Decorators are special functions in Python used to modify or enhance function behavior, and their core lies in closures and function nesting. 1. The decorator is essentially a higher-order function that accepts functions as parameters and returns new functions; 2. The basic decorator implements functional extensions by defining internal wrapper functions, such as adding log output before and after function execution; 3. To support any parameters of the decorator function, args and *kwargs need to be passed; 4. If the decorator itself needs to receive parameters, it needs to adopt a three-layer nested structure; 5. Use functools.wraps to retain the meta information of the decorating function (such as name and document string) for easy debugging and document generation. After mastering these key points, you can gradually write various decorators from simple to complex.
It is actually not difficult to write a Python decorator. The core is to understand that it is essentially a function that is used to modify the behavior of other functions or classes without modifying its source code. The key to a decorator is closures and function nesting. After mastering these two points, it will be easy to use.
What is a decorator
Simply put, a decorator is a function that accepts a function as an argument and then returns a new function. You can think of it as a way to "add functions" to a function.
To give the simplest example: you have a function that wants to print log information before and after execution. At this time, you can write a decorator to handle it uniformly, instead of adding print to each function.
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()Output result:
Before function call Hello After function call
How to decorate with parameters
The above example can only handle functions without parameters. If the function you want to decorate has parameters, then make wrapper support any parameters.
def my_decorator(func):
def wrapper(*args, **kwargs):
print("Before function call")
result = func(*args, **kwargs)
print("After function call")
return result
Return wrapper
@my_decorator
def greet(name):
print(f"Hello, {name}")
greet("Alice")In this way, no matter how many parameters the objective function has, it can run normally.
How to write a decorator with parameters
Sometimes you want the decorator itself to receive parameters, such as control behavior switches or configuration options. At this time, three layers of nested functions are needed.
def repeat(times):
def decorator(func):
def wrapper(*args, **kwargs):
for _ in range(times):
func(*args, **kwargs)
return None
Return wrapper
Return decorator
@repeat(3)
def says_hi():
print("Hi")
say_hi()This decorator will cause the function to execute a specified number of times. You can adjust the logic as needed, such as adding a delay, recording logs, etc.
Use functools.wraps to preserve meta information
When you finish writing the decorator, you may find that the decorating function name has changed or the document string has been lost. This is because the decorator replaces the function object.
The solution is to use functools.wraps from the standard library:
from functools import wraps
def my_decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
print("Doing something before")
return func(*args, **kwargs)
Return wrapperAfter adding this, the function name, docstring and other information will not be lost, which is very helpful for debugging and automatic document generation.
Basically that's it. The decorator looks a bit tangled, but as long as you remember that it is a "function wrapping function" and step by step, it is not difficult to understand. At the beginning, you can start with writing without parameters, and then slowly expand to the situation where parameters and decorator parameters are supported.
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