Develop Reference

how __name__ change in python decorator

Recently I'm learning Python decorator and the use of functools.wraps.
def a():
def b():
def c():
print('hello')
return c
return b
print a.__name__
#output:a
I understand why the output is a.But I don't know how __name__ change in the following code.
def log(text):
def decorator(func):
def wrapper(*args, **kw):
print('%s %s():' % (text, func.__name__))
return func(*args, **kw)
return wrapper
return decorator
#log('...')
def simple():
print('*' * 20)
print simple.__name__
#output:wrapper
Why the output is 'wrapper' rather than 'decorator' or 'log'?

Some basics:
#decorator
def f():
pass
is equivalent to:
def f():
pass
f = decorator(f)
Decorator with args:
#decorator(*args, **kwargs)
def f():
pass
is equivalent to:
def f():
pass
decorator_instance = decorator(*args, **kwargs)
f = decorator_instance(f)
With knowing so, we may rewrite your example to:
def simple():
print('*' * 20)
log_instance = log('...')
simple = log_instance(simple)
Let's analyze what happens in last two lines:
log_instance is a decorator function, and text variable within it is equal to '...'
Since decorator (regardless of text value) returns function named wrapper, simple is replaced with function named wrapper

The point of decorators is to replace a function or class with what is returned by the decorator, when called with that function/class as argument. Decorators with arguments are a bit more convoluted, as you first call the outer method (log) to obtain a "parameterized" decorator (decorator), then you call that one and obtain the final function (wrapper), which will replace the decorated function (simple).
So, to give it some structure,
call log with '...' as argument and obtain decorator
call decorator with simple as argument and obtain wrapper
replace simple with wrapper

#log('...')
def simple(...
is equivalent to
def simple(...
simple = log('...')(simple)
so log is actually called, returning decorator, which is called with simple as argument which is then replaced by decorator's return value, which is the function wrapper, thus its __name__ is wrapper.

How do I pass extra arguments to a Python decorator?

I have a decorator like below.
def myDecorator(test_func):
return callSomeWrapper(test_func)
def callSomeWrapper(test_func):
return test_func
#myDecorator
def someFunc():
print 'hello'
I want to enhance this decorator to accept another argument like below
def myDecorator(test_func,logIt):
if logIt:
print "Calling Function: " + test_func.__name__
return callSomeWrapper(test_func)
#myDecorator(False)
def someFunc():
print 'Hello'
But this code gives the error,
TypeError: myDecorator() takes exactly 2 arguments (1 given)
Why is the function not automatically passed? How do I explicitly pass the function to the decorator function?

Since you are calling the decorator like a function, it needs to return another function which is the actual decorator:
def my_decorator(param):
def actual_decorator(func):
print("Decorating function {}, with parameter {}".format(func.__name__, param))
return function_wrapper(func) # assume we defined a wrapper somewhere
return actual_decorator
The outer function will be given any arguments you pass explicitly, and should return the inner function. The inner function will be passed the function to decorate, and return the modified function.
Usually you want the decorator to change the function behavior by wrapping it in a wrapper function. Here's an example that optionally adds logging when the function is called:
def log_decorator(log_enabled):
def actual_decorator(func):
#functools.wraps(func)
def wrapper(*args, **kwargs):
if log_enabled:
print("Calling Function: " + func.__name__)
return func(*args, **kwargs)
return wrapper
return actual_decorator
The functools.wraps call copies things like the name and docstring to the wrapper function, to make it more similar to the original function.
Example usage:
>>> #log_decorator(True)
... def f(x):
... return x+1
...
>>> f(4)
Calling Function: f
5

Just to provide a different viewpoint: the syntax
#expr
def func(...): #stuff
is equivalent to
def func(...): #stuff
func = expr(func)
In particular, expr can be anything you like, as long as it evaluates to a callable. In particular particular, expr can be a decorator factory: you give it some parameters and it gives you a decorator. So maybe a better way to understand your situation is as
dec = decorator_factory(*args)
#dec
def func(...):
which can then be shortened to
#decorator_factory(*args)
def func(...):
Of course, since it looks like decorator_factory is a decorator, people tend to name it to reflect that. Which can be confusing when you try to follow the levels of indirection.

Just want to add some usefull trick that will allow to make decorator arguments optional. It will also alows to reuse decorator and decrease nesting
import functools
def myDecorator(test_func=None,logIt=None):
if test_func is None:
return functools.partial(myDecorator, logIt=logIt)
#functools.wraps(test_func)
def f(*args, **kwargs):
if logIt==1:
print 'Logging level 1 for {}'.format(test_func.__name__)
if logIt==2:
print 'Logging level 2 for {}'.format(test_func.__name__)
return test_func(*args, **kwargs)
return f
#new decorator
myDecorator_2 = myDecorator(logIt=2)
#myDecorator(logIt=2)
def pow2(i):
return i**2
#myDecorator
def pow3(i):
return i**3
#myDecorator_2
def pow4(i):
return i**4
print pow2(2)
print pow3(2)
print pow4(2)

Just another way of doing decorators.
I find this way the easiest to wrap my head around.
class NiceDecorator:
def __init__(self, param_foo='a', param_bar='b'):
self.param_foo = param_foo
self.param_bar = param_bar
def __call__(self, func):
def my_logic(*args, **kwargs):
# whatever logic your decorator is supposed to implement goes in here
print('pre action baz')
print(self.param_bar)
# including the call to the decorated function (if you want to do that)
result = func(*args, **kwargs)
print('post action beep')
return result
return my_logic
# usage example from here on
#NiceDecorator(param_bar='baaar')
def example():
print('example yay')
example()

Now if you want to call a function function1 with a decorator decorator_with_arg and in this case both the function and the decorator take arguments,
def function1(a, b):
print (a, b)
decorator_with_arg(10)(function1)(1, 2)

Methods with the same name in one class in Python

How can I declare a few methods with the same name, but with different numbers of parameters or different types in one class?
What must I change in the following class?
class MyClass:
""""""
#----------------------------------------------------------------------
def __init__(self):
"""Constructor"""
def my_method(self,parameter_A_that_Must_Be_String):
print parameter_A_that_Must_Be_String
def my_method(self,parameter_A_that_Must_Be_String,parameter_B_that_Must_Be_String):
print parameter_A_that_Must_Be_String
print parameter_B_that_Must_Be_String
def my_method(self,parameter_A_that_Must_Be_String,parameter_A_that_Must_Be_Int):
print parameter_A_that_Must_Be_String * parameter_A_that_Must_Be_Int

You can have a function that takes in a variable number of arguments.
def my_method(*args, **kwds):
# Do something
# When you call the method
my_method(a1, a2, k1=a3, k2=a4)
# You get:
args = (a1, a2)
kwds = {'k1':a3, 'k2':a4}
So you can modify your function as follows:
def my_method(*args):
if len(args) == 1 and isinstance(args[0], str):
# Case 1
elif len(args) == 2 and isinstance(args[1], int):
# Case 2
elif len(args) == 2 and isinstance(args[1], str):
# Case 3

You can't. There are not overloads or multimethods or similar things. One name refers to one thing. As far as the language is concerned anyway, you can always emulate them yourself... You could check types with isinstance (but please do it properly - e.g. in Python 2, use basestring to detect both strings and unicode), but it's ugly, generally discouraged and rarely useful. If the methods do different things, give them different names. Consider polymorphism as well.

Using Python 3.5 or higher, you can use #typing.overload to provide type annotations for overloaded functions/methods.
From the docs:
#overload
def process(response: None) -> None:
...
#overload
def process(response: int) -> tuple[int, str]:
...
#overload
def process(response: bytes) -> str:
...
def process(response):
<actual implementation>

Short answer: you can't (see this previous discussion). Typically you'd use something like (you could add more type checking and reorder):
def my_method(self,parameter_A, parameter_B=None):
if isinstance(parameter_B, int):
print parameter_A * parameter_B
else:
print parameter_A
if parameter_B is not None:
print parameter_B

You can try multimethods in Python:
http://www.artima.com/weblogs/viewpost.jsp?thread=101605
But I don't believe multimethod is a way to go. Rather objects that you pass to a method should have common interface. You are trying to achieve method overloading similar to the one in C++, but it is very rarely required in Python. One way to do this is a cascade of ifs using isinstance, but that's ugly.

Python is nothing like Java.
There are not really types, just objects with methods.
There is a way to test if a passed object is from a class, but it is mainly bad practices.
However, the code you want to produce for the two first methods should be something like
class MyClass(object):
def my_method(self, str1, str2=None):
print str1
if str2: print str2
For the third, well... Use a different name...

This cannot work. No matter how many arguments you have, the name m will be overriden with the second m method.
class C:
def m(self):
print('m first')
def m(self, x):
print(f'm second {x}')
ci=C();
#ci.m() # will not work TypeError: m() missing 1 required positional argument: 'x'
ci.m(1) # works
The output will simple be:
m second 1

You probably want a pattern similar to the following:
Note that adding '_' to the beginning of a method name is convention for marking a private method.
class MyClass:
""""""
#----------------------------------------------------------------------
def __init__(self):
"""Constructor"""
def my_method(self,parameter_A_that_Must_Be_String, param2=None):
if type(param2) == str:
return self._my_method_extra_string_version(parameter_A_that_Must_Be_String, param2)
elif type(param2) == int:
return self._my_method_extra_int_version(parameter_A_that_Must_Be_String, param2)
else:
pass # use the default behavior in this function
print parameter_A_that_Must_Be_String
def _my_method_extra_string_version(self,parameter_A_that_Must_Be_String, parameter_B_that_Must_Be_String):
print parameter_A_that_Must_Be_String
print parameter_B_that_Must_Be_String
def _my_method_extra_int_version(self,parameter_A_that_Must_Be_String, parameter_A_that_Must_Be_Int):
print parameter_A_that_Must_Be_String * parameter_A_that_Must_Be_Int

class MyClass:
def __init__(this, foo_str, bar_int):
this.__foo = foo_str
this.__bar = bar_int
def foo(this, new=None):
if new != None:
try:
this.__foo = str(new)
except ValueError:
print("Illegal value. foo unchanged.")
return this.__foo
def bar(this, new=None):
if new != None:
try:
this.__bar = int(new)
except ValueError:
print("Illegal value. bar unchanged.")
return this.__bar
obj = MyClass("test", 42)
print(obj.foo(), obj.bar())
print(obj.foo("tset"), obj.bar(24))
print(obj.foo(42), obj.bar("test"))
Output:
test 42
tset 24
Illegal value. bar unchanged.
42 24

I think one very simple example is missing from all the answers, and that is: what to do when the only difference between variations on the method is the number of arguments. The answer still is to use a method with variable number of arguments.
Say, you start with a method that requires use of two arguments
def method(int_a, str_b):
print("Got arguments: '{0}' and '{1}'".format(int_a, str_b)
then you need to add a variant with just the second argument (say, because the integer is redundant), the solution is very simple:
def _method_2_param(int_a, str_b):
print("Got arguments: '{0}' and '{1}'".format(int_a, str_b))
def _method_1_param(str_b):
print("Got argument: '{0}'".format(str_b))
def method(*args, **kwargs):
if len(args) + len(kwargs) == 2:
return _method_2_param(args, kwargs)
elif len(args) + len(kwargs) == 1:
return _method_1_param(args, kwargs)
else:
raise TypeError("Method requires one or two arguments")
The nice thing about this solution is that no matter if the calling code used keyword arguments or positional arguments before, it will still continue to work.

As of Python 3.10 a more elegant solution would be to use Structural Pattern Matching.
def my_method(parameters):
match parameters:
case str():
# Case 1
case (str(), str()):
# Case 2
case (str(), int()):
# Case 3
case _:
print('no match')

How are these type of python decorators written?

I'd like to write a decorator that would limit the number of times a function can be executed, something along the following syntax :
#max_execs(5)
def my_method(*a,**k):
# do something here
pass
I think it's possible to write this type of decorator, but I don't know how. I think a function won't be this decorator's first argument, right? I'd like a "plain decorator" implementation, not some class with a call method.
The reason for this is to learn how they are written. Please explain the syntax, and how that decorator works.

This is what I whipped up. It doesn't use a class, but it does use function attributes:
def max_execs(n=5):
def decorator(fn):
fn.max = n
fn.called = 0
def wrapped(*args, **kwargs):
fn.called += 1
if fn.called <= fn.max:
return fn(*args, **kwargs)
else:
# Replace with your own exception, or something
# else that you want to happen when the limit
# is reached
raise RuntimeError("max executions exceeded")
return wrapped
return decorator
max_execs returns a functioned called decorator, which in turn returns wrapped. decoration stores the max execs and current number of execs in two function attributes, which then get checked in wrapped.
Translation: When using the decorator like this:
#max_execs(5)
def f():
print "hi!"
You're basically doing something like this:
f = max_execs(5)(f)

Decorator is merely a callable that transforms a function into something else. In your case, max_execs(5) must be a callable that transforms a function into another callable object that will count and forward the calls.
class helper:
def __init__(self, i, fn):
self.i = i
self.fn = fn
def __call__(self, *args, **kwargs):
if self.i > 0:
self.i = self.i - 1
return self.fn(*args, **kwargs)
class max_execs:
def __init__(self, i):
self.i = i
def __call__(self, fn):
return helper(self.i, fn)
I don't see why you would want to limit yourself to a function (and not a class). But if you really want to...
def max_execs(n):
return lambda fn, i=n: return helper(i, fn)

There are two ways of doing it. The object-oriented way is to make a class:
class max_execs:
def __init__(self, max_executions):
self.max_executions = max_executions
self.executions = 0
def __call__(self, func):
#wraps(func)
def maybe(*args, **kwargs):
if self.executions < self.max_executions:
self.executions += 1
return func(*args, **kwargs)
else:
print "fail"
return maybe
See this question for an explanation of wraps.
I prefer the above OOP approach for this kind of decorator, since you've basically got a private count variable tracking the number of executions. However, the other approach is to use a closure, such as
def max_execs(max_executions):
executions = [0]
def actual_decorator(func):
#wraps(func)
def maybe(*args, **kwargs):
if executions[0] < max_executions:
executions[0] += 1
return func(*args, **kwargs)
else:
print "fail"
return maybe
return actual_decorator
This involved three functions. The max_execs function is given a parameter for the number of executions and returns a decorator that will restrict you to that many calls. That function, the actual_decorator, does the same thing as our __call__ method in the OOP example. The only weirdness is that since we don't have a class with private variables, we need to mutate the executions variable which is in the outer scope of our closure. Python 3.0 supports this with the nonlocal statement, but in Python 2.6 or earlier, we need to wrap our executions count in a list so that it can be mutated.

Without relying to a state in a class, you have to save the state (count) in the function itself:
def max_execs(count):
def new_meth(meth):
meth.count = count
def new(*a,**k):
meth.count -= 1
print meth.count
if meth.count>=0:
return meth(*a,**k)
return new
return new_meth
#max_execs(5)
def f():
print "invoked"
[f() for _ in range(10)]
It gives:
5
invoked
4
invoked
3
invoked
2
invoked
1
invoked
0
-1
-2
-3
-4

This method does not modify function internals, instead wraps it into a callable object.
Using class slows down execution by ~20% vs using the patched function!
def max_execs(n=1):
class limit_wrapper:
def __init__(self, fn, max):
self.calls_left = max
self.fn = fn
def __call__(self,*a,**kw):
if self.calls_left > 0:
self.calls_left -= 1
return self.fn(*a,**kw)
raise Exception("max num of calls is %d" % self.i)
def decorator(fn):
return limit_wrapper(fn,n)
return decorator
#max_execs(2)
def fun():
print "called"

I know you said you didn't want a class, but unfortunately that's the only way I can think of how to do it off the top of my head.
class mymethodwrapper:
def __init__(self):
self.maxcalls = 0
def mymethod(self):
self.maxcalls += 1
if self.maxcalls > 5:
return
#rest of your code
print "Code fired!"
Fire it up like this
a = mymethodwrapper
for x in range(1000):
a.mymethod()
The output would be:
>>> Code fired!
>>> Code fired!
>>> Code fired!
>>> Code fired!
>>> Code fired!

How do I make function decorators and chain them together?

How do I make two decorators in Python that would do the following?
#make_bold
#make_italic
def say():
return "Hello"
Calling say() should return:
"<b><i>Hello</i></b>"

If you are not into long explanations, see Paolo Bergantino’s answer.
Decorator Basics
Python’s functions are objects
To understand decorators, you must first understand that functions are objects in Python. This has important consequences. Let’s see why with a simple example :
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 as e:
print(e)
#outputs: "name 'shout' is not defined"
print(scream())
# outputs: 'Yes!'
Keep this in mind. We’ll circle back to it shortly.
Another interesting property of Python functions is they can be defined inside another function!
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 as e:
print(e)
#outputs : "name 'whisper' is not defined"*
#Python's functions are objects
Functions references
Okay, still here? Now the fun part...
You’ve seen that functions are objects. Therefore, functions:
can be assigned to a variable
can be defined in another function
That means that a function can return another function.
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...
There’s more!
If you can return a function, you can pass one as a parameter:
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!
Well, you just have everything needed to understand decorators. You see, decorators are “wrappers”, which means that they let you execute code before and after the function they decorate without modifying the function itself.
Handcrafted decorators
How you’d do it manually:
# 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
Now, you probably want that every time you call a_stand_alone_function, a_stand_alone_function_decorated is called instead. That’s easy, just overwrite a_stand_alone_function with the function returned by 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!
Decorators demystified
The previous example, using the decorator syntax:
#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
Yes, that’s all, it’s that simple. #decorator is just a shortcut to:
another_stand_alone_function = my_shiny_new_decorator(another_stand_alone_function)
Decorators are just a pythonic variant of the decorator design pattern. There are several classic design patterns embedded in Python to ease development (like iterators).
Of course, you can accumulate decorators:
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~
#<\______/>
Using the Python decorator syntax:
#bread
#ingredients
def sandwich(food="--ham--"):
print(food)
sandwich()
#outputs:
#</''''''\>
# #tomatoes#
# --ham--
# ~salad~
#<\______/>
The order you set the decorators MATTERS:
#ingredients
#bread
def strange_sandwich(food="--ham--"):
print(food)
strange_sandwich()
#outputs:
##tomatoes#
#</''''''\>
# --ham--
#<\______/>
# ~salad~
Now: to answer the question...
As a conclusion, you can easily see how to answer the question:
# 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>
You can now just leave happy, or burn your brain a little bit more and see advanced uses of decorators.
Taking decorators to the next level
Passing arguments to the decorated function
# 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
Decorating methods
One nifty thing about Python is that methods and functions are really the same. The only difference is that methods expect that their first argument is a reference to the current object (self).
That means you can build a decorator for methods the same way! Just remember to take self into consideration:
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?
If you’re making general-purpose decorator--one you’ll apply to any function or method, no matter its arguments--then just use *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?
Passing arguments to the decorator
Great, now what would you say about passing arguments to the decorator itself?
This can get somewhat twisted, since a decorator must accept a function as an argument. Therefore, you cannot pass the decorated function’s arguments directly to the decorator.
Before rushing to the solution, let’s write a little reminder:
# 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
It’s exactly the same. "my_decorator" is called. So when you #my_decorator, you are telling Python to call the function 'labelled by the variable "my_decorator"'.
This is important! The label you give can point directly to the decorator—or not.
Let’s get evil. ☺
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.
No surprise here.
Let’s do EXACTLY the same thing, but skip all the pesky intermediate variables:
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.
Let’s make it even shorter:
#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.
Hey, did you see that? We used a function call with the "#" syntax! :-)
So, back to decorators with arguments. If we can use functions to generate the decorator on the fly, we can pass arguments to that function, right?
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
Here it is: a decorator with arguments. Arguments can be set as variable:
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
As you can see, you can pass arguments to the decorator like any function using this trick. You can even use *args, **kwargs if you wish. But remember decorators are called only once. Just when Python imports the script. You can't dynamically set the arguments afterwards. When you do "import x", the function is already decorated, so you can't
change anything.
Let’s practice: decorating a decorator
Okay, as a bonus, I'll give you a snippet to make any decorator accept generically any argument. After all, in order to accept arguments, we created our decorator using another function.
We wrapped the decorator.
Anything else we saw recently that wrapped function?
Oh yes, decorators!
Let’s have some fun and write a decorator for the decorators:
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
It can be used as follows:
# 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!
I know, the last time you had this feeling, it was after listening a guy saying: "before understanding recursion, you must first understand recursion". But now, don't you feel good about mastering this?
Best practices: decorators
Decorators were introduced in Python 2.4, so be sure your code will be run on >= 2.4.
Decorators slow down the function call. Keep that in mind.
You cannot un-decorate a function. (There are hacks to create decorators that can be removed, but nobody uses them.) So once a function is decorated, it’s decorated for all the code.
Decorators wrap functions, which can make them hard to debug. (This gets better from Python >= 2.5; see below.)
The functools module was introduced in Python 2.5. It includes the function functools.wraps(), which copies the name, module, and docstring of the decorated function to its wrapper.
(Fun fact: functools.wraps() is a decorator! ☺)
# 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
How can the decorators be useful?
Now the big question: What can I use decorators for?
Seem cool and powerful, but a practical example would be great. Well, there are 1000 possibilities. Classic uses are extending a function behavior from an external lib (you can't modify it), or for debugging (you don't want to modify it because it’s temporary).
You can use them to extend several functions in a DRY’s way, like so:
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
Of course the good thing with decorators is that you can use them right away on almost anything without rewriting. DRY, I said:
#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 itself provides several decorators: property, staticmethod, etc.
Django uses decorators to manage caching and view permissions.
Twisted to fake inlining asynchronous functions calls.
This really is a large playground.

Check out the documentation to see how decorators work. Here is what you asked for:
from functools import wraps
def makebold(fn):
#wraps(fn)
def wrapper(*args, **kwargs):
return "<b>" + fn(*args, **kwargs) + "</b>"
return wrapper
def makeitalic(fn):
#wraps(fn)
def wrapper(*args, **kwargs):
return "<i>" + fn(*args, **kwargs) + "</i>"
return wrapper
#makebold
#makeitalic
def hello():
return "hello world"
#makebold
#makeitalic
def log(s):
return s
print hello() # returns "<b><i>hello world</i></b>"
print hello.__name__ # with functools.wraps() this returns "hello"
print log('hello') # returns "<b><i>hello</i></b>"

Alternatively, you could write a factory function which return a decorator which wraps the return value of the decorated function in a tag passed to the factory function. For example:
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
This enables you to write:
#wrap_in_tag('b')
#wrap_in_tag('i')
def say():
return 'hello'
or
makebold = wrap_in_tag('b')
makeitalic = wrap_in_tag('i')
#makebold
#makeitalic
def say():
return 'hello'
Personally I would have written the decorator somewhat differently:
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
which would yield:
#wrap_in_tag('b')
#wrap_in_tag('i')
def say(val):
return val
say('hello')
Don't forget the construction for which decorator syntax is a shorthand:
say = wrap_in_tag('b')(wrap_in_tag('i')(say)))

Decorators are just syntactical sugar.
This
#decorator
def func():
...
expands to
def func():
...
func = decorator(func)

And of course you can return lambdas as well from a decorator function:
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 decorators add extra functionality to another function
An italics decorator could be like
def makeitalic(fn):
def newFunc():
return "<i>" + fn() + "</i>"
return newFunc
Note that a function is defined inside a function.
What it basically does is replace a function with the newly defined one. For example, I have this class
class foo:
def bar(self):
print "hi"
def foobar(self):
print "hi again"
Now say, I want both functions to print "---" after and before they are done.
I could add a print "---" before and after each print statement.
But because I don't like repeating myself, I will make a decorator
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
So now I can change my class to
class foo:
#addDashes
def bar(self):
print "hi"
#addDashes
def foobar(self):
print "hi again"
For more on decorators, check
http://www.ibm.com/developerworks/linux/library/l-cpdecor.html

You could make two separate decorators that do what you want as illustrated directly below. Note the use of *args, **kwargs in the declaration of the wrapped() function which supports the decorated function having multiple arguments (which isn't really necessary for the example say() function, but is included for generality).
For similar reasons, the functools.wraps decorator is used to change the meta attributes of the wrapped function to be those of the one being decorated. This makes error messages and embedded function documentation (func.__doc__) be those of the decorated function instead of wrapped()'s.
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>
Refinements
As you can see there's a lot of duplicate code in these two decorators. Given this similarity it would be better for you to instead make a generic one that was actually a decorator factory—in other words, a decorator function that makes other decorators. That way there would be less code repetition—and allow the DRY principle to be followed.
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>
To make the code more readable, you can assign a more descriptive name to the factory-generated decorators:
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>
or even combine them like this:
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>
Efficiency
While the above examples do all work, the code generated involves a fair amount of overhead in the form of extraneous function calls when multiple decorators are applied at once. This may not matter, depending the exact usage (which might be I/O-bound, for instance).
If speed of the decorated function is important, the overhead can be kept to a single extra function call by writing a slightly different decorator factory-function which implements adding all the tags at once, so it can generate code that avoids the addtional function calls incurred by using separate decorators for each tag.
This requires more code in the decorator itself, but this only runs when it's being applied to function definitions, not later when they themselves are called. This also applies when creating more readable names by using lambda functions as previously illustrated. Sample:
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>

Another way of doing the same thing:
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'
Or, more flexibly:
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'

How can I make two decorators in Python that would do the following?
You want the following function, when called:
#makebold
#makeitalic
def say():
return "Hello"
To return:
<b><i>Hello</i></b>
Simple solution
To most simply do this, make decorators that return lambdas (anonymous functions) that close over the function (closures) and call it:
def makeitalic(fn):
return lambda: '<i>' + fn() + '</i>'
def makebold(fn):
return lambda: '<b>' + fn() + '</b>'
Now use them as desired:
#makebold
#makeitalic
def say():
return 'Hello'
and now:
>>> say()
'<b><i>Hello</i></b>'
Problems with the simple solution
But we seem to have nearly lost the original function.
>>> say
<function <lambda> at 0x4ACFA070>
To find it, we'd need to dig into the closure of each lambda, one of which is buried in the other:
>>> say.__closure__[0].cell_contents
<function <lambda> at 0x4ACFA030>
>>> say.__closure__[0].cell_contents.__closure__[0].cell_contents
<function say at 0x4ACFA730>
So if we put documentation on this function, or wanted to be able to decorate functions that take more than one argument, or we just wanted to know what function we were looking at in a debugging session, we need to do a bit more with our wrapper.
Full featured solution - overcoming most of these problems
We have the decorator wraps from the functools module in the standard library!
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
It is unfortunate that there's still some boilerplate, but this is about as simple as we can make it.
In Python 3, you also get __qualname__ and __annotations__ assigned by default.
So now:
#makebold
#makeitalic
def say():
"""This function returns a bolded, italicized 'hello'"""
return 'Hello'
And now:
>>> say
<function say at 0x14BB8F70>
>>> help(say)
Help on function say in module __main__:
say(*args, **kwargs)
This function returns a bolded, italicized 'hello'
Conclusion
So we see that wraps makes the wrapping function do almost everything except tell us exactly what the function takes as arguments.
There are other modules that may attempt to tackle the problem, but the solution is not yet in the standard library.

A decorator takes the function definition and creates a new function that executes this function and transforms the result.
#deco
def do():
...
is equivalent to:
do = deco(do)
Example:
def deco(func):
def inner(letter):
return func(letter).upper() #upper
return inner
This
#deco
def do(number):
return chr(number) # number to letter
is equivalent to this
def do2(number):
return chr(number)
do2 = deco(do2)
65 <=> 'a'
print(do(65))
print(do2(65))
>>> B
>>> B
To understand the decorator, it is important to notice, that decorator created a new function do which is inner that executes function and transforms the result.

This answer has long been answered, but I thought I would share my Decorator class which makes writing new decorators easy and compact.
from abc import ABCMeta, abstractclassmethod
class Decorator(metaclass=ABCMeta):
""" Acts as a base class for all decorators """
def __init__(self):
self.method = None
def __call__(self, method):
self.method = method
return self.call
#abstractclassmethod
def call(self, *args, **kwargs):
return self.method(*args, **kwargs)
For one I think this makes the behavior of decorators very clear, but it also makes it easy to define new decorators very concisely. For the example listed above, you could then solve it as:
class MakeBold(Decorator):
def call():
return "<b>" + self.method() + "</b>"
class MakeItalic(Decorator):
def call():
return "<i>" + self.method() + "</i>"
#MakeBold()
#MakeItalic()
def say():
return "Hello"
You could also use it to do more complex tasks, like for instance a decorator which automatically makes the function get applied recursively to all arguments in an iterator:
class ApplyRecursive(Decorator):
def __init__(self, *types):
super().__init__()
if not len(types):
types = (dict, list, tuple, set)
self._types = types
def call(self, arg):
if dict in self._types and isinstance(arg, dict):
return {key: self.call(value) for key, value in arg.items()}
if set in self._types and isinstance(arg, set):
return set(self.call(value) for value in arg)
if tuple in self._types and isinstance(arg, tuple):
return tuple(self.call(value) for value in arg)
if list in self._types and isinstance(arg, list):
return list(self.call(value) for value in arg)
return self.method(arg)
#ApplyRecursive(tuple, set, dict)
def double(arg):
return 2*arg
print(double(1))
print(double({'a': 1, 'b': 2}))
print(double({1, 2, 3}))
print(double((1, 2, 3, 4)))
print(double([1, 2, 3, 4, 5]))
Which prints:
2
{'a': 2, 'b': 4}
{2, 4, 6}
(2, 4, 6, 8)
[1, 2, 3, 4, 5, 1, 2, 3, 4, 5]
Notice that this example didn't include the list type in the instantiation of the decorator, so in the final print statement the method gets applied to the list itself, not the elements of the list.

#decorator.py
def makeHtmlTag(tag, *args, **kwds):
def real_decorator(fn):
css_class = " class='{0}'".format(kwds["css_class"]) \
if "css_class" in kwds else ""
def wrapped(*args, **kwds):
return "<"+tag+css_class+">" + fn(*args, **kwds) + "</"+tag+">"
return wrapped
# return decorator dont call it
return real_decorator
#makeHtmlTag(tag="b", css_class="bold_css")
#makeHtmlTag(tag="i", css_class="italic_css")
def hello():
return "hello world"
print hello()
You can also write decorator in Class
#class.py
class makeHtmlTagClass(object):
def __init__(self, tag, css_class=""):
self._tag = tag
self._css_class = " class='{0}'".format(css_class) \
if css_class != "" else ""
def __call__(self, fn):
def wrapped(*args, **kwargs):
return "<" + self._tag + self._css_class+">" \
+ fn(*args, **kwargs) + "</" + self._tag + ">"
return wrapped
#makeHtmlTagClass(tag="b", css_class="bold_css")
#makeHtmlTagClass(tag="i", css_class="italic_css")
def hello(name):
return "Hello, {}".format(name)
print hello("Your name")

Here is a simple example of chaining decorators. Note the last line - it shows what is going on under the covers.
############################################################
#
# decorators
#
############################################################
def bold(fn):
def decorate():
# surround with bold tags before calling original function
return "<b>" + fn() + "</b>"
return decorate
def uk(fn):
def decorate():
# swap month and day
fields = fn().split('/')
date = fields[1] + "/" + fields[0] + "/" + fields[2]
return date
return decorate
import datetime
def getDate():
now = datetime.datetime.now()
return "%d/%d/%d" % (now.day, now.month, now.year)
#bold
def getBoldDate():
return getDate()
#uk
def getUkDate():
return getDate()
#bold
#uk
def getBoldUkDate():
return getDate()
print getDate()
print getBoldDate()
print getUkDate()
print getBoldUkDate()
# what is happening under the covers
print bold(uk(getDate))()
The output looks like:
17/6/2013
<b>17/6/2013</b>
6/17/2013
<b>6/17/2013</b>
<b>6/17/2013</b>

Speaking of the counter example - as given above, the counter will be shared between all functions that use the decorator:
def counter(func):
def wrapped(*args, **kws):
print 'Called #%i' % wrapped.count
wrapped.count += 1
return func(*args, **kws)
wrapped.count = 0
return wrapped
That way, your decorator can be reused for different functions (or used to decorate the same function multiple times: func_counter1 = counter(func); func_counter2 = counter(func)), and the counter variable will remain private to each.

Decorate functions with different number of arguments:
def frame_tests(fn):
def wrapper(*args):
print "\nStart: %s" %(fn.__name__)
fn(*args)
print "End: %s\n" %(fn.__name__)
return wrapper
#frame_tests
def test_fn1():
print "This is only a test!"
#frame_tests
def test_fn2(s1):
print "This is only a test! %s" %(s1)
#frame_tests
def test_fn3(s1, s2):
print "This is only a test! %s %s" %(s1, s2)
if __name__ == "__main__":
test_fn1()
test_fn2('OK!')
test_fn3('OK!', 'Just a test!')
Result:
Start: test_fn1
This is only a test!
End: test_fn1
Start: test_fn2
This is only a test! OK!
End: test_fn2
Start: test_fn3
This is only a test! OK! Just a test!
End: test_fn3

Paolo Bergantino's answer has the great advantage of only using the stdlib, and works for this simple example where there are no decorator arguments nor decorated function arguments.
However it has 3 major limitations if you want to tackle more general cases:
as already noted in several answers, you can not easily modify the code to add optional decorator arguments. For example creating a makestyle(style='bold') decorator is non-trivial.
besides, wrappers created with #functools.wraps do not preserve the signature, so if bad arguments are provided they will start executing, and might raise a different kind of error than the usual TypeError.
finally, it is quite difficult in wrappers created with #functools.wraps to access an argument based on its name. Indeed the argument can appear in *args, in **kwargs, or may not appear at all (if it is optional).
I wrote decopatch to solve the first issue, and wrote makefun.wraps to solve the other two. Note that makefun leverages the same trick than the famous decorator lib.
This is how you would create a decorator with arguments, returning truly signature-preserving wrappers:
from decopatch import function_decorator, DECORATED
from makefun import wraps
#function_decorator
def makestyle(st='b', fn=DECORATED):
open_tag = "<%s>" % st
close_tag = "</%s>" % st
#wraps(fn)
def wrapped(*args, **kwargs):
return open_tag + fn(*args, **kwargs) + close_tag
return wrapped
decopatch provides you with two other development styles that hide or show the various python concepts, depending on your preferences. The most compact style is the following:
from decopatch import function_decorator, WRAPPED, F_ARGS, F_KWARGS
#function_decorator
def makestyle(st='b', fn=WRAPPED, f_args=F_ARGS, f_kwargs=F_KWARGS):
open_tag = "<%s>" % st
close_tag = "</%s>" % st
return open_tag + fn(*f_args, **f_kwargs) + close_tag
In both cases you can check that the decorator works as expected:
#makestyle
#makestyle('i')
def hello(who):
return "hello %s" % who
assert hello('world') == '<b><i>hello world</i></b>'
Please refer to the documentation for details.

I add a case when you need to add custom parameters in decorator, pass it to final function and then work it with.
the very decorators:
def jwt_or_redirect(fn):
#wraps(fn)
def decorator(*args, **kwargs):
...
return fn(*args, **kwargs)
return decorator
def jwt_refresh(fn):
#wraps(fn)
def decorator(*args, **kwargs):
...
new_kwargs = {'refreshed_jwt': 'xxxxx-xxxxxx'}
new_kwargs.update(kwargs)
return fn(*args, **new_kwargs)
return decorator
and the final function:
#app.route('/')
#jwt_or_redirect
#jwt_refresh
def home_page(*args, **kwargs):
return kwargs['refreched_jwt']

Yet another example of nested decorators for plotting an image:
import matplotlib.pylab as plt
def remove_axis(func):
def inner(img, alpha):
plt.axis('off')
func(img, alpha)
return inner
def plot_gray(func):
def inner(img, alpha):
plt.gray()
func(img, alpha)
return inner
#remove_axis
#plot_gray
def plot_image(img, alpha):
plt.imshow(img, alpha=alpha)
plt.show()
Now, let's show a color image first without axis labels using the nested decorators:
plot_image(plt.imread('lena_color.jpg'), 0.4)
Next, let's show a gray scale image without axis labels using the nested decorators remove_axis and plot_gray (we need to cmap='gray', otherwise the default colormap is viridis, so a grayscale image is by default not displayed in black and white shades, unless explicitly specified)
plot_image(plt.imread('lena_bw.jpg'), 0.8)
The above function call reduces down to the following nested call
remove_axis(plot_gray(plot_image))(img, alpha)

With make_bold() and make_italic() below:
def make_bold(func):
def core(*args, **kwargs):
result = func(*args, **kwargs)
return "<b>" + result + "</b>"
return core
def make_italic(func):
def core(*args, **kwargs):
result = func(*args, **kwargs)
return "<i>" + result + "</i>"
return core
You can use them as decorators with say() as shown below:
#make_bold
#make_italic
def say():
return "Hello"
print(say())
Output:
<b><i>Hello</i></b>
And of course, you can directly use make_bold() and make_italic() without decorators as shown below:
def say():
return "Hello"
f1 = make_italic(say)
f2 = make_bold(f1)
result = f2()
print(result)
In short:
def say():
return "Hello"
result = make_bold(make_italic(say))()
print(result)
Output:
<b><i>Hello</i></b>

Consider the following decorator, note that we are returning the wrapper() function as an object
def make_bold(func):
def wrapper():
return '<b>'+func()+'</b>'
return wrapper
So This
#make_bold
def say():
return "Hello"
evaluates to this
x = make_bold(say)
Note that x is not the say() but the wrapper object that calls say() internally. That is how decorator works. It always returns the wrapper object which calls the actual function.
In case of chaining this
#make_italic
#make_bold
def say():
return "Hello"
gets converted to this
x = make_bold(say)
y = make_italic(x)
Below is the complete code
def make_italic(func):
def wrapper():
return '<i>'+func()+'</i>'
return wrapper
def make_bold(func):
def wrapper():
return '<b>'+func()+'</b>'
return wrapper
#make_italic
#make_bold
def say():
return "Hello"
if __name__ == '__main__':
# x = make_bold(say) When you wrap say with make_bold decorator
# y = make_italic(x) When you also add make_italic as part of chaining
# print(y())
print(say())
The above code will return
<i><b>Hello</b></i>
Hope this helps