装饰器(Decorators)是Python的一个重要部分。简单地说：他们是修改其他函数的功能的函数。他们有助于让我们的代码更简短，也更符合Python规范（Pythonic）。

def a_new_decorator(a_func):

    def wrapTheFunction():
        print("I am doing some boring work before executing a_func()")

        a_func()

        print("I am doing some boring work after executing a_func()")

    return wrapTheFunction

def a_function_requiring_decoration():
    print("I am the function which needs some decoration to remove my foul smell")

a_function_requiring_decoration()
#outputs: "I am the function which needs some decoration to remove my foul smell"

a_function_requiring_decoration = a_new_decorator(a_function_requiring_decoration)
#now a_function_requiring_decoration is wrapped by wrapTheFunction()

a_function_requiring_decoration()
#outputs:I am doing some boring work before executing a_func()
#        I am the function which needs some decoration to remove my foul smell
#        I am doing some boring work after executing a_func()

上面的代码可以更简洁一些：

def a_new_decorator(a_func):

    def wrapTheFunction():
        print("I am doing some boring work before executing a_func()")

        a_func()

        print("I am doing some boring work after executing a_func()")

    return wrapTheFunction

@a_new_decorator
def a_function_requiring_decoration():
    print("I am the function which needs some decoration to remove my foul smell")

a_function_requiring_decoration()
#outputs:I am doing some boring work before executing a_func()
#        I am the function which needs some decoration to remove my foul smell
#        I am doing some boring work after executing a_func()

其中@a_new_decorator（注意语句位置，须位于被装修函数之前）等价于下列语句：

a_function_requiring_decoration = a_new_decorator(a_function_requiring_decoration)

如果需要获取被装饰函数的函数名，需要使用functools.wraps函数：

from functools import wraps

def a_new_decorator(a_func):
    @wraps(a_func)
    def wrapTheFunction():
        print("I am doing some boring work before executing a_func()")
        a_func()
        print("I am doing some boring work after executing a_func()")
    return wrapTheFunction

@a_new_decorator
def a_function_requiring_decoration():
    """Hey yo! Decorate me!"""
    print("I am the function which needs some decoration to "
          "remove my foul smell")

print(a_function_requiring_decoration.__name__)
# Output: a_function_requiring_decoration

否则，print(a_function_requiring_decoration.__name__)的返回结果将是wrapTheFunction。

装饰器能有助于检查某个人是否被授权去使用一个web应用的端点(endpoint)。它们被大量使用于Flask和Django web框架中。这里是一个例子来使用基于装饰器的授权：

from functools import wraps

def requires_auth(f):
    @wraps(f)
    def decorated(*args, **kwargs):
        auth = request.authorization
        if not auth or not check_auth(auth.username, auth.password):
            authenticate()
        return f(*args, **kwargs)
    return decorated

日志是装饰器运用的另一个亮点。这是个例子：

from functools import wraps

def logit(func):
    @wraps(func)
    def with_logging(*args, **kwargs):
        print(func.__name__ + " was called")
        return func(*args, **kwargs)
    return with_logging

@logit
def addition_func(x):
   """Do some math."""
   return x + x


result = addition_func(4)
# Output: addition_func was called

装饰器也可以带参数，我们将上面日志的例子修改一下，允许指定保存日志的位置：

from functools import wraps

def logit(logfile='out.log'):
    def logging_decorator(func):
        @wraps(func)
        def wrapped_function(*args, **kwargs):
            log_string = func.__name__ + " was called"
            print(log_string)
            # Open the logfile and append
            with open(logfile, 'a') as opened_file:
                # Now we log to the specified logfile
                opened_file.write(log_string + '
')
            return func(*args, **kwargs)
        return wrapped_function
    return logging_decorator

@logit()
def myfunc1():
    pass

myfunc1()
# Output: myfunc1 was called
# A file called out.log now exists, with the above string

@logit(logfile='func2.log')
def myfunc2():
    pass

myfunc2()
# Output: myfunc2 was called
# A file called func2.log now exists, with the above string

类也可以用来构建装饰器：

class logit(object):

    _logfile = 'out.log'

    def __init__(self, func):
        self.func = func

    def __call__(self, *args):
        log_string = self.func.__name__ + " was called"
        print(log_string)
        # Open the logfile and append
        with open(self._logfile, 'a') as opened_file:
            # Now we log to the specified logfile
            opened_file.write(log_string + '
')
        # Now, send a notification
        self.notify()

        # return base func
        return self.func(*args)



    def notify(self):
        # logit only logs, no more
        pass

这个实现有一个附加优势，在于比嵌套函数的方式更加整洁，而且包裹一个函数还是使用跟以前一样的语法：

logit._logfile = 'out2.log' # if change log file
@logit
def myfunc1():
    pass

myfunc1()
# Output: myfunc1 was called

我们给logit创建子类，来添加email的功能。

class email_logit(logit):
    '''
    A logit implementation for sending emails to admins
    when the function is called.
    '''
    def __init__(self, email='admin@myproject.com', *args, **kwargs):
        self.email = email
        super(email_logit, self).__init__(*args, **kwargs)

    def notify(self):
        # Send an email to self.email
        # Will not be implemented here
        pass