- 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)**

- Synchronizing Threads

>> Common Pitfalls

>> Conclusion

Multithreading in Python is a powerful tool for concurrent execution, especially for I/O-bound tasks. By understanding and correctly

implementing threading, you can significantly improve the performance and responsiveness of your applications.

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.

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:

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.|

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:

try:

# Code that might raise an exception

result = 10 / 0

except:

print("An error occured!")

Output

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.

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:

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

Oops! You tried to divide by zero.

If you comment on the line `result = 10 / 0`, then the output will be:

There's a variable named 'undefined_variable' that hasn't been defined yet.

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:

try:

# Code that might raise ZeroDivisionError or NameError

result = 10 / 0

name = undefined_variable

except (ZeroDivisionError, NameError):

print("An error occured!")

Output

An error occured!

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:

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

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

The average is: 20.0

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:

try:

a = 10 // 0

print(a)

except ZeroDivisionError:

print("Cannot be divided by zero.")

finally:

print("Program executed!")

Output

Cannot be divided by zero.

Program executed!

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:

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

An error occured: Can't divide by zero!

- **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.

- **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.

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 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.

Function generators can be represented mathematically using set-builder notation. The general form is:

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.

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.

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.

def fibonacci():

a, b = 0, 1

while True:

yield a

a, b = b, a + b

fib_gen = fibonacci()

for _ in range(10):

print(next(fib_gen))

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.

Generator expressions can also be represented mathematically using set-builder notation. The general form is the same as for function generators.

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.

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.

square_gen = (x**2 for x in range(10))

for num in square_gen:

print(num)

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.

- [OOPs](oops.md)

<<<<<<< HEAD

- [Protocols](protocols.md)

- [Exception Handling in Python](exception-handling.md)

- [Generators](generators.md)