A decorator in Python is a function or class that wraps another function to extend or modify its behavior without altering its source code. It works by taking a function as an argument and returning a new function, often using the @decorator_name syntax. 1. A basic decorator adds functionality before and after a function call. 2. Decorators help avoid repetitive code by enabling reusable logic such as logging, timing, access control, or caching. 3. Custom decorators can be created by defining a wrapper function that accepts args and *kwargs for flexibility. 4. Built-in decorators like @staticmethod, @classmethod, and @property are commonly used in classes. 5. Third-party libraries like Flask and FastAPI use decorators for routing and validation, improving readability and reducing boilerplate code.
A decorator in Python is a design pattern that allows you to modify or enhance the behavior of functions or classes without modifying their source code. It’s essentially a function (or class) that wraps another function to extend its behavior. This is done using the @decorator_name syntax in Python, which makes it both clean and readable.
How Do Decorators Work?
At its core, a decorator is just a callable that takes another function as an argument and returns a new function.
Here’s a basic example:
def my_decorator(func):
def wrapper():
print("Before function call")
func()
print("After function call")
return wrapper
@my_decorator
def say_hello():
print("Hello")
say_hello()This will output:
Before function call Hello After function call
What’s happening here is that say_hello is being replaced by the result of calling my_decorator(say_hello). So when you call say_hello(), you're actually calling the wrapper function defined inside my_decorator.
Why Use Decorators?
Decorators are useful for adding common functionality to multiple functions in a clean way. Some common use cases include:
- Logging function calls
- Timing how long a function runs
- Enforcing access control or authentication
- Caching results (memoization)
They help avoid repeating code and keep your logic organized and reusable.
For example, if you want to time different functions, you can write a single @timer decorator and apply it wherever needed, instead of copying timing code into each function.
How to Create Your Own Decorator
Creating a decorator is straightforward. Just define a function that accepts another function and returns a wrapped version.
Here’s a simple logging decorator:
def log_call(func):
def wrapper(*args, **kwargs):
print(f"Calling {func.__name__}")
return func(*args, **kwargs)
return wrapper
@log_call
def add(a, b):
return a b
print(add(3, 4))You’ll see:
Calling add 7
A few things to note:
- Use
*argsand**kwargsso your wrapper works with any number of arguments. - Always return the result of the original function unless you have a reason not to.
- If you need decorators with arguments, you'll need a function that returns a decorator — that is, a nested function three levels deep.
Common Built-in and Third-party Decorators
Python comes with some built-in decorators like:
-
@staticmethodand@classmethod– for defining methods in classes that don’t require instance or class instantiation -
@property– for making methods behave like attributes
Third-party libraries also make heavy use of decorators. For example:
- Flask uses
@app.route('/path')to bind URLs to functions - FastAPI uses similar patterns for API routing and request validation
These make code more readable and reduce boilerplate.
That's basically what a decorator is in Python — a powerful tool for wrapping and extending function behavior cleanly. They might look a bit magical at first, but once you understand they’re just functions wrapping other functions, they become much easier to work with.
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