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-
