ALX Backend Python

This repository contains different projects.
The projects focus on advanced Python concepts, testing, and backend development practices.

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📌 Topics Covered

This repository covers the following major topics:

1. Unit Tests and Integration Tests
2. Context Managers
3. Decorators
4. Generators
5. Asynchronous Programming (async/await)

Each topic is explained separately below.

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1️⃣ Unit Tests and Integration Tests

🔹 Introduction

• Unit testing: Test individual functions in isolation. External dependencies (e.g., databases, APIs) should be mocked.
• Integration testing: Test how multiple components interact together, mocking only external requests.

🔹 Why Important?

• Ensures reliability of functions.
• Protects against regressions.
• Encourages modular and maintainable code.

2️⃣Context Managers

🔹 What They Are

A context manager handles setup and cleanup actions automatically (e.g., opening/closing a file). They are commonly used with the with statement.

🔹 Example

with open("data.txt", "r") as f:
    content = f.read()
# file is automatically closed here

🔹 Custom Context Manager

class MyContext:
    def __enter__(self):
        print("Entering context")
        return self
    def __exit__(self, exc_type, exc_value, traceback):
        print("Exiting context")

with MyContext():
    print("Inside block")

3️⃣ Decorators

🔹 What They Are

A decorator is a function that takes another function and extends its behavior without modifying it.

🔹 Example

    def wrapper():
        print("Before function call")
        func()
        print("After function call")
    return wrapper

@my_decorator
def say_hello():
    print("Hello!")

say_hello()

🔹 Usage in Repo

@memoize in utils.py is a decorator that caches method results.

4️⃣ Generators

🔹 What They Are

A generator is a function that yields values one at a time using yield, instead of returning them all at once.

🔹 Example

def count_up_to(n):
    i = 1
    while i <= n:
        yield i
        i += 1

for num in count_up_to(5):
    print(num)

🔹 Why Useful?

Saves memory (lazy evaluation).

Perfect for streaming data or infinite sequences.

5️⃣ Asynchronous Programming

🔹 What It Is

Asynchronous programming allows multiple tasks to run concurrently without blocking each other. Python uses async and await keywords for this.

🔹 Example

import asyncio

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

async def main():
    await asyncio.gather(say_hello(), say_hello())

asyncio.run(main())

🔹 Why Important?

Essential for backend services handling multiple requests.

Efficient handling of I/O-bound operations (APIs, databases).