A decorator is a tool that modifies or enhances the behavior of a function or class without changing its source code. It implements function extension by accepting the objective function or class as parameters and returning a new wrapped function or class. It is often used to add logs, permission control, timing and other functions. To create a decorator: 1. Define a function that receives a function or class; 2. Define a wrapper function in it to add additional operations; 3. Call the original function or class; 4. Return the wrapped result. Just use the @decorator_name syntax to apply it to the objective function or class. For functions with parameters, compatibility can be ensured using *args and **kwargs in the decorator. Python has built-in commonly used decorators such as @staticmethod, @classmethod and @property. Third-party libraries such as Flask also widely use decorators for routing mapping. Decorators are suitable for scenarios where general logic is required to separate core business, but excessive stacking of complex decorators should be avoided to avoid affecting readability and maintenance.
Decorators in Python are a way to modify or enhance functions or classes without changing their source code. They're commonly used to add functionality like logging, access control, timing, and more — all while keeping your code clean and reusable.
What Exactly Is a Decorator?
At its core, a decorator is just a function (or class) that wraps another function or class, modifying its behavior. The key idea is that you pass a function into another function, and the decorator returns a new function that usually does something extra before or after calling the original one.
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 says_hello():
print("Hello")
say_hello()This will output:
Before function call Hello After function call
So when you use @my_decorator above say_hello , it's equivalent to writing:
say_hello = my_decorator(say_hello)
How to Create and Use Function Decorators
Creating your own decorator starts with understanding how functions can be passed around as objects. Here's how to build a simple one:
- Define a function that takes another function as an argument.
- Inside it, define a wrapper function that does something extra.
- Call the original function inside the wrapper.
- Return the wrapper function.
You can then apply this decorator using the @decorator_name syntax right above the function definition.
If your decorated function needs arguments, make sure your wrapper handles them. You can use *args and **kwargs for flexibility:
def my_decorator(func):
def wrapper(*args, **kwargs):
print("Something before")
result = func(*args, **kwargs)
print("Something after")
return result
Return wrapperThis version works with any function, regardless of what parameters it accepts.
Using Built-in and Third-Party Decorators
Python comes with some handy decorators built in. For example:
-
@staticmethodand@classmethod– Used in classes to define methods that don't require instance or class instance respectively. -
@property– Makes a method behave like an attribute, useful for computed properties and encapsulation.
Third-party libraries also use decorators heavily. For instance, Flask uses them to map URLs to functions:
@app.route('/')
def home():
return "Welcome!"These tools help reduce boilerplate and make your intentions clear without cluttering logic.
When Should You Use Decorators?
Use decorators when you want to separate cross-cutting concerns from your main logic — things like authentication, logging, caching, input validation, etc.
They're especially useful when:
- You need to apply the same behavior to multiple functions.
- You want to keep your core logic clean and readable.
- You're working on frameworks or libraries where extension matters.
Just remember: a decorator should ideally do one thing well. If you find yourself stacking many complex decorators, consider refactoring or adding comments so others (or future you) can understand what's going on.
That's basically it. They might seem a bit confusing at first, but once you get the pattern down, they become a powerful part of your Python toolkit.
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