Advanced Python Series: Episode 1

🎓 Advanced Python Series: Episode 1

🔥 Topic: Understanding Decorators in Python

Welcome to the first episode of our Advanced Python series on DarchumsTech! In this episode, we're diving into one of the most powerful features in Python — decorators. If you've ever seen a @ symbol in Python and wondered what it's doing, this lesson is for you.

---

📹 Watch the Full Video Tutorial:

Don’t forget to like, subscribe, and share if you enjoy the content!

---

🧠 What You'll Learn (with Explanations):

• What decorators are:
  Decorators in Python are a type of function that take another function as input and extend or modify its behavior without changing its source code. They are often used for logging, access control, memoization, and more.
• How to create your own decorators:
  To create a decorator, you define a function that returns a wrapper function. This wrapper function adds behavior before or after the original function runs.
• Using functools.wraps to preserve function metadata:
  When wrapping a function, the original name and docstring can get lost. @wraps from the functools module copies that metadata so debugging and documentation stay intact.
• Real-life examples:
  We'll explore decorators for timing how long functions take to run (great for performance testing), logging actions, and enforcing simple access control in applications.

---

🧪 Code Example (with inline explanation):

import time
from functools import wraps

# This is our decorator
def timer(func):
    @wraps(func)  # Preserves original function's metadata
    def wrapper(*args, **kwargs):
        start = time.time()                  # Start timing
        result = func(*args, **kwargs)       # Call the original function
        end = time.time()                    # End timing
        print(f"{func.__name__} took {end - start:.4f} seconds")  # Show result
        return result
    return wrapper

# Apply the decorator to a sample function
@timer
def long_function():
    time.sleep(2)   # Simulate a long-running task
    return "Done"

print(long_function())

    

This code shows how decorators can help track performance without touching the original function’s logic.

---

🧰 Tip:

Use decorators when you want to cleanly extend functionality — for example, adding logging, authentication, or input validation — in a way that’s modular and reusable.

---

📬 Stay Tuned:

In the next episode, we’ll explore generators and iterators — essential tools for memory-efficient looping and data streaming in Python applications.

---

🔔 Subscribe for updates at:

📧 darchumstech.blogspot.com