq)}.") -# Output: Cost of Optimal BST is 142. ``` + +#### Output +``` +Cost of Optimal BST is 142. +``` + ## Optimal Binary Search Tree Code in Python (Bottom-Up Approach) + ```python def optimal_bst(keys, freq): n = len(keys) @@ -414,12 +441,13 @@ def optimal_bst(keys, freq): keys = [10, 12, 20] freq = [34, 8, 50] print(f"Cost of Optimal BST is {optimal_bst(keys, freq)}.") -# Output: Cost of Optimal BST is 142. ``` -## **Complexity Analysis:** -- **Time Complexity:** O(n^3) where n is the number of keys in the binary search tree. -- **Space Complexity:** O(n^2) for both top-down and bottom-up approaches -
    -
    -
    +#### Output +``` +Cost of Optimal BST is 142. +``` + +### Complexity Analysis +- **Time Complexity**: O(n^3) where n is the number of keys in the binary search tree. +- **Space Complexity**: O(n^2) for both top-down and bottom-up approaches From 1954374803a806c58c96c192b1d0621176bb257a Mon Sep 17 00:00:00 2001 From: mankalavaishnavi Date: Thu, 27 Jun 2024 20:00:20 +0530 Subject: [PATCH 642/656] changes as per request --- contrib/ds-algorithms/deque.md | 156 +++++++++++++++++++++------------ 1 file changed, 102 insertions(+), 54 deletions(-) diff --git a/contrib/ds-algorithms/deque.md b/contrib/ds-algorithms/deque.md index db489a82..2a5a77d2 100644 --- a/contrib/ds-algorithms/deque.md +++ b/contrib/ds-algorithms/deque.md @@ -19,86 +19,134 @@ d = deque(iterable) # Create deque from iterable (optional) - append(x): Adds element x to the right end of the deque. - appendleft(x): Adds element x to the left end of the deque. + ### Program + ```py + from collections import deque + + # Initialize a deque + d = deque([1, 2, 3, 4, 5]) + print("Initial deque:", d) + + # Append elements + d.append(6) + print("After append(6):", d) + + # Append left + d.appendleft(0) + print("After appendleft(0):", d) + + ``` + ### Output + ```py + Initial deque: deque([1, 2, 3, 4, 5]) + After append(6): deque([1, 2, 3, 4, 5, 6]) + After appendleft(0): deque([0, 1, 2, 3, 4, 5, 6]) + ``` + 2. **Removing Elements**: - pop(): Removes and returns the rightmost element. - popleft(): Removes and returns the leftmost element. + ### Program + ```py + from collections import deque + + # Initialize a deque + d = deque([1, 2, 3, 4, 5]) + print("Initial deque:", d) + + # Pop from the right end + rightmost = d.pop() + print("Popped from right end:", rightmost) + print("Deque after pop():", d) + + # Pop from the left end + leftmost = d.popleft() + print("Popped from left end:", leftmost) + print("Deque after popleft():", d) + + ``` + + ### Output + ```py + Initial deque: deque([1, 2, 3, 4, 5]) + Popped from right end: 5 + Deque after pop(): deque([1, 2, 3, 4]) + Popped from left end: 1 + Deque after popleft(): deque([2, 3, 4]) + ``` + 3. **Accessing Elements**: - deque[index]: Accesses element at index. + ### Program + ```py + from collections import deque + + # Initialize a deque + d = deque([1, 2, 3, 4, 5]) + print("Initial deque:", d) + + # Accessing elements + print("Element at index 2:", d[2]) + + ``` + + ### Output + ```py + Initial deque: deque([1, 2, 3, 4, 5]) + Element at index 2: 3 + + ``` + 4. **Other Operations**: - extend(iterable): Extends deque by appending elements from iterable. - extendleft(iterable): Extends deque by appending elements from iterable to the left. - rotate(n): Rotates deque n steps to the right (negative n rotates left). -## Example - -### 1. showing all the operations -```py -from collections import deque + ### Program + ```py + from collections import deque -# Initialize a deque -d = deque([1, 2, 3, 4, 5]) -print("Initial deque:", d) + # Initialize a deque + d = deque([1, 2, 3, 4, 5]) + print("Initial deque:", d) -# Append elements -d.append(6) -print("After append(6):", d) + # Extend deque + d.extend([6, 7, 8]) + print("After extend([6, 7, 8]):", d) -# Append left -d.appendleft(0) -print("After appendleft(0):", d) + # Extend left + d.extendleft([-1, 0]) + print("After extendleft([-1, 0]):", d) -# Pop from the right end -rightmost = d.pop() -print("Popped from right end:", rightmost) -print("Deque after pop():", d) + # Rotate deque + d.rotate(2) + print("After rotate(2):", d) -# Pop from the left end -leftmost = d.popleft() -print("Popped from left end:", leftmost) -print("Deque after popleft():", d) + # Rotate left + d.rotate(-3) + print("After rotate(-3):", d) -# Accessing elements -print("Element at index 2:", d[2]) + ``` -# Extend deque -d.extend([6, 7, 8]) -print("After extend([6, 7, 8]):", d) + ### Output + ```py + Initial deque: deque([1, 2, 3, 4, 5]) + After extend([6, 7, 8]): deque([1, 2, 3, 4, 5, 6, 7, 8]) + After extendleft([-1, 0]): deque([0, -1, 1, 2, 3, 4, 5, 6, 7, 8]) + After rotate(2): deque([7, 8, 0, -1, 1, 2, 3, 4, 5, 6]) + After rotate(-3): deque([1, 2, 3, 4, 5, 6, 7, 8, 0, -1]) -# Extend left -d.extendleft([-1, 0]) -print("After extendleft([-1, 0]):", d) + ``` -# Rotate deque -d.rotate(2) -print("After rotate(2):", d) -# Rotate left -d.rotate(-3) -print("After rotate(-3):", d) -``` -Output -```py -Initial deque: deque([1, 2, 3, 4, 5]) -After append(6): deque([1, 2, 3, 4, 5, 6]) -After appendleft(0): deque([0, 1, 2, 3, 4, 5, 6]) -Popped from right end: 6 -Deque after pop(): deque([0, 1, 2, 3, 4, 5]) -Popped from left end: 0 -Deque after popleft(): deque([1, 2, 3, 4, 5]) -Element at index 2: 3 -After extend([6, 7, 8]): deque([1, 2, 3, 4, 5, 6, 7, 8]) -After extendleft([-1, 0]): deque([0, -1, 1, 2, 3, 4, 5, 6, 7, 8]) -After rotate(2): deque([7, 8, 0, -1, 1, 2, 3, 4, 5, 6]) -After rotate(-3): deque([1, 2, 3, 4, 5, 6, 7, 8, 0, -1]) - -``` +## Example -### 2. Finding Maximum in Sliding Window +### 1. Finding Maximum in Sliding Window ```py from collections import deque From 53de9d412448d4225c12e5d4911f22d1fb192688 Mon Sep 17 00:00:00 2001 From: srilekha279 <158455553+srilekha279@users.noreply.github.com> Date: Thu, 27 Jun 2024 22:05:00 +0530 Subject: [PATCH 643/656] Update linked-list.md Added function reverseList --- contrib/ds-algorithms/linked-list.md | 32 ++++++++++++++++++++++------ 1 file changed, 25 insertions(+), 7 deletions(-) diff --git a/contrib/ds-algorithms/linked-list.md b/contrib/ds-algorithms/linked-list.md index ddbc6d56..31cd2e57 100644 --- a/contrib/ds-algorithms/linked-list.md +++ b/contrib/ds-algorithms/linked-list.md @@ -1,6 +1,6 @@ # Linked List Data Structure -Link list is a linear data Structure which can be defined as collection of objects called nodes that are randomly stored in the memory. +Linked list is a linear data Structure which can be defined as collection of objects called nodes that are randomly stored in the memory. A node contains two types of metadata i.e. data stored at that particular address and the pointer which contains the address of the next node in the memory. The last element in a linked list features a null pointer. @@ -36,10 +36,10 @@ The smallest Unit: Node Now, we will see the types of linked list. There are mainly four types of linked list, -1. Singly Link list -2. Doubly link list -3. Circular link list -4. Doubly circular link list +1. Singly linked list +2. Doubly linked list +3. Circular linked list +4. Doubly circular linked list ## 1. Singly linked list. @@ -160,6 +160,18 @@ check the list is empty otherwise shift the head to next node. temp.next = None # Remove the last node by setting the next pointer of the second-last node to None ``` +### Reversing the linked list +```python + def reverseList(self): + prev = None + temp = self.head + while(temp): + nextNode = temp.next #Store the next node + temp.next = prev #Reverse the pointer of current node + prev = temp #Move `prev` one step forward + temp = nextNode #Move `temp` one step forward. + self.head = prev #Update the head pointer to last node +``` ### Search in a linked list ```python @@ -174,6 +186,8 @@ check the list is empty otherwise shift the head to next node. return f"Value '{value}' not found in the list" ``` +Connect all the code. + ```python if __name__ == '__main__': llist = LinkedList() @@ -197,13 +211,17 @@ check the list is empty otherwise shift the head to next node. #delete at the end llist.deleteFromEnd() # 2 3 56 9 4 10 - # Print the list + # Print the original list + llist.printList() + llist.reverseList() #10 4 9 56 3 2 + # Print the reversed list llist.printList() ``` ## Output: -2 3 56 9 4 10 +2 3 56 9 4 10 +10 4 9 56 3 2 ## Real Life uses of Linked List From 20d36f661bb269b64ca0f134954bfce1e47a7926 Mon Sep 17 00:00:00 2001 From: srilekha279 <158455553+srilekha279@users.noreply.github.com> Date: Thu, 27 Jun 2024 22:06:20 +0530 Subject: [PATCH 644/656] Update linked-list.md --- contrib/ds-algorithms/linked-list.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/contrib/ds-algorithms/linked-list.md b/contrib/ds-algorithms/linked-list.md index 31cd2e57..59631e7c 100644 --- a/contrib/ds-algorithms/linked-list.md +++ b/contrib/ds-algorithms/linked-list.md @@ -167,10 +167,10 @@ check the list is empty otherwise shift the head to next node. temp = self.head while(temp): nextNode = temp.next #Store the next node - temp.next = prev #Reverse the pointer of current node - prev = temp #Move `prev` one step forward - temp = nextNode #Move `temp` one step forward. - self.head = prev #Update the head pointer to last node + temp.next = prev # Reverse the pointer of current node + prev = temp # Move prev pointer one step forward + temp = nextNode # Move temp pointer one step forward. + self.head = prev # Update the head pointer to last node ``` ### Search in a linked list From 3048f561ebc44714a7b8cb8f46db0cd2475f4327 Mon Sep 17 00:00:00 2001 From: Divyanshi <126316406+DivyanshiSingh00@users.noreply.github.com> Date: Thu, 27 Jun 2024 22:19:46 +0530 Subject: [PATCH 645/656] Update naive-bayes.md Updating some of the markdown equations. Please consider and please also review this under GSSOC and give this updations some level. --- contrib/machine-learning/naive-bayes.md | 85 +++++++------------------ 1 file changed, 22 insertions(+), 63 deletions(-) diff --git a/contrib/machine-learning/naive-bayes.md b/contrib/machine-learning/naive-bayes.md index 82525997..4bf0f04c 100644 --- a/contrib/machine-learning/naive-bayes.md +++ b/contrib/machine-learning/naive-bayes.md @@ -93,13 +93,9 @@ $$ - Rain: -$$ -P(Rain|Yes) = \frac{2}{6} -$$ +$$P(Rain|Yes) = \frac{2}{6}$$ -$$ -P(Rain|No) = \frac{4}{4} -$$ +$$P(Rain|No) = \frac{4}{4}$$ - Overcast: @@ -111,10 +107,7 @@ $$ $$ -Here, we can see that - $$ - P(Overcast|No) = 0 - $$ +Here, we can see that P(Overcast|No) = 0 This is a zero probability error! Since probability is 0, naive bayes model fails to predict. @@ -124,13 +117,9 @@ Since probability is 0, naive bayes model fails to predict. In Laplace's correction, we scale the values for 1000 instances. - **Calculate prior probabilities** - $$ - P(Yes) = \frac{600}{1002} - $$ + $$P(Yes) = \frac{600}{1002}$$ - $$ - P(No) = \frac{402}{1002} - $$ + $$P(No) = \frac{402}{1002}$$ - **Calculate likelihoods** @@ -151,21 +140,13 @@ Since probability is 0, naive bayes model fails to predict. - **Rain:** - $$ - P(Rain|Yes) = \frac{200}{600} - $$ - $$ - P(Rain|No) = \frac{401}{402} - $$ + $$P(Rain|Yes) = \frac{200}{600}$$ + $$P(Rain|No) = \frac{401}{402}$$ - **Overcast:** - $$ - P(Overcast|Yes) = \frac{400}{600} - $$ - $$ - P(Overcast|No) = \frac{1}{402} - $$ + $$P(Overcast|Yes) = \frac{400}{600}$$ + $$P(Overcast|No) = \frac{1}{402}$$ 2. **Wind (B):** @@ -181,49 +162,27 @@ Since probability is 0, naive bayes model fails to predict. - **Weak:** - $$ - P(Weak|Yes) = \frac{500}{600} - $$ - $$ - P(Weak|No) = \frac{200}{400} - $$ + $$P(Weak|Yes) = \frac{500}{600}$$ + $$P(Weak|No) = \frac{200}{400}$$ - **Strong:** - $$ - P(Strong|Yes) = \frac{100}{600} - $$ - $$ - P(Strong|No) = \frac{200}{400} - $$ + $$P(Strong|Yes) = \frac{100}{600}$$ + $$P(Strong|No) = \frac{200}{400}$$ - **Calculting probabilities:** - $$ - P(PlayTennis|Yes) = P(Yes) * P(Overcast|Yes) * P(Weak|Yes) - $$ - $$ - = \frac{600}{1002} * \frac{400}{600} * \frac{500}{600} - $$ - $$ - = 0.3326 - $$ - - $$ - P(PlayTennis|No) = P(No) * P(Overcast|No) * P(Weak|No) - $$ - $$ - = \frac{402}{1002} * \frac{1}{402} * \frac{200}{400} - $$ - $$ - = 0.000499 = 0.0005 - $$ + $$P(PlayTennis|Yes) = P(Yes) * P(Overcast|Yes) * P(Weak|Yes)$$ + $$= \frac{600}{1002} * \frac{400}{600} * \frac{500}{600}$$ + $$= 0.3326$$ + + $$P(PlayTennis|No) = P(No) * P(Overcast|No) * P(Weak|No)$$ + $$= \frac{402}{1002} * \frac{1}{402} * \frac{200}{400}$$ + $$= 0.000499 = 0.0005$$ Since , -$$ - P(PlayTennis|Yes) > P(PlayTennis|No) -$$ +$$P(PlayTennis|Yes) > P(PlayTennis|No)$$ we can conclude that tennis can be played if outlook is overcast and wind is weak. @@ -366,4 +325,4 @@ print("Confusion matrix: \n",confusion_matrix(y_train,y_pred)) ## Conclusion We can conclude that naive bayes may limit in some cases due to the assumption that the features are independent of each other but still reliable in many cases. Naive Bayes is an efficient classifier and works even on small datasets. - \ No newline at end of file + From 0ab0668f271d05c60351258b73c497641fa12ca2 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Thu, 27 Jun 2024 23:03:11 +0530 Subject: [PATCH 646/656] Update README.md --- README.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/README.md b/README.md index a025510c..5cb7379d 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,5 @@ [![Discord Server Invite](https://img.shields.io/badge/DISCORD-JOIN%20SERVER-5663F7?style=for-the-badge&logo=discord&logoColor=white)](https://bit.ly/heyfoss) -This project is participating in GSSoC 2024. - -![gssoc-logo](https://github.com/foss42/awesome-generative-ai-apis/assets/1382619/670b651a-15d7-4869-a4d1-6613df09fa37) - Contributors should go through the [Contributing Guide](https://github.com/animator/learn-python/blob/main/CONTRIBUTING.md) to learn how you can contribute to the project. ![Learn Python 3 Logo](images/learn-python.png) From 3d25e5a030ec966a6e933bad055262415e0deb62 Mon Sep 17 00:00:00 2001 From: RajKhanke <137288727+RajKhanke@users.noreply.github.com> Date: Sat, 29 Jun 2024 17:50:02 +0530 Subject: [PATCH 647/656] Update index.md --- contrib/plotting-visualization/index.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 02324f22..b1074166 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -10,4 +10,5 @@ - [Seaborn Plotting Functions](seaborn-plotting.md) - [Getting started with Seaborn](seaborn-basics.md) - [Bar Plots in Plotly](plotly-bar-plots.md) -- [Pie Charts in Plotly](plotly-pie-charts.md) \ No newline at end of file +- [Pie Charts in Plotly](plotly-pie-charts.md) +- [Violin Plots in Matplotlib](matplotlib-violin_plots.md) From 3680553331fa1149ed59f84403594ea8dcbcc13f Mon Sep 17 00:00:00 2001 From: Manas Deshpande 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zau)TjgR(-eyl8TAS{F%{Nrr4`TRR8>f@#6=%mmxF2#B;H7m?ar4~`N+KZb}oH9SHb zYp%r}8;faUGM>!?y#mzJo^MzQ`Rs2P7;T8rc(y6P`PR-tXwN$5ZxMh>)@y3e)Nkf! z^p@*(ibsh2T4_hcR@!*}(^Z>^iGi<1ZrS%BZK2iq4OkkLyz38frydc2^T`3zL++Fh zy4O&PUv!&!#LO3-*hQJS-p;JvMQ*6Nn{)e%+n+SZb}A*2@IPjsDz zkPvcPh5+WztIm6hXj|glT?jaPdPuhqWpJ#epiFNn^JrL^{nPDU9VuiBHu7dDG|7QV z6aW^A;jJOFO+OKhlZ23upygr5S^cd)mMa4=B?hs#2Wy^(?zAr&w-xr zuFb(>eFyp;Dk;2LI2dziT7E6)cF@gd^2Bw-!I*omLGK>2iwy^@6rT$cBJT;`s{W(- zBP%d7HloBo`u*Gk{TEZ2zpWpP5t9HfOXB&BKl&+7f|*@8N+o*WT0Y(dP6G03^xy%- zVhG?LV=oTUK6;Ia$A2B5XJx~dxB;;>r!XLf( EKZ2*rBme*a literal 0 HcmV?d00001 From ba1b163b6ec66507d91a1e996f36fd84e8dd136b Mon Sep 17 00:00:00 2001 From: Manas Deshpande <66067507+manasdeshpande@users.noreply.github.com> Date: Mon, 1 Jul 2024 16:02:10 +0530 Subject: [PATCH 649/656] Created plotly-scatter-plots.md --- .../plotly-scatter-plots.md | 198 ++++++++++++++++++ 1 file changed, 198 insertions(+) create mode 100644 contrib/plotting-visualization/plotly-scatter-plots.md diff --git a/contrib/plotting-visualization/plotly-scatter-plots.md b/contrib/plotting-visualization/plotly-scatter-plots.md new file mode 100644 index 00000000..dc40b1f7 --- /dev/null +++ b/contrib/plotting-visualization/plotly-scatter-plots.md @@ -0,0 +1,198 @@ +# Scatter Plots in Plotly + +* A scatter plot is a type of data visualization that uses dots to show values for two variables, with one variable on the x-axis and the other on the y-axis. It's useful for identifying relationships, trends, and correlations, as well as spotting clusters and outliers. +* The dots on the plot shows how the variables are related. A scatter plot is made with the plotly library's `px.scatter()`. + +## Prerequisites + +Before creating Scatter plots in Plotly you must ensure that you have Python, Plotly and Pandas installed on your system. + +## Introduction + +There are various ways to create Scatter plots in `plotly`. One of the prominent and easiest one is using `plotly.express`. Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures. On the other hand you can also use `plotly.graph_objects` to create various plots. + +Here, we'll be using `plotly.express` to create the Scatter Plots. Also we'll be converting our datasets into pandas DataFrames which makes it extremely convenient and easy to create charts. + +Also, note that when you execute the codes in a simple python file, the output plot will be shown in your **browser**, rather than a pop-up window like in matplotlib. If you do not want that, it is **recommended to create the plots in a notebook (like jupyter)**. For this, install an additional library `nbformat`. This way you can see the output on the notebook itself, and can also render its format to png, jpg, etc. + +## Creating a simple Scatter Plot using `plotly.express.scatter` + +In `plotly.express.scatter`, each data point is represented as a marker point, whose location is given by the x and y columns. + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] + +# Converting dataset to pandas DataFrame +dataset = {"Years": years, "Number of Cars sold": num_of_cars_sold} +df = pd.DataFrame(dataset) + +# Creating scatter plot +fig = px.scatter(df, x='Years', y='Number of Cars sold') + +# Showing plot +fig.show() +``` +![Basic Scatter Plot](images/plotly-basic-scatter-plot.png) + +Here, we are first creating the dataset and converting it into a pandas DataFrame using a dictionary, with its keys being DataFrame columns. Next, we are plotting the scatter plot by using `px.scatter`. In the `x` and `y` parameters, we have to specify a column name in the DataFrame. + +`px.scatter(df, x='Years', y='Number of Cars sold')` is used to specify that the scatter plot is to be plotted by taking the values from column `Years` for the x-axis and the values from column `Number of Cars sold` for the y-axis. + +Note: When you generate the image using the above code, it will show you an interactive plot. If you want an image, you can download it from the interactive plot itself. + +## Customizing Scatter Plots + +### Adding title to the plot + +Simply pass the title of your plot as a parameter in `px.scatter`. + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] + +# Converting dataset to pandas DataFrame +dataset = {"Years": years, "Number of Cars sold": num_of_cars_sold} +df = pd.DataFrame(dataset) + +# Creating scatter plot +fig = px.scatter(df, x='Years', y='Number of Cars sold' ,title='Number of cars sold in various years') + +# Showing plot +fig.show() +``` +![Scatter Plot title](images/plotly-scatter-title.png) + +### Adding bar colors and legends + +* To add different colors to different bars, simply pass the column name of the x-axis or a custom column which groups different bars in `color` parameter. +* There are a lot of beautiful color scales available in plotly and can be found here [plotly color scales](https://plotly.com/python/builtin-colorscales/). Choose your favourite colorscale apply it like this: + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +flowers = ['Rose','Tulip','Marigold','Sunflower','Daffodil'] +petals = [11,9,17,4,7] + +# Converting dataset to pandas DataFrame +dataset = {'flowers':flowers, 'petals':petals} +df = pd.DataFrame(dataset) + +# Creating pie chart +fig = px.pie(df, values='petals', names='flowers', + title='Number of Petals in Flowers', + color_discrete_sequence=px.colors.sequential.Agsunset) + +# Showing plot +fig.show() +``` +![Scatter Plot Colors-1](images/plotly-scatter-colour.png) + +You can also set custom colors for each label by passing it as a dictionary(map) in `color_discrete_map`, like this: + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] + +# Converting dataset to pandas DataFrame +dataset = {"Years": years, "Number of Cars sold": num_of_cars_sold} +df = pd.DataFrame(dataset) + +# Creating scatter plot +fig = px.scatter(df, x='Years', + y='Number of Cars sold' , + title='Number of cars sold in various years', + color='Years', + color_discrete_map={'1998':'red', + '1999':'magenta', + '2000':'green', + '2001':'yellow', + '2002':'royalblue'}) + +# Showing plot +fig.show() +``` +![Scatter Plot Colors-1](images/plotly-scatter-colour-2.png) + +### Setting Size of Scatter + +We may want to set the size of different scatters for visibility differences between categories. This can be done by using the `size` parameter in `px.scatter`, where we specify a column in the DataFrame that determines the size of each scatter point. + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] + +# Converting dataset to pandas DataFrame +dataset = {"Years": years, "Number of Cars sold": num_of_cars_sold} +df = pd.DataFrame(dataset) + +# Creating scatter plot +fig = px.scatter(df, x='Years', + y='Number of Cars sold' , + title='Number of cars sold in various years', + color='Years', + color_discrete_map={'1998':'red', + '1999':'magenta', + '2000':'green', + '2001':'yellow', + '2002':'royalblue'}, + size='Number of Cars sold') + +# Showing plot +fig.show() +``` +![Scatter plot size](images/plotly-scatter-size.png) + +### Giving a hover effect + +you can use the `hover_name` and `hover_data` parameters in `px.scatter`. The `hover_name` parameter specifies the column to use for the `hover text`, and the `hover_data` parameter allows you to specify additional data to display when hovering over a point + +```Python +import plotly.express as px +import pandas as pd + +# Creating dataset +years = ['1998', '1999', '2000', '2001', '2002'] +num_of_cars_sold = [200, 300, 500, 700, 1000] + +# Converting dataset to pandas DataFrame +dataset = {"Years": years, "Number of Cars sold": num_of_cars_sold} +df = pd.DataFrame(dataset) + +# Creating scatter plot +fig = px.scatter(df, x='Years', + y='Number of Cars sold' , + title='Number of cars sold in various years', + color='Years', + color_discrete_map={'1998':'red', + '1999':'magenta', + '2000':'green', + '2001':'yellow', + '2002':'royalblue'}, + size='Number of Cars sold', + hover_name='Years', + hover_data={'Number of Cars sold': True}) + +# Showing plot +fig.show() +``` +![Scatter Hover](images/plotly-scatter-hover.png) + From 47690b7af47947878119756e6bb554cab6d13998 Mon Sep 17 00:00:00 2001 From: Manas Deshpande <66067507+manasdeshpande@users.noreply.github.com> Date: Mon, 1 Jul 2024 16:07:37 +0530 Subject: [PATCH 650/656] Update Index.md with plotly scatter plots --- contrib/plotting-visualization/index.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 02324f22..db7f33a7 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -10,4 +10,5 @@ - [Seaborn Plotting Functions](seaborn-plotting.md) - [Getting started with Seaborn](seaborn-basics.md) - [Bar Plots in Plotly](plotly-bar-plots.md) -- [Pie Charts in Plotly](plotly-pie-charts.md) \ No newline at end of file +- [Pie Charts in Plotly](plotly-pie-charts.md) +- [Scatter Plots in Plotly](plotly-scatter-plots.md) From 7b209363dd6e8eb4768d3eb2a19e5a607174cbf3 Mon Sep 17 00:00:00 2001 From: Salma Mamdoh 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to Line Charts in Plotly --- .../images/plotly-basic-line-chart.png | Bin 0 -> 30266 bytes .../images/plotly-line-color.png | Bin 0 -> 31860 bytes .../images/plotly-line-dashed.png | Bin 0 -> 28808 bytes .../images/plotly-line-dasheddotted.png | Bin 0 -> 31375 bytes .../images/plotly-line-datapoint-label.png | Bin 0 -> 33605 bytes .../images/plotly-line-dotted.png | Bin 0 -> 31483 bytes .../images/plotly-line-markers.png | Bin 0 -> 31924 bytes .../images/plotly-line-multiple-lines.png | Bin 0 -> 38031 bytes .../images/plotly-line-title.png | Bin 0 -> 31062 bytes .../images/plotly-line-width.png | Bin 0 -> 31028 bytes contrib/plotting-visualization/index.md | 3 +- .../plotly-line-charts.md | 300 ++++++++++++++++++ 12 files changed, 302 insertions(+), 1 deletion(-) create mode 100644 contrib/plotting-visualization/images/plotly-basic-line-chart.png create mode 100644 contrib/plotting-visualization/images/plotly-line-color.png create mode 100644 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zvf~AJ{cKvk0HalkSUrHak^x8qm^!@L`}6Wg;Ie$Id!_cbg@iU~9tP}j ze8pLdpO=5P1UJ733((l{PbwwJ0wVn%geFZ2$*!(ApD$e+obB5$@rYn`V9L8P%Arrj zjo|2aZ|IN0!OZ;#rY*c745X|By!y38t+Kns6gre=`dK(em*ikY zXiwFj`&ESy%B7yRE(LL4sUU>{8ignFD3wCDu|9aO@b0)Z$^?`unfH}6A-(KZ#>0sebr1RVuy2N|ztkod zb5Zy9EJ4vLu$pY!RUQ+*qq0)}v39Z^>uLU`1aCL9rWF466rn6Yqek;5;Rhy>?jI)~ zmfrjQ<<>Rgi~+v8Kl%Ei-{6@Ve3!S<)Om-5IloHQ&y=1C?{VvXo1!fv@9Md6%aLR6 zbd7teT&e+muXnR-dT@=oSAf99d+L)fNuYb-V|L2cyWFlhbMbWW|=YHMP;0Uxc$ zG8}e5b0)sVJSp~;`4pWLd)0|w49j|X3_>HM7rVvssu}Ub>CNq zkE8mRnBWIuy2EKH?(6MaKCq|z@?Kk*+Zv%4LG^^*gOyPDbI>4FEKW!at!T-D7_@UYUOX&X+8WM*8WC>@HX;g-31tr-Od Date: Thu, 4 Jul 2024 22:48:37 +0530 Subject: [PATCH 653/656] Rename exploratory_dataAnalysis.md to eda.md --- contrib/machine-learning/{exploratory_dataAnalysis.md => eda.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/machine-learning/{exploratory_dataAnalysis.md => eda.md} (100%) diff --git a/contrib/machine-learning/exploratory_dataAnalysis.md b/contrib/machine-learning/eda.md similarity index 100% rename from contrib/machine-learning/exploratory_dataAnalysis.md rename to contrib/machine-learning/eda.md From 3843fcad54cc71d466b831e722415093824c22fb Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Thu, 4 Jul 2024 22:49:17 +0530 Subject: [PATCH 654/656] Update index.md --- contrib/machine-learning/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 37df2a8e..7ee61ebe 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -27,4 +27,4 @@ - [Transformers](transformers.md) - [Reinforcement Learning](reinforcement-learning.md) - [Neural network regression](neural-network-regression.md) -- [exploratory data analysis](exploratory_dataAnalysis.md) \ No newline at end of file +- [Exploratory Data Analysis](eda.md) From 4ac12fefc85382ff3a1d0532f57e721a1474efbe Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Thu, 4 Jul 2024 23:05:47 +0530 Subject: [PATCH 655/656] Update and rename matplotlib-violin_plots.md to matplotlib-violin-plots.md --- .../{matplotlib-violin_plots.md => matplotlib-violin-plots.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/plotting-visualization/{matplotlib-violin_plots.md => matplotlib-violin-plots.md} (100%) diff --git a/contrib/plotting-visualization/matplotlib-violin_plots.md b/contrib/plotting-visualization/matplotlib-violin-plots.md similarity index 100% rename from contrib/plotting-visualization/matplotlib-violin_plots.md rename to contrib/plotting-visualization/matplotlib-violin-plots.md From edeffda1886757833b88c997cf52587859978cd2 Mon Sep 17 00:00:00 2001 From: Ashita Prasad Date: Thu, 4 Jul 2024 23:08:05 +0530 Subject: [PATCH 656/656] Delete contrib/plotting-visualization/images/matplotlib-sub-plots.md --- .../images/matplotlib-sub-plots.md | 216 ------------------ 1 file changed, 216 deletions(-) delete mode 100644 contrib/plotting-visualization/images/matplotlib-sub-plots.md diff --git a/contrib/plotting-visualization/images/matplotlib-sub-plots.md b/contrib/plotting-visualization/images/matplotlib-sub-plots.md deleted file mode 100644 index 97d45cea..00000000 --- a/contrib/plotting-visualization/images/matplotlib-sub-plots.md +++ /dev/null @@ -1,216 +0,0 @@ -# Bar Plots in Matplotlib -A bar plot or a bar chart is a type of data visualisation that represents data in the form of rectangular bars, with lengths or heights proportional to the values and data which they represent. The bar plots can be plotted both vertically and horizontally. - -It is one of the most widely used type of data visualisation as it is easy to interpret and is pleasing to the eyes. - -Matplotlib provides a very easy and intuitive method to create highly customized bar plots. - -## Prerequisites - -Before creating bar plots in matplotlib you must ensure that you have Python as well as Matplotlib installed on your system. - -## Creating a simple Bar Plot with `bar()` method - -A very basic Bar Plot can be created with `bar()` method in `matplotlib.pyplot` - -```Python -import matplotlib.pyplot as plt - -# Creating dataset -x = ["A", "B", "C", "D"] -y = [2, 7, 9, 11] - -# Creating bar plot -plt.bar(x,y) -plt.show() # Shows the plot -``` -When executed, this would show the following bar plot: - -![Basic Bar Plot](images/basic_bar_plot.png) - -The `bar()` function takes arguments that describes the layout of the bars. - -Here, `plt.bar(x,y)` is used to specify that the bar chart is to be plotted by taking the `x` array as X-axis and `y` array as Y-axis. You can customize the graph further like adding labels to the axes, color of the bars, etc. These will be explored in the upcoming sections. - -Additionally, you can also use `numpy` arrays for faster generation when handling large datasets. - -```Python -import matplotlib.pyplot as plt -import numpy as np - -# Using numpy array -x = np.array(["A", "B", "C", "D"]) -y = np.array([2, 7, 9, 11]) - -plt.bar(x,y) -plt.show() -``` -Its output would be the same as above. - -## Customizing Bar Plots - -For creating customized bar plots, it is **highly recommended** to create the plots using `matplotlib.pyplot.subplots()`, otherwise it is difficult to apply the customizations in the newer versions of Matplotlib. - -### Adding title to the graph and labeling the axes - -Let us create an imaginary graph of number of cars sold in a various years. - -```Python -import matplotlib.pyplot as plt - -fig, ax = plt.subplots() - -years = ['1999', '2000', '2001', '2002'] -num_of_cars_sold = [300, 500, 700, 1000] - -# Creating bar plot -ax.bar(years, num_of_cars_sold) - -# Adding axis labels -ax.set_xlabel("Years") -ax.set_ylabel("Number of cars sold") - -# Adding plot title -ax.set_title("Number of cars sold in various years") - -plt.show() -``` - -![Title and axis labels](images/title_and_axis_labels.png) - -Here, we have created a `matplotlib.pyplot.subplots()` object which returns a `Figure` object `fig` as well as an `Axes` object `ax` both of which are used for customizing the bar plot. `ax.set_xlabel`, `ax.set_ylabel` and `ax.set_title` are respectively used for adding labels of X, Y axis and adding title to the graph. - -### Adding bar colors and legends - -Let us consider our previous example of number of cars sold in various years and suppose that we want to add different colors to the bars from different centuries and respective legends for better interpretation. - -This can be achieved by creating two separate arrays `bar_colors` for bar colors and `bar_labels` for legend labels and passing them as arguments to parameters color and label respectively in `ax.bar` method. - -```Python -import matplotlib.pyplot as plt - -fig, ax = plt.subplots() - -years = ['1998', '1999', '2000', '2001', '2002'] -num_of_cars_sold = [200, 300, 500, 700, 1000] -bar_colors = ['tab:green', 'tab:green', 'tab:blue', 'tab:blue', 'tab:blue'] -bar_labels = ['1900s', '_1900s', '2000s', '_2000s', '_2000s'] - -# Creating the customized bar plot -ax.bar(years, num_of_cars_sold, color=bar_colors, label=bar_labels) - -# Adding axis labels -ax.set_xlabel("Years") -ax.set_ylabel("Number of cars sold") - -# Adding plot title -ax.set_title("Number of cars sold in various years") - -# Adding legend title -ax.legend(title='Centuries') - -plt.show() -``` - -![Bar colors and Legends](images/bar_colors_and_legends.png) - -Note that the labels with a preceding underscore won't show up in the legend. Legend titles can be added by simply passing `title` argument in `ax.legend()`, as shown. Also, you can have a different color for all the bars by passing the `HEX` value of that color in the `color` parameter. - -### Adding labels to bars - -We may want to add labels to bars representing their absolute (or truncated) values for instant and accurate reading. This can be achieved by passing the `BarContainer` object (returned by `ax.bar()` method) which is basically a aontainer with all the bars and optionally errorbars to `ax.bar_label` method. - -```Python -import matplotlib.pyplot as plt - -fig, ax = plt.subplots() - -years = ['1998', '1999', '2000', '2001', '2002'] -num_of_cars_sold = [200, 300, 500, 700, 1000] -bar_colors = ['tab:green', 'tab:green', 'tab:blue', 'tab:blue', 'tab:blue'] -bar_labels = ['1900s', '_1900s', '2000s', '_2000s', '_2000s'] - -# BarContainer object -bar_container = ax.bar(years, num_of_cars_sold, color=bar_colors, label=bar_labels) - -ax.set_xlabel("Years") -ax.set_ylabel("Number of cars sold") -ax.set_title("Number of cars sold in various years") -ax.legend(title='Centuries') - -# Adding bar labels -ax.bar_label(bar_container) - -plt.show() -``` - -![Bar Labels](images/bar_labels.png) - -**Note:** There are various other methods of adding bar labels in matplotlib. - -## Horizontal Bar Plot - -We can create horizontal bar plots by using the `barh()` method in `matplotlib.pyplot`. All the relevant customizations are applicable here also. - -```Python -import matplotlib.pyplot as plt - -fig, ax = plt.subplots(figsize=(10,5)) # figsize is used to alter the size of figure - -years = ['1998', '1999', '2000', '2001', '2002'] -num_of_cars_sold = [200, 300, 500, 700, 1000] -bar_colors = ['tab:green', 'tab:green', 'tab:blue', 'tab:blue', 'tab:blue'] -bar_labels = ['1900s', '_1900s', '2000s', '_2000s', '_2000s'] - -# Creating horizontal bar plot -bar_container = ax.barh(years, num_of_cars_sold, color=bar_colors, label=bar_labels) - -# Adding axis labels -ax.set_xlabel("Years") -ax.set_ylabel("Number of cars sold") - -# Adding Title -ax.set_title("Number of cars sold in various years") -ax.legend(title='Centuries') - -# Adding bar labels -ax.bar_label(bar_container) - -plt.show() -``` - -![Horizontal Bar Plot-1](images/horizontal_bar_plot_1.png) - -We can also invert the Y-axis labels here to show the top values first. - -```Python -import matplotlib.pyplot as plt - -fig, ax = plt.subplots(figsize=(10,5)) # figsize is used to alter the size of figure - -years = ['1998', '1999', '2000', '2001', '2002'] -num_of_cars_sold = [200, 300, 500, 700, 1000] -bar_colors = ['tab:green', 'tab:green', 'tab:blue', 'tab:blue', 'tab:blue'] -bar_labels = ['1900s', '_1900s', '2000s', '_2000s', '_2000s'] - -# Creating horizontal bar plot -bar_container = ax.barh(years, num_of_cars_sold, color=bar_colors, label=bar_labels) - -# Adding axis labels -ax.set_xlabel("Years") -ax.set_ylabel("Number of cars sold") - -# Adding Title -ax.set_title("Number of cars sold in various years") -ax.legend(title='Centuries') - -# Adding bar labels -ax.bar_label(bar_container) - -# Inverting Y-axis -ax.invert_yaxis() - -plt.show() -``` - -![Horizontal Bar Plot-2](images/horizontal_bar_plot_2.png)

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    - Model Architecture
    -Source:     Attention Is All You Need - Figure 1 -

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zuFEpuNSBM<${~P9R!J9%R&ZPH-CX zLGg6Lpyx#ZK3&0Iff(GBcu4r@!KRDHu(boc6n<4)U!4ZJJ|i}B=+`W4tOhz3)Jh9$ zz49B}`x2dRw|O#;P>{;Ly7RwLJnvD_NXmQwFJi-18glhgZzDem+v5&JPaKf}j1;gl zShZZwl5`|l!B+0+AEX!IOkAys`D|QFZch^ZDN#){67`K5NA@303BXc78I_aK<|W7G zK^VjzNgnH;PwUNWs6*XQZ4{N`GUwV|4hFj4n!nO|!2D)oQ7SelcxCZ zJ4TutyRPfbKE@DI=dIe~-ey#yHtA0pJ{S)kbr9>kYw- zjs0(Dx4f3U8UZvzM@K`jdSht5*6v$aktcHo0AxKSd|IB`Ag|jfuTN6>Fd7j8RCwkU zpEHtFas+vELja7MszjF^WvGI0c1%H4T3%sc zB#PtR@wD&pi@9I_YX7RedGkiSdT#qqu`ejY@~7cVG Date: Fri, 31 May 2024 04:50:16 +0530 Subject: [PATCH 295/656] Update index.md --- contrib/pandas/index.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 58b21a53..051f3b0d 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -5,6 +5,7 @@ - [Pandas Descriptive Statistics](Descriptive_Statistics.md) - [Group By Functions with Pandas](GroupBy_Functions_Pandas.md) - [Excel using Pandas DataFrame](excel_with_pandas.md) -- [Working with datetime using Pandas](datetime_with_pandas.md) +- [Working with datetime in Pandas](datetime.md) - [Importing and Exporting Data in Pandas](import-export.md) -- [Handling Missing Values in Pandas](handling-missing-values.md) \ No newline at end of file +- [Handling Missing Values in Pandas](handling-missing-values.md) + From eba755cb6370a9642efdc6298d8a3d46eb3564a6 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 05:06:11 +0530 Subject: [PATCH 296/656] Update and rename datetime_with_pandas.md to datetime.md --- .../{datetime_with_pandas.md => datetime.md} | 46 ++++++++----------- 1 file changed, 19 insertions(+), 27 deletions(-) rename contrib/pandas/{datetime_with_pandas.md => datetime.md} (58%) diff --git a/contrib/pandas/datetime_with_pandas.md b/contrib/pandas/datetime.md similarity index 58% rename from contrib/pandas/datetime_with_pandas.md rename to contrib/pandas/datetime.md index d87d9ec4..008d6fc5 100644 --- a/contrib/pandas/datetime_with_pandas.md +++ b/contrib/pandas/datetime.md @@ -1,10 +1,6 @@ -# Pandas DateTime +# Working with Date & Time in Pandas -Pandas is a robust Python library that is available as free source. The Pandas library is used to manipulate and analyse data. Pandas are made up of data structures and functions that allow for efficient data processing. - -While working with data, it is common to come across time series data. Pandas is a very handy tool for dealing with time series data. Pandas is a strong Python data analysis toolkit that provides a wide range of date and time data processing options. Many data science jobs require working with time series data, time zones, and date arithmetic, and pandas simplifies these processes. - -Features of Pandas `Date_Time`: +While working with data, it is common to come across data containing date and time. Pandas is a very handy tool for dealing with such data and provides a wide range of date and time data processing options. - **Parsing dates and times**: Pandas provides a number of functions for parsing dates and times from strings, including `to_datetime()` and `parse_dates()`. These functions can handle a variety of date and time formats, Unix timestamps, and human-readable formats. @@ -12,13 +8,9 @@ Features of Pandas `Date_Time`: - **Visualizing dates and times**: Pandas provides a number of functions for visualizing dates and times, including `plot()`, `hist()`, and `bar()`. These functions can be used to create line charts, histograms, and bar charts of date and time data. +### `Timestamp` function - -### Installation of libraries - -`pip install pandas` - -- **Note**: There is no need to install a seperate library for date_time operations, pandas module itself has built-in functions. +The timestamp function in Pandas is used to convert a datetime object to a Unix timestamp. A Unix timestamp is a numerical representation of datetime. Example for retrieving day, month and year from given date: @@ -35,15 +27,13 @@ print('Day is: ', d) ``` Output: + ```python Year is: 2024 Month is: 5 Day is: 5 ``` -- **Note**: The timestamp function in Pandas is used to convert a datetime object to a Unix timestamp. A Unix timestamp is a numerical representation of datetime. - - Example for extracting time related data from given date: ```python @@ -57,6 +47,7 @@ print('Quarter is: ', ts.quarter) ``` Output: + ```python Hour is: 12 Minute is: 0 @@ -64,6 +55,8 @@ Weekday is: 1 Quarter is: 4 ``` +### `Timestamp.now()` + Example for getting current date and time: ```python @@ -78,6 +71,8 @@ Output: Current date and time is: 2024-05-25 11:48:25.593213 ``` +### `date_range` function + Example for generating dates' for next five days: ```python @@ -89,6 +84,7 @@ for i in ts: ``` Output: + ```python 2024-05-25 2024-05-26 @@ -116,11 +112,11 @@ Output: 2024-05-25 ``` -### Pandas `DateTime` is Efficient than Built-in `DateTime` library in various aspects like: +### Built-in vs pandas date & time operations -- **In pandas, you may add a time delta to a full column of dates in a single action, but Python's datetime requires a loop.** +In `pandas`, you may add a time delta to a full column of dates in a single action, but Python's datetime requires a loop. -Example using Pandas DateTime: +Example in Pandas: ```python import pandas as pd @@ -146,7 +142,7 @@ Output: 99999 2023-03-12 10:39:00 ``` -Example using Built-In datetime library: +Example using Built-in datetime library: ```python from datetime import datetime, timedelta @@ -155,12 +151,8 @@ dates = [datetime(2023, 1, 1) + timedelta(minutes=i) for i in range(100000)] dates = [date + timedelta(days=1) for date in dates] ``` -Output: -```The output is very large to display and taking more time to display``` - - -- **Pandas employs NumPy's datetime64 dtype, which takes up a set amount of bytes (usually 8 bytes per date), to store datetime data more compactly and efficiently.** -- **Each datetime object in Python takes up extra memory since it contains not only the date and time but also the additional metadata and overhead associated with Python objects.** +Why use pandas functions? -- **Pandas Offers a wide range of convenient functions and methods for date manipulation, extraction, and conversion, such as `pd.to_datetime()`, `date_range()`, `timedelta_range()`, and more.** -- **datetime library requires manual implementation for many of these operations, leading to longer and less efficient code.** +- Pandas employs NumPy's datetime64 dtype, which takes up a set amount of bytes (usually 8 bytes per date), to store datetime data more compactly and efficiently. +- Each datetime object in Python takes up extra memory since it contains not only the date and time but also the additional metadata and overhead associated with Python objects. +- Pandas Offers a wide range of convenient functions and methods for date manipulation, extraction, and conversion, such as `pd.to_datetime()`, `date_range()`, `timedelta_range()`, and more. datetime library requires manual implementation for many of these operations, leading to longer and less efficient code. From 90aa15145b2289d9c203bbddf79de917cd7daa8d Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 05:07:26 +0530 Subject: [PATCH 297/656] Update index.md --- contrib/pandas/index.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md index 051f3b0d..c17d8393 100644 --- a/contrib/pandas/index.md +++ b/contrib/pandas/index.md @@ -5,7 +5,6 @@ - [Pandas Descriptive Statistics](Descriptive_Statistics.md) - [Group By Functions with Pandas](GroupBy_Functions_Pandas.md) - [Excel using Pandas DataFrame](excel_with_pandas.md) -- [Working with datetime in Pandas](datetime.md) +- [Working with Date & Time in Pandas](datetime.md) - [Importing and Exporting Data in Pandas](import-export.md) - [Handling Missing Values in Pandas](handling-missing-values.md) - From 976eb8cffd558f544efd27d2be4e30303a236191 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 05:10:48 +0530 Subject: [PATCH 298/656] Update index.md --- contrib/ds-algorithms/index.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 3645f825..31cff39b 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -1,7 +1,6 @@ # List of sections -- [Section title](filename.md) -- [Time Space Complexity](time-space-complexity.md) +- [Time & Space Complexity](time-space-complexity.md) - [Queues in Python](Queues.md) - [Graphs](graph.md) - [Sorting Algorithms](sorting-algorithms.md) From db796bfc2b1a30f5167e5f5b4e4cbe73db7ed0ca Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 05:19:31 +0530 Subject: [PATCH 299/656] Update time-space-complexity.md --- .../ds-algorithms/time-space-complexity.md | 86 +++++++++++-------- 1 file changed, 51 insertions(+), 35 deletions(-) diff --git a/contrib/ds-algorithms/time-space-complexity.md b/contrib/ds-algorithms/time-space-complexity.md index 79a072f2..eeadd649 100644 --- a/contrib/ds-algorithms/time-space-complexity.md +++ b/contrib/ds-algorithms/time-space-complexity.md @@ -1,7 +1,10 @@ # Time and Space Complexity We can solve a problem using one or more algorithms. It's essential to learn how to compare the performance of different algorithms and select the best one for a specific task. -Therefore, it is highly required to use a method to compare the solutions in order to judge which one is more optimal.
    The method must be: + +Therefore, it is highly required to use a method to compare the solutions in order to judge which one is more optimal. + +The method must be: - Regardless of the system or its settings on which the algorithm is executing. - Demonstrate a direct relationship with the quantity of inputs. @@ -35,9 +38,7 @@ $$ Graphical representation of Big Oh: -![BigOh Notation Graph](./images/Time-And-Space-Complexity-BigOh.png) - -Image Credit: www.programiz.com +![BigOh Notation Graph](images/Time-And-Space-Complexity-BigOh.png) ### 2. Big Omega (Ω) Notation @@ -51,9 +52,7 @@ $$ Graphical representation of Big Omega: -![BigOmega Notation Graph](./images/Time-And-Space-Complexity-BigOmega.png) - -Image Credit: www.programiz.com +![BigOmega Notation Graph](images/Time-And-Space-Complexity-BigOmega.png) ### 3. Big Theta (Θ) Notation @@ -67,61 +66,64 @@ $$ Graphical representation of Big Theta: -![Big Theta Notation Graph](./images/Time-And-Space-Complexity-BigTheta.png) - - -Image Credit: www.programiz.com - +![Big Theta Notation Graph](images/Time-And-Space-Complexity-BigTheta.png) ## Best Case, Worst Case and Average Case + ### 1. Best-Case Scenario: + The best-case scenario refers to the situation where an algorithm performs optimally, achieving the lowest possible time or space complexity. It represents the most favorable conditions under which an algorithm operates. #### Characteristics: -- Represents the minimum time or space required by an algorithm to solve a problem. + +- Represents the minimum time or space required by an algorithm to solve a problem. - Occurs when the input data is structured in such a way that the algorithm can exploit its strengths fully. - Often used to analyze the lower bound of an algorithm's performance. #### Example: -Consider the `linear search algorithm` where we're searching for a `target element` in an array. The best-case scenario occurs when the target element is found `at the very beginning of the array`. In this case, the algorithm would only need to make one comparison, resulting in a time complexity of O(1). +Consider the `linear search algorithm` where we're searching for a `target element` in an array. The best-case scenario occurs when the target element is found `at the very beginning of the array`. In this case, the algorithm would only need to make one comparison, resulting in a time complexity of `O(1)`. ### 2. Worst-Case Scenario: + The worst-case scenario refers to the situation where an algorithm performs at its poorest, achieving the highest possible time or space complexity. It represents the most unfavorable conditions under which an algorithm operates. #### Characteristics: + - Represents the maximum time or space required by an algorithm to solve a problem. - Occurs when the input data is structured in such a way that the algorithm encounters the most challenging conditions. - Often used to analyze the upper bound of an algorithm's performance. #### Example: -Continuing with the `linear search algorithm`, the worst-case scenario occurs when the `target element` is either not present in the array or located `at the very end`. In this case, the algorithm would need to iterate through the entire array, resulting in a time complexity of O(n), where n is the size of the array. +Continuing with the `linear search algorithm`, the worst-case scenario occurs when the `target element` is either not present in the array or located `at the very end`. In this case, the algorithm would need to iterate through the entire array, resulting in a time complexity of `O(n)`, where `n` is the size of the array. ### 3. Average-Case Scenario: + The average-case scenario refers to the expected performance of an algorithm over all possible inputs, typically calculated as the arithmetic mean of the time or space complexity. #### Characteristics: + - Represents the typical performance of an algorithm across a range of input data. - Takes into account the distribution of inputs and their likelihood of occurrence. - Provides a more realistic measure of an algorithm's performance compared to the best-case or worst-case scenarios. #### Example: -- For the `linear search algorithm`, the average-case scenario considers the probability distribution of the target element's position within the array. If the `target element is equally likely to be found at any position in the array`, the average-case time complexity would be O(n/2), as the algorithm would, on average, need to search halfway through the array. +For the `linear search algorithm`, the average-case scenario considers the probability distribution of the target element's position within the array. If the `target element is equally likely to be found at any position in the array`, the average-case time complexity would be `O(n/2)`, as the algorithm would, on average, need to search halfway through the array. ## Space Complexity + The memory space that a code utilizes as it is being run is often referred to as space complexity. Additionally, space complexity depends on the machine, therefore rather than using the typical memory units like MB, GB, etc., we will express space complexity using the Big O notation. #### Examples of Space Complexity -1. `Constant Space Complexity (O(1))`: Algorithms that operate on a fixed-size array or use a constant number of variables have O(1) space complexity. +1. `Constant Space Complexity (O(1))`: Algorithms that operate on a fixed-size array or use a constant number of variables have O(1) space complexity. 2. `Linear Space Complexity (O(n))`: Algorithms that store each element of the input array in a separate variable or data structure have O(n) space complexity. - 3. `Quadratic Space Complexity (O(n^2))`: Algorithms that create a two-dimensional array or matrix with dimensions based on the input size have O(n^2) space complexity. - #### Analyzing Space Complexity + To analyze space complexity: - Identify the variables, data structures, and recursive calls used by the algorithm. @@ -130,18 +132,23 @@ To analyze space complexity: ## Examples to calculate time and space complexity -#### 1. Print all elements of given array : +#### 1. Print all elements of given array + Consider each line takes one unit of time to run. So, to simply iterate over an array to print all elements it will take `O(n)` time, where n is the size of array. + Code: + ```python arr = [1,2,3,4] #1 for x in arr: #2 print(x) #3 ``` -Here, the 1st statement executes only once. So, it takes one unit of time to run. The for loop consisting of 2nd and 3rd statements executes 4 times. + +Here, the 1st statement executes only once. So, it takes one unit of time to run. The for loop consisting of 2nd and 3rd statements executes 4 times. Also, as the code dosen't take any additional space except the input arr its Space Complexity is O(1) constant. #### 2. Linear Search + Linear search is a simple algorithm for finding an element in an array by sequentially checking each element until a match is found or the end of the array is reached. Here's an example of calculating the time and space complexity of linear search: ```python @@ -156,17 +163,20 @@ arr = [1, 3, 5, 7, 9] target = 5 print(linear_search(arr, target)) ``` -`Time Complexity Analysis:` -The for loop iterates through the entire array, which takes O(n) time in the worst case, where n is the size of the array. -Inside the loop, each operation takes constant time (O(1)). + +**Time Complexity Analysis** + +The for loop iterates through the entire array, which takes O(n) time in the worst case, where n is the size of the array. +Inside the loop, each operation takes constant time (O(1)). Therefore, the time complexity of linear search is `O(n)`. +**Space Complexity Analysis** -`Space Complexity Analysis:` The space complexity of linear search is `O(1)` since it only uses a constant amount of additional space for variables regardless of the input size. #### 3. Binary Search + Binary search is an efficient algorithm for finding an element in a sorted array by repeatedly dividing the search interval in half. Here's an example of calculating the time and space complexity of binary search: ```python @@ -190,18 +200,21 @@ def binary_search(arr, target): arr = [1, 3, 5, 7, 9] target = 5 print(binary_search(arr, target)) - ``` -`Time Complexity Analysis:` -The initialization of left and right takes constant time (O(1)). -The while loop runs for log(n) iterations in the worst case, where n is the size of the array. -Inside the loop, each operation takes constant time (O(1)). + +**Time Complexity Analysis** + +The initialization of left and right takes constant time (O(1)). +The while loop runs for log(n) iterations in the worst case, where n is the size of the array. +Inside the loop, each operation takes constant time (O(1)). Therefore, the time complexity of binary search is `O(log n)`. -`Space Complexity Analysis:` +**Space Complexity Analysis** + The space complexity of binary search is `O(1)` since it only uses a constant amount of additional space for variables regardless of the input size. #### 4. Fibbonaci Sequence + Let's consider an example of a function that generates Fibonacci numbers up to a given index and stores them in a list. In this case, the space complexity will not be constant because the size of the list grows with the Fibonacci sequence. ```python @@ -219,9 +232,12 @@ n = 10 fib_sequence = fibonacci_sequence(n) print(fib_sequence) ``` -`Time Complexity Analysis:` + +**Time Complexity Analysis** + The while loop iterates until the length of the Fibonacci sequence list reaches n, so it takes `O(n)` iterations in the `worst case`.Inside the loop, each operation takes constant time (O(1)). -`Space Complexity Analysis:` -The space complexity of this function is not constant because it creates and stores a list of Fibonacci numbers. -As n grows, the size of the list also grows, so the space complexity is O(n), where n is the index of the last Fibonacci number generated. \ No newline at end of file +**Space Complexity Analysis** + +The space complexity of this function is not constant because it creates and stores a list of Fibonacci numbers. +As n grows, the size of the list also grows, so the space complexity is O(n), where n is the index of the last Fibonacci number generated. From dc015fb32fdb5c8d13beb1f6db76cc669f7d4f5e Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 05:36:53 +0530 Subject: [PATCH 300/656] Update index.md --- contrib/plotting-visualization/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index e7c57452..cabea954 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -1,4 +1,4 @@ # List of sections -- [Installing Matplotlib](matplotlib_installation.md) -- [Bar Plots in Matplotlib](matplotlib_bar_plots.md) +- [Installing Matplotlib](installation.md) +- [Bar Plots in Matplotlib](bar-plots.md) From 5f8a9d01ea69a7eb70ccfdc2c1f4690486f6cf77 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 05:37:07 +0530 Subject: [PATCH 301/656] Rename matplotlib_bar_plots.md to bar-plots.md --- .../{matplotlib_bar_plots.md => bar-plots.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/plotting-visualization/{matplotlib_bar_plots.md => bar-plots.md} (99%) diff --git a/contrib/plotting-visualization/matplotlib_bar_plots.md b/contrib/plotting-visualization/bar-plots.md similarity index 99% rename from contrib/plotting-visualization/matplotlib_bar_plots.md rename to contrib/plotting-visualization/bar-plots.md index 104001d9..97d45cea 100644 --- a/contrib/plotting-visualization/matplotlib_bar_plots.md +++ b/contrib/plotting-visualization/bar-plots.md @@ -213,4 +213,4 @@ ax.invert_yaxis() plt.show() ``` -![Horizontal Bar Plot-2](images/horizontal_bar_plot_2.png) \ No newline at end of file +![Horizontal Bar Plot-2](images/horizontal_bar_plot_2.png) From 077e569deafbb0b0beb244e2c9ddb45df3549723 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 05:37:52 +0530 Subject: [PATCH 302/656] Rename matplotlib_installation.md to matplotlib-installation.md --- .../{matplotlib_installation.md => matplotlib-installation.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/plotting-visualization/{matplotlib_installation.md => matplotlib-installation.md} (100%) diff --git a/contrib/plotting-visualization/matplotlib_installation.md b/contrib/plotting-visualization/matplotlib-installation.md similarity index 100% rename from contrib/plotting-visualization/matplotlib_installation.md rename to contrib/plotting-visualization/matplotlib-installation.md From 7bb7b05ea1de0d1a6d15d985df652579d20a5724 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 05:38:17 +0530 Subject: [PATCH 303/656] Update index.md --- contrib/plotting-visualization/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index cabea954..4d7fda85 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -1,4 +1,4 @@ # List of sections -- [Installing Matplotlib](installation.md) -- [Bar Plots in Matplotlib](bar-plots.md) +- [Installing Matplotlib](matplotlib-installation.md) +- [Bar Plots in Matplotlib](matplotlib-bar-plots.md) From a904e73c65f8bd1941794fe9a87295b4c0493f5e Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 06:07:45 +0530 Subject: [PATCH 304/656] Update concatenation-of-arrays.md --- contrib/numpy/concatenation-of-arrays.md | 82 ++++++++++++++++-------- 1 file changed, 55 insertions(+), 27 deletions(-) diff --git a/contrib/numpy/concatenation-of-arrays.md b/contrib/numpy/concatenation-of-arrays.md index a0c50511..bf27512d 100644 --- a/contrib/numpy/concatenation-of-arrays.md +++ b/contrib/numpy/concatenation-of-arrays.md @@ -20,31 +20,39 @@ Args: ### Example +#### Concatenate along axis 0 + ```python import numpy as np #creating 2 arrays arr1 = np.array([1 2 3],[7 8 9]) arr2 = np.array([4 5 6],[10 11 12]) -#concatenate along axis 0 result_1 = np.concatenate((arr1, arr2), axis=0) -print("Concatenation along axis 0:") print(result_1) -""" Output- Concatenation along axis 0: +``` + +#### Output +``` [[ 1 2 3] [ 7 8 9] [ 4 5 6] - [10 11 12]] """ + [10 11 12]] +``` -#concatenate along axis 1 +#### Concatenate along axis 1 + +```python result_2 = np.concatenate((arr1, arr2), axis=1) -print("Concatenation along axis 1:") print(result_2) -""" Output- Concatenation along axis 1: -[[ 1 2 3 4 5 6 ] - [ 7 8 9 10 11 12]] """ +``` +#### Output ``` +[[ 1 2 3 4 5 6 ] + [ 7 8 9 10 11 12]] +``` + ## np.vstack Vertical stacking of arrays (row-wise). @@ -59,6 +67,7 @@ Args: - arrays: Sequence of arrays to stack. ### Example + ```python import numpy as np #create arrays @@ -67,12 +76,16 @@ arr2 = np.array([4 5 6],[10 11 12]) result = np.vstack((arr1, arr2)) print(result) -"""output- +``` + +#### Output +``` [[ 1 2 3] [ 7 8 9] [ 4 5 6] - [10 11 12]] """ + [10 11 12]] ``` + ## 3. np.hstack Stacks arrays horizontally (column-wise). @@ -96,9 +109,12 @@ arr2 = np.array([4 5 6],[10 11 12]) result = np.hstack((arr1, arr2)) print(result) -"""output- +``` + +#### Output +``` [[ 1 2 3] [ 4 5 6] - [ 7 8 9] [10 11 12]] """ + [ 7 8 9] [10 11 12]] ``` ## np.dstack @@ -123,14 +139,17 @@ arr2 = np.array([4 5 6],[10 11 12]) result = np.dstack((arr1, arr2)) print(result) -""" output- +``` + +#### Output +``` [[[ 1 4] [ 2 5] [ 3 6]] [[ 7 10] [ 8 11] - [ 9 12]]] """ + [ 9 12]]] ``` ## np.stack @@ -154,12 +173,15 @@ arr2 = np.array([4 5 6],[10 11 12]) result = np.stack((arr1, arr2), axis=0) print(result) -""" output- +``` + +#### Output +``` [[[ 1 2 3] [ 7 8 9]] [[ 4 5 6] - [10 11 12]]] """ + [10 11 12]]] ``` # Concatenation with Mixed Dimensions @@ -168,28 +190,34 @@ When concatenating arrays with different shapes, it's often necessary to reshape ## Example +#### Concatenate along axis 0 + ```python arr1 = np.array([[1, 2, 3], [4, 5, 6]]) arr2 = np.array([7, 8, 9]) -# Concatenate along axis 0 result_0= np.concatenate((arr1, arr2[np.newaxis, :]), axis=0) -print("Concatenation along axis 0:") print(result_0) -""" output- -Concatenation along axis 0: +``` + +#### Output +``` [[1 2 3] [4 5 6] - [7 8 9]] """ + [7 8 9]] +``` + +#### Concatenate along axis 1 -# Concatenate along axis 1 +```python result_1 = np.concatenate((arr1, arr2[:, np.newaxis]), axis=1) -print("\nConcatenation along axis 1:") print(result_1) -""" output- -Concatenation along axis 1: +``` + +#### Output +``` [[1 2 3 7] - [4 5 6 8]] """ + [4 5 6 8]] ``` From 75423bc5ba6e4a698ebc53df6f13af52f4cfc21f Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 06:14:48 +0530 Subject: [PATCH 305/656] Rename numpy_array_iteration.md to array-iteration.md --- contrib/numpy/{numpy_array_iteration.md => array-iteration.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/numpy/{numpy_array_iteration.md => array-iteration.md} (100%) diff --git a/contrib/numpy/numpy_array_iteration.md b/contrib/numpy/array-iteration.md similarity index 100% rename from contrib/numpy/numpy_array_iteration.md rename to contrib/numpy/array-iteration.md From 3bebec30e0529dac743073495a12ad85cd1e3904 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 06:18:47 +0530 Subject: [PATCH 306/656] Update array-iteration.md --- contrib/numpy/array-iteration.md | 35 +++++++++++++++++++++----------- 1 file changed, 23 insertions(+), 12 deletions(-) diff --git a/contrib/numpy/array-iteration.md b/contrib/numpy/array-iteration.md index ac35d958..b0a499f5 100644 --- a/contrib/numpy/array-iteration.md +++ b/contrib/numpy/array-iteration.md @@ -7,7 +7,7 @@ Understanding these methods is crucial for performing operations on array elemen - Iterating using basic `for` loop. -**Single-dimensional array iteration**: +### Single-dimensional array Iterating over a single-dimensional array is straightforward using a basic `for` loop @@ -18,11 +18,18 @@ arr = np.array([1, 2, 3, 4, 5]) for i in arr: print(i) ``` -**Output** : + +#### Output + ```python -[ 1 2 3 4 5 ] +1 +2 +3 +4 +5 ``` -**Multi-dimensional array**: + +### Multi-dimensional array Iterating over multi-dimensional arrays, each iteration returns a sub-array along the first axis. @@ -32,14 +39,16 @@ marr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) for arr in marr: print(arr) ``` -**Output** : + +#### Output + ```python [1 2 3] [4 5 6] [7 8 9] ``` -## 2. Iterating with nditer +## 2. Iterating with `nditer` - `nditer` is a powerful iterator provided by NumPy for iterating over multi-dimensional arrays. - In each interation it gives each element. @@ -51,7 +60,9 @@ arr = np.array([[1, 2, 3], [4, 5, 6]]) for i in np.nditer(arr): print(i) ``` -**Output** : + +#### Output + ```python 1 2 @@ -61,7 +72,7 @@ for i in np.nditer(arr): 6 ``` -## 3. Iterating with ndenumerate +## 3. Iterating with `ndenumerate` - `ndenumerate` allows you to iterate with both the index and the value of each element. - It gives index and value as output in each iteration @@ -74,7 +85,7 @@ for index,value in np.ndenumerate(arr): print(index,value) ``` -**Output** : +#### Output ```python (0, 0) 1 @@ -86,7 +97,6 @@ for index,value in np.ndenumerate(arr): ## 4. Iterating with flat - The `flat` attribute returns a 1-D iterator over the array. -- ```python import numpy as np @@ -96,7 +106,7 @@ for element in arr.flat: print(element) ``` -**Output** : +#### Output ```python 1 @@ -105,5 +115,6 @@ for element in arr.flat: 4 ``` -Understanding the various ways to iterate over NumPy arrays can significantly enhance your data processing efficiency. +Understanding the various ways to iterate over NumPy arrays can significantly enhance your data processing efficiency. + Whether you are working with single-dimensional or multi-dimensional arrays, NumPy provides versatile tools to iterate and manipulate array elements effectively. From d1bc3127082c3a27b7f2eb27a52fbb4b1013cc36 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 06:21:12 +0530 Subject: [PATCH 307/656] Rename Logistic_Regression.md to logistic-regression.md --- .../{Logistic_Regression.md => logistic-regression.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/machine-learning/{Logistic_Regression.md => logistic-regression.md} (100%) diff --git a/contrib/machine-learning/Logistic_Regression.md b/contrib/machine-learning/logistic-regression.md similarity index 100% rename from contrib/machine-learning/Logistic_Regression.md rename to contrib/machine-learning/logistic-regression.md From ad01516cf8f834d7c4afbf907cd72320bc432f1e Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 06:21:28 +0530 Subject: [PATCH 308/656] Update index.md --- contrib/machine-learning/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 0b9c2b06..46100dfb 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -9,4 +9,4 @@ - [TensorFlow.md](tensorFlow.md) - [PyTorch.md](pytorch.md) - [Types of optimizers](Types_of_optimizers.md) -- [Logistic Regression](Logistic_Regression.md) +- [Logistic Regression](logistic-regression.md) From 70547423e3af49f0c65c7b444f8d2987239e87a5 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 06:23:46 +0530 Subject: [PATCH 309/656] Update logistic-regression.md --- contrib/machine-learning/logistic-regression.md | 6 ------ 1 file changed, 6 deletions(-) diff --git a/contrib/machine-learning/logistic-regression.md b/contrib/machine-learning/logistic-regression.md index 31115507..2e45e984 100644 --- a/contrib/machine-learning/logistic-regression.md +++ b/contrib/machine-learning/logistic-regression.md @@ -113,9 +113,3 @@ print("Classification Report:\n", class_report) ## Conclusion Logistic regression is a fundamental classification technique that is easy to implement and interpret. It is a powerful tool for binary classification problems and provides a probabilistic framework for predicting binary outcomes. - -## References - -- [Scikit-learn Documentation](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression) -- [Wikipedia: Logistic Regression](https://en.wikipedia.org/wiki/Logistic_regression) -- [Towards Data Science: Understanding Logistic Regression](https://towardsdatascience.com/understanding-logistic-regression-9b02c2aec102) From 1646a8c0330695ef4750a2ca533062a7c1c61599 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 06:32:34 +0530 Subject: [PATCH 310/656] Update recursion.md --- contrib/ds-algorithms/recursion.md | 41 +++++++----------------------- 1 file changed, 9 insertions(+), 32 deletions(-) diff --git a/contrib/ds-algorithms/recursion.md b/contrib/ds-algorithms/recursion.md index 67c630b3..4233242a 100644 --- a/contrib/ds-algorithms/recursion.md +++ b/contrib/ds-algorithms/recursion.md @@ -2,7 +2,7 @@ When a function calls itself to solve smaller instances of the same problem until a specified condition is fulfilled is called recursion. It is used for tasks that can be divided into smaller sub-tasks. -# How Recursion Works +## How Recursion Works To solve a problem using recursion we must define: - Base condition :- The condition under which recursion ends. @@ -17,7 +17,7 @@ When a recursive function is called, the following sequence of events occurs: - Stack Management: Each recursive call is placed on the call stack. The stack keeps track of each function call, its argument, and the point to return to once the call completes. - Unwinding the Stack: When the base case is eventually met, the function returns a value, and the stack starts unwinding, returning values to previous function calls until the initial call is resolved. -# Python Code: Factorial using Recursion +## Python Code: Factorial using Recursion ```python def fact(n): @@ -30,7 +30,7 @@ if __name__ == "__main__": print("Factorial of", n, "is", fact(n)) ``` -## Explanation +### Explanation This Python script calculates the factorial of a given number using recursion. @@ -43,8 +43,7 @@ This Python script calculates the factorial of a given number using recursion. - The main section prompts the user to enter a positive number. - It then calls the `fact` function with the input number and prints the result. -### Example : Let n = 4 - +#### Example : Let n = 4 The recursion unfolds as follows: 1. When `fact(4)` is called, it computes `4 * fact(3)`. @@ -55,48 +54,26 @@ The recursion unfolds as follows: 6. `fact(4)` receives the value from `fact(3)`, resulting in `4 * 6` i.e. `24`. 7. Finally, `fact(4)` returns 24 to the main function. - #### So, the result is 24. - - -# What is Stack Overflow in Recursion +#### What is Stack Overflow in Recursion? Stack overflow is an error that occurs when the call stack memory limit is exceeded. During execution of recursion calls they are simultaneously stored in a recursion stack waiting for the recursive function to be completed. Without a base case, the function would call itself indefinitely, leading to a stack overflow. -# Example - -- Factorial of a Number - - The factorial of i natural numbers is nth integer multiplied by factorial of (i-1) numbers. The base case is if i=0 we return 1 as factorial of 0 is 1. - -```python -def factorial(i): - #base case - if i==0 : - return 1 - #recursive case - else : - return i * factorial(i-1) -i = 6 -print("Factorial of i is :", factorial(i)) # Output- Factorial of i is :720 -``` -# What is Backtracking +## What is Backtracking Backtracking is a recursive algorithmic technique used to solve problems by exploring all possible solutions and discarding those that do not meet the problem's constraints. It is particularly useful for problems involving combinations, permutations, and finding paths in a grid. -# How Backtracking Works +## How Backtracking Works - Incremental Solution Building: Solutions are built one step at a time. - Feasibility Check: At each step, a check is made to see if the current partial solution is valid. - Backtracking: If a partial solution is found to be invalid, the algorithm backtracks by removing the last added part of the solution and trying the next possibility. - Exploration of All Possibilities: The process continues recursively, exploring all possible paths, until a solution is found or all possibilities are exhausted. -# Example - -- Word Search +## Example: Word Search - Given a 2D grid of characters and a word, determine if the word exists in the grid. The word can be constructed from letters of sequentially adjacent cells, where "adjacent" cells are horizontally or vertically neighboring. The same letter cell may not be used more than once. +Given a 2D grid of characters and a word, determine if the word exists in the grid. The word can be constructed from letters of sequentially adjacent cells, where "adjacent" cells are horizontally or vertically neighboring. The same letter cell may not be used more than once. Algorithm for Solving the Word Search Problem with Backtracking: - Start at each cell: Attempt to find the word starting from each cell. From 73955bd969f1f799832976af4177d24c3f09b9ff Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 07:10:57 +0530 Subject: [PATCH 311/656] Update index.md --- contrib/numpy/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md index df759d97..2e418abc 100644 --- a/contrib/numpy/index.md +++ b/contrib/numpy/index.md @@ -3,7 +3,7 @@ - [Installing NumPy](installing-numpy.md) - [Introduction](introduction.md) - [NumPy Data Types](datatypes.md) -- [Numpy Array Shape and Reshape](array_reshape.md) +- [Numpy Array Shape and Reshape](reshape-array.md) - [Basic Mathematics](basic_math.md) - [Operations on Arrays in NumPy](operations-on-arrays.md) - [Loading Arrays from Files](loading_arrays_from_files.md) From bf4fa5a067c550d3a76403789e33839827c96021 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 07:11:13 +0530 Subject: [PATCH 312/656] Rename array_reshape.md to reshape-array.md --- contrib/numpy/{array_reshape.md => reshape-array.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/numpy/{array_reshape.md => reshape-array.md} (100%) diff --git a/contrib/numpy/array_reshape.md b/contrib/numpy/reshape-array.md similarity index 100% rename from contrib/numpy/array_reshape.md rename to contrib/numpy/reshape-array.md From 02c7f2c3a202fc7234c5000dd010b1f3df6b9910 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 07:19:32 +0530 Subject: [PATCH 313/656] Update reshape-array.md --- contrib/numpy/reshape-array.md | 47 ++++++++++++++++++---------------- 1 file changed, 25 insertions(+), 22 deletions(-) diff --git a/contrib/numpy/reshape-array.md b/contrib/numpy/reshape-array.md index d3d7f595..91da3660 100644 --- a/contrib/numpy/reshape-array.md +++ b/contrib/numpy/reshape-array.md @@ -1,54 +1,57 @@ # Numpy Array Shape and Reshape + In NumPy, the primary data structure is the ndarray (N-dimensional array). An array can have one or more dimensions, and it organizes your data efficiently. -Code to create a 2D array +Let us create a 2D array + ``` python import numpy as np numbers = np.array([[1, 2, 3, 4], [5, 6, 7, 8]]) print(numbers) +``` + +#### Output: -# Output: -# array([[1, 2, 3, 4],[5, 6, 7, 8]]) +``` python +array([[1, 2, 3, 4],[5, 6, 7, 8]]) ``` -## Changing Array Shape using Reshape() +## Changing Array Shape using `reshape()` + The `reshape()` function allows you to rearrange the data within a NumPy array. -It take 2 arguements, row and columns. The `reshape()` can add or remove the dimensions. For instance, array can convert a 1D array into a 2D array or vice versa. + +It take 2 arguments, row and columns. The `reshape()` can add or remove the dimensions. For instance, array can convert a 1D array into a 2D array or vice versa. ``` python arr_1d = np.array([1, 2, 3, 4, 5, 6]) # 1D array -arr_2d = arr_1d.reshape(2, 3) # Reshaping with 2rows and 3cols +arr_2d = arr_1d.reshape(2, 3) # Reshaping with 2 rows and 3 cols print(arr_2d) +``` -# Output: -# array([[1, 2, 3],[4, 5, 6]]) +#### Output: +``` python +array([[1, 2, 3],[4, 5, 6]]) ``` -## Changing Array Shape using Resize() +## Changing Array Shape using `resize()` + The `resize()` function allows you to modify the shape of a NumPy array directly. + It take 2 arguements, row and columns. ``` python import numpy as np arr_1d = np.array([1, 2, 3, 4, 5, 6]) -arr_1d.resize((2, 3)) # 2rows and 3cols +arr_1d.resize((2, 3)) # 2 rows and 3 cols print(arr_1d) - -# Output: -# array([[1, 2, 3],[4, 5, 6]]) - ``` -## Reshape() VS Resize() +#### Output: -| Reshape | Resize | -| ----------- | ----------- | -| Does not modify the original array | Modifies the original array in-place | -| Creates a new array | Changes the shape of the array | -| Returns a reshaped array | Doesn't return anything | -| Compatibility between dimensions | Does not compatibility between dimensions | -| Syntax: reshape(row,col) | Syntax: resize((row,col)) | +``` python +array([[1, 2, 3],[4, 5, 6]]) +``` From 1f515ff7fc489e95ced9d41b9c20e9911e454d54 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 07:29:48 +0530 Subject: [PATCH 314/656] Rename matplotlib_pie_charts.md to matplotlib-pie-charts.md --- .../{matplotlib_pie_charts.md => matplotlib-pie-charts.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename contrib/plotting-visualization/{matplotlib_pie_charts.md => matplotlib-pie-charts.md} (99%) diff --git a/contrib/plotting-visualization/matplotlib_pie_charts.md b/contrib/plotting-visualization/matplotlib-pie-charts.md similarity index 99% rename from contrib/plotting-visualization/matplotlib_pie_charts.md rename to contrib/plotting-visualization/matplotlib-pie-charts.md index 0a1e56d0..66f2aa15 100644 --- a/contrib/plotting-visualization/matplotlib_pie_charts.md +++ b/contrib/plotting-visualization/matplotlib-pie-charts.md @@ -230,4 +230,4 @@ plt.legend(labels, title="Flowers") # Show plot plt.show() ``` -![Legends](images/legends.png) \ No newline at end of file +![Legends](images/legends.png) From 3a1c46a09552b785d6192f3582e91d515238375e Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 07:32:57 +0530 Subject: [PATCH 315/656] Update index.md --- contrib/database/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/database/index.md b/contrib/database/index.md index 2d57e4d2..bc3d7e67 100644 --- a/contrib/database/index.md +++ b/contrib/database/index.md @@ -1,4 +1,4 @@ # List of sections - [Introduction to MySQL and Queries](intro_mysql_queries.md) -- [SQLAlchemy and Aggregation Functions](sqlalchemy_aggregation.md) +- [SQLAlchemy and Aggregation Functions](sqlalchemy-aggregation.md) From f0db474c8fc1e6536ba78da8057b5590dee43a93 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 07:33:07 +0530 Subject: [PATCH 316/656] Update and rename sqlalchemy_aggregation.md to sqlalchemy-aggregation.md --- .../{sqlalchemy_aggregation.md => sqlalchemy-aggregation.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename contrib/database/{sqlalchemy_aggregation.md => sqlalchemy-aggregation.md} (100%) diff --git a/contrib/database/sqlalchemy_aggregation.md b/contrib/database/sqlalchemy-aggregation.md similarity index 100% rename from contrib/database/sqlalchemy_aggregation.md rename to contrib/database/sqlalchemy-aggregation.md From 4eefdc057efe9f519bf860293820880539db9566 Mon Sep 17 00:00:00 2001 From: Ankit Mahato Date: Fri, 31 May 2024 07:34:49 +0530 Subject: [PATCH 317/656] Update sqlalchemy-aggregation.md --- contrib/database/sqlalchemy-aggregation.md | 20 +++++++++++++++++++- 1 file changed, 19 insertions(+), 1 deletion(-) diff --git a/contrib/database/sqlalchemy-aggregation.md b/contrib/database/sqlalchemy-aggregation.md index c18e8dfb..9fce96c0 100644 --- a/contrib/database/sqlalchemy-aggregation.md +++ b/contrib/database/sqlalchemy-aggregation.md @@ -1,5 +1,5 @@ # SQLAlchemy -SQLAlchemy is a powerful and flexible SQL toolkit and Object-Relational Mapping (ORM) library for Python. It is a versatile library that bridges the gap between Python applications and relational databases.
    +SQLAlchemy is a powerful and flexible SQL toolkit and Object-Relational Mapping (ORM) library for Python. It is a versatile library that bridges the gap between Python applications and relational databases. SQLAlchemy allows the user to write database-agnostic code that can work with a variety of relational databases such as SQLite, MySQL, PostgreSQL, Oracle, and Microsoft SQL Server. The ORM layer in SQLAlchemy allows developers to map Python classes to database tables. This means you can interact with your database using Python objects instead of writing raw SQL queries. @@ -10,9 +10,11 @@ SQLAlchemy allows the user to write database-agnostic code that can work with a ```bash pip install sqlalchemy mysql-connector-python ``` + * If not installed, you can install them using the above command in terminal, ## Establishing Connection with Database + * Create a connection with the database using the following code snippet: ```python from sqlalchemy import create_engine @@ -27,11 +29,13 @@ session = Session() Base = declarative_base() ``` + * The connection string **DATABASE_URL** is passed as an argument to **create_engine** function which is used to create a connection to the database. This connection string contains the database credentials such as the database type, username, password, and database name. * The **sessionmaker** function is used to create a session object which is used to interact with the database * The **declarative_base** function is used to create a base class for all the database models. This base class is used to define the structure of the database tables. ## Creating Tables + * The following code snippet creates a table named **"products"** in the database: ```python from sqlalchemy import Column, Integer, String, Float @@ -46,10 +50,12 @@ class Product(Base): Base.metadata.create_all(engine) ``` + * The **Product class** inherits from **Base**, which is a base class for all the database models. * The **Base.metadata.create_all(engine)** statement is used to create the table in the database. The engine object is a connection to the database that was created earlier. ## Inserting Data for Aggregation Functions + * The following code snippet inserts data into the **"products"** table: ```python products = [ @@ -63,13 +69,17 @@ products = [ session.add_all(products) session.commit() ``` + * A list of **Product** objects is created. Each Product object represents a row in the **products table** in the database. * The **add_all** method of the session object is used to add all the Product objects to the session. This method takes a **list of objects as an argument** and adds them to the session. * The **commit** method of the session object is used to commit the changes made to the database. ## Aggregation Functions + SQLAlchemy provides functions that correspond to SQL aggregation functions and are available in the **sqlalchemy.func module**. + ### COUNT + The **COUNT** function returns the number of rows in a result set. It can be demonstrated using the following code snippet: ```python from sqlalchemy import func @@ -77,25 +87,33 @@ from sqlalchemy import func total_products = session.query(func.count(Product.id)).scalar() print(f'Total products: {total_products}') ``` + ### SUM + The **SUM** function returns the sum of all values in a column. It can be demonstrated using the following code snippet: ```python total_price = session.query(func.sum(Product.price)).scalar() print(f'Total price of all products: {total_price}') ``` + ### AVG + The **AVG** function returns the average of all values in a column. It can be demonstrated by the following code snippet: ```python average_price = session.query(func.avg(Product.price)).scalar() print(f'Average price of products: {average_price}') ``` + ### MAX + The **MAX** function returns the maximum value in a column. It can be demonstrated using the following code snippet : ```python max_price = session.query(func.max(Product.price)).scalar() print(f'Maximum price of products: {max_price}') ``` + ### MIN + The **MIN** function returns the minimum value in a column. It can be demonstrated using the following code snippet: ```python min_price = session.query(func.min(Product.price)).scalar() From 21612e4b81468a127feea94ee419d22efcb1133c Mon Sep 17 00:00:00 2001 From: Vrisha Shah <74671946+Vrisha213@users.noreply.github.com> Date: Fri, 31 May 2024 07:58:39 +0530 Subject: [PATCH 318/656] Update index.md --- contrib/machine-learning/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 46100dfb..e3a8f0b6 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -10,3 +10,4 @@ - [PyTorch.md](pytorch.md) - [Types of optimizers](Types_of_optimizers.md) - [Logistic Regression](logistic-regression.md) +- [Grid Search](grid-search.md) From a953122b2681a430e0805de2031f5c20af2ee8cc Mon Sep 17 00:00:00 2001 From: Vrisha Shah <74671946+Vrisha213@users.noreply.github.com> Date: Fri, 31 May 2024 07:59:40 +0530 Subject: [PATCH 319/656] Create grid-search.md --- contrib/machine-learning/grid-search.md | 68 +++++++++++++++++++++++++ 1 file changed, 68 insertions(+) create mode 100644 contrib/machine-learning/grid-search.md diff --git a/contrib/machine-learning/grid-search.md b/contrib/machine-learning/grid-search.md new file mode 100644 index 00000000..3bf53fff --- /dev/null +++ b/contrib/machine-learning/grid-search.md @@ -0,0 +1,68 @@ +# Grid Search + +Grid Search is a hyperparameter tuning technique in Machine Learning that helps to find the best combination of hyperparameters for a given model. It works by defining a grid of hyperparameters and then training the model with all the possible combinations of hyperparameters to find the best performing set. +The Grid Search Method considers some hyperparameter combinations and selects the one returning a lower error score. This method is specifically useful when there are only some hyperparameters in order to optimize. However, it is outperformed by other weighted-random search methods when the Machine Learning model grows in complexity. + +## Implementation + +Before applying Grid Searching on any algorithm, Data is used to divided into training and validation set, a validation set is used to validate the models. A model with all possible combinations of hyperparameters is tested on the validation set to choose the best combination. +Grid Searching can be applied to any hyperparameters algorithm whose performance can be improved by tuning hyperparameter. For example, we can apply grid searching on K-Nearest Neighbors by validating its performance on a set of values of K in it. Same thing we can do with Logistic Regression by using a set of values of learning rate to find the best learning rate at which Logistic Regression achieves the best accurac +Let us consider that the model accepts the below three parameters in the form of input: +1. Number of hidden layers [2, 4] +2. Number of neurons in every layer [5, 10] +3. Number of epochs [10, 50] + +If we want to try out two options for every parameter input (as specified in square brackets above), it estimates different combinations. For instance, one possible combination can be [2, 5, 10]. Finding such combinations manually would be a headache. +Now, suppose that we had ten different parameters as input, and we would like to try out five possible values for each and every parameter. It would need manual input from the programmer's end every time we like to alter the value of a parameter, re-execute the code, and keep a record of the outputs for every combination of the parameters. +Grid Search automates that process, as it accepts the possible value for every parameter and executes the code in order to try out each and every possible combination outputs the result for the combinations and outputs the combination having the best accuracy. +Higher values of C tell the model, the training data resembles real world information, place a greater weight on the training data. While lower values of C do the opposite. + +## Explaination of the Code + +The code provided performs hyperparameter tuning for a Logistic Regression model using a manual grid search approach. It evaluates the model's performance for different values of the regularization strength hyperparameter C on the Iris dataset. +1. datasets from sklearn is imported to load the Iris dataset. +2. LogisticRegression from sklearn.linear_model is imported to create and fit the logistic regression model. +3. The Iris dataset is loaded, with X containing the features and y containing the target labels. +4. A LogisticRegression model is instantiated with max_iter=10000 to ensure convergence during the fitting process, as the default maximum iterations (100) might not be sufficient. +5. A list of different values for the regularization strength C is defined. The hyperparameter C controls the regularization strength, with smaller values specifying stronger regularization. +6. An empty list scores is initialized to store the model's performance scores for different values of C. +7. A for loop iterates over each value in the C list: +8. logit.set_params(C=choice) sets the C parameter of the logistic regression model to the current value in the loop. +9. logit.fit(X, y) fits the logistic regression model to the entire Iris dataset (this is typically done on training data in a real scenario, not the entire dataset). +10. logit.score(X, y) calculates the accuracy of the fitted model on the dataset and appends this score to the scores list. +11. After the loop, the scores list is printed, showing the accuracy for each value of C. + +## Python Code + +from sklearn import datasets + +from sklearn.linear_model import LogisticRegression + +iris = datasets.load_iris() + +X = iris['data'] + +y = iris['target'] + +logit = LogisticRegression(max_iter = 10000) + +C = [0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2] + +scores = [] + +for choice in C: + + logit.set_params(C=choice) + + logit.fit(X, y) + + scores.append(logit.score(X, y)) + +print(scores) + +## Results + +[0.9666666666666667, 0.9666666666666667, 0.9733333333333334, 0.9733333333333334, 0.98, 0.98, 0.9866666666666667, 0.9866666666666667] + +We can see that the lower values of C performed worse than the base parameter of 1. However, as we increased the value of C to 1.75 the model experienced increased accuracy. +It seems that increasing C beyond this amount does not help increase model accuracy. From 50d2b654e03d777ef20c20a210732a9b1a44c54e Mon Sep 17 00:00:00 2001 From: Vrisha Shah <74671946+Vrisha213@users.noreply.github.com> Date: Fri, 31 May 2024 08:05:09 +0530 Subject: [PATCH 320/656] Update index.md --- contrib/plotting-visualization/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 32261d6e..a8416a85 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -3,3 +3,4 @@ - [Installing Matplotlib](matplotlib-installation.md) - [Bar Plots in Matplotlib](matplotlib-bar-plots.md) - [Pie Charts in Matplotlib](matplotlib-pie-charts.md) +- [Box Plots in Matplotlib](matplotlib-box-plots.md) From c54f6128e9cdee0834d265c7b6ca7a42a105c540 Mon Sep 17 00:00:00 2001 From: Vrisha Shah <74671946+Vrisha213@users.noreply.github.com> Date: Fri, 31 May 2024 08:06:46 +0530 Subject: [PATCH 321/656] Create matplotlib-box-plots.md --- .../matplotlib-box-plots.md | 104 ++++++++++++++++++ 1 file changed, 104 insertions(+) create mode 100644 contrib/plotting-visualization/matplotlib-box-plots.md diff --git a/contrib/plotting-visualization/matplotlib-box-plots.md b/contrib/plotting-visualization/matplotlib-box-plots.md new file mode 100644 index 00000000..323b5a4b --- /dev/null +++ b/contrib/plotting-visualization/matplotlib-box-plots.md @@ -0,0 +1,104 @@ +# Box Plot + +A box plot represents the distribution of a dataset in a graph. It displays the summary statistics of a dataset, including the minimum, first quartile (Q1), median (Q2), third quartile (Q3), and maximum. The box represents the interquartile range (IQR) between the first and third quartiles, while whiskers extend from the box to the minimum and maximum values. Outliers, if present, may be displayed as individual points beyond the whiskers. + +For example - Imagine you have the exam scores of students from three classes. A box plot is a way to show how these scores are spread out. + +## Key Ranges in Data Distribution + +The data can be distributed between five key ranges, which are as follows - +1. Minimum: Q1-1.5*IQR +2. 1st quartile (Q1): 25th percentile +3. Median: 50th percentile +4. 3rd quartile(Q3): 75th percentile +5. Maximum: Q3+1.5*IQR + +## Purpose of Box Plots + +We can create the box plot of the data to determine the following- +1. The number of outliers in a dataset +2. Is the data skewed or not (skewness is a measure of asymmetry of the distribution) +3. The range of the data + +## Creating Box Plots using Matplotlib + +By using inbuilt funtion boxplot() of pyplot module of matplotlib - + +Syntax - matplotlib.pyplot.boxplot(data,notch=none,vert=none,patch_artist,widths=none) + +1. data: The data should be an array or sequence of arrays which will be plotted. +2. notch: This parameter accepts only Boolean values, either true or false. +3. vert: This attribute accepts a Boolean value. If it is set to true, then the graph will be vertical. Otherwise, it will be horizontal. +4. position: It accepts the array of integers which defines the position of the box. +5. widths: It accepts the array of integers which defines the width of the box. +6. patch_artist: this parameter accepts Boolean values, either true or false, and this is an optional parameter. +7. labels: This accepts the strings which define the labels for each data point +8. meanline: It accepts a boolean value, and it is optional. +9. order: It sets the order of the boxplot. +10. bootstrap: It accepts the integer value, which specifies the range of the notched boxplot. + +## Implementation of Box Plot in Python + +### Import libraries +import matplotlib.pyplot as plt +import numpy as np + +### Creating dataset +np.random.seed(10) +data = np.random.normal(100, 20, 200) +fig = plt.figure(figsize =(10, 7)) + +### Creating plot +plt.boxplot(data) + +### show plot +plt.show() + +### Implementation of Multiple Box Plot in Python +import matplotlib.pyplot as plt +import numpy as np +np.random.seed(10) +dataSet1 = np.random.normal(100, 10, 220) +dataSet2 = np.random.normal(80, 20, 200) +dataSet3 = np.random.normal(60, 35, 220) +dataSet4 = np.random.normal(50, 40, 200) +dataSet = [dataSet1, dataSet2, dataSet3, dataSet4] +figure = plt.figure(figsize =(10, 7)) +ax = figure.add_axes([0, 0, 1, 1]) +bp = ax.boxplot(dataSet) +plt.show() + +### Implementation of Box Plot with Outliers (visual representation of the sales distribution for each product, and the outliers highlight months with exceptionally high or low sales) +import matplotlib.pyplot as plt +import numpy as np + +### Data for monthly sales +product_A_sales = [100, 110, 95, 105, 115, 90, 120, 130, 80, 125, 150, 200] +product_B_sales = [90, 105, 100, 98, 102, 105, 110, 95, 112, 88, 115, 250] +product_C_sales = [80, 85, 90, 78, 82, 85, 88, 92, 75, 85, 200, 95] + +### Introducing outliers +product_A_sales.extend([300, 80]) +product_B_sales.extend([50, 300]) +product_C_sales.extend([70, 250]) + +### Creating a box plot with outliers +plt.boxplot([product_A_sales, product_B_sales, product_C_sales], sym='o') +plt.title('Monthly Sales Performance by Product with Outliers') +plt.xlabel('Products') +plt.ylabel('Sales') +plt.show() + +### Implementation of Grouped Box Plot (to compare the exam scores of students from three different classes (A, B, and C)) +import matplotlib.pyplot as plt +import numpy as np +class_A_scores = [75, 80, 85, 90, 95] +class_B_scores = [70, 75, 80, 85, 90] +class_C_scores = [65, 70, 75, 80, 85] + +### Creating a grouped box plot +plt.boxplot([class_A_scores, class_B_scores, class_C_scores], labels=['Class A', 'Class B', 'Class C']) +plt.title('Exam Scores by Class') +plt.xlabel('Classes') +plt.ylabel('Scores') +plt.show() From c3f715e98321ff5afe0fb50d5dd80e7ec7366a59 Mon Sep 17 00:00:00 2001 From: Vrisha Shah <74671946+Vrisha213@users.noreply.github.com> Date: Fri, 31 May 2024 08:09:21 +0530 Subject: [PATCH 322/656] Update matplotlib-box-plots.md --- .../matplotlib-box-plots.md | 39 ++++++++++++++++++- 1 file changed, 38 insertions(+), 1 deletion(-) diff --git a/contrib/plotting-visualization/matplotlib-box-plots.md b/contrib/plotting-visualization/matplotlib-box-plots.md index 323b5a4b..4d762430 100644 --- a/contrib/plotting-visualization/matplotlib-box-plots.md +++ b/contrib/plotting-visualization/matplotlib-box-plots.md @@ -40,60 +40,97 @@ Syntax - matplotlib.pyplot.boxplot(data,notch=none,vert=none,patch_artist,widths ## Implementation of Box Plot in Python ### Import libraries + import matplotlib.pyplot as plt + import numpy as np ### Creating dataset + np.random.seed(10) + data = np.random.normal(100, 20, 200) + fig = plt.figure(figsize =(10, 7)) ### Creating plot + plt.boxplot(data) ### show plot + plt.show() ### Implementation of Multiple Box Plot in Python + import matplotlib.pyplot as plt + import numpy as np + np.random.seed(10) + dataSet1 = np.random.normal(100, 10, 220) -dataSet2 = np.random.normal(80, 20, 200) + +dataSet2 = np.random.normal(80, 20, 200) + dataSet3 = np.random.normal(60, 35, 220) + dataSet4 = np.random.normal(50, 40, 200) + dataSet = [dataSet1, dataSet2, dataSet3, dataSet4] + figure = plt.figure(figsize =(10, 7)) + ax = figure.add_axes([0, 0, 1, 1]) + bp = ax.boxplot(dataSet) + plt.show() ### Implementation of Box Plot with Outliers (visual representation of the sales distribution for each product, and the outliers highlight months with exceptionally high or low sales) + import matplotlib.pyplot as plt + import numpy as np ### Data for monthly sales + product_A_sales = [100, 110, 95, 105, 115, 90, 120, 130, 80, 125, 150, 200] + product_B_sales = [90, 105, 100, 98, 102, 105, 110, 95, 112, 88, 115, 250] + product_C_sales = [80, 85, 90, 78, 82, 85, 88, 92, 75, 85, 200, 95] ### Introducing outliers + product_A_sales.extend([300, 80]) + product_B_sales.extend([50, 300]) + product_C_sales.extend([70, 250]) ### Creating a box plot with outliers + plt.boxplot([product_A_sales, product_B_sales, product_C_sales], sym='o') + plt.title('Monthly Sales Performance by Product with Outliers') + plt.xlabel('Products') + plt.ylabel('Sales') + plt.show() ### Implementation of Grouped Box Plot (to compare the exam scores of students from three different classes (A, B, and C)) + import matplotlib.pyplot as plt + import numpy as np + class_A_scores = [75, 80, 85, 90, 95] + class_B_scores = [70, 75, 80, 85, 90] + class_C_scores = [65, 70, 75, 80, 85] ### Creating a grouped box plot From d552e04ded85206b731474e0e272f1701641c13a Mon Sep 17 00:00:00 2001 From: Vrisha Shah <74671946+Vrisha213@users.noreply.github.com> Date: Fri, 31 May 2024 08:09:57 +0530 Subject: [PATCH 323/656] Update matplotlib-box-plots.md --- contrib/plotting-visualization/matplotlib-box-plots.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/contrib/plotting-visualization/matplotlib-box-plots.md b/contrib/plotting-visualization/matplotlib-box-plots.md index 4d762430..1780cab2 100644 --- a/contrib/plotting-visualization/matplotlib-box-plots.md +++ b/contrib/plotting-visualization/matplotlib-box-plots.md @@ -134,8 +134,13 @@ class_B_scores = [70, 75, 80, 85, 90] class_C_scores = [65, 70, 75, 80, 85] ### Creating a grouped box plot + plt.boxplot([class_A_scores, class_B_scores, class_C_scores], labels=['Class A', 'Class B', 'Class C']) + plt.title('Exam Scores by Class') + plt.xlabel('Classes') + plt.ylabel('Scores') + plt.show() From 208ff1ea06192dc51ebc45f1e32b29a185d4b561 Mon Sep 17 00:00:00 2001 From: Vrisha Shah <74671946+Vrisha213@users.noreply.github.com> Date: Fri, 31 May 2024 08:13:42 +0530 Subject: [PATCH 324/656] Delete contrib/plotting-visualization/matplotlib-box-plots.md --- .../matplotlib-box-plots.md | 146 ------------------ 1 file changed, 146 deletions(-) delete mode 100644 contrib/plotting-visualization/matplotlib-box-plots.md diff --git a/contrib/plotting-visualization/matplotlib-box-plots.md b/contrib/plotting-visualization/matplotlib-box-plots.md deleted file mode 100644 index 1780cab2..00000000 --- a/contrib/plotting-visualization/matplotlib-box-plots.md +++ /dev/null @@ -1,146 +0,0 @@ -# Box Plot - -A box plot represents the distribution of a dataset in a graph. It displays the summary statistics of a dataset, including the minimum, first quartile (Q1), median (Q2), third quartile (Q3), and maximum. The box represents the interquartile range (IQR) between the first and third quartiles, while whiskers extend from the box to the minimum and maximum values. Outliers, if present, may be displayed as individual points beyond the whiskers. - -For example - Imagine you have the exam scores of students from three classes. A box plot is a way to show how these scores are spread out. - -## Key Ranges in Data Distribution - -The data can be distributed between five key ranges, which are as follows - -1. Minimum: Q1-1.5*IQR -2. 1st quartile (Q1): 25th percentile -3. Median: 50th percentile -4. 3rd quartile(Q3): 75th percentile -5. Maximum: Q3+1.5*IQR - -## Purpose of Box Plots - -We can create the box plot of the data to determine the following- -1. The number of outliers in a dataset -2. Is the data skewed or not (skewness is a measure of asymmetry of the distribution) -3. The range of the data - -## Creating Box Plots using Matplotlib - -By using inbuilt funtion boxplot() of pyplot module of matplotlib - - -Syntax - matplotlib.pyplot.boxplot(data,notch=none,vert=none,patch_artist,widths=none) - -1. data: The data should be an array or sequence of arrays which will be plotted. -2. notch: This parameter accepts only Boolean values, either true or false. -3. vert: This attribute accepts a Boolean value. If it is set to true, then the graph will be vertical. Otherwise, it will be horizontal. -4. position: It accepts the array of integers which defines the position of the box. -5. widths: It accepts the array of integers which defines the width of the box. -6. patch_artist: this parameter accepts Boolean values, either true or false, and this is an optional parameter. -7. labels: This accepts the strings which define the labels for each data point -8. meanline: It accepts a boolean value, and it is optional. -9. order: It sets the order of the boxplot. -10. bootstrap: It accepts the integer value, which specifies the range of the notched boxplot. - -## Implementation of Box Plot in Python - -### Import libraries - -import matplotlib.pyplot as plt - -import numpy as np - -### Creating dataset - -np.random.seed(10) - -data = np.random.normal(100, 20, 200) - -fig = plt.figure(figsize =(10, 7)) - -### Creating plot - -plt.boxplot(data) - -### show plot - -plt.show() - -### Implementation of Multiple Box Plot in Python - -import matplotlib.pyplot as plt - -import numpy as np - -np.random.seed(10) - -dataSet1 = np.random.normal(100, 10, 220) - -dataSet2 = np.random.normal(80, 20, 200) - -dataSet3 = np.random.normal(60, 35, 220) - -dataSet4 = np.random.normal(50, 40, 200) - -dataSet = [dataSet1, dataSet2, dataSet3, dataSet4] - -figure = plt.figure(figsize =(10, 7)) - -ax = figure.add_axes([0, 0, 1, 1]) - -bp = ax.boxplot(dataSet) - -plt.show() - -### Implementation of Box Plot with Outliers (visual representation of the sales distribution for each product, and the outliers highlight months with exceptionally high or low sales) - -import matplotlib.pyplot as plt - -import numpy as np - -### Data for monthly sales - -product_A_sales = [100, 110, 95, 105, 115, 90, 120, 130, 80, 125, 150, 200] - -product_B_sales = [90, 105, 100, 98, 102, 105, 110, 95, 112, 88, 115, 250] - -product_C_sales = [80, 85, 90, 78, 82, 85, 88, 92, 75, 85, 200, 95] - -### Introducing outliers - -product_A_sales.extend([300, 80]) - -product_B_sales.extend([50, 300]) - -product_C_sales.extend([70, 250]) - -### Creating a box plot with outliers - -plt.boxplot([product_A_sales, product_B_sales, product_C_sales], sym='o') - -plt.title('Monthly Sales Performance by Product with Outliers') - -plt.xlabel('Products') - -plt.ylabel('Sales') - -plt.show() - -### Implementation of Grouped Box Plot (to compare the exam scores of students from three different classes (A, B, and C)) - -import matplotlib.pyplot as plt - -import numpy as np - -class_A_scores = [75, 80, 85, 90, 95] - -class_B_scores = [70, 75, 80, 85, 90] - -class_C_scores = [65, 70, 75, 80, 85] - -### Creating a grouped box plot - -plt.boxplot([class_A_scores, class_B_scores, class_C_scores], labels=['Class A', 'Class B', 'Class C']) - -plt.title('Exam Scores by Class') - -plt.xlabel('Classes') - -plt.ylabel('Scores') - -plt.show() From dd24cd3a2e82ef40f55ead219868947afdfc12c6 Mon Sep 17 00:00:00 2001 From: Vrisha Shah <74671946+Vrisha213@users.noreply.github.com> Date: Fri, 31 May 2024 08:14:00 +0530 Subject: [PATCH 325/656] Update index.md --- contrib/plotting-visualization/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index a8416a85..92ec1064 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -3,4 +3,4 @@ - [Installing Matplotlib](matplotlib-installation.md) - [Bar Plots in Matplotlib](matplotlib-bar-plots.md) - [Pie Charts in Matplotlib](matplotlib-pie-charts.md) -- [Box Plots in Matplotlib](matplotlib-box-plots.md) + From bdbe355c485de10dfea943108434b756e624a889 Mon Sep 17 00:00:00 2001 From: Soubeer Koley Date: Fri, 31 May 2024 08:23:22 +0530 Subject: [PATCH 326/656] updated index --- contrib/machine-learning/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 94ca1e2f..073bca93 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -9,3 +9,4 @@ - [TensorFlow.md](tensorFlow.md) - [PyTorch.md](pytorch.md) - [Types of optimizers](Types_of_optimizers.md) +- [Random Forest](Random_Forest.md) \ No newline at end of file From 4888094a059aad26f1eba062b4765b70b9b19ee8 Mon Sep 17 00:00:00 2001 From: saiumasankar Date: Fri, 31 May 2024 09:26:56 +0530 Subject: [PATCH 327/656] Create Trie.md --- contrib/ds-algorithms/Trie.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 contrib/ds-algorithms/Trie.md diff --git a/contrib/ds-algorithms/Trie.md b/contrib/ds-algorithms/Trie.md new file mode 100644 index 00000000..e69de29b From 3f51f48ee55690c7f64e591a729d9cb034264ca4 Mon Sep 17 00:00:00 2001 From: SAM <8dmasters@gmail.com> Date: Fri, 31 May 2024 10:00:10 +0530 Subject: [PATCH 328/656] Create Transformers.md Added a empty .md file to be served as a guide for Transformers --- contrib/machine-learning/Transformers.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 contrib/machine-learning/Transformers.md diff --git a/contrib/machine-learning/Transformers.md b/contrib/machine-learning/Transformers.md new file mode 100644 index 00000000..e69de29b From cd74a77397850249e8910638f41149a86476f37b Mon Sep 17 00:00:00 2001 From: Ritesh Date: Fri, 31 May 2024 10:17:29 +0530 Subject: [PATCH 329/656] Create clustering.md --- contrib/machine-learning/clustering.md | 115 +++++++++++++++++++++++++ 1 file changed, 115 insertions(+) create mode 100644 contrib/machine-learning/clustering.md diff --git a/contrib/machine-learning/clustering.md b/contrib/machine-learning/clustering.md new file mode 100644 index 00000000..7d5577ca --- /dev/null +++ b/contrib/machine-learning/clustering.md @@ -0,0 +1,115 @@ +# Clustering + +Clustering is an unsupervised machine learning technique that groups a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups (clusters). This README provides an overview of clustering, including its fundamental concepts, types, algorithms, and how to implement it using Python. + +## Table of Contents + +1. [Introduction](#introduction) +2. [Concepts](#concepts) +3. [Types of Clustering](#types-of-clustering) +4. [Clustering Algorithms](#clustering-algorithms) +5. [Implementation](#implementation) + - [Using Scikit-learn](#using-scikit-learn) + - [Code Example](#code-example) +6. [Evaluation Metrics](#evaluation-metrics) +7. [Conclusion](#conclusion) +8. [References](#references) + +## Introduction + +Clustering is a technique used to find inherent groupings within data without pre-labeled targets. It is widely used in exploratory data analysis, pattern recognition, image analysis, information retrieval, and bioinformatics. + +## Concepts + +### Centroid + +A centroid is the center of a cluster. In the k-means clustering algorithm, for example, each cluster is represented by its centroid, which is the mean of all the data points in the cluster. + +### Distance Measure + +Distance measures are used to quantify the similarity or dissimilarity between data points. Common distance measures include Euclidean distance, Manhattan distance, and cosine similarity. + +### Inertia + +Inertia is a metric used to assess the quality of the clusters formed. It is the sum of squared distances of samples to their nearest cluster center. + +## Types of Clustering + +1. **Hard Clustering**: Each data point either belongs to a cluster completely or not at all. +2. **Soft Clustering (Fuzzy Clustering)**: Each data point can belong to multiple clusters with varying degrees of membership. + +## Clustering Algorithms + +### K-Means Clustering + +K-Means is a popular clustering algorithm that partitions the data into k clusters, where each data point belongs to the cluster with the nearest mean. The algorithm follows these steps: +1. Initialize k centroids randomly. +2. Assign each data point to the nearest centroid. +3. Recalculate the centroids as the mean of all data points assigned to each cluster. +4. Repeat steps 2 and 3 until convergence. + +### Hierarchical Clustering + +Hierarchical clustering builds a tree of clusters. There are two types: +- **Agglomerative (bottom-up)**: Starts with each data point as a separate cluster and merges the closest pairs of clusters iteratively. +- **Divisive (top-down)**: Starts with all data points in one cluster and splits the cluster iteratively into smaller clusters. + +### DBSCAN (Density-Based Spatial Clustering of Applications with Noise) + +DBSCAN groups together points that are close to each other based on a distance measurement and a minimum number of points. It can find arbitrarily shaped clusters and is robust to noise. + +## Implementation + +### Using Scikit-learn + +Scikit-learn is a popular machine learning library in Python that provides tools for clustering. + +### Code Example + +```python +import numpy as np +import pandas as pd +from sklearn.cluster import KMeans +from sklearn.preprocessing import StandardScaler +from sklearn.metrics import silhouette_score + +# Load dataset +data = pd.read_csv('path/to/your/dataset.csv') + +# Preprocess the data +scaler = StandardScaler() +data_scaled = scaler.fit_transform(data) + +# Initialize and fit KMeans model +kmeans = KMeans(n_clusters=3, random_state=42) +kmeans.fit(data_scaled) + +# Get cluster labels +labels = kmeans.labels_ + +# Calculate silhouette score +silhouette_avg = silhouette_score(data_scaled, labels) +print("Silhouette Score:", silhouette_avg) + +# Add cluster labels to the original data +data['Cluster'] = labels + +print(data.head()) +``` + +## Evaluation Metrics + +- **Silhouette Score**: Measures how similar a data point is to its own cluster compared to other clusters. +- **Inertia (Within-cluster Sum of Squares)**: Measures the compactness of the clusters. +- **Davies-Bouldin Index**: Measures the average similarity ratio of each cluster with the cluster that is most similar to it. +- **Dunn Index**: Ratio of the minimum inter-cluster distance to the maximum intra-cluster distance. + +## Conclusion + +Clustering is a powerful technique for discovering structure in data. Understanding different clustering algorithms and their evaluation metrics is crucial for selecting the appropriate method for a given problem. + +## References + +- [Scikit-learn Documentation](https://scikit-learn.org/stable/modules/clustering.html) +- [Wikipedia: Cluster Analysis](https://en.wikipedia.org/wiki/Cluster_analysis) +- [Towards Data Science: A Comprehensive Guide to Clustering](https://towardsdatascience.com/a-comprehensive-guide-to-clustering-9789897f8b88) From 90e238ebf593caf7cf99ed1d213823f5219a062f Mon Sep 17 00:00:00 2001 From: Ritesh Date: Fri, 31 May 2024 10:18:24 +0530 Subject: [PATCH 330/656] Update index.md --- contrib/machine-learning/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 46100dfb..1e0004d3 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -10,3 +10,4 @@ - [PyTorch.md](pytorch.md) - [Types of optimizers](Types_of_optimizers.md) - [Logistic Regression](logistic-regression.md) +- [Clustering](clustering.md) From 97f852c99616cab741e8c06469f239bd34b74dd6 Mon Sep 17 00:00:00 2001 From: rohit Date: Fri, 31 May 2024 10:26:09 +0530 Subject: [PATCH 331/656] Added sklearn.md file --- contrib/machine-learning/index.md | 1 + contrib/machine-learning/sklearn.md | 144 ++++++++++++++++++++++++++++ 2 files changed, 145 insertions(+) create mode 100644 contrib/machine-learning/sklearn.md diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 46100dfb..68bd24d2 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -10,3 +10,4 @@ - [PyTorch.md](pytorch.md) - [Types of optimizers](Types_of_optimizers.md) - [Logistic Regression](logistic-regression.md) +- [sklearn.md](sklearn.md) diff --git a/contrib/machine-learning/sklearn.md b/contrib/machine-learning/sklearn.md new file mode 100644 index 00000000..8174d938 --- /dev/null +++ b/contrib/machine-learning/sklearn.md @@ -0,0 +1,144 @@ +# scikit-learn (sklearn) Python Library + +## Overview + +scikit-learn, also known as sklearn, is a popular open-source Python library that provides simple and efficient tools for data mining and data analysis. It is built on NumPy, SciPy, and matplotlib. The library is designed to interoperate with the Python numerical and scientific libraries. + +## Key Features + +- **Classification**: Identifying which category an object belongs to. Example algorithms include SVM, nearest neighbors, random forest. +- **Regression**: Predicting a continuous-valued attribute associated with an object. Example algorithms include support vector regression (SVR), ridge regression, Lasso. +- **Clustering**: Automatic grouping of similar objects into sets. Example algorithms include k-means, spectral clustering, mean-shift. +- **Dimensionality Reduction**: Reducing the number of random variables to consider. Example algorithms include PCA, feature selection, non-negative matrix factorization. +- **Model Selection**: Comparing, validating, and choosing parameters and models. Example methods include grid search, cross-validation, metrics. +- **Preprocessing**: Feature extraction and normalization. + +## When to Use scikit-learn + +- **Use scikit-learn if**: + - You are working on machine learning tasks such as classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. + - You need an easy-to-use, well-documented library. + - You require tools that are compatible with NumPy and SciPy. + +- **Do not use scikit-learn if**: + - You need to perform deep learning tasks. In such cases, consider using TensorFlow or PyTorch. + - You need out-of-the-box support for large-scale data. scikit-learn is designed to work with in-memory data, so for very large datasets, you might want to consider libraries like Dask-ML. + +## Installation + +You can install scikit-learn using pip: + +```bash +pip install scikit-learn +``` + +Or via conda: + +```bash +conda install scikit-learn +``` + +## Basic Usage with Code Snippets + +### Importing the Library + +```python +import numpy as np +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import accuracy_score +``` + +### Loading Data + +For illustration, let's create a simple synthetic dataset: + +```python +from sklearn.datasets import make_classification + +X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42) +``` + +### Splitting Data + +Split the dataset into training and testing sets: + +```python +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) +``` + +### Preprocessing + +Standardizing the features: + +```python +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_test = scaler.transform(X_test) +``` + +### Training a Model + +Train a Logistic Regression model: + +```python +model = LogisticRegression() +model.fit(X_train, y_train) +``` + +### Making Predictions + +Make predictions on the test set: + +```python +y_pred = model.predict(X_test) +``` + +### Evaluating the Model + +Evaluate the accuracy of the model: + +```python +accuracy = accuracy_score(y_test, y_pred) +print(f"Accuracy: {accuracy * 100:.2f}%") +``` + +### Putting it All Together + +Here is a complete example from data loading to model evaluation: + +```python +import numpy as np +from sklearn.datasets import make_classification +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import accuracy_score + +# Load data +X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42) + +# Split data +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) + +# Preprocess data +scaler = StandardScaler() +X_train = scaler.fit_transform(X_train) +X_test = scaler.transform(X_test) + +# Train model +model = LogisticRegression() +model.fit(X_train, y_train) + +# Make predictions +y_pred = model.predict(X_test) + +# Evaluate model +accuracy = accuracy_score(y_test, y_pred) +print(f"Accuracy: {accuracy * 100:.2f}%") +``` + +## Conclusion + +scikit-learn is a powerful and versatile library that can be used for a wide range of machine learning tasks. It is particularly well-suited for beginners due to its easy-to-use interface and extensive documentation. Whether you are working on a simple classification task or a more complex clustering problem, scikit-learn provides the tools you need to build and evaluate your models effectively. \ No newline at end of file From b72cf4ad5a778264701ce634df9d7a6c6f5c5d49 Mon Sep 17 00:00:00 2001 From: saiumasankar Date: Fri, 31 May 2024 11:12:37 +0530 Subject: [PATCH 332/656] adding Trie Data structure --- contrib/ds-algorithms/Trie.md | 152 ++++++++++++++++++++++++++++++++++ 1 file changed, 152 insertions(+) diff --git a/contrib/ds-algorithms/Trie.md b/contrib/ds-algorithms/Trie.md index e69de29b..43f04157 100644 --- a/contrib/ds-algorithms/Trie.md +++ b/contrib/ds-algorithms/Trie.md @@ -0,0 +1,152 @@ +# Trie + +A Trie is a tree-like data structure used for storing a dynamic set of strings where the keys are usually strings. It is also known as prefix tree or digital tree. + +>Trie is a type of search tree, where each node represents a single character of a string. + +>Nodes are linked in such a way that they form a tree, where each path from the root to a leaf node represents a unique string stored in the Trie. + +## Characteristics of Trie +- **Prefix Matching**: Tries are particularly useful for prefix matching operations. Any node in the Trie represents a common prefix of all strings below it. +- **Space Efficiency**: Tries can be more space-efficient than other data structures like hash tables for storing large sets of strings with common prefixes. +- **Time Complexity**: Insertion, deletion, and search operations in a Trie have a time complexity of +𝑂(𝑚), where m is the length of the string. This makes Tries very efficient for these operations. + +## Structure of Trie + +Trie mainly consists of three parts: +- **Root**: The root of a Trie is an empty node that does not contain any character. +- **Edges**: Each edge in the Trie represents a character in the alphabet of the stored strings. +- **Nodes**: Each node contains a character and possibly additional information, such as a boolean flag indicating if the node represents the end of a valid string. + +To implement the nodes of trie. We use Classes in Python. Each node is an object of the Node Class. + +Node Class have mainly two components +- *Array of size 26*: It is used to represent the 26 alphabets. Initially all are None. While inserting the words, then array will be filled with object of child nodes. +- *End of word*: It is used to represent the end of word while inserting. + +Code Block of Node Class : + +``` +class Node: + def __init__(self): + self.alphabets = [None] * 26 + self.end_of_word = 0 +``` + +Now we need to implement Trie. We create another class named Trie with some methods like Insertion, Searching and Deletion. + +**Initialization:** In this, we initializes the Trie with a `root` node. + +Code Implementation of Initialization: + +``` +class Trie: + def __init__(self): + self.root = Node() +``` + +## Operations on Trie + +1. **Insertion**: Inserts the word into the Trie. This method takes `word` as parameter. For each character in the word, it checks if there is a corresponding child node. If not, it creates a new `Node`. After processing all the characters in word, it increments the `end_of_word` value of the last node. + +Code Implementation of Insertion: +``` +def insert(self, word): + node = self.root + for char in word: + index = ord(char) - ord('a') + if not node.alphabets[index]: + node.alphabets[index] = Node() + node = node.alphabets[index] + node.end_of_word += 1 +``` + +2. **Searching**: Search the `word` in trie. Searching process starts from the `root` node. Each character of the `word` is processed. After traversing the whole word in trie, it return the count of words. + +There are two cases in Searching: +- *Word Not found*: It happens when the word we search not present in the trie. This case will occur, if the value of `alphabets` array at that character is `None` or if the value of `end_of_word` of the node, reached after traversing the whole word is `0`. +- *Word found*: It happens when the search word is present in the Trie. This case will occur, when the `end_of_word` value is greater than `0` of the node after traversing the whole word. + +Code Implementation of Searching: +``` + def Search(self, word): + node = self.root + for char in word: + index = ord(char) - ord('a') + if not node.alphabets[index]: + return 0 + node = node.alphabets[index] + return node.end_of_word +``` + +3. **Deletion**: To delete a string, follow the path of the string. If the end node is reached and `end_of_word` is greater than `0` then decrement the value. + +Code Implementation of Deletion: + +``` +def delete(self, word): + node = self.root + for char in word: + index = ord(char) - ord('a') + node = node.alphabets[index] + if node.end_of_word: + node.end_of_word-=1 +``` + +Python Code to implement Trie: + +``` +class Node: + def __init__(self): + self.alphabets = [None] * 26 + self.end_of_word = 0 + +class Trie: + def __init__(self): + self.root = Node() + + def insert(self, word): + node = self.root + for char in word: + index = ord(char) - ord('a') + if not node.alphabets[index]: + node.alphabets[index] = Node() + node = node.alphabets[index] + node.end_of_word += 1 + + def Search(self, word): + node = self.root + for char in word: + index = ord(char) - ord('a') + if not node.alphabets[index]: + return 0 + node = node.alphabets[index] + return node.end_of_word + + def delete(self, word): + node = self.root + for char in word: + index = ord(char) - ord('a') + node = node.alphabets[index] + if node.end_of_word: + node.end_of_word-=1 + +if __name__ == "__main__": + trie = Trie() + + word1 = "apple" + word2 = "app" + word3 = "bat" + + trie.insert(word1) + trie.insert(word2) + trie.insert(word3) + + print(trie.Search(word1)) + print(trie.Search(word2)) + print(trie.Search(word3)) + + trie.delete(word2) + print(trie.Search(word2)) +``` From 0a6d9cd1bfedd86cd85a0414e92f43014e9e8c19 Mon Sep 17 00:00:00 2001 From: saiumasankar Date: Fri, 31 May 2024 11:25:29 +0530 Subject: [PATCH 333/656] update index.md --- contrib/ds-algorithms/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 31cff39b..653cc986 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -10,3 +10,4 @@ - [Greedy Algorithms](greedy-algorithms.md) - [Dynamic Programming](dynamic-programming.md) - [Linked list](linked-list.md) +- [Trie](Trie.md) From d00f146401afba7514f183291d40a62de92866ba Mon Sep 17 00:00:00 2001 From: Yogesh Vishwakarma Date: Fri, 31 May 2024 11:34:39 +0530 Subject: [PATCH 334/656] created Sliding_Window.md and updated index.md --- contrib/ds-algorithms/Sliding_Window.md | 249 ++++++++++++++++++++++++ contrib/ds-algorithms/index.md | 1 + 2 files changed, 250 insertions(+) create mode 100644 contrib/ds-algorithms/Sliding_Window.md diff --git a/contrib/ds-algorithms/Sliding_Window.md b/contrib/ds-algorithms/Sliding_Window.md new file mode 100644 index 00000000..72aa1915 --- /dev/null +++ b/contrib/ds-algorithms/Sliding_Window.md @@ -0,0 +1,249 @@ +# Sliding Window Technique + +The sliding window technique is a fundamental approach used to solve problems involving arrays, lists, or sequences. It's particularly useful when you need to calculate something over a subarray or sublist of fixed size that slides over the entire array. + +In easy words, It is the transformation of the nested loops into the single loop +## Concept + +The sliding window technique involves creating a window (a subarray or sublist) that moves or "slides" across the entire array. This window can either be fixed in size or dynamically resized. By maintaining and updating this window as it moves, you can optimize certain computations, reducing time complexity. + +## Types of Sliding Windows + +1. **Fixed Size Window**: The window size remains constant as it slides from the start to the end of the array. +2. **Variable Size Window**: The window size can change based on certain conditions, such as the sum of elements within the window meeting a specified target. + +## Steps to Implement a Sliding Window + +1. **Initialize the Window**: Set the initial position of the window and any required variables (like sum, count, etc.). +2. **Expand the Window**: Add the next element to the window and update the relevant variables. +3. **Shrink the Window**: If needed, remove elements from the start of the window and update the variables. +4. **Slide the Window**: Move the window one position to the right by including the next element and possibly excluding the first element. +5. **Repeat**: Continue expanding, shrinking, and sliding the window until you reach the end of the array. + +## Example Problems + +### 1. Maximum Sum Subarray of Fixed Size K + +Given an array of integers and an integer k, find the maximum sum of a subarray of size k. + +**Steps:** + +1. Initialize the sum of the first k elements. +2. Slide the window from the start of the array to the end, updating the sum by subtracting the element that is left behind and adding the new element. +3. Track the maximum sum encountered. + +**Python Code:** + +```python +def max_sum_subarray(arr, k): + n = len(arr) + if n < k: + return None + + # Compute the sum of the first window + window_sum = sum(arr[:k]) + max_sum = window_sum + + # Slide the window from start to end + for i in range(n - k): + window_sum = window_sum - arr[i] + arr[i + k] + max_sum = max(max_sum, window_sum) + + return max_sum + +# Example usage: +arr = [1, 3, 2, 5, 1, 1, 6, 2, 8, 5] +k = 3 +print(max_sum_subarray(arr, k)) # Output: 16 +``` + +### 2. Longest Substring Without Repeating Characters + +Given a string, find the length of the longest substring without repeating characters. + +**Steps:** + +1. Use two pointers to represent the current window. +2. Use a set to track characters in the current window. +3. Expand the window by moving the right pointer. +4. If a duplicate character is found, shrink the window by moving the left pointer until the duplicate is removed. + +**Python Code:** + +```python +def longest_unique_substring(s): + n = len(s) + char_set = set() + left = 0 + max_length = 0 + + for right in range(n): + while s[right] in char_set: + char_set.remove(s[left]) + left += 1 + char_set.add(s[right]) + max_length = max(max_length, right - left + 1) + + return max_length + +# Example usage: +s = "abcabcbb" +print(longest_unique_substring(s)) # Output: 3 +``` +## 3. Minimum Size Subarray Sum + +Given an array of positive integers and a positive integer `s`, find the minimal length of a contiguous subarray of which the sum is at least `s`. If there isn't one, return 0 instead. + +### Steps: +1. Use two pointers, `left` and `right`, to define the current window. +2. Expand the window by moving `right` and adding `arr[right]` to `current_sum`. +3. If `current_sum` is greater than or equal to `s`, update `min_length` and shrink the window from the left by moving `left` and subtracting `arr[left]` from `current_sum`. +4. Repeat until `right` has traversed the array. + +### Python Code: +```python +def min_subarray_len(s, arr): + n = len(arr) + left = 0 + current_sum = 0 + min_length = float('inf') + + for right in range(n): + current_sum += arr[right] + + while current_sum >= s: + min_length = min(min_length, right - left + 1) + current_sum -= arr[left] + left += 1 + + return min_length if min_length != float('inf') else 0 + +# Example usage: +arr = [2, 3, 1, 2, 4, 3] +s = 7 +print(min_subarray_len(s, arr)) # Output: 2 (subarray [4, 3]) +``` + +## 4. Longest Substring with At Most K Distinct Characters + +Given a string `s` and an integer `k`, find the length of the longest substring that contains at most `k` distinct characters. + +### Steps: +1. Use two pointers, `left` and `right`, to define the current window. +2. Use a dictionary `char_count` to count characters in the window. +3. Expand the window by moving `right` and updating `char_count`. +4. If `char_count` has more than `k` distinct characters, shrink the window from the left by moving `left` and updating `char_count`. +5. Keep track of the maximum length of the window with at most `k` distinct characters. + +### Python Code: +```python +def longest_substring_k_distinct(s, k): + n = len(s) + char_count = {} + left = 0 + max_length = 0 + + for right in range(n): + char_count[s[right]] = char_count.get(s[right], 0) + 1 + + while len(char_count) > k: + char_count[s[left]] -= 1 + if char_count[s[left]] == 0: + del char_count[s[left]] + left += 1 + + max_length = max(max_length, right - left + 1) + + return max_length + +# Example usage: +s = "eceba" +k = 2 +print(longest_substring_k_distinct(s, k)) # Output: 3 (substring "ece") +``` + +## 5. Maximum Number of Vowels in a Substring of Given Length + +Given a string `s` and an integer `k`, return the maximum number of vowel letters in any substring of `s` with length `k`. + +### Steps: +1. Use a sliding window of size `k`. +2. Keep track of the number of vowels in the current window. +3. Expand the window by adding the next character and update the count if it's a vowel. +4. If the window size exceeds `k`, remove the leftmost character and update the count if it's a vowel. +5. Track the maximum number of vowels found in any window of size `k`. + +### Python Code: +```python +def max_vowels(s, k): + vowels = set('aeiou') + max_vowel_count = 0 + current_vowel_count = 0 + + for i in range(len(s)): + if s[i] in vowels: + current_vowel_count += 1 + if i >= k: + if s[i - k] in vowels: + current_vowel_count -= 1 + max_vowel_count = max(max_vowel_count, current_vowel_count) + + return max_vowel_count + +# Example usage: +s = "abciiidef" +k = 3 +print(max_vowels(s, k)) # Output: 3 (substring "iii") +``` + +## 6. Subarray Product Less Than K + +Given an array of positive integers `nums` and an integer `k`, return the number of contiguous subarrays where the product of all the elements in the subarray is less than `k`. + +### Steps: +1. Use two pointers, `left` and `right`, to define the current window. +2. Expand the window by moving `right` and multiplying `product` by `nums[right]`. +3. If `product` is greater than or equal to `k`, shrink the window from the left by moving `left` and dividing `product` by `nums[left]`. +4. For each position of `right`, the number of valid subarray ending at `right` is `right - left + 1`. +5. Sum these counts to get the total number of subarray with product less than `k`. + +### Python Code: +```python +def num_subarray_product_less_than_k(nums, k): + if k <= 1: + return 0 + + product = 1 + left = 0 + count = 0 + + for right in range(len(nums)): + product *= nums[right] + + while product >= k: + product /= nums[left] + left += 1 + + count += right - left + 1 + + return count + +# Example usage: +nums = [10, 5, 2, 6] +k = 100 +print(num_subarray_product_less_than_k(nums, k)) # Output: 8 +``` + +## Advantages + +- **Efficiency**: Reduces the time complexity from O(n^2) to O(n) for many problems. +- **Simplicity**: Provides a straightforward way to manage subarrays/substrings with overlapping elements. + +## Applications + +- Finding the maximum or minimum sum of subarrays of fixed size. +- Detecting unique elements in a sequence. +- Solving problems related to dynamic programming with fixed constraints. +- Efficiently managing and processing streaming data or real-time analytics. + +By using the sliding window technique, you can tackle a wide range of problems in a more efficient manner. diff --git a/contrib/ds-algorithms/index.md b/contrib/ds-algorithms/index.md index 31cff39b..d3c3c271 100644 --- a/contrib/ds-algorithms/index.md +++ b/contrib/ds-algorithms/index.md @@ -10,3 +10,4 @@ - [Greedy Algorithms](greedy-algorithms.md) - [Dynamic Programming](dynamic-programming.md) - [Linked list](linked-list.md) +- [Sliding Window Technique](Sliding_Window.md) From 56e972133ff33ca0532c6d0637fb465425b41c09 Mon Sep 17 00:00:00 2001 From: manishh12 Date: Fri, 31 May 2024 12:02:09 +0530 Subject: [PATCH 335/656] added types of cost functions issue#625 --- .../Types_of_Cost_Functions.md | 227 ++++++++++++++++++ contrib/machine-learning/index.md | 1 + 2 files changed, 228 insertions(+) create mode 100644 contrib/machine-learning/Types_of_Cost_Functions.md diff --git a/contrib/machine-learning/Types_of_Cost_Functions.md b/contrib/machine-learning/Types_of_Cost_Functions.md new file mode 100644 index 00000000..547a05e3 --- /dev/null +++ b/contrib/machine-learning/Types_of_Cost_Functions.md @@ -0,0 +1,227 @@ + +# Cost Functions in Machine Learning + +Cost functions, also known as loss functions, play a crucial role in training machine learning models. They measure how well the model performs on the training data by quantifying the difference between predicted and actual values. Different types of cost functions are used depending on the problem domain and the nature of the data. + +## Types of Cost Functions + +### 1. Mean Squared Error (MSE) + +**Explanation:** +MSE is one of the most commonly used cost functions, particularly in regression problems. It calculates the average squared difference between the predicted and actual values. + +**Mathematical Formulation:** +The MSE is defined as: +$$ MSE = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 $$ +Where: +- \( n \) is the number of samples. +- \( y_i \) is the actual value. +- \( y^i\) is the predicted value. + +**Advantages:** +- Sensitive to large errors due to squaring. +- Differentiable and convex, facilitating optimization. + +**Disadvantages:** +- Sensitive to outliers, as the squared term amplifies their impact. + +**Python Implementation:** +```python +import numpy as np + +def mean_squared_error(y_true, y_pred): + n = len(y_true) + return np.mean((y_true - y_pred) ** 2) +``` + +### 2. Mean Absolute Error (MAE) + +**Explanation:** +MAE is another commonly used cost function for regression tasks. It measures the average absolute difference between predicted and actual values. + +**Mathematical Formulation:** +The MAE is defined as: +$$ MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i| $$ +Where: +- \( n \) is the number of samples. +- \( y_i \) is the actual value. +- \( y^i\) is the predicted value. + +**Advantages:** +- Less sensitive to outliers compared to MSE. +- Provides a linear error term, which can be easier to interpret. + + +**Disadvantages:** +- Not differentiable at zero, which can complicate optimization. + +**Python Implementation:** +```python +import numpy as np + +def mean_absolute_error(y_true, y_pred): + n = len(y_true) + return np.mean(np.abs(y_true - y_pred)) +``` + +### 3. Cross-Entropy Loss (Binary) + +**Explanation:** +Cross-entropy loss is commonly used in binary classification problems. It measures the dissimilarity between the true and predicted probability distributions. + +**Mathematical Formulation:** +For binary classification, the cross-entropy loss is defined as: +$$ \text{Cross-Entropy} = -\frac{1}{n} \sum_{i=1}^{n} [y_i \log(\hat{y}_i) + (1 - y_i) \log(1 - \hat{y}_i)] $$ +Where: +- \( n \) is the number of samples. +- \( y_i \) is the actual class label (0 or 1). +- \( y^i\) is the predicted probability of the positive class. + + +**Advantages:** +- Penalizes confident wrong predictions heavily. +- Suitable for probabilistic outputs. + +**Disadvantages:** +- Sensitive to class imbalance. + +**Python Implementation:** +```python +import numpy as np + +def binary_cross_entropy(y_true, y_pred): + n = len(y_true) + return -np.mean(y_true * np.log(y_pred) + (1 - y_true) * np.log(1 - y_pred)) +``` + +### 4. Cross-Entropy Loss (Multiclass) + +**Explanation:** +For multiclass classification problems, the cross-entropy loss is adapted to handle multiple classes. + +**Mathematical Formulation:** +The multiclass cross-entropy loss is defined as: +$$ \text{Cross-Entropy} = -\frac{1}{n} \sum_{i=1}^{n} \sum_{c=1}^{C} y_{i,c} \log(\hat{y}_{i,c}) $$ +Where: +- \( n \) is the number of samples. +- \( C \) is the number of classes. +- \( y_{i,c} \) is the indicator function for the true class of sample \( i \). + +- (y^i,c) is the predicted probability of sample \( i \) belonging to class \( c \). + +**Advantages:** +- Handles multiple classes effectively. +- Encourages the model to assign high probabilities to the correct classes. + +**Disadvantages:** +- Requires one-hot encoding for class labels, which can increase computational complexity. + +**Python Implementation:** +```python +import numpy as np + +def categorical_cross_entropy(y_true, y_pred): + n = len(y_true) + return -np.mean(np.sum(y_true * np.log(y_pred), axis=1)) +``` + +### 5. Hinge Loss (SVM) + +**Explanation:** +Hinge loss is commonly used in support vector machines (SVMs) for binary classification tasks. It penalizes misclassifications by a linear margin. + +**Mathematical Formulation:** +For binary classification, the hinge loss is defined as: +$$ \text{Hinge Loss} = \frac{1}{n} \sum_{i=1}^{n} \max(0, 1 - y_i \cdot \hat{y}_i) $$ +Where: +- \( n \) is the number of samples. +- \( y_i \) is the actual class label (-1 or 1). +- \( \hat{y}_i \) is the predicted score for sample \( i \). + +**Advantages:** +- Encourages margin maximization in SVMs. +- Robust to outliers due to the linear penalty. + +**Disadvantages:** +- Not differentiable at the margin, which can complicate optimization. + +**Python Implementation:** +```python +import numpy as np + +def hinge_loss(y_true, y_pred): + n = len(y_true) + loss = np.maximum(0, 1 - y_true * y_pred) + return np.mean(loss) +``` + +### 6. Huber Loss + +**Explanation:** +Huber loss is a combination of MSE and MAE, providing a compromise between the two. It is less sensitive to outliers than MSE and provides a smooth transition to MAE for large errors. + +**Mathematical Formulation:** + +The Huber loss is defined as: + + +$$ +\text{Huber Loss} = \frac{1}{n} \sum_{i=1}^{n} \left\{ +\begin{array}{ll} +\frac{1}{2} (y_i - \hat{y}_i)^2 & \text{if } |y_i - \hat{y}_i| \leq \delta \\ +\delta(|y_i - \hat{y}_i| - \frac{1}{2} \delta) & \text{otherwise} +\end{array} +\right. +$$ +Where: +- \( n \) is the number of samples. +- \( \delta \) is a threshold parameter. + +**Advantages:** +- Provides a smooth loss function. +- Less sensitive to outliers than MSE. + +**Disadvantages:** +- Requires tuning of the threshold parameter. + +**Python Implementation:** +```python +import numpy as np + +def huber_loss(y_true, y_pred, delta): + error = y_true - y_pred + loss = np.where(np.abs(error) <= delta, 0.5 * error ** 2, delta * (np.abs(error) - 0.5 * delta)) + return np.mean(loss) +``` + +### 7. Log-Cosh Loss + +**Explanation:** +Log-Cosh loss is a smooth approximation of the MAE and is less sensitive to outliers than MSE. It provides a smooth transition from quadratic for small errors to linear for large errors. + +**Mathematical Formulation:** +The Log-Cosh loss is defined as: +$$ \text{Log-Cosh Loss} = \frac{1}{n} \sum_{i=1}^{n} \log(\cosh(y_i - \hat{y}_i)) $$ +Where: +- \( n \) is the number of samples. + +**Advantages:** +- Smooth and differentiable everywhere. +- Less sensitive to outliers. + +**Disadvantages:** +- Computationally more expensive than simple losses like MSE. + +**Python Implementation:** +```python +import numpy as np + +def logcosh_loss(y_true, y_pred): + error = y_true - y_pred + loss = np.log(np.cosh(error)) + return np.mean(loss) +``` + +These implementations provide various options for cost functions suitable for different machine learning tasks. Each function has its advantages and disadvantages, making them suitable for different scenarios and problem domains. + +--- \ No newline at end of file diff --git a/contrib/machine-learning/index.md b/contrib/machine-learning/index.md index 46100dfb..cfe9a67d 100644 --- a/contrib/machine-learning/index.md +++ b/contrib/machine-learning/index.md @@ -10,3 +10,4 @@ - [PyTorch.md](pytorch.md) - [Types of optimizers](Types_of_optimizers.md) - [Logistic Regression](logistic-regression.md) +-[Types_of_Cost_Functions](Types_of_Cost_Functions.md) From d23389a8ea17ca0d16b96b14b710ac41606e8383 Mon Sep 17 00:00:00 2001 From: Manish kumar gupta <97523900+manishh12@users.noreply.github.com> Date: Fri, 31 May 2024 12:07:34 +0530 Subject: [PATCH 336/656] Updated maths formulas --- .../Types_of_Cost_Functions.md | 35 ++++++++++++------- 1 file changed, 23 insertions(+), 12 deletions(-) diff --git a/contrib/machine-learning/Types_of_Cost_Functions.md b/contrib/machine-learning/Types_of_Cost_Functions.md index 547a05e3..f6507268 100644 --- a/contrib/machine-learning/Types_of_Cost_Functions.md +++ b/contrib/machine-learning/Types_of_Cost_Functions.md @@ -12,7 +12,7 @@ MSE is one of the most commonly used cost functions, particularly in regression **Mathematical Formulation:** The MSE is defined as: -$$ MSE = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 $$ +$$MSE = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$$ Where: - \( n \) is the number of samples. - \( y_i \) is the actual value. @@ -41,7 +41,7 @@ MAE is another commonly used cost function for regression tasks. It measures the **Mathematical Formulation:** The MAE is defined as: -$$ MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i| $$ +$$MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i|$$ Where: - \( n \) is the number of samples. - \( y_i \) is the actual value. @@ -70,8 +70,11 @@ def mean_absolute_error(y_true, y_pred): Cross-entropy loss is commonly used in binary classification problems. It measures the dissimilarity between the true and predicted probability distributions. **Mathematical Formulation:** + For binary classification, the cross-entropy loss is defined as: -$$ \text{Cross-Entropy} = -\frac{1}{n} \sum_{i=1}^{n} [y_i \log(\hat{y}_i) + (1 - y_i) \log(1 - \hat{y}_i)] $$ + +$$\text{Cross-Entropy} = -\frac{1}{n} \sum_{i=1}^{n} [y_i \log(\hat{y}_i) + (1 - y_i) \log(1 - \hat{y}_i)]$$ + Where: - \( n \) is the number of samples. - \( y_i \) is the actual class label (0 or 1). @@ -100,8 +103,11 @@ def binary_cross_entropy(y_true, y_pred): For multiclass classification problems, the cross-entropy loss is adapted to handle multiple classes. **Mathematical Formulation:** + The multiclass cross-entropy loss is defined as: -$$ \text{Cross-Entropy} = -\frac{1}{n} \sum_{i=1}^{n} \sum_{c=1}^{C} y_{i,c} \log(\hat{y}_{i,c}) $$ + +$$\text{Cross-Entropy} = -\frac{1}{n} \sum_{i=1}^{n} \sum_{c=1}^{C} y_{i,c} \log(\hat{y}_{i,c})$$ + Where: - \( n \) is the number of samples. - \( C \) is the number of classes. @@ -131,8 +137,11 @@ def categorical_cross_entropy(y_true, y_pred): Hinge loss is commonly used in support vector machines (SVMs) for binary classification tasks. It penalizes misclassifications by a linear margin. **Mathematical Formulation:** + For binary classification, the hinge loss is defined as: -$$ \text{Hinge Loss} = \frac{1}{n} \sum_{i=1}^{n} \max(0, 1 - y_i \cdot \hat{y}_i) $$ + +$$\text{Hinge Loss} = \frac{1}{n} \sum_{i=1}^{n} \max(0, 1 - y_i \cdot \hat{y}_i)$$ + Where: - \( n \) is the number of samples. - \( y_i \) is the actual class label (-1 or 1). @@ -165,17 +174,16 @@ Huber loss is a combination of MSE and MAE, providing a compromise between the t The Huber loss is defined as: -$$ -\text{Huber Loss} = \frac{1}{n} \sum_{i=1}^{n} \left\{ +$$\text{Huber Loss} = \frac{1}{n} \sum_{i=1}^{n} \left\{ \begin{array}{ll} \frac{1}{2} (y_i - \hat{y}_i)^2 & \text{if } |y_i - \hat{y}_i| \leq \delta \\ \delta(|y_i - \hat{y}_i| - \frac{1}{2} \delta) & \text{otherwise} \end{array} -\right. -$$ +\right.$$ + Where: - \( n \) is the number of samples. -- \( \delta \) is a threshold parameter. +- \(delta\) is a threshold parameter. **Advantages:** - Provides a smooth loss function. @@ -200,8 +208,11 @@ def huber_loss(y_true, y_pred, delta): Log-Cosh loss is a smooth approximation of the MAE and is less sensitive to outliers than MSE. It provides a smooth transition from quadratic for small errors to linear for large errors. **Mathematical Formulation:** + The Log-Cosh loss is defined as: -$$ \text{Log-Cosh Loss} = \frac{1}{n} \sum_{i=1}^{n} \log(\cosh(y_i - \hat{y}_i)) $$ + +$$\text{Log-Cosh Loss} = \frac{1}{n} \sum_{i=1}^{n} \log(\cosh(y_i - \hat{y}_i))$$ + Where: - \( n \) is the number of samples. @@ -224,4 +235,4 @@ def logcosh_loss(y_true, y_pred): These implementations provide various options for cost functions suitable for different machine learning tasks. 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Matplotlib along with NumPy can be considered as the open source equivalent of MATLAB. + +- Matplotlib has a procedural interface named the Pylab, which is designed to resemble MATLAB. However, it is completely independent of Matlab. + +## Starting with Matplotlib + +### 1.] Install and import the neccasary libraries - mayplotlib.pylplot +```python +pip install matplotlib +``` + +```python +import maptplotlib.pyplot as plt +import numpy as np +``` + + + +### 2.] Scatter plot +Scatter plot is a type of plot that uses the cartesian coordinates between x and y to describe the relation between them. It uses dots to represent relation between the data variables of the data set. + +```python +x = [5,4,5,8,9,8,6,7,3,2] +y = [9,1,7,3,5,7,6,1,2,8] + +plt.scatter(x,y, color = "red") + +plt.title("Scatter plot") +plt.xlabel("X values") +plt.ylabel("Y values") + +plt.tight_layout() +plt.show() +``` + +![img](scatterplot.png) + + + +### 3.] Bar plot +Bar plot is a type of plot that plots the frequency distrubution of the categorical variables. Each entity of the categoric variable is represented as a bar. The size of the bar represents its numeric value. + +```python +x = np.array(['A','B','C','D']) +y = np.array([42,50,15,35]) + +plt.bar(x,y,color = "red") + +plt.title("Bar plot") +plt.xlabel("X values") +plt.ylabel("Y values") + +plt.show() +``` +![img](barplot.png) + + +### 4.] Histogram +Histogram is the representation of frequency distribution of qualitative data. The height of each rectangle defines the amount, or how often that variable appears. + +```python +x = [9,1,7,3,5,7,6,1,2,8] + +plt.hist(x, color = "red", edgecolor= "white", bins =5) + +plt.title("Histogram") +plt.xlabel("X values") +plt.ylabel("Frequency Distribution") + +plt.show() +``` + +![img](histogram.png) + + + + + + diff --git a/contrib/plotting-visualization/scatterplot.png b/contrib/plotting-visualization/scatterplot.png new file mode 100644 index 0000000000000000000000000000000000000000..94c91484f54e9dc6e492862c036bab2a804add3b GIT binary patch literal 13739 zcmdUWXINBO+HGNM1K0*cQLt5z3@Rcy7!go%jtU}BI+Mxh45b{--1}I8+kZ`T1KW*)doQJc#GARaY{zxQl5`D9=hu z%tm}tjE6R!;uyAV+qO%t3*%VVo*pioN|L-lcKe+ukr2b(q=WP0beLGHhzmx~K9`q= zgoGsAe8CvvGCti`zVIYJ9d?l*1jF%LzUw@KOILcWPSzxvw}BP^_jR zCEQnrUkG+LZmh2k*a-c!5jw)akW^cHdw%@8N?>qc;173;jkV?OA&-@jq{4*`b9!dx zj9s*h$@r=6mEpJmS>woy#=a-%ym6CVDN1oSInJE9s-f>}{Iv5f+-4_qa%H;wfMbLB zVzNxoxxM@LwOHm;63X%#Wp+w`FK&-F>(?V_mv@^Uj;g 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zW#!}=kxV0L46GumIQJkZ7~(H`daj|{E`_dEt%Li%eOaqHOC59h@Y{<+fKw|IKJG-z zZRHgfN3fo)yH61v38G5~i#7o%KU-dEmKk3hO? zJcu{(U_X9?u`;9$LlmM*8ldM5IlG5IbOYwB-*!0+HI&DzCQ!Q2NIHcN4rs!%uam$B zE)4k|>r=;xX@X5%ui;L-WF$rl{;yvB*47txM?GRsyKxJeg(M>-e=haRmAn5238{EC From 57c369bd0ec3d02e283224f903e760abb368b51f Mon Sep 17 00:00:00 2001 From: Harshita Jain <122027571+Harshita297@users.noreply.github.com> Date: Fri, 31 May 2024 13:00:40 +0530 Subject: [PATCH 342/656] Update matplotlib.md --- contrib/plotting-visualization/matplotlib.md | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/contrib/plotting-visualization/matplotlib.md b/contrib/plotting-visualization/matplotlib.md index 720dff84..02f17f56 100644 --- a/contrib/plotting-visualization/matplotlib.md +++ b/contrib/plotting-visualization/matplotlib.md @@ -41,9 +41,10 @@ plt.tight_layout() plt.show() ``` -![img](scatterplot.png) +![scatterplot](https://github.com/Harshita297/learn-python/assets/122027571/aa57eb31-4377-4f75-9a97-e4fa7a54a938) + ### 3.] Bar plot Bar plot is a type of plot that plots the frequency distrubution of the categorical variables. Each entity of the categoric variable is represented as a bar. The size of the bar represents its numeric value. @@ -60,7 +61,8 @@ plt.ylabel("Y values") plt.show() ``` -![img](barplot.png) +![barplot](https://github.com/Harshita297/learn-python/assets/122027571/00cea3cd-a962-4bfe-b158-921389ee564c) + ### 4.] Histogram @@ -77,8 +79,11 @@ plt.ylabel("Frequency Distribution") plt.show() ``` +![histogram](https://github.com/Harshita297/learn-python/assets/122027571/32470819-bac5-4bb2-9f36-79521fa40bf3) + + + -![img](histogram.png) From 9470b8c1a255f8e36c51ae1db4b263a8ca05297f Mon Sep 17 00:00:00 2001 From: Harshita Jain <122027571+Harshita297@users.noreply.github.com> Date: Fri, 31 May 2024 13:02:02 +0530 Subject: [PATCH 343/656] Update index.md --- contrib/plotting-visualization/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 32261d6e..84d07f9d 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -3,3 +3,4 @@ - [Installing Matplotlib](matplotlib-installation.md) - [Bar Plots in Matplotlib](matplotlib-bar-plots.md) - [Pie Charts in Matplotlib](matplotlib-pie-charts.md) +- [Introducing Matplotlib](matplotlib.md) From b832f8f0c90673462ecc990f45595761565b027d Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Fri, 31 May 2024 13:40:20 +0530 Subject: [PATCH 344/656] Update Transformers.md Added topics --- contrib/machine-learning/Transformers.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/contrib/machine-learning/Transformers.md b/contrib/machine-learning/Transformers.md index e69de29b..7bcc102c 100644 --- a/contrib/machine-learning/Transformers.md +++ b/contrib/machine-learning/Transformers.md @@ -0,0 +1,21 @@ +# Transformers +## Introduction +A transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism. It is based on the softmax-based attention +mechanism. Before transformers, predecessors of attention mechanism were added to gated recurrent neural networks, such as LSTMs and gated recurrent units (GRUs), which +processed datasets sequentially. Dependency on previous token computations prevented them from being able to parallelize the attention mechanism. + +## Key Concepts + +## Architecture + +## Implementation +### Theory +Text is converted to numerical representations called tokens, and each token is converted into a vector via looking up from a word embedding table. +At each layer, each token is then contextualized within the scope of the context window with other tokens via a parallel multi-head attention mechanism +allowing the signal for key tokens to be amplified and less important tokens to be diminished. + +### HuggingFace + +### Tensorflow and Keras + +### PyTorch From 540163c863037f28afa4b9b7463198bf57e41866 Mon Sep 17 00:00:00 2001 From: Dishika Vaishkiyar <152963337+Dishika18@users.noreply.github.com> Date: Fri, 31 May 2024 14:09:16 +0530 Subject: [PATCH 345/656] Added Content: Line Charts in Matplotlib --- .../plotting-visualization/images/dot-line.png | Bin 0 -> 1180 bytes .../images/line-asymptote.png | Bin 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.../matplotlib-line-plot.md | 268 ++++++++++++++++++ 1 file changed, 268 insertions(+) create mode 100644 contrib/plotting-visualization/matplotlib-line-plot.md diff --git a/contrib/plotting-visualization/matplotlib-line-plot.md b/contrib/plotting-visualization/matplotlib-line-plot.md new file mode 100644 index 00000000..00ddc903 --- /dev/null +++ b/contrib/plotting-visualization/matplotlib-line-plot.md @@ -0,0 +1,268 @@ +# Line Chart in Matplotlib + +A line chart is a simple way to visualize data where we connect individual data points. It helps us to see trends and patterns over time or across categories. +
    This type of chart is particularly useful for:
    +* Comparing Data: Comparing multiple datasets on the same axes. +* Highlighting Changes: Illustrating changes and patterns in data. +* Visualizing Trends: Showing trends over time or other continuous variables. + +## Prerequisites +Line plots can be created in Python with Matplotlib's ``pyplot`` library. To build a line plot, first import ``Matplotlib``. It is a standard convention to import Matplotlib's pyplot library as ``plt``. +``` +import matplotlib.pyplot as plt + +``` + +## Creating a simple Line Plot + +First import matplotlib and numpy, these are useful for charting. +
    You can use the ``plot(x,y)`` method to create a line chart.
    + +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.linspace(-1, 1, 50) +print(x) +y = 2*x + 1 + +plt.plot(x, y) +plt.show() +``` +When executed, this will show the following line plot: + +![Basic line Chart](contrib/plotting-visualization/images/simple_line.png) + + +## Curved line + +The ``plot()`` method also works for other types of line charts. It doesn’t need to be a straight line, y can have any type of values. +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.linspace(-1, 1, 50) +y = 2**x + 1 + +plt.plot(x, y) +plt.show() +``` +When executed, this will show the following Curved line plot: + +![Curved line](contrib/plotting-visualization/images/line-curve.png) + + +## Line with Labels + +To know what you are looking at, you need meta data. Labels are a type of meta data. They show what the chart is about. The chart has an ``x label``, ``y label`` and ``title``. +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.linspace(-1, 1, 50) +y1 = 2*x + 1 +y2 = 2**x + 1 + +plt.figure() +plt.plot(x, y1) + +plt.xlabel("I am x") +plt.ylabel("I am y") +plt.title("With Labels") + +plt.show() +``` +When executed, this will show the following line with labels plot: + +![line with labels](contrib/plotting-visualization/images/line-labels.png) + +## Multiple lines + +More than one line can be in the plot. To add another line, just call the ``plot(x,y)`` function again. In the example below we have two different values for ``y(y1,y2)`` that are plotted onto the chart. + +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.linspace(-1, 1, 50) +y1 = 2*x + 1 +y2 = 2**x + 1 + +plt.figure(num = 3, figsize=(8, 5)) +plt.plot(x, y2) +plt.plot(x, y1, + color='red', + linewidth=1.0, + linestyle='--' + ) + +plt.show() +``` +When executed, this will show the following Multiple lines plot: + +![multiple lines](contrib/plotting-visualization/images/two-lines.png) + + +## Dotted line + +Lines can be in the form of dots like the image below. Instead of calling ``plot(x,y)`` call the ``scatter(x,y)`` method. The ``scatter(x,y)`` method can also be used to (randomly) plot points onto the chart. + +``` +import matplotlib.pyplot as plt +import numpy as np + +n = 1024 +X = np.random.normal(0, 1, n) +Y = np.random.normal(0, 1, n) +T = np.arctan2(X, Y) + +plt.scatter(np.arange(5), np.arange(5)) + +plt.xticks(()) +plt.yticks(()) + +plt.show() +``` + +When executed, this will show the following Dotted line plot: + +![dotted lines](contrib/plotting-visualization/images/dot-line.png) + +## Line ticks + +You can change the ticks on the plot. Set them on the ``x-axis``, ``y-axis`` or even change their color. The line can be more thick and have an alpha value. + +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.linspace(-1, 1, 50) +y = 2*x - 1 + +plt.figure(figsize=(12, 8)) +plt.plot(x, y, color='r', linewidth=10.0, alpha=0.5) + +ax = plt.gca() + +ax.spines['right'].set_color('none') +ax.spines['top'].set_color('none') + +ax.xaxis.set_ticks_position('bottom') +ax.yaxis.set_ticks_position('left') + +ax.spines['bottom'].set_position(('data', 0)) +ax.spines['left'].set_position(('data', 0)) + +for label in ax.get_xticklabels() + ax.get_yticklabels(): + label.set_fontsize(12) + label.set_bbox(dict(facecolor='y', edgecolor='None', alpha=0.7)) + +plt.show() +``` + +When executed, this will show the following line ticks plot: + +![line ticks](contrib/plotting-visualization/images/line-ticks.png) + +## Line with asymptote + +An asymptote can be added to the plot. To do that, use ``plt.annotate()``. There’s lso a dotted line in the plot below. You can play around with the code to see how it works. + +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.linspace(-1, 1, 50) +y1 = 2*x + 1 +y2 = 2**x + 1 + +plt.figure(figsize=(12, 8)) +plt.plot(x, y2) +plt.plot(x, y1, color='red', linewidth=1.0, linestyle='--') + +ax = plt.gca() + +ax.spines['right'].set_color('none') +ax.spines['top'].set_color('none') + +ax.xaxis.set_ticks_position('bottom') +ax.yaxis.set_ticks_position('left') + +ax.spines['bottom'].set_position(('data', 0)) +ax.spines['left'].set_position(('data', 0)) + + +x0 = 1 +y0 = 2*x0 + 1 + +plt.scatter(x0, y0, s = 66, color = 'b') +plt.plot([x0, x0], [y0, 0], 'k-.', lw= 2.5) + +plt.annotate(r'$2x+1=%s$' % + y0, + xy=(x0, y0), + xycoords='data', + + xytext=(+30, -30), + textcoords='offset points', + fontsize=16, + arrowprops=dict(arrowstyle='->',connectionstyle='arc3,rad=.2') + ) + +plt.text(0, 3, + r'$This\ is\ a\ good\ idea.\ \mu\ \sigma_i\ \alpha_t$', + fontdict={'size':16,'color':'r'}) + +plt.show() +``` + +When executed, this will show the following Line with asymptote plot: + +![Line with asymptote](contrib/plotting-visualization/images/line-asymptote.png) + +## Line with text scale + +It doesn’t have to be a numeric scale. The scale can also contain textual words like the example below. In ``plt.yticks()`` we just pass a list with text values. These values are then show against the ``y axis``. + +``` +import matplotlib.pyplot as plt +import numpy as np + +x = np.linspace(-1, 1, 50) +y1 = 2*x + 1 +y2 = 2**x + 1 + +plt.figure(num = 3, figsize=(8, 5)) +plt.plot(x, y2) + +plt.plot(x, y1, + color='red', + linewidth=1.0, + linestyle='--' + ) + +plt.xlim((-1, 2)) +plt.ylim((1, 3)) + +new_ticks = np.linspace(-1, 2, 5) +plt.xticks(new_ticks) +plt.yticks([-2, -1.8, -1, 1.22, 3], + [r'$really\ bad$', r'$bad$', r'$normal$', r'$good$', r'$readly\ good$']) + +ax = plt.gca() +ax.spines['right'].set_color('none') +ax.spines['top'].set_color('none') + +ax.xaxis.set_ticks_position('bottom') +ax.yaxis.set_ticks_position('left') + +ax.spines['bottom'].set_position(('data', 0)) +ax.spines['left'].set_position(('data', 0)) + +plt.show() +``` +When executed, this will show the following Line with text scale plot: + +![Line with text scale](contrib/plotting-visualization/images/line-with-text-scale.png) + + From 310ff8f340c15e0eef66b8b5acc383c586846662 Mon Sep 17 00:00:00 2001 From: Dishika Vaishkiyar <152963337+Dishika18@users.noreply.github.com> Date: Fri, 31 May 2024 14:29:39 +0530 Subject: [PATCH 347/656] Update index.md --- contrib/plotting-visualization/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/contrib/plotting-visualization/index.md b/contrib/plotting-visualization/index.md index 32261d6e..61dd5371 100644 --- a/contrib/plotting-visualization/index.md +++ b/contrib/plotting-visualization/index.md @@ -3,3 +3,4 @@ - [Installing Matplotlib](matplotlib-installation.md) - [Bar Plots in Matplotlib](matplotlib-bar-plots.md) - [Pie Charts in Matplotlib](matplotlib-pie-charts.md) +- [Line Charts in Matplotlib](matplotlib-line-plot.md) From 34d7bc39c9aa7ed30565cded96c14566ed7e9f80 Mon Sep 17 00:00:00 2001 From: Dishika Vaishkiyar <152963337+Dishika18@users.noreply.github.com> Date: Fri, 31 May 2024 14:32:11 +0530 Subject: [PATCH 348/656] Update matplotlib-line-plot.md --- .../matplotlib-line-plot.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/contrib/plotting-visualization/matplotlib-line-plot.md b/contrib/plotting-visualization/matplotlib-line-plot.md index 00ddc903..566fdbba 100644 --- a/contrib/plotting-visualization/matplotlib-line-plot.md +++ b/contrib/plotting-visualization/matplotlib-line-plot.md @@ -31,7 +31,7 @@ plt.show() ``` When executed, this will show the following line plot: -![Basic line Chart](contrib/plotting-visualization/images/simple_line.png) +![Basic line Chart](images/simple_line.png) ## Curved line @@ -49,7 +49,7 @@ plt.show() ``` When executed, this will show the following Curved line plot: -![Curved line](contrib/plotting-visualization/images/line-curve.png) +![Curved line](images/line-curve.png) ## Line with Labels @@ -74,7 +74,7 @@ plt.show() ``` When executed, this will show the following line with labels plot: -![line with labels](contrib/plotting-visualization/images/line-labels.png) +![line with labels](images/line-labels.png) ## Multiple lines @@ -100,7 +100,7 @@ plt.show() ``` When executed, this will show the following Multiple lines plot: -![multiple lines](contrib/plotting-visualization/images/two-lines.png) +![multiple lines](images/two-lines.png) ## Dotted line @@ -126,7 +126,7 @@ plt.show() When executed, this will show the following Dotted line plot: -![dotted lines](contrib/plotting-visualization/images/dot-line.png) +![dotted lines](images/dot-line.png) ## Line ticks @@ -162,7 +162,7 @@ plt.show() When executed, this will show the following line ticks plot: -![line ticks](contrib/plotting-visualization/images/line-ticks.png) +![line ticks](images/line-ticks.png) ## Line with asymptote @@ -218,7 +218,7 @@ plt.show() When executed, this will show the following Line with asymptote plot: -![Line with asymptote](contrib/plotting-visualization/images/line-asymptote.png) +![Line with asymptote](images/line-asymptote.png) ## Line with text scale @@ -263,6 +263,6 @@ plt.show() ``` When executed, this will show the following Line with text scale plot: -![Line with text scale](contrib/plotting-visualization/images/line-with-text-scale.png) +![Line with text scale](images/line-with-text-scale.png) From 5b3c8d7ad108d4a65b4a946d719d242fe9eae308 Mon Sep 17 00:00:00 2001 From: SAM <60264918+SAM-DEV007@users.noreply.github.com> Date: Fri, 31 May 2024 15:14:09 +0530 Subject: [PATCH 349/656] Update Transformers.md Added information about Model Architecture --- contrib/machine-learning/Transformers.md | 59 +++++++++++++++++++++++- 1 file changed, 57 insertions(+), 2 deletions(-) diff --git a/contrib/machine-learning/Transformers.md b/contrib/machine-learning/Transformers.md index 7bcc102c..49a1b976 100644 --- a/contrib/machine-learning/Transformers.md +++ b/contrib/machine-learning/Transformers.md @@ -4,9 +4,53 @@ A transformer is a deep learning architecture developed by Google and based on t mechanism. Before transformers, predecessors of attention mechanism were added to gated recurrent neural networks, such as LSTMs and gated recurrent units (GRUs), which processed datasets sequentially. Dependency on previous token computations prevented them from being able to parallelize the attention mechanism. -## Key Concepts +## Model Architecture +

    +Click to expand + +- [Introduction](#introduction) +- [CNN Architecture](#cnn-architecture) + -
    + Convolutional Layer + + - [Input Shape](#input-shape) + - [Stride](#strides) + - [Padding](#padding) + - [Filter](#filters) + - [Output](#output) + +
    + +- [Pooling Layer](#pooling-layer) +- [Flatten Layer](#flatten-layer) +- [Dropout Layer](#dropout-layer) + +- [Implementation](#implementation) + +