如果您没有详细解释，请参阅 Paolo Bergantino的回答。

装饰家基础知识

Python的功能是对象

要理解装饰器，首先必须了解函数是Python中的对象。这具有重要的后果。让我们通过一个简单的例子来看看为什么：

def shout(word="yes"):
    return word.capitalize()+"!"

print(shout())
# outputs : 'Yes!'

# As an object, you can assign the function to a variable like any other object 
scream = shout

# Notice we don't use parentheses: we are not calling the function,
# we are putting the function "shout" into the variable "scream".
# It means you can then call "shout" from "scream":

print(scream())
# outputs : 'Yes!'

# More than that, it means you can remove the old name 'shout',
# and the function will still be accessible from 'scream'

del shout
try:
    print(shout())
except NameError, e:
    print(e)
    #outputs: "name 'shout' is not defined"

print(scream())
# outputs: 'Yes!'

记住这一点。我们很快就会回过头来。

Python函数的另一个有趣的属性是它们可以在另一个函数中定义！

def talk():

    # You can define a function on the fly in "talk" ...
    def whisper(word="yes"):
        return word.lower()+"..."

    # ... and use it right away!
    print(whisper())

# You call "talk", that defines "whisper" EVERY TIME you call it, then
# "whisper" is called in "talk". 
talk()
# outputs: 
# "yes..."

# But "whisper" DOES NOT EXIST outside "talk":

try:
    print(whisper())
except NameError, e:
    print(e)
    #outputs : "name 'whisper' is not defined"*
    #Python's functions are objects

函数参考

好的，还在吗？现在有趣的部分......

你已经看到函数是对象。因此，功能：

• 可以分配给变量
• 可以在另一个函数中定义

这意味着 一个功能可以 return 另一个功能。

def getTalk(kind="shout"):

    # We define functions on the fly
    def shout(word="yes"):
        return word.capitalize()+"!"

    def whisper(word="yes") :
        return word.lower()+"...";

    # Then we return one of them
    if kind == "shout":
        # We don't use "()", we are not calling the function,
        # we are returning the function object
        return shout  
    else:
        return whisper

# How do you use this strange beast?

# Get the function and assign it to a variable
talk = getTalk()      

# You can see that "talk" is here a function object:
print(talk)
#outputs : <function shout at 0xb7ea817c>

# The object is the one returned by the function:
print(talk())
#outputs : Yes!

# And you can even use it directly if you feel wild:
print(getTalk("whisper")())
#outputs : yes...

还有更多！

如果你可以的话 return 一个函数，你可以传递一个作为参数：

def doSomethingBefore(func): 
    print("I do something before then I call the function you gave me")
    print(func())

doSomethingBefore(scream)
#outputs: 
#I do something before then I call the function you gave me
#Yes!

好吧，你只需要了解装饰器所需的一切。你看，装饰器是“包装器”，这意味着 他们让你在他们装饰的功能之前和之后执行代码 没有修改功能本身。

手工装饰

你是如何手动完成的：

# A decorator is a function that expects ANOTHER function as parameter
def my_shiny_new_decorator(a_function_to_decorate):

    # Inside, the decorator defines a function on the fly: the wrapper.
    # This function is going to be wrapped around the original function
    # so it can execute code before and after it.
    def the_wrapper_around_the_original_function():

        # Put here the code you want to be executed BEFORE the original function is called
        print("Before the function runs")

        # Call the function here (using parentheses)
        a_function_to_decorate()

        # Put here the code you want to be executed AFTER the original function is called
        print("After the function runs")

    # At this point, "a_function_to_decorate" HAS NEVER BEEN EXECUTED.
    # We return the wrapper function we have just created.
    # The wrapper contains the function and the code to execute before and after. It’s ready to use!
    return the_wrapper_around_the_original_function

# Now imagine you create a function you don't want to ever touch again.
def a_stand_alone_function():
    print("I am a stand alone function, don't you dare modify me")

a_stand_alone_function() 
#outputs: I am a stand alone function, don't you dare modify me

# Well, you can decorate it to extend its behavior.
# Just pass it to the decorator, it will wrap it dynamically in 
# any code you want and return you a new function ready to be used:

a_stand_alone_function_decorated = my_shiny_new_decorator(a_stand_alone_function)
a_stand_alone_function_decorated()
#outputs:
#Before the function runs
#I am a stand alone function, don't you dare modify me
#After the function runs

现在，你可能每次打电话都想要 a_stand_alone_function， a_stand_alone_function_decorated 被称为。这很容易，只是覆盖 a_stand_alone_function 使用返回的函数 my_shiny_new_decorator：

a_stand_alone_function = my_shiny_new_decorator(a_stand_alone_function)
a_stand_alone_function()
#outputs:
#Before the function runs
#I am a stand alone function, don't you dare modify me
#After the function runs

# That’s EXACTLY what decorators do!

装饰者神秘化了

上一个示例，使用装饰器语法：

@my_shiny_new_decorator
def another_stand_alone_function():
    print("Leave me alone")

another_stand_alone_function()  
#outputs:  
#Before the function runs
#Leave me alone
#After the function runs

是的，就是这样，就这么简单。 @decorator 只是一个快捷方式：

another_stand_alone_function = my_shiny_new_decorator(another_stand_alone_function)

装饰者只是一个pythonic变种 装饰设计模式。 Python中嵌入了几种经典设计模式以简化开发（如迭代器）。

当然，你可以积累装饰器：

def bread(func):
    def wrapper():
        print("</''''''\>")
        func()
        print("<\______/>")
    return wrapper

def ingredients(func):
    def wrapper():
        print("#tomatoes#")
        func()
        print("~salad~")
    return wrapper

def sandwich(food="--ham--"):
    print(food)

sandwich()
#outputs: --ham--
sandwich = bread(ingredients(sandwich))
sandwich()
#outputs:
#</''''''\>
# #tomatoes#
# --ham--
# ~salad~
#<\______/>

使用Python装饰器语法：

@bread
@ingredients
def sandwich(food="--ham--"):
    print(food)

sandwich()
#outputs:
#</''''''\>
# #tomatoes#
# --ham--
# ~salad~
#<\______/>

您设置装饰器MATTERS的顺序：

@ingredients
@bread
def strange_sandwich(food="--ham--"):
    print(food)

strange_sandwich()
#outputs:
##tomatoes#
#</''''''\>
# --ham--
#<\______/>
# ~salad~

现在：回答这个问题......

总之，您可以轻松地看到如何回答这个问题：

# The decorator to make it bold
def makebold(fn):
    # The new function the decorator returns
    def wrapper():
        # Insertion of some code before and after
        return "<b>" + fn() + "</b>"
    return wrapper

# The decorator to make it italic
def makeitalic(fn):
    # The new function the decorator returns
    def wrapper():
        # Insertion of some code before and after
        return "<i>" + fn() + "</i>"
    return wrapper

@makebold
@makeitalic
def say():
    return "hello"

print(say())
#outputs: <b><i>hello</i></b>

# This is the exact equivalent to 
def say():
    return "hello"
say = makebold(makeitalic(say))

print(say())
#outputs: <b><i>hello</i></b>

你现在可以离开快乐，或者更多地燃烧你的大脑并看到装饰器的高级用途。

将装饰器提升到一个新的水平

将参数传递给修饰函数

# It’s not black magic, you just have to let the wrapper 
# pass the argument:

def a_decorator_passing_arguments(function_to_decorate):
    def a_wrapper_accepting_arguments(arg1, arg2):
        print("I got args! Look: {0}, {1}".format(arg1, arg2))
        function_to_decorate(arg1, arg2)
    return a_wrapper_accepting_arguments

# Since when you are calling the function returned by the decorator, you are
# calling the wrapper, passing arguments to the wrapper will let it pass them to 
# the decorated function

@a_decorator_passing_arguments
def print_full_name(first_name, last_name):
    print("My name is {0} {1}".format(first_name, last_name))

print_full_name("Peter", "Venkman")
# outputs:
#I got args! Look: Peter Venkman
#My name is Peter Venkman

装饰方法

关于Python的一个好消息是方法和函数真的是一样的。唯一的区别是方法期望它们的第一个参数是对当前对象的引用（self）。

这意味着您可以以相同的方式为方法构建装饰器！记住要记住 self 考虑到：

def method_friendly_decorator(method_to_decorate):
    def wrapper(self, lie):
        lie = lie - 3 # very friendly, decrease age even more :-)
        return method_to_decorate(self, lie)
    return wrapper


class Lucy(object):

    def __init__(self):
        self.age = 32

    @method_friendly_decorator
    def sayYourAge(self, lie):
        print("I am {0}, what did you think?".format(self.age + lie))

l = Lucy()
l.sayYourAge(-3)
#outputs: I am 26, what did you think?

如果你正在制作通用装饰 - 一个你将适用于任何函数或方法，无论它的论点 - 然后只是使用 *args, **kwargs：

def a_decorator_passing_arbitrary_arguments(function_to_decorate):
    # The wrapper accepts any arguments
    def a_wrapper_accepting_arbitrary_arguments(*args, **kwargs):
        print("Do I have args?:")
        print(args)
        print(kwargs)
        # Then you unpack the arguments, here *args, **kwargs
        # If you are not familiar with unpacking, check:
        # http://www.saltycrane.com/blog/2008/01/how-to-use-args-and-kwargs-in-python/
        function_to_decorate(*args, **kwargs)
    return a_wrapper_accepting_arbitrary_arguments

@a_decorator_passing_arbitrary_arguments
def function_with_no_argument():
    print("Python is cool, no argument here.")

function_with_no_argument()
#outputs
#Do I have args?:
#()
#{}
#Python is cool, no argument here.

@a_decorator_passing_arbitrary_arguments
def function_with_arguments(a, b, c):
    print(a, b, c)

function_with_arguments(1,2,3)
#outputs
#Do I have args?:
#(1, 2, 3)
#{}
#1 2 3 

@a_decorator_passing_arbitrary_arguments
def function_with_named_arguments(a, b, c, platypus="Why not ?"):
    print("Do {0}, {1} and {2} like platypus? {3}".format(a, b, c, platypus))

function_with_named_arguments("Bill", "Linus", "Steve", platypus="Indeed!")
#outputs
#Do I have args ? :
#('Bill', 'Linus', 'Steve')
#{'platypus': 'Indeed!'}
#Do Bill, Linus and Steve like platypus? Indeed!

class Mary(object):

    def __init__(self):
        self.age = 31

    @a_decorator_passing_arbitrary_arguments
    def sayYourAge(self, lie=-3): # You can now add a default value
        print("I am {0}, what did you think?".format(self.age + lie))

m = Mary()
m.sayYourAge()
#outputs
# Do I have args?:
#(<__main__.Mary object at 0xb7d303ac>,)
#{}
#I am 28, what did you think?

将参数传递给装饰器

好的，现在您对将参数传递给装饰器本身有什么看法？

这可能会有些扭曲，因为装饰器必须接受函数作为参数。因此，您无法将装饰函数的参数直接传递给装饰器。

在急于解决之前，让我们写一点提醒：

# Decorators are ORDINARY functions
def my_decorator(func):
    print("I am an ordinary function")
    def wrapper():
        print("I am function returned by the decorator")
        func()
    return wrapper

# Therefore, you can call it without any "@"

def lazy_function():
    print("zzzzzzzz")

decorated_function = my_decorator(lazy_function)
#outputs: I am an ordinary function

# It outputs "I am an ordinary function", because that’s just what you do:
# calling a function. Nothing magic.

@my_decorator
def lazy_function():
    print("zzzzzzzz")

#outputs: I am an ordinary function

它完全一样。 “my_decorator“被叫了。所以当你 @my_decorator，你告诉Python调用函数'由变量标记'my_decorator“”。

这个很重要！你给的标签可以直接指向装饰 - 或不。

让我们变得邪恶。

def decorator_maker():

    print("I make decorators! I am executed only once: "
          "when you make me create a decorator.")

    def my_decorator(func):

        print("I am a decorator! I am executed only when you decorate a function.")

        def wrapped():
            print("I am the wrapper around the decorated function. "
                  "I am called when you call the decorated function. "
                  "As the wrapper, I return the RESULT of the decorated function.")
            return func()

        print("As the decorator, I return the wrapped function.")

        return wrapped

    print("As a decorator maker, I return a decorator")
    return my_decorator

# Let’s create a decorator. It’s just a new function after all.
new_decorator = decorator_maker()       
#outputs:
#I make decorators! I am executed only once: when you make me create a decorator.
#As a decorator maker, I return a decorator

# Then we decorate the function

def decorated_function():
    print("I am the decorated function.")

decorated_function = new_decorator(decorated_function)
#outputs:
#I am a decorator! I am executed only when you decorate a function.
#As the decorator, I return the wrapped function

# Let’s call the function:
decorated_function()
#outputs:
#I am the wrapper around the decorated function. I am called when you call the decorated function.
#As the wrapper, I return the RESULT of the decorated function.
#I am the decorated function.

这里不足为奇。

让我们做同样的事情，但跳过所有讨厌的中间变量：

def decorated_function():
    print("I am the decorated function.")
decorated_function = decorator_maker()(decorated_function)
#outputs:
#I make decorators! I am executed only once: when you make me create a decorator.
#As a decorator maker, I return a decorator
#I am a decorator! I am executed only when you decorate a function.
#As the decorator, I return the wrapped function.

# Finally:
decorated_function()    
#outputs:
#I am the wrapper around the decorated function. I am called when you call the decorated function.
#As the wrapper, I return the RESULT of the decorated function.
#I am the decorated function.

我们做吧 甚至更短：

@decorator_maker()
def decorated_function():
    print("I am the decorated function.")
#outputs:
#I make decorators! I am executed only once: when you make me create a decorator.
#As a decorator maker, I return a decorator
#I am a decorator! I am executed only when you decorate a function.
#As the decorator, I return the wrapped function.

#Eventually: 
decorated_function()    
#outputs:
#I am the wrapper around the decorated function. I am called when you call the decorated function.
#As the wrapper, I return the RESULT of the decorated function.
#I am the decorated function.

嘿，你看到了吗？我们使用函数调用“@“ 句法！ ：-）

所以，回到带有参数的装饰器。如果我们可以使用函数动态生成装饰器，我们可以将参数传递给该函数，对吧？

def decorator_maker_with_arguments(decorator_arg1, decorator_arg2):

    print("I make decorators! And I accept arguments: {0}, {1}".format(decorator_arg1, decorator_arg2))

    def my_decorator(func):
        # The ability to pass arguments here is a gift from closures.
        # If you are not comfortable with closures, you can assume it’s ok,
        # or read: https://stackoverflow.com/questions/13857/can-you-explain-closures-as-they-relate-to-python
        print("I am the decorator. Somehow you passed me arguments: {0}, {1}".format(decorator_arg1, decorator_arg2))

        # Don't confuse decorator arguments and function arguments!
        def wrapped(function_arg1, function_arg2) :
            print("I am the wrapper around the decorated function.\n"
                  "I can access all the variables\n"
                  "\t- from the decorator: {0} {1}\n"
                  "\t- from the function call: {2} {3}\n"
                  "Then I can pass them to the decorated function"
                  .format(decorator_arg1, decorator_arg2,
                          function_arg1, function_arg2))
            return func(function_arg1, function_arg2)

        return wrapped

    return my_decorator

@decorator_maker_with_arguments("Leonard", "Sheldon")
def decorated_function_with_arguments(function_arg1, function_arg2):
    print("I am the decorated function and only knows about my arguments: {0}"
           " {1}".format(function_arg1, function_arg2))

decorated_function_with_arguments("Rajesh", "Howard")
#outputs:
#I make decorators! And I accept arguments: Leonard Sheldon
#I am the decorator. Somehow you passed me arguments: Leonard Sheldon
#I am the wrapper around the decorated function. 
#I can access all the variables 
#   - from the decorator: Leonard Sheldon 
#   - from the function call: Rajesh Howard 
#Then I can pass them to the decorated function
#I am the decorated function and only knows about my arguments: Rajesh Howard

这是：带参数的装饰器。参数可以设置为变量：

c1 = "Penny"
c2 = "Leslie"

@decorator_maker_with_arguments("Leonard", c1)
def decorated_function_with_arguments(function_arg1, function_arg2):
    print("I am the decorated function and only knows about my arguments:"
           " {0} {1}".format(function_arg1, function_arg2))

decorated_function_with_arguments(c2, "Howard")
#outputs:
#I make decorators! And I accept arguments: Leonard Penny
#I am the decorator. Somehow you passed me arguments: Leonard Penny
#I am the wrapper around the decorated function. 
#I can access all the variables 
#   - from the decorator: Leonard Penny 
#   - from the function call: Leslie Howard 
#Then I can pass them to the decorated function
#I am the decorated function and only know about my arguments: Leslie Howard

如您所见，您可以像使用此技巧的任何函数一样将参数传递给装饰器。你甚至可以使用 *args, **kwargs 如果你希望。但请记住装饰者被召唤 只有一次。就在Python导入脚本的时候。之后您无法动态设置参数。当你做“导入x”时， 功能已经装饰好了，所以你不能 改变一切。

让我们练习：装饰装饰

好吧，作为奖励，我会给你一个片段，让任何装饰者一般都接受任何争论。毕竟，为了接受参数，我们使用另一个函数创建了装饰器。

我们包装了装饰者。

我们最近看到的其他包装功能还有什么？

哦，是的，装饰者！

让我们玩得开心，为装饰者写一个装饰器：

def decorator_with_args(decorator_to_enhance):
    """ 
    This function is supposed to be used as a decorator.
    It must decorate an other function, that is intended to be used as a decorator.
    Take a cup of coffee.
    It will allow any decorator to accept an arbitrary number of arguments,
    saving you the headache to remember how to do that every time.
    """

    # We use the same trick we did to pass arguments
    def decorator_maker(*args, **kwargs):

        # We create on the fly a decorator that accepts only a function
        # but keeps the passed arguments from the maker.
        def decorator_wrapper(func):

            # We return the result of the original decorator, which, after all, 
            # IS JUST AN ORDINARY FUNCTION (which returns a function).
            # Only pitfall: the decorator must have this specific signature or it won't work:
            return decorator_to_enhance(func, *args, **kwargs)

        return decorator_wrapper

    return decorator_maker

它可以使用如下：

# You create the function you will use as a decorator. And stick a decorator on it :-)
# Don't forget, the signature is "decorator(func, *args, **kwargs)"
@decorator_with_args 
def decorated_decorator(func, *args, **kwargs): 
    def wrapper(function_arg1, function_arg2):
        print("Decorated with {0} {1}".format(args, kwargs))
        return func(function_arg1, function_arg2)
    return wrapper

# Then you decorate the functions you wish with your brand new decorated decorator.

@decorated_decorator(42, 404, 1024)
def decorated_function(function_arg1, function_arg2):
    print("Hello {0} {1}".format(function_arg1, function_arg2))

decorated_function("Universe and", "everything")
#outputs:
#Decorated with (42, 404, 1024) {}
#Hello Universe and everything

# Whoooot!

我知道，你最后一次有这种感觉，是在听了一个人说：“在理解递归之前，你必须先了解递归”。但是现在，你掌握这个并不是很好吗？

最佳实践：装饰者

• 装饰器是在Python 2.4中引入的，因此请确保您的代码将在> = 2.4上运行。
• 装饰器减慢了函数调用。记住这一点。
• 你不能解开一个功能。 （那里 是 hacks来创建可以删除的装饰器，但没有人使用它们。）所以一旦一个函数被装饰，它就会被装饰 对于所有代码。
• 装饰器包装函数，这使得它们难以调试。 （这从Python> = 2.5变得更好;见下文。）

该 functools 模块是在Python 2.5中引入的。它包括功能 functools.wraps()，将装饰函数的名称，模块和文档字符串复制到其包装器。

（有趣的事实： functools.wraps() 是装饰者！ ）

# For debugging, the stacktrace prints you the function __name__
def foo():
    print("foo")

print(foo.__name__)
#outputs: foo

# With a decorator, it gets messy    
def bar(func):
    def wrapper():
        print("bar")
        return func()
    return wrapper

@bar
def foo():
    print("foo")

print(foo.__name__)
#outputs: wrapper

# "functools" can help for that

import functools

def bar(func):
    # We say that "wrapper", is wrapping "func"
    # and the magic begins
    @functools.wraps(func)
    def wrapper():
        print("bar")
        return func()
    return wrapper

@bar
def foo():
    print("foo")

print(foo.__name__)
#outputs: foo

装饰器如何有用？

现在这个大问题： 我可以使用装饰器做什么？

看起来酷而有力，但一个实际的例子会很棒。嗯，有1000种可能性。经典用法是从外部库扩展函数行为（您无法修改它），或者用于调试（您不想修改它，因为它是临时的）。

您可以使用它们以DRY的方式扩展多个功能，如下所示：

def benchmark(func):
    """
    A decorator that prints the time a function takes
    to execute.
    """
    import time
    def wrapper(*args, **kwargs):
        t = time.clock()
        res = func(*args, **kwargs)
        print("{0} {1}".format(func.__name__, time.clock()-t))
        return res
    return wrapper


def logging(func):
    """
    A decorator that logs the activity of the script.
    (it actually just prints it, but it could be logging!)
    """
    def wrapper(*args, **kwargs):
        res = func(*args, **kwargs)
        print("{0} {1} {2}".format(func.__name__, args, kwargs))
        return res
    return wrapper


def counter(func):
    """
    A decorator that counts and prints the number of times a function has been executed
    """
    def wrapper(*args, **kwargs):
        wrapper.count = wrapper.count + 1
        res = func(*args, **kwargs)
        print("{0} has been used: {1}x".format(func.__name__, wrapper.count))
        return res
    wrapper.count = 0
    return wrapper

@counter
@benchmark
@logging
def reverse_string(string):
    return str(reversed(string))

print(reverse_string("Able was I ere I saw Elba"))
print(reverse_string("A man, a plan, a canoe, pasta, heros, rajahs, a coloratura, maps, snipe, percale, macaroni, a gag, a banana bag, a tan, a tag, a banana bag again (or a camel), a crepe, pins, Spam, a rut, a Rolo, cash, a jar, sore hats, a peon, a canal: Panama!"))

#outputs:
#reverse_string ('Able was I ere I saw Elba',) {}
#wrapper 0.0
#wrapper has been used: 1x 
#ablE was I ere I saw elbA
#reverse_string ('A man, a plan, a canoe, pasta, heros, rajahs, a coloratura, maps, snipe, percale, macaroni, a gag, a banana bag, a tan, a tag, a banana bag again (or a camel), a crepe, pins, Spam, a rut, a Rolo, cash, a jar, sore hats, a peon, a canal: Panama!',) {}
#wrapper 0.0
#wrapper has been used: 2x
#!amanaP :lanac a ,noep a ,stah eros ,raj a ,hsac ,oloR a ,tur a ,mapS ,snip ,eperc a ,)lemac a ro( niaga gab ananab a ,gat a ,nat a ,gab ananab a ,gag a ,inoracam ,elacrep ,epins ,spam ,arutaroloc a ,shajar ,soreh ,atsap ,eonac a ,nalp a ,nam A

当然，装饰器的好处在于你几乎可以在没有重写的情况下立即使用它们。干，我说：

@counter
@benchmark
@logging
def get_random_futurama_quote():
    from urllib import urlopen
    result = urlopen("http://subfusion.net/cgi-bin/quote.pl?quote=futurama").read()
    try:
        value = result.split("<br><b><hr><br>")[1].split("<br><br><hr>")[0]
        return value.strip()
    except:
        return "No, I'm ... doesn't!"


print(get_random_futurama_quote())
print(get_random_futurama_quote())

#outputs:
#get_random_futurama_quote () {}
#wrapper 0.02
#wrapper has been used: 1x
#The laws of science be a harsh mistress.
#get_random_futurama_quote () {}
#wrapper 0.01
#wrapper has been used: 2x
#Curse you, merciful Poseidon!

Python本身提供了几个装饰器： property， staticmethod等

• Django使用装饰器来管理缓存和查看权限。
• 扭曲伪造内联异步函数调用。

这真的是一个大型游乐场。

或者，您可以编写一个返回装饰器的工厂函数，该装饰器将装饰函数的返回值包装在传递给工厂函数的标记中。例如：

from functools import wraps

def wrap_in_tag(tag):
    def factory(func):
        @wraps(func)
        def decorator():
            return '<%(tag)s>%(rv)s</%(tag)s>' % (
                {'tag': tag, 'rv': func()})
        return decorator
    return factory

这使您可以写：

@wrap_in_tag('b')
@wrap_in_tag('i')
def say():
    return 'hello'

要么

makebold = wrap_in_tag('b')
makeitalic = wrap_in_tag('i')

@makebold
@makeitalic
def say():
    return 'hello'

就个人而言，我会以不同的方式编写装饰器：

from functools import wraps

def wrap_in_tag(tag):
    def factory(func):
        @wraps(func)
        def decorator(val):
            return func('<%(tag)s>%(val)s</%(tag)s>' %
                        {'tag': tag, 'val': val})
        return decorator
    return factory

会产生：

@wrap_in_tag('b')
@wrap_in_tag('i')
def say(val):
    return val
say('hello')

不要忘记装饰器语法是简写的结构：

say = wrap_in_tag('b')(wrap_in_tag('i')(say)))

看起来其他人已经告诉过你如何解决这个问题。我希望这能帮助你理解装饰器是什么。

装饰者只是语法糖。

这个

@decorator
def func():
    ...

扩展到

def func():
    ...
func = decorator(func)

当然，您也可以从装饰器函数返回lambdas：

def makebold(f): 
    return lambda: "<b>" + f() + "</b>"
def makeitalic(f): 
    return lambda: "<i>" + f() + "</i>"

@makebold
@makeitalic
def say():
    return "Hello"

print say()

Python装饰器为另一个函数添加了额外的功能

斜体装饰器可能就像

def makeitalic(fn):
    def newFunc():
        return "<i>" + fn() + "</i>"
    return newFunc

请注意，函数是在函数内定义的。 它基本上做的是用新定义的函数替换函数。例如，我有这门课

class foo:
    def bar(self):
        print "hi"
    def foobar(self):
        print "hi again"

现在说，我希望两个函数在完成之后和之前打印“---”。 我可以在每个print语句之前和之后添加一个打印“---”。 但因为我不喜欢重复自己，我会做一个装饰师

def addDashes(fn): # notice it takes a function as an argument
    def newFunction(self): # define a new function
        print "---"
        fn(self) # call the original function
        print "---"
    return newFunction
    # Return the newly defined function - it will "replace" the original

所以现在我可以改变我的课程

class foo:
    @addDashes
    def bar(self):
        print "hi"

    @addDashes
    def foobar(self):
        print "hi again"

有关装饰器的更多信息，请检查 http://www.ibm.com/developerworks/linux/library/l-cpdecor.html

您 可以 制作两个单独的装饰器，按照下面的说明进行操作。注意使用 *args, **kwargs 在宣言中 wrapped() 支持具有多个参数的修饰函数的函数（对于该示例而言，这不是必需的） say() 功能，但包含在一般性）。

出于类似的原因， functools.wraps decorator用于将包装函数的元属性更改为正在装饰的函数的元属性。这会产生错误消息和嵌入式功能文档（func.__doc__）是装饰函数的代替 wrapped()的。

from functools import wraps

def makebold(fn):
    @wraps(fn)
    def wrapped(*args, **kwargs):
        return "<b>" + fn(*args, **kwargs) + "</b>"
    return wrapped

def makeitalic(fn):
    @wraps(fn)
    def wrapped(*args, **kwargs):
        return "<i>" + fn(*args, **kwargs) + "</i>"
    return wrapped

@makebold
@makeitalic
def say():
    return 'Hello'

print(say())  # -> <b><i>Hello</i></b>

改进

正如您所看到的，这两个装饰器中存在大量重复代码。鉴于这种相似性，你最好制作一个实际上是通用的 装饰工厂 - 换句话说，一个装饰器，使其他装饰器。这样就可以减少代码重复次数 - 并允许 干 要遵循的原则。

def html_deco(tag):
    def decorator(fn):
        @wraps(fn)
        def wrapped(*args, **kwargs):
            return '<%s>' % tag + fn(*args, **kwargs) + '</%s>' % tag
        return wrapped
    return decorator

@html_deco('b')
@html_deco('i')
def greet(whom=''):
    return 'Hello' + (' ' + whom) if whom else ''

print(greet('world'))  # -> <b><i>Hello world</i></b>

为了使代码更具可读性，您可以为工厂生成的装饰器分配更具描述性的名称：

makebold = html_deco('b')
makeitalic = html_deco('i')

@makebold
@makeitalic
def greet(whom=''):
    return 'Hello' + (' ' + whom) if whom else ''

print(greet('world'))  # -> <b><i>Hello world</i></b>

或者甚至将它们组合成这样：

makebolditalic = lambda fn: makebold(makeitalic(fn))

@makebolditalic
def greet(whom=''):
    return 'Hello' + (' ' + whom) if whom else ''

print(greet('world'))  # -> <b><i>Hello world</i></b>

效率

虽然上面的示例都可以正常工作，但是当一次应用多个装饰器时，生成的代码会以无关函数调用的形式涉及相当大的开销。这可能无关紧要，具体取决于具体用法（例如，可能是I / O绑定）。

如果修饰函数的速度很重要，可以通过编写稍微不同的装饰器工厂函数来保持开销到一个额外的函数调用，该函数实现一次添加所有标记，因此它可以生成代码以避免发生的附加函数调用通过为每个标签使用单独的装饰器。

这需要装饰器本身有更多的代码，但这只在它被应用于函数定义时运行，而不是在它们自己被调用时运行。这在使用时创建更可读的名称时也适用 lambda 功能如前所述。样品：

def multi_html_deco(*tags):
    start_tags, end_tags = [], []
    for tag in tags:
        start_tags.append('<%s>' % tag)
        end_tags.append('</%s>' % tag)
    start_tags = ''.join(start_tags)
    end_tags = ''.join(reversed(end_tags))

    def decorator(fn):
        @wraps(fn)
        def wrapped(*args, **kwargs):
            return start_tags + fn(*args, **kwargs) + end_tags
        return wrapped
    return decorator

makebolditalic = multi_html_deco('b', 'i')

@makebolditalic
def greet(whom=''):
    return 'Hello' + (' ' + whom) if whom else ''

print(greet('world'))  # -> <b><i>Hello world</i></b>

另一种做同样事情的方法：

class bol(object):
  def __init__(self, f):
    self.f = f
  def __call__(self):
    return "<b>{}</b>".format(self.f())

class ita(object):
  def __init__(self, f):
    self.f = f
  def __call__(self):
    return "<i>{}</i>".format(self.f())

@bol
@ita
def sayhi():
  return 'hi'

或者，更灵活：

class sty(object):
  def __init__(self, tag):
    self.tag = tag
  def __call__(self, f):
    def newf():
      return "<{tag}>{res}</{tag}>".format(res=f(), tag=self.tag)
    return newf

@sty('b')
@sty('i')
def sayhi():
  return 'hi'

如何在Python中创建两个装饰器来执行以下操作？

调用时，您需要以下函数：

@makebold
@makeitalic
def say():
    return "Hello"

回来：

<b><i>Hello</i></b>

简单解决方案

为了最简单地做到这一点，让make decorators返回关闭函数（闭包）并调用它的lambdas（匿名函数）并调用它：

def makeitalic(fn):
    return lambda: '<i>' + fn() + '</i>'

def makebold(fn):
    return lambda: '<b>' + fn() + '</b>'

现在根据需要使用它们：

@makebold
@makeitalic
def say():
    return 'Hello'

现在：

>>> say()
'<b><i>Hello</i></b>'

简单解决方案的问题

但我们似乎几乎失去了原有的功能。

>>> say
<function <lambda> at 0x4ACFA070>

为了找到它，我们需要深入研究每个lambda的闭包，其中一个被埋在另一个中：

>>> say.__closure__[0].cell_contents
<function <lambda> at 0x4ACFA030>
>>> say.__closure__[0].cell_contents.__closure__[0].cell_contents
<function say at 0x4ACFA730>

因此，如果我们将文档放在这个函数上，或者希望能够装饰带有多个参数的函数，或者我们只是想知道我们在调试会话中看到了什么函数，我们需要对我们的函数做更多的事情。包装。

全功能解决方案 - 克服大多数这些问题

我们有装饰师 wraps 来自 functools 标准库中的模块！

from functools import wraps

def makeitalic(fn):
    # must assign/update attributes from wrapped function to wrapper
    # __module__, __name__, __doc__, and __dict__ by default
    @wraps(fn) # explicitly give function whose attributes it is applying
    def wrapped(*args, **kwargs):
        return '<i>' + fn(*args, **kwargs) + '</i>'
    return wrapped

def makebold(fn):
    @wraps(fn)
    def wrapped(*args, **kwargs):
        return '<b>' + fn(*args, **kwargs) + '</b>'
    return wrapped

不幸的是，仍然有一些样板，但这很简单，我们可以做到。

在Python 3中，您也可以获得 __qualname__ 和 __annotations__ 默认分配。

所以现在：

@makebold
@makeitalic
def say():
    """This function returns a bolded, italicized 'hello'"""
    return 'Hello'

现在：

>>> say
<function say at 0x14BB8F70>
>>> help(say)
Help on function say in module __main__:

say(*args, **kwargs)
    This function returns a bolded, italicized 'hello'

结论

所以我们看到了 wraps 使包装函数几乎完成所有操作，除了告诉我们函数作为参数的确切内容。

还有其他模块可能尝试解决该问题，但该解决方案尚未出现在标准库中。