diff --git a/001_Python_NumPy.ipynb b/001_Python_NumPy.ipynb
index f795c0f..a070ee7 100644
--- a/001_Python_NumPy.ipynb
+++ b/001_Python_NumPy.ipynb
@@ -15,9 +15,9 @@
"source": [
"# Python NumPy\n",
"\n",
- "In this class, you will learn various NumPy concepts like how to install NumPy, arrays, functions, matrix multiplication, etc. This NumPy in Python tutorial will help you learn all Python NumPy basics..\n",
+ "In this class, you will learn various NumPy concepts like how to install NumPy, arrays, functions, matrix multiplication, etc. This NumPy in Python tutorial will help you learn all Python NumPy basics.\n",
"\n",
- "**[Numpy](http://www.numpy.org/)** (‘Numerical Python’) is the core open source library for scientific computing in Python. It is a very useful library to perform mathematical and statistical operations in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. In this part, we will review the essential functions that you need to know for the tutorial on 'TensorFlow.'"
+ "**[Numpy](http://www.numpy.org/)** (‘Numerical Python’) is the core open source library for scientific computing in Python. It is a Linear Algebra Library for Python, it is so important for Finance with Python. It is a very useful library to perform mathematical and statistical operations in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. In this part, we will review the essential functions that you need to know for the tutorial on 'TensorFlow.'"
]
},
{
@@ -48,26 +48,14 @@
"\n",
"NumPy is installed by default with Anaconda.\n",
"\n",
- "In remote case, NumPy not installed-\n",
- "\n",
- "You can install NumPy using Anaconda:\n",
- "\n",
- "```puthon\n",
- "conda install -c anaconda numpy\t\t\n",
- "```\n",
- "In Jupyter Notebook :\n",
- "\n",
- "```python\n",
- ">>> import sys\n",
- ">>> !conda install --yes --prefix {sys.prefix} numpy\n",
- "```"
+ "**It is highly recommended you install Python using the Anaconda distribution to make sure all underlying dependencies (such as Linear Algebra libraries like numpy) sync up with the use of a conda install. If you have Anaconda, install NumPy by going to your terminal or command prompt and typing:**\n",
+ " \n",
+ "`conda install numpy`"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "heading_collapsed": true
- },
+ "metadata": {},
"source": [
"### Import NumPy and Check Version\n",
"\n",
@@ -76,20 +64,27 @@
"```python\n",
"import numpy as np\n",
"```\n",
- "Above code renames the Numpy namespace to np. This permits us to prefix Numpy function, methods, and attributes with \" np \" instead of typing \" numpy.\" It is the standard shortcut you will find in the numpy literature"
+ "Above code renames the **`numpy`** namespace to **`np`**."
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {
"ExecuteTime": {
- "end_time": "2021-05-27T16:41:31.257895Z",
- "start_time": "2021-05-27T16:41:31.250082Z"
- },
- "hidden": true
+ "end_time": "2021-07-03T05:22:44.173245Z",
+ "start_time": "2021-07-03T05:22:44.166407Z"
+ }
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.20.1\n"
+ ]
+ }
+ ],
"source": [
"# To check your installed version of Numpy use the command\n",
"\n",
diff --git a/002_Python_NumPy_Array.ipynb b/002_Python_NumPy_Array.ipynb
deleted file mode 100644
index 1590877..0000000
--- a/002_Python_NumPy_Array.ipynb
+++ /dev/null
@@ -1,3803 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2021-05-27T18:42:32.348446Z",
- "start_time": "2021-05-27T18:42:32.329894Z"
- }
- },
- "source": [
- "\n",
- "All the IPython Notebooks in this lecture series by Dr. Milan Parmar are available @ **[GitHub](https://github.com/milaan9/09_Python_NumPy_Module)**\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Python NumPy Array: \n",
- "\n",
- "A numpy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. The number of dimensions is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each dimension.\n",
- "\n",
- "Numpy array is a powerful N-dimensional array object which is in the form of rows and columns. We can initialize NumPy arrays from nested Python lists and access it elements."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2021-05-27T18:42:42.296170Z",
- "start_time": "2021-05-27T18:42:42.277617Z"
- }
- },
- "source": [
- "## NumPy Array Types:\n",
- "\n",
- "\n",
- "
![](\"img/array.png\")
\n",
- "
\n",
+ "
\n",
+ "
\n",
+ "
![](\"http://docs.scipy.org/doc/numpy/_images/dtype-hierarchy.png\")
\n",
+ "
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
# 09_Python_NumPy_Module
## Introduction 👋
### What is NumPy?
-NumPy stands for numeric python which is a python package for the computation and processing of the multidimensional and single dimensional array elements.
+[](https://numpy.org/) stands for numeric python which is a python package for the computation and processing of the multidimensional and single dimensional array elements.
**Travis Oliphant** created NumPy package in 2005 by injecting the features of the ancestor module Numeric into another module Numarray.
-It is an extension module of Python which is mostly written in C. It provides various functions which are capable of performing the numeric computations with a high speed.
+It is an extension module of Python which is mostly written in "C". It provides various functions which are capable of performing the numeric computations with a high speed.
NumPy provides various powerful data structures, implementing multi-dimensional arrays and matrices. These data structures are used for the optimal computations regarding arrays and matrices.
@@ -28,49 +49,65 @@ There are the following advantages of using NumPy for data analysis.
Nowadays, NumPy in combination with SciPy and Mat-plotlib is used as the replacement to MATLAB as Python is more complete and easier programming language than MATLAB.
+---
## Table of contents 📋
-
-[001_Python_NumPy](https://github.com/milaan9/09_Python_NumPy_Module/blob/main/001_Python_NumPy.ipynb)
+| **No.** | **Name** |
+| ------- | -------- |
+| 01 | **[Python_NumPy](https://github.com/milaan9/09_Python_NumPy_Module/blob/main/001_Python_NumPy.ipynb)** |
+| 02 | **[Python_NumPy_Array_Part1](https://github.com/milaan9/09_Python_NumPy_Module/blob/main/002_Python_NumPy_Array_Part1.ipynb)** |
+| 03 | **[Python_NumPy_Array_Part2](https://github.com/milaan9/09_Python_NumPy_Module/blob/main/003_Python_NumPy_Array_Part2.ipynb)** |
+| 04 | **[Python_NumPy_Array_Part3](https://github.com/milaan9/09_Python_NumPy_Module/blob/main/004_Python_NumPy_Array_Part3.ipynb)** |
+| 05 | **[Python_NumPy_Array_Part4](https://github.com/milaan9/09_Python_NumPy_Module/blob/main/005_Python_NumPy_Array_Part4.ipynb)** |
+| | **[data](https://github.com/milaan9/09_Python_NumPy_Module/blob/main/data.txt)** |
+| 06 | **[Python_Numpy_Exercises_with_hints](https://github.com/milaan9/09_Python_NumPy_Module/blob/main/Python_Numpy_Exercises_with_hints.ipynb)** |
+| 07 | **[Python_Numpy_Exercises_with_hints](https://github.com/milaan9/09_Python_NumPy_Module/blob/main/Python_Numpy_Exercises_with_hints.ipynb)** |
+| 08 | **[NumPy Cheat Sheet Data Analysis in Python.pdf](https://github.com/milaan9/09_Python_NumPy_Module/blob/main/NumPy%20Cheat%20Sheet%20Data%20Analysis%20in%20Python.pdf)** |
-[002_Python_NumPy_Array](https://github.com/milaan9/09_Python_NumPy_Module/blob/main/002_Python_NumPy_Array.ipynb)
+These are online **read-only** versions. However you can **`Run ▶`** all the codes **online** by clicking here ➞
+---
-[data](https://github.com/milaan9/09_Python_NumPy_Module/blob/main/data.txt)
+## Install Numpy Module:
+Open your [](https://www.anaconda.com/products/individual) Prompt 
and type and run the following command (individually):
+ - pip install numpy
+
-These are online read-only versions.
+Once Installed now we can import it inside our python code.
+---
## Frequently asked questions ❔
### How can I thank you for writing and sharing this tutorial? 🌷
-You can ⭐ star this tutorial. Starring is free for you, but it tells me and other people that it was helpful and you like this tutorial.
+You can 
and 
Starring and Forking is free for you, but it tells me and other people that it was helpful and you like this tutorial.
-Go [here](https://github.com/milaan9/09_Python_NumPy_Module) if you aren't here already and click the "⭐ Star" button in the top right corner. You will be asked to create a GitHub account if you don't already have one.
+Go [**`here`**](https://github.com/milaan9/09_Python_NumPy_Module) if you aren't here already and click ➞ **`✰ Star`** and **`ⵖ Fork`** button in the top right corner. You'll be asked to create a GitHub account if you don't already have one.
-### How can I read this tutorial without an Internet connection? 🤔
+---
-1. Go [here](https://github.com/milaan9/09_Python_NumPy_Module) if you aren't here already.
-
-2. Click the big green "Clone or download" button in the top right of the page, then click "[Download ZIP](https://github.com/milaan9/09_Python_NumPy_Module/archive/refs/heads/main.zip)".
+### How can I read this tutorial without an Internet connection? 
+
+1. Go [**`here`**](https://github.com/milaan9/09_Python_NumPy_Module) and click the big green ➞ **`Code`** button in the top right of the page, then click ➞ [**`Download ZIP`**](https://github.com/milaan9/09_Python_NumPy_Module/archive/refs/heads/main.zip).
-3. Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run.
+2. Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run.
-4. Launch ipython notebook from the folder which contains the notebooks. Open each one of them
+3. Launch ipython notebook from the folder which contains the notebooks. Open each one of them
- `Cell > All Output > Clear`
+ **`Kernel > Restart & Clear Output`**
This will clear all the outputs and now you can understand each statement and learn interactively.
If you have git and you know how to use it, you can also clone the repository instead of downloading a zip and extracting it. An advantage with doing it this way is that you don't need to download the whole tutorial again to get the latest version of it, all you need to do is to pull with git and run ipython notebook again.
+---
## Authors ✍️
@@ -78,11 +115,13 @@ I'm Dr. Milaan Parmar and I have written this tutorial. If you think you can add
See [github's contributors page](https://github.com/milaan9/09_Python_NumPy_Module/graphs/contributors) for details.
-If you have trouble with this tutorial please tell me about it by [Create an issue on GitHub](https://github.com/milaan9/11_Python_Matplotlib_Module/issues/new). and I'll make this tutorial better. This is probably the best choice if you had trouble following the tutorial, and something in it should be explained better. You will be asked to create a GitHub account if you don't already have one.
+If you have trouble with this tutorial please tell me about it by [Create an issue on GitHub](https://github.com/milaan9/09_Python_NumPy_Module/issues/new). and I'll make this tutorial better. This is probably the best choice if you had trouble following the tutorial, and something in it should be explained better. You will be asked to create a GitHub account if you don't already have one.
If you like this tutorial, please [give it a ⭐ star](https://github.com/milaan9/09_Python_NumPy_Module).
+---
## Licence 📜
You may use this tutorial freely at your own risk. See [LICENSE](./LICENSE).
+Copyright (c) 2020 Dr. Milan Parmar