Python #5

In this session, we'll cover some specialized and advanced Python topics, including error handling with custom exceptions, context managers, and an introduction to asynchronous programming.

Custom Exceptions

Creating custom exceptions can make your error handling more precise and meaningful.

class CustomError(Exception):
    """A custom exception class."""
    pass

def risky_function():
    raise CustomError("Something went wrong!")

try:
    risky_function()
except CustomError as e:
    print(e)  # Output: Something went wrong!

Context Managers

Context managers are used to manage resources, such as files or network connections, ensuring they are properly acquired and released. The with statement is commonly used with context managers.

Using Built-in Context Managers

with open('example.txt', 'r') as file:
    content = file.read()
    print(content)

Creating a Custom Context Manager

You can create a custom context manager using a class with __enter__ and __exit__ methods or by using the contextlib module.

from contextlib import contextmanager

@contextmanager
def my_context_manager():
    print("Entering the context")
    yield
    print("Exiting the context")

with my_context_manager():
    print("Inside the context")

Asynchronous Programming

Asynchronous programming allows you to write code that performs tasks concurrently, which can be useful for I/O-bound operations like web requests or database queries.

Using asyncio

Python's asyncio library provides tools for writing asynchronous code.

import asyncio

async def say_hello():
    print("Hello")
    await asyncio.sleep(1)
    print("World")

async def main():
    await say_hello()

# Run the main function
asyncio.run(main())

Concurrent Tasks

You can run multiple asynchronous tasks concurrently using asyncio.gather.

async def task1():
    await asyncio.sleep(1)
    return "Task 1 completed"

async def task2():
    await asyncio.sleep(2)
    return "Task 2 completed"

async def main():
    results = await asyncio.gather(task1(), task2())
    print(results)

asyncio.run(main())

These topics will help you write more efficient and robust Python applications, particularly when dealing with resource management and concurrent operations.