【Related learning recommendations: python tutorial】
Decorator
- is essentially a function that accepts parameters as functions.
- Function: Add additional general functions to an already implemented method, such as logging, running timing, etc.
Example
Decorator without parameters, without @
# 不带参数的装饰器def deco_test(func): def wrapper(*args, **kwargs): print("before function") f = func(*args, **kwargs) print("after function") return f return wrapperdef do_something(a,b,c): print(a) time.sleep(1) print(b) time.sleep(1) print(c) return aif __name__ == '__main__': # 不用@ f = deco_test(do_something)("1","2","3")
Output:
before function 1 2 3 after function
Personal understanding:
is equivalent to putting two outputs outside the do_something
function: before function
and after function
.
Decorator without parameters, use @
# 不带参数的装饰器def deco_test(func): def wrapper(*args, **kwargs): print("before function") f = func(*args, **kwargs) print("after function") return f return wrapper @deco_testdef do_something(a,b,c): print(a) time.sleep(1) print(b) time.sleep(1) print(c) return aif __name__ == '__main__': # 使用@ f = do_something("1","2","3")
to output:
before function 1 2 3 after function
Personal understanding:
Equivalent to when executing the do_something
function, because of @
reasons, we already know that there is a layer of decorator deco_test
, so there is no need to write it separately deco_test(do_something)
is gone.
Decorator with parameters
# 带参数的装饰器def logging(level): def wrapper(func): def inner_wrapper(*args, **kwargs): print("[{level}]: enter function {func}()".format(level=level, func=func.__name__)) f = func(*args, **kwargs) print("after function: [{level}]: enter function {func}()".format(level=level, func=func.__name__)) return f return inner_wrapper return wrapper @logging(level="debug")def do_something(a,b,c): print(a) time.sleep(1) print(b) time.sleep(1) print(c) return aif __name__ == '__main__': # 使用@ f = do_something("1","2","3")
Output:
[debug]: enter function do_something() 1 2 3 after function: [debug]: enter function do_something()
Personal understanding:
Decorator With a parameter level = "debug"
.
The outermost function logging()
accepts parameters and applies them to the inner decorator function. The inner function wrapper()
accepts a function as a parameter, and then places a decorator on the function. The key point here is that the decorator can use the parameters passed to logging()
.
Class Decorator
# 类装饰器class deco_cls(object): def __init__(self, func): self._func = func def __call__(self, *args, **kwargs): print("class decorator before function") f = self._func(*args, **kwargs) print("class decorator after function") return f @deco_clsdef do_something(a,b,c): print(a) time.sleep(1) print(b) time.sleep(1) print(c) return aif __name__ == '__main__': # 使用@ f = do_something("1","2","3")
Output:
class decorator before function 1 2 3 class decorator after function
Personal understanding:
Use a decorator To wrap a function, return a callable instance. Therefore a class decorator is defined.
Two-layer decorator
# 不带参数的装饰器def deco_test(func): def wrapper(*args, **kwargs): print("before function") f = func(*args, **kwargs) print("after function") return f return wrapper# 带参数的装饰器def logging(level): def wrapper(func): def inner_wrapper(*args, **kwargs): print("[{level}]: enter function {func}()".format(level=level, func=func.__name__)) f = func(*args, **kwargs) print("after function: [{level}]: enter function {func}()".format(level=level, func=func.__name__)) return f return inner_wrapper return wrapper @logging(level="debug")@deco_testdef do_something(a,b,c): print(a) time.sleep(1) print(b) time.sleep(1) print(c) return aif __name__ == '__main__': # 使用@ f = do_something("1","2","3")
Output:
[debug]: enter function wrapper() before function 1 2 3 after function after function: [debug]: enter function wrapper()
Personal understanding:
In functiondo_something()
First put a layer of deco_test()
decorator on the outside, and then put a layer of logging()
decorator on the outside.
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Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

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Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

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