Decorators
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Decorators in Python are a powerful feature that allows us to modify or enhance the behavior of functions or classes without modifying their actual code. They are often used to wrap another function or method, adding extra functionality in a clean and reusable manner.
A decorator in Python is a callable (usually a function) that takes another function (or class) as its argument and returns a modified or extended version of that function (or class).
Why Use Decorators?
- Code Reusability: Common functionality (e.g., logging, authentication, performance monitoring) can be extracted and reused across multiple functions or classes.
- Separation of Concerns: The original function’s core logic is preserved, while additional features are handled externally.
- Improved Readability: Decorators make it easier to apply functionality without cluttering the main code.
How Do Decorators Work?
A decorator is essentially a higher-order function, meaning it either:
- Takes a function as an argument, or
- Returns a function.
At its core, a decorator works like this:
def decorator(func):
def wrapper():
# Code to execute BEFORE the original function
print("Before the function call.")
# Call the original function
func()
# Code to execute AFTER the original function
print("After the function call.")
return wrapper
If you apply this decorator to a function:
@decorator
def my_function():
print("This is my function.")
my_function()
Output:
Before the function call.
This is my function.
After the function call.
Creating and Using Decorators
Basic Decorator
Here is a basic example of a decorator that logs when a function is called.
def logger(func):
def wrapper(*args, **kwargs):
print(f"Function '{func.__name__}' is being called.")
result = func(*args, **kwargs)
print(f"Function '{func.__name__}' finished execution.")
return result
return wrapper
@logger
def greet(name):
print(f"Hello, {name}!")
greet("Abhishek")
Output:
Function 'greet' is being called.
Hello, Abhishek!
Function 'greet' finished execution.
Using Arguments in Decorators
Decorators can also handle arguments passed to the wrapped function using *args and **kwargs.
def logger(func):
def wrapper(*args, **kwargs):
print(f"Calling {func.__name__} with arguments {args} and {kwargs}")
return func(*args, **kwargs)
return wrapper
@logger
def add(a, b):
return a + b
result = add(10, 20)
print(f"Result: {result}")
Output:
Calling add with arguments (10, 20) and {}
Result: 30
Chaining Decorators
Multiple decorators can be applied to a single function. They are applied in the order they are listed, from top to bottom.
def uppercase(func):
def wrapper():
result = func()
return result.upper()
return wrapper
def exclaim(func):
def wrapper():
result = func()
return f"{result}!"
return wrapper
@uppercase
@exclaim
def greet():
return "hello"
print(greet())
Output:
HELLO!
Here, the exclaim decorator is applied first, followed by uppercase.
Using Decorators with Functions that Return Values
When the wrapped function returns a value, the decorator must ensure it returns the result correctly.
def double_result(func):
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
return result * 2
return wrapper
@double_result
def multiply(a, b):
return a * b
print(multiply(5, 3))
Output:
30
Decorators with Arguments
A decorator itself can accept arguments by wrapping it inside another function.
def repeat(n):
def decorator(func):
def wrapper(*args, **kwargs):
for _ in range(n):
func(*args, **kwargs)
return wrapper
return decorator
@repeat(3)
def say_hello():
print("Hello!")
say_hello()
Output:
Hello!
Hello!
Hello!
Built-in Decorators in Python
Python provides some commonly used decorators:
- @staticmethod: Defines a static method within a class (does not access class or instance attributes).
- @classmethod: Defines a class method that takes the class (
cls) as the first argument instead of the instance (self). - @property: Converts a method into a read-only property.
Example:
class Circle:
def __init__(self, radius):
self._radius = radius
@property
def radius(self):
return self._radius
@radius.setter
def radius(self, value):
if value < 0:
raise ValueError("Radius cannot be negative.")
self._radius = value
circle = Circle(5)
print(circle.radius) # Access property
circle.radius = 10 # Modify property
print(circle.radius)
Practical Examples of Decorators
Logging
def log(func):
def wrapper(*args, **kwargs):
print(f"Logging: {func.__name__} was called with arguments {args} and {kwargs}")
return func(*args, **kwargs)
return wrapper
@log
def divide(a, b):
return a / b
print(divide(10, 2))
Authentication
def requires_login(func):
def wrapper(user, *args, **kwargs):
if not user.get("logged_in"):
raise PermissionError("User is not logged in!")
return func(user, *args, **kwargs)
return wrapper
@requires_login
def view_profile(user):
print(f"Viewing profile of {user['username']}")
user = {"username": "Abhishek", "logged_in": True}
view_profile(user)
Performance Monitoring
import time
def timer(func):
def wrapper(*args, **kwargs):
start = time.time()
result = func(*args, **kwargs)
end = time.time()
print(f"Execution time: {end - start:.4f} seconds")
return result
return wrapper
@timer
def slow_function():
time.sleep(2)
print("Function complete!")
slow_function()
Best Practices for Using Decorators
-
Use
functools.wraps:- When defining a decorator, use
functools.wrapsto preserve the metadata (e.g., function name, docstring) of the original function.
from functools import wrapsdef decorator(func):@wraps(func)def wrapper(*args, **kwargs):return func(*args, **kwargs)return wrapper - When defining a decorator, use
-
Keep Decorators Simple:
- Avoid making the decorator too complex. Its purpose is to add functionality while keeping the code readable.
-
Test the Decorator:
- Test the decorator separately to ensure it behaves as expected.