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Revisions

  1. @Zearin Zearin revised this gist Sep 10, 2014. 1 changed file with 624 additions and 624 deletions.
    1,248 changes: 624 additions & 624 deletions python_decorator_guide.md
    View file
    Original file line number Diff line number Diff line change
    @@ -17,67 +17,67 @@ To understand decorators, you must first understand that functions are objects i
    This has important consequences. Let’s see why with a simple example :

    ```python
    def shout(word='yes'):
    return word.capitalize() + '!'
    def shout(word='yes'):
    return word.capitalize() + '!'

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

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

    scream = shout
    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`:
    # 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!'
    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`
    # 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"
    del shout
    try:
    print shout()
    except NameError as e:
    print e
    #outputs: "name 'shout' is not defined"

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

    Okay! 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!

    ```python
    def talk():
    def talk():

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

    # ... and use it right away!
    # ... and use it right away!

    print whisper()
    print whisper()

    # You call `talk`, that defines `whisper` EVERY TIME you call it, then
    # `whisper` is called in `talk`.
    talk()
    # outputs:
    # "yes..."
    # 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`:
    # 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
    try:
    print whisper()
    except NameError as e:
    print e
    #outputs : "name 'whisper' is not defined"*
    Python's functions are objects
    ```

    ## Functions references
    @@ -92,54 +92,54 @@ You’ve seen that functions are objects. Therefore, functions:
    That means that **a function can `return` another function**. Have a look! ☺

    ```python
    def getTalk(kind='shout'):
    def getTalk(kind='shout'):

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

    def whisper(word='yes'):
    return word.lower() + '...'
    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;
    # instead, we’re returning the function object
    return shout
    else:
    return whisper
    # Then we return one of them
    if kind == 'shout':
    # We don’t use '()'. We are not calling the function;
    # instead, we’re returning the function object
    return shout
    else:
    return whisper

    # How do you use this strange beast?
    # How do you use this strange beast?

    # Get the function and assign it to a variable
    talk = getTalk()
    # 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>
    # 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!
    # 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...
    # And you can even use it directly if you feel wild:
    print getTalk('whisper')()
    #outputs : yes...
    ```

    But wait...there’s more!

    If you can `return` a function, you can pass one as a parameter:

    ```python
    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!
    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.
    @@ -150,151 +150,151 @@ Well, you just have everything needed to understand decorators. You see, decorat
    How you’d do it manually:

    ```python
    # 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
    # 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`:

    ```python
    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

    # And guess what? That’s EXACTLY what decorators do!
    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

    # And guess what? That’s EXACTLY what decorators do!
    ```

    ## Decorators demystified

    The previous example, using the decorator syntax:

    ```python
    @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
    @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:

    ```python
    another_stand_alone_function = my_shiny_new_decorator(another_stand_alone_function)
    another_stand_alone_function = my_shiny_new_decorator(another_stand_alone_function)
    ```

    Decorators are just a pythonic variant of the [decorator design pattern][3]. There are several classic design patterns embedded in Python to ease development (like iterators).

    Of course, you can accumulate decorators:

    ```python
    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~
    #<\______/>
    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:

    ```python
    @bread
    @ingredients
    def sandwich(food='--ham--'):
    print food

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

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

    The order you set the decorators MATTERS:

    ```python
    @ingredients
    @bread
    def strange_sandwich(food='--ham--'):
    print food

    strange_sandwich()
    #outputs:
    ##tomatoes#
    #</''''''\>
    # --ham--
    #<\______/>
    # ~salad~
    @ingredients
    @bread
    def strange_sandwich(food='--ham--'):
    print food

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

    ----
    @@ -304,37 +304,37 @@ The order you set the decorators MATTERS:
    As a conclusion, you can easily see how to answer the question:

    ```python
    # 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>
    # 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.
    @@ -346,27 +346,27 @@ You can now just leave happy, or burn your brain a little bit more and see advan
    ## Passing arguments to the decorated function

    ```python
    # 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:', 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', first_name, last_name
    print_full_name('Peter', 'Venkman')
    # outputs:
    #I got args! Look: Peter Venkman
    #My name is Peter Venkman
    # 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:', 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', first_name, last_name

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

    ## Decorating methods
    @@ -376,91 +376,91 @@ One nifty thing about Python is that methods and functions are really the same.
    That means you can build a decorator for methods the same way! Just remember to take `self` into consideration:

    ```python
    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

    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

    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)

    @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?
    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`:

    ```python
    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!

    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

    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?
    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
    @@ -472,30 +472,30 @@ This can get somewhat twisted, since a decorator must accept a function as an ar
    Before rushing to the solution, let’s write a little reminder:

    ```python
    # 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
    # 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`”*.
    @@ -505,162 +505,162 @@ This is important! The label you give can point directly to the decorator—**or
    Let’s get evil. ☺

    ```python
    def decorator_maker():

    print 'I make decorators! I am executed only once: '+\
    'when you make me create a decorator.'

    def my_decorator(func):
    def decorator_maker():

    print 'I make decorators! I am executed only once: '+\
    'when you make me create a decorator.'

    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()
    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

    print 'As the decorator, I return the wrapped function.'
    # 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

    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.
    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:

    ```python
    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.
    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*:

    ```python
    @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.
    @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?

    ```python
    def decorator_maker_with_arguments(decorator_arg1, decorator_arg2):

    print 'I make decorators! And I accept arguments:', 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: http://stackoverflow.com/questions/13857/can-you-explain-closures-as-they-relate-to-python
    print 'I am the decorator. Somehow you passed me arguments:', 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)
    def decorator_maker_with_arguments(decorator_arg1, decorator_arg2):

    print 'I make decorators! And I accept arguments:', decorator_arg1, decorator_arg2

    return wrapped
    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: http://stackoverflow.com/questions/13857/can-you-explain-closures-as-they-relate-to-python
    print 'I am the decorator. Somehow you passed me arguments:', 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 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
    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:

    ```python
    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 knows about my arguments: Leslie Howard
    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 knows 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
    @@ -681,56 +681,56 @@ Oh yes, decorators!
    Let’s have some fun and write a decorator for the decorators:

    ```python
    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
    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_maker
    return decorator_wrapper

    return decorator_maker
    ```

    It can be used as follows:

    ```python
    # 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', args, kwargs
    return func(function_arg1, function_arg2)
    return wrapper
    # Then you decorate the functions you wish with your brand new decorated decorator.
    # 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', 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', function_arg1, function_arg2
    @decorated_decorator(42, 404, 1024)
    def decorated_function(function_arg1, function_arg2):
    print 'Hello', function_arg1, function_arg2

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

    # Whoooot!
    # 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?
    @@ -749,46 +749,46 @@ The `functools` module was introduced in Python 2.5. It includes the function `f
    (Fun fact: `functools.wraps()` is a decorator! ☺)

    ```python
    # 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 with 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
    # 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 with 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
    ```

    ----
    @@ -802,92 +802,92 @@ Seem cool and powerful, but a practical example would be great. Well, there are
    You can use them to extend several functions in a DRY’s way, like so:

    ```python
    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 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 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
    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 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 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:

    ```python
    @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!'
    @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!
    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.
  2. @Zearin Zearin revised this gist Sep 10, 2014. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion python_decorator_guide.md
    View file
    Original file line number Diff line number Diff line change
    @@ -873,7 +873,7 @@ Of course the good thing with decorators is that you can use them right away on
    value = result.split('<br><b><hr><br>')[1].split('<br><br><hr>')[0]
    return value.strip()
    except:
    return 'No, I’m ... doesn't!'
    return 'No, I’m ... doesn’t!'


    print get_random_futurama_quote()
  3. @Zearin Zearin revised this gist Sep 10, 2014. 1 changed file with 99 additions and 99 deletions.
    198 changes: 99 additions & 99 deletions python_decorator_guide.md
    View file
    Original file line number Diff line number Diff line change
    @@ -17,8 +17,8 @@ To understand decorators, you must first understand that functions are objects i
    This has important consequences. Let’s see why with a simple example :

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

    print shout()
    # outputs : 'Yes!'
    @@ -29,8 +29,8 @@ This has important consequences. Let’s see why with a simple example :
    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":
    # putting the function `shout` into the variable `scream`.
    # It means you can then call `shout` from `scream`:

    print scream()
    # outputs : 'Yes!'
    @@ -56,21 +56,21 @@ Another interesting property of Python functions is they can be defined... insid
    ```python
    def talk():

    # You can define a function on the fly in "talk" ...
    def whisper(word="yes"):
    return word.lower()+"..."
    # 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".
    # 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":
    # But `whisper` DOES NOT EXIST outside `talk`:

    try:
    print whisper()
    @@ -92,19 +92,19 @@ You’ve seen that functions are objects. Therefore, functions:
    That means that **a function can `return` another function**. Have a look! ☺

    ```python
    def getTalk(kind="shout"):
    def getTalk(kind='shout'):

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

    def whisper(word="yes") :
    return word.lower()+"...";
    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
    if kind == 'shout':
    # We don’t use '()'. We are not calling the function;
    # instead, we’re returning the function object
    return shout
    else:
    return whisper
    @@ -114,7 +114,7 @@ That means that **a function can `return` another function**. Have a look! ☺
    # Get the function and assign it to a variable
    talk = getTalk()

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

    @@ -123,7 +123,7 @@ That means that **a function can `return` another function**. Have a look! ☺
    #outputs : Yes!

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

    @@ -133,7 +133,7 @@ If you can `return` a function, you can pass one as a parameter:

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

    doSomethingBefore(scream)
    @@ -160,24 +160,24 @@ How you’d do it manually:

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

    # At this point, "a_function_to_decorate" HAS NEVER BEEN EXECUTED.
    # 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"
    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
    @@ -214,7 +214,7 @@ The previous example, using the decorator syntax:
    ```python
    @my_shiny_new_decorator
    def another_stand_alone_function():
    print "Leave me alone"
    print 'Leave me alone'

    another_stand_alone_function()
    #outputs:
    @@ -243,12 +243,12 @@ Of course, you can accumulate decorators:

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

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

    sandwich()
    @@ -268,7 +268,7 @@ Using the Python decorator syntax:
    ```python
    @bread
    @ingredients
    def sandwich(food="--ham--"):
    def sandwich(food='--ham--'):
    print food

    sandwich()
    @@ -285,7 +285,7 @@ The order you set the decorators MATTERS:
    ```python
    @ingredients
    @bread
    def strange_sandwich(food="--ham--"):
    def strange_sandwich(food='--ham--'):
    print food

    strange_sandwich()
    @@ -309,28 +309,28 @@ As a conclusion, you can easily see how to answer the question:
    # The new function the decorator returns
    def wrapper():
    # Insertion of some code before and after
    return "<b>" + fn() + "</b>"
    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 '<i>' + fn() + '</i>'
    return wrapper

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

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

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

    print say()
    @@ -351,7 +351,7 @@ You can now just leave happy, or burn your brain a little bit more and see advan

    def a_decorator_passing_arguments(function_to_decorate):
    def a_wrapper_accepting_arguments(arg1, arg2):
    print "I got args! Look:", arg1, arg2
    print 'I got args! Look:', arg1, arg2
    function_to_decorate(arg1, arg2)
    return a_wrapper_accepting_arguments

    @@ -361,9 +361,9 @@ You can now just leave happy, or burn your brain a little bit more and see advan

    @a_decorator_passing_arguments
    def print_full_name(first_name, last_name):
    print "My name is", first_name, last_name
    print 'My name is', first_name, last_name

    print_full_name("Peter", "Venkman")
    print_full_name('Peter', 'Venkman')
    # outputs:
    #I got args! Look: Peter Venkman
    #My name is Peter Venkman
    @@ -389,7 +389,7 @@ That means you can build a decorator for methods the same way! Just remember to

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

    l = Lucy()
    l.sayYourAge(-3)
    @@ -402,7 +402,7 @@ If you’re making general-purpose decorator--one you’ll apply to any function
    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 'Do I have args?:'
    print args
    print kwargs
    # Then you unpack the arguments, here *args, **kwargs
    @@ -413,7 +413,7 @@ If you’re making general-purpose decorator--one you’ll apply to any function

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

    function_with_no_argument()
    #outputs
    @@ -434,11 +434,11 @@ If you’re making general-purpose decorator--one you’ll apply to any function
    #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(
    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!")
    function_with_named_arguments('Bill', 'Linus', 'Steve', platypus='Indeed!')
    #outputs
    #Do I have args ? :
    #('Bill', 'Linus', 'Steve')
    @@ -452,7 +452,7 @@ If you’re making general-purpose decorator--one you’ll apply to any function

    @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)
    print 'I am {0}, what did you think?'.format(self.age + lie)

    m = Mary()
    m.sayYourAge()
    @@ -474,31 +474,31 @@ Before rushing to the solution, let’s write a little reminder:
    ```python
    # Decorators are ORDINARY functions
    def my_decorator(func):
    print "I am an ordinary function"
    print 'I am an ordinary function'
    def wrapper():
    print "I am function returned by the decorator"
    print 'I am function returned by the decorator'
    func()
    return wrapper

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

    def lazy_function():
    print "zzzzzzzz"
    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:
    # 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"
    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`"*.
    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**.

    @@ -507,24 +507,24 @@ Let’s get evil. ☺
    ```python
    def decorator_maker():

    print "I make decorators! I am executed only once: "+\
    "when you make me create a decorator."
    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."
    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.")
    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."
    print 'As the decorator, I return the wrapped function.'

    return wrapped

    print "As a decorator maker, I return a decorator"
    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.
    @@ -536,7 +536,7 @@ Let’s get evil. ☺
    # Then we decorate the function

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

    decorated_function = new_decorator(decorated_function)
    #outputs:
    @@ -557,7 +557,7 @@ Let’s do EXACTLY the same thing, but skip all the pesky intermediate variables

    ```python
    def decorated_function():
    print "I am the 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.
    @@ -578,7 +578,7 @@ Let’s make it *even shorter*:
    ```python
    @decorator_maker()
    def decorated_function():
    print "I am the 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
    @@ -593,28 +593,28 @@ Let’s make it *even shorter*:
    #I am the decorated function.
    ```

    Hey, did you see that? We used a function call with the "`@`" syntax! :-)
    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?

    ```python
    def decorator_maker_with_arguments(decorator_arg1, decorator_arg2):

    print "I make decorators! And I accept arguments:", decorator_arg1, decorator_arg2
    print 'I make decorators! And I accept arguments:', 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: http://stackoverflow.com/questions/13857/can-you-explain-closures-as-they-relate-to-python
    print "I am the decorator. Somehow you passed me arguments:", decorator_arg1, decorator_arg2
    print 'I am the decorator. Somehow you passed me arguments:', 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"
    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)
    @@ -623,12 +623,12 @@ So, back to decorators with arguments. If we can use functions to generate the d

    return my_decorator

    @decorator_maker_with_arguments("Leonard", "Sheldon")
    @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))
    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")
    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
    @@ -643,15 +643,15 @@ So, back to decorators with arguments. If we can use functions to generate the d
    Here it is: a decorator with arguments. Arguments can be set as variable:

    ```python
    c1 = "Penny"
    c2 = "Leslie"
    c1 = 'Penny'
    c2 = 'Leslie'

    @decorator_maker_with_arguments("Leonard", c1)
    @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))
    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")
    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
    @@ -711,29 +711,29 @@ It can be used as follows:

    ```python
    # 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)"
    # 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", args, kwargs
    print 'Decorated with', 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", function_arg1, function_arg2
    print 'Hello', function_arg1, function_arg2

    decorated_function("Universe and", "everything")
    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?
    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?

    ----

    @@ -751,41 +751,41 @@ The `functools` module was introduced in Python 2.5. It includes the function `f
    ```python
    # For debugging, the stacktrace prints you the function __name__
    def foo():
    print "foo"
    print 'foo'

    print foo.__name__
    #outputs: foo

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

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

    print foo.__name__
    #outputs: wrapper

    # "functools" can help for that
    # `functools` can help with that

    import functools

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

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

    print foo.__name__
    #outputs: foo
    @@ -835,7 +835,7 @@ You can use them to extend several functions in a DRY’s way, like so:
    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)
    print '{0} has been used: {1}x'.format(func.__name__, wrapper.count)
    return res
    wrapper.count = 0
    return wrapper
    @@ -846,8 +846,8 @@ You can use them to extend several functions in a DRY’s way, like so:
    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!")
    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',) {}
    @@ -868,12 +868,12 @@ Of course the good thing with decorators is that you can use them right away on
    @logging
    def get_random_futurama_quote():
    from urllib import urlopen
    result = urlopen("http://subfusion.net/cgi-bin/quote.pl?quote=futurama").read()
    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]
    value = result.split('<br><b><hr><br>')[1].split('<br><br><hr>')[0]
    return value.strip()
    except:
    return "No, I'm ... doesn't!"
    return 'No, I’m ... doesn't!'


    print get_random_futurama_quote()
  4. @Zearin Zearin revised this gist Sep 10, 2014. 1 changed file with 7 additions and 8 deletions.
    15 changes: 7 additions & 8 deletions python_decorator_guide.md
    View file
    Original file line number Diff line number Diff line change
    @@ -381,16 +381,15 @@ That means you can build a decorator for methods the same way! Just remember to
    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 %s, what did you think?" % (self.age + lie)
    print "I am {0}, what did you think?".format(self.age + lie)

    l = Lucy()
    l.sayYourAge(-3)
    @@ -436,8 +435,8 @@ If you’re making general-purpose decorator--one you’ll apply to any function

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

    function_with_named_arguments("Bill", "Linus", "Steve", platypus="Indeed!")
    #outputs
    @@ -446,14 +445,14 @@ If you’re making general-purpose decorator--one you’ll apply to any function
    #{'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 %s, what did you think ?" % (self.age + lie)
    print "I am {0}, what did you think ?".format(self.age + lie)

    m = Mary()
    m.sayYourAge()
  5. @Zearin Zearin revised this gist Sep 10, 2014. 1 changed file with 17 additions and 17 deletions.
    34 changes: 17 additions & 17 deletions python_decorator_guide.md
    View file
    Original file line number Diff line number Diff line change
    @@ -28,20 +28,20 @@ This has important consequences. Let’s see why with a simple example :

    scream = shout

    # Notice we don't use parentheses: we are not calling the function, we are
    # 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'
    # 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:
    except NameError as e:
    print e
    #outputs: "name 'shout' is not defined"

    @@ -74,7 +74,7 @@ Another interesting property of Python functions is they can be defined... insid

    try:
    print whisper()
    except NameError, e:
    except NameError as e:
    print e
    #outputs : "name 'whisper' is not defined"*
    Python's functions are objects
    @@ -103,7 +103,7 @@ That means that **a function can `return` another function**. Have a look! ☺

    # Then we return one of them
    if kind == "shout":
    # We don't use "()", we are not calling the function,
    # We don’t use "()", we are not calling the function,
    # we are returning the function object
    return shout
    else:
    @@ -175,7 +175,7 @@ How you’d do it manually:
    # 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.
    # 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"

    @@ -201,7 +201,7 @@ Now, you probably want that every time you call `a_stand_alone_function`, `a_sta
    a_stand_alone_function()
    #outputs:
    #Before the function runs
    #I am a stand alone function, don't you dare modify me
    #I am a stand alone function, don’t you dare modify me
    #After the function runs

    # And guess what? That’s EXACTLY what decorators do!
    @@ -499,7 +499,7 @@ Before rushing to the solution, let’s write a little reminder:
    #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`"'.
    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**.

    @@ -609,8 +609,8 @@ So, back to decorators with arguments. If we can use functions to generate the d
    # or read: http://stackoverflow.com/questions/13857/can-you-explain-closures-as-they-relate-to-python
    print "I am the decorator. Somehow you passed me arguments:", decorator_arg1, decorator_arg2

    # Don't confuse decorator arguments and function arguments!
    def wrapped(function_arg1, function_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"
    @@ -664,14 +664,14 @@ Here it is: a decorator with arguments. Arguments can be set as variable:
    #I am the decorated function and only knows 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
    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.
    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.

    @@ -700,7 +700,7 @@ Let’s have some fun and write a decorator for the decorators:

    # 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:
    # Only pitfall: the decorator must have this specific signature or it won’t work:
    return decorator_to_enhance(func, *args, **kwargs)

    return decorator_wrapper
    @@ -712,7 +712,7 @@ It can be used as follows:

    ```python
    # 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)"
    # 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):
    @@ -734,7 +734,7 @@ It can be used as follows:
    # 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?
    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?

    ----

    @@ -798,7 +798,7 @@ The `functools` module was introduced in Python 2.5. It includes the function `f

    **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).
    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:

  6. @Zearin Zearin revised this gist Sep 10, 2014. 1 changed file with 54 additions and 11 deletions.
    65 changes: 54 additions & 11 deletions python_decorator_guide.md
    View file
    Original file line number Diff line number Diff line change
    @@ -53,6 +53,7 @@ Okay! 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!

    ```python
    def talk():

    # You can define a function on the fly in "talk" ...
    @@ -77,7 +78,7 @@ Another interesting property of Python functions is they can be defined... insid
    print e
    #outputs : "name 'whisper' is not defined"*
    Python's functions are objects

    ```

    ## Functions references

    @@ -90,6 +91,7 @@ You’ve seen that functions are objects. Therefore, functions:

    That means that **a function can `return` another function**. Have a look! ☺

    ```python
    def getTalk(kind="shout"):

    # We define functions on the fly
    @@ -123,11 +125,13 @@ That means that **a function can `return` another function**. Have a look! ☺
    # And you can even use it directly if you feel wild:
    print getTalk("whisper")()
    #outputs : yes...
    ```

    But wait...there’s more!

    If you can `return` a function, you can pass one as a parameter:

    ```python
    def doSomethingBefore(func):
    print "I do something before then I call the function you gave me"
    print func()
    @@ -136,6 +140,7 @@ If you can `return` a function, you can pass one as a parameter:
    #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.

    @@ -144,6 +149,7 @@ Well, you just have everything needed to understand decorators. You see, decorat

    How you’d do it manually:

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

    @@ -186,9 +192,11 @@ How you’d do it manually:
    #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`:

    ```python
    a_stand_alone_function = my_shiny_new_decorator(a_stand_alone_function)
    a_stand_alone_function()
    #outputs:
    @@ -197,12 +205,13 @@ Now, you probably want that every time you call `a_stand_alone_function`, `a_sta
    #After the function runs

    # And guess what? That’s EXACTLY what decorators do!

    ```

    ## Decorators demystified

    The previous example, using the decorator syntax:

    ```python
    @my_shiny_new_decorator
    def another_stand_alone_function():
    print "Leave me alone"
    @@ -212,15 +221,19 @@ The previous example, using the decorator syntax:
    #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:

    ```python
    another_stand_alone_function = my_shiny_new_decorator(another_stand_alone_function)
    ```

    Decorators are just a pythonic variant of the [decorator design pattern][3]. There are several classic design patterns embedded in Python to ease development (like iterators).

    Of course, you can accumulate decorators:

    ```python
    def bread(func):
    def wrapper():
    print "</''''''\>"
    @@ -248,9 +261,11 @@ Of course, you can accumulate decorators:
    # --ham--
    # ~salad~
    #<\______/>
    ```

    Using the Python decorator syntax:

    ```python
    @bread
    @ingredients
    def sandwich(food="--ham--"):
    @@ -263,9 +278,11 @@ Using the Python decorator syntax:
    # --ham--
    # ~salad~
    #<\______/>
    ```

    The order you set the decorators MATTERS:

    ```python
    @ingredients
    @bread
    def strange_sandwich(food="--ham--"):
    @@ -278,14 +295,15 @@ The order you set the decorators MATTERS:
    # --ham--
    #<\______/>
    # ~salad~

    ```

    ----

    # Now: to answer the question...

    As a conclusion, you can easily see how to answer the question:

    ```python
    # The decorator to make it bold
    def makebold(fn):
    # The new function the decorator returns
    @@ -317,7 +335,7 @@ As a conclusion, you can easily see how to answer the question:

    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.

    @@ -327,6 +345,7 @@ You can now just leave happy, or burn your brain a little bit more and see advan

    ## Passing arguments to the decorated function

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

    @@ -348,14 +367,15 @@ You can now just leave happy, or burn your brain a little bit more and see advan
    # 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:


    ```python
    def method_friendly_decorator(method_to_decorate):
    def wrapper(self, lie):
    lie = lie - 3 # very friendly, decrease age even more :-)
    @@ -375,9 +395,11 @@ That means you can build a decorator for methods the same way! Just remember to
    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`:

    ```python
    def a_decorator_passing_arbitrary_arguments(function_to_decorate):
    # The wrapper accepts any arguments
    def a_wrapper_accepting_arbitrary_arguments(*args, **kwargs):
    @@ -440,7 +462,7 @@ If you’re making general-purpose decorator--one you’ll apply to any function
    #(<__main__.Mary object at 0xb7d303ac>,)
    #{}
    #I am 28, what did you think?

    ```

    ## Passing arguments to the decorator

    @@ -450,6 +472,7 @@ This can get somewhat twisted, since a decorator must accept a function as an ar

    Before rushing to the solution, let’s write a little reminder:

    ```python
    # Decorators are ORDINARY functions
    def my_decorator(func):
    print "I am an ordinary function"
    @@ -474,13 +497,15 @@ Before rushing to the solution, let’s write a little reminder:
    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. ☺

    ```python
    def decorator_maker():

    print "I make decorators! I am executed only once: "+\
    @@ -525,11 +550,13 @@ Let’s get evil. ☺
    #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:

    ```python
    def decorated_function():
    print "I am the decorated function."
    decorated_function = decorator_maker()(decorated_function)
    @@ -545,9 +572,11 @@ Let’s do EXACTLY the same thing, but skip all the pesky intermediate variables
    #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*:

    ```python
    @decorator_maker()
    def decorated_function():
    print "I am the decorated function."
    @@ -563,11 +592,13 @@ Let’s make it *even shorter*:
    #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?

    ```python
    def decorator_maker_with_arguments(decorator_arg1, decorator_arg2):

    print "I make decorators! And I accept arguments:", decorator_arg1, decorator_arg2
    @@ -608,9 +639,11 @@ So, back to decorators with arguments. If we can use functions to generate the d
    # - 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:

    ```python
    c1 = "Penny"
    c2 = "Leslie"

    @@ -629,6 +662,7 @@ Here it is: a decorator with arguments. Arguments can be set as variable:
    # - from the function call: Leslie Howard
    #Then I can pass them to the decorated function
    #I am the decorated function and only knows 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.
    @@ -647,6 +681,7 @@ Oh yes, decorators!

    Let’s have some fun and write a decorator for the decorators:

    ```python
    def decorator_with_args(decorator_to_enhance):
    """
    This function is supposed to be used as a decorator.
    @@ -671,9 +706,11 @@ Let’s have some fun and write a decorator for the decorators:
    return decorator_wrapper

    return decorator_maker
    ```

    It can be used as follows:


    ```python
    # 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
    @@ -695,7 +732,8 @@ It can be used as follows:
    #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?

    ----
    @@ -711,6 +749,7 @@ The `functools` module was introduced in Python 2.5. It includes the function `f

    (Fun fact: `functools.wraps()` is a decorator! ☺)

    ```python
    # For debugging, the stacktrace prints you the function __name__
    def foo():
    print "foo"
    @@ -751,6 +790,7 @@ The `functools` module was introduced in Python 2.5. It includes the function `f

    print foo.__name__
    #outputs: foo
    ```

    ----

    @@ -762,7 +802,7 @@ Seem cool and powerful, but a practical example would be great. Well, there are

    You can use them to extend several functions in a DRY’s way, like so:

    ```python
    def benchmark(func):
    """
    A decorator that prints the time a function takes
    @@ -819,9 +859,11 @@ You can use them to extend several functions in a DRY’s way, like so:
    #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:

    ```python
    @counter
    @benchmark
    @logging
    @@ -847,6 +889,7 @@ Of course the good thing with decorators is that you can use them right away on
    #wrapper 0.01
    #wrapper has been used: 2x
    #Curse you, merciful Poseidon!
    ```

    Python itself provides several decorators: `property`, `staticmethod`, etc.

  7. @Zearin Zearin revised this gist Sep 10, 2014. 1 changed file with 2 additions and 0 deletions.
    2 changes: 2 additions & 0 deletions python_decorator_guide.md
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    @@ -16,6 +16,7 @@ If you are not into long explanations, see [Paolo Bergantino’s answer][2].
    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 :

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

    @@ -46,6 +47,7 @@ This has important consequences. Let’s see why with a simple example :

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

    Okay! Keep this in mind. We’ll circle back to it shortly.

  8. @Zearin Zearin revised this gist Sep 10, 2014. 1 changed file with 1 addition and 1 deletion.
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    @@ -1,4 +1,4 @@
    This is
    *__NOTE:__ This is [a question I found on StackOverflow]() which I’ve archived here, because the answer is so effing phenomenal.*

    ----

  9. @Zearin Zearin created this gist Sep 10, 2014.
    860 changes: 860 additions & 0 deletions python_decorator_guide.md
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    @@ -0,0 +1,860 @@
    This is

    ----

    # Q: How can I make a chain of function decorators in Python?

    ----

    If you are not into long explanations, see [Paolo Bergantino’s answer][2].


    # 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, e:
    print e
    #outputs: "name 'shout' is not defined"

    print scream()
    # outputs: 'Yes!'

    Okay! 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, 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**. Have a look! ☺

    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...

    But wait...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

    # And guess what? 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][3]. 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:", 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", 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 %s, what did you think?" % (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 %s, %s and %s like platypus? %s" %\
    (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 %s, what did you think ?" % (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:", 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: http://stackoverflow.com/questions/13857/can-you-explain-closures-as-they-relate-to-python
    print "I am the decorator. Somehow you passed me arguments:", 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 knows 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", 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", 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 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 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.


    [1]: http://stackoverflow.com/questions/231767/can-somebody-explain-me-the-python-yield-statement/231855#231855
    [2]: http://stackoverflow.com/questions/739654/understanding-python-decorators#answer-739665
    [3]: http://en.wikipedia.org/wiki/Decorator_pattern