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Python Decorators: A Complete Guide

A

is a design pattern tool in Python for wrapping code around functions or classes (defined blocks). This design pattern allows a programmer to add new functionality to existing functions or classes without modifying the existing structure. The section provides an overview of what decorators are, how to decorate functions and classes, and what problem can it solve.

A decorator is a function that takes another function as an argument, does some actions, and then returns the argument based on the actions performed. Since functions are

object in Python, they can be passed as arguments to another functions. Hence we can say that

Below code shows a simple decorator that add additional notes to the my_function

:

def decorated_docstring(function):
    function.__doc__ += '\n Hi George, I''m a simple Decorator.'
    return function

def my_function(string1):
    """Return the string."""
    return string1

def main():
    myFunc = decorated_docstring(my_function)
    myFunc('Hai')
    help(myFunc)

if __name__ == "__main__":
    main()

Help on function my_function in module __main__:

my_function(string1)
    Return the string.
    Hi George, Im a simple Decorator.

From the above explanation, you can understand how to decorate a function. But it is unacceptable to define a function, assign it to a variable, then reassign the decorated function to the same variable. Hence Python 2.5 introduced a syntax for decorating function by prepending an

character with the decorator name and adding directly above the function declaration. Below code shows how to use the syntax:

def reverse_decorator(function):

    def reverse_wrapper():
        make_reverse = "".join(reversed(function()))
        return make_reverse

    return reverse_wrapper

@reverse_decorator
def say_hi():
    return 'Hi George'

def main():
    print(say_hi())

if __name__ == "__main__":
    main()

egroeG iH

Here we use @ character for decorating function.

@reverse_decorator
def say_hi():
    return 'Hi George'

The above code is syntactically equivalent to the following code

In this case, the reverse_decorator function executes and creates the reference for revserse_wrapper. Let's look into the below example for better understanding:

def reverse_decorator(function):
    print('Inside reverse_decorator function')

    def reverse_wrapper():
        print('Inside reverse_wrapper function')
        return 'Return reverse_wrapper function'

    return reverse_wrapper


@reverse_decorator
def say_hi():
    return 'Inside say_hi'

Inside reverse_decorator function

def reverse_decorator(function):
    print('Inside reverse_decorator function')
  
    def reverse_wrapper():
        print('Inside reverse_wrapper function')
        return 'reverse_wrapper'

    return reverse_wrapper


@reverse_decorator
def say_hi():
    return 'Inside say_hi'

    
def main():
    print('Inside main()')
    print(say_hi)
    print(''); 
    print(say_hi()) # main executes the reference

main()

Inside reverse_decorator function Inside main() <function reverse_decorator..reverse_wrapper at 0x0000015762A16708> Inside reverse_wrapper function reverse_wrapper

Now you are clear about how to use the @syntax to decorate a function. Another cool stuff is that we can use multiple decorators on a single function. One important thing to note here is

applied is important, which is

. Let’s look into multiple decorators.

def reverse_decorator(function):

    def reverse_wrapper():
        make_reverse = "".join(reversed(function()))
        return make_reverse

    return reverse_wrapper

def uppercase_decorator(function):

    def uppercase_wrapper():
        var_uppercase = function().upper()
        return var_uppercase

    return uppercase_wrapper

@uppercase_decorator
@reverse_decorator
def say_hi():
    return 'hi george'

def main():
    print(say_hi())

if __name__ == "__main__":
    main()

EGROEG IH

Here the string is reversed first and foremost, and secondarily it converts to uppercase.

@uppercase_decorator
@reverse_decorator
def say_hi():
    return 'hi george'

The above code is syntactically equivalent to

So far, you have seen decorators without any arguments. In certain cases, it's necessary to pass arguments to decorate the method accordingly.

def decorator_arguments(function):
    def wrapper_arguments(arg1, arg2):
        print("Arguments accepted are: {0}, {1}".format(arg1, arg2))
        function(arg1, arg2)  # calls the function hobbies

    return wrapper_arguments

@decorator_arguments
def hobbies(hobby_one, hobby_two):
    print("My Hobbies are {0} and {1}".format(hobby_one, hobby_two))

def main():
    hobbies("Travelling", "Reading")

if __name__ == "__main__":
    main()

Arguments accepted are: Travelling, Reading My Hobbies are Travelling and Reading

In the above case, the decorator will not take any arguments instead it is passed to the wrapper function by the callable. The below code provides more clarity in the process of passing the arguments using reference.

def decorator_arguments(function):

    def wrapper_arguments(arg1, arg2):
        print(" Arguments accepted are: {0}, {1}".format(arg1, arg2))

    return wrapper_arguments

@decorator_arguments
def hobbies(hobby_one, hobby_two):
    print("My Hobbies are {0} and {1}".format(hobby_one, hobby_two))

def main():

    # the reference of wrapper_arguments
    # is returned
    hob = hobbies
    print(hob)

    # passing the arguments to the 
    # wrapper_arguments
    hob('Travelling', 'Reading')

main()

<function decorator_arguments..wrapper_arguments at 0x7f100315e158> Arguments accepted are: Travelling, Reading

The standard library provides many modules that include decorators and many Python tools and frameworks that make use of decorators. Few examples are:

• You can use @classmethod or @staticmethod decorator for creating a method without creating an instance
• The mock module permits the use of @mock.patch or @mock.patch.object as a decorator which is used for unit testing.
• Common tools such as Django (uses @login_required) for setting login privileges and Flask (uses @app.route) for function registry uses decorators.
• To identify a function as an asynchronous task Celery uses @task decorator.

Two important reasons to write this reusable piece of Python functionality are when written properly the decorators are modular and explicit.

• Modularity of Decorators
• Decorators are Explicit

Using decorators’ programmers can add or remove functionalities easily in defined blocks of code, which refrains the repetition of boilerplate setup.

Programmers can apply Decorators to all callable based on the need. This ensures readability and provides clean code.

Here we will discuss a few decorator use-cases which help you to understand When to write Decorators.

1. Functional Addons
2. Data Sanitization
3. Function Registration

The first and foremost reason to write a decorator is its flexibility to add extra functionality to the defined blocks of code(functions and classes).

With proper data sanitization methods, you can remove or destroy the data that is stored on memory and make it impossible to recover it. For example, you may use the facility of cache to avoid running the same function or you may use methods to validate the user login credentials and so on, which points to the importance of data sanitization. A decorator can properly sanitize the arguments that passed to the decorated function and also sanitize the data returned from the function.

Another point to write a decorator is to register a function. Here decorator allows two or more subsystems to communicate without having much information about each other.

The above section shows how decorators help in decorating functions. We have seen that decorators are callable that accepts and returns a callable. Since classes are callable, decorators are also used to decorate classes. Some of the uses of decorating classes are:

• Addition or enhancement of attributes
• Attribute interaction
• Alter the API of a class (alter the way of declaring classes and its instance use)

Let's go through how to decorate a function using class.

class MyDecorator:

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

    def __call__(self):
        print("Inside Function Call")
        self.function()

@MyDecorator
def function():
    print("GeeksforGeeks")

def main():
    function()

if __name__ == "__main__":
    main()

Inside Function Call
GeeksforGeeks

Let's look into an another example. Below code, sort the instance based on their creation time. Here we require three additional attributes- the instantiation timestamp, __lt__ and __gt__ methods.

import functools
import time

def sort_by_creation_time(cl):
    org_init = cl.__init__

    # Enhance the class to store the creation
    # time based on the instantiation.
    @functools.wraps(org_init)
    def new_init(self, *args, **kwargs):
        org_init(self, *args, **kwargs)
        self._created = time.time()

    # __lt__ and __gt__ methods return true or false 
    # based on the creation time.
    cl.__init__ = new_init
    cl.__lt = lambda self, other: self._created < other._created
    cl.__gt__ = lambda self, other: self._created > other._created
    return cl

@sort_by_creation_time
class Sort(object):
    def __init__(self, identifier):
        self.identifier = identifier

    def __repr__(self):
        return self.identifier


def main():

    first = Sort('Python')
    second = Sort('Data Analysis')
    third = Sort('Machine Learning')

    sortables = [third, first, second]
    print(sorted(sortables))

if __name__ == "__main__":
    main()

[Python, Data Analysis, Machine Learning]

,

. The

update the wrapper function to reflect the look of wrapped function. Check

for detail understanding.

    @functools.wraps(org_init)
    def new_init(self, *args, **kwargs):

        # calls the init method of class
        org_init(self, *args, **kwargs)

        # add an extra attribute and store 
        # creation time  
        self._created = time.time() 

    # reference of new_init is assigned to
    # __init__ method and executes when
    # callable creates the class object.
    cl.__init__ = new_init  

Sometimes it's necessary to decorate a function and return a class. Say, for example, advanced cases developers can subclass a class in an API. It can also avoid an increase in boilerplate code.

class Addition(object):

    def __call__(self, *args, **kwargs):
        return self.run(*args, **kwargs)

    def run(self, *args, **kwargs):
        raise NotImplementedError('Subclass must implement `run`.')
        
    def identity(self):
        return 'Hi George, I''m here to help you with addition !'

def addition(decorated):
    class AddSubclass(Addition):
        def run(self, *args, **kwargs):

            if args:
                add = 0
                for arg in args:
                    add = arg + add   # add arguments 
                return add
                
            else:
                # func is called if there are no arguments passed.
                return decorated(*args, **kwargs) 
                                                    

    return AddSubclass()

@addition
def func(*args):
    return 1 + 1

print(func.identity())
print(func())
print(func(2, 2))
print(func(2, 1, 4))

Hi George, Im here to help you with addition!
2
4
7

Here the decorator creates a subclass of Addition and returns the instance of the subclass rather than reference (

).

def addition(decorated):
    class AddSubclass(Addition):
        def run(self, *args, **kwargs):

            if args:
                add = 0
                for arg in args:
                    add = arg + add
                return add
                
            else:
                return decorated(*args, **kwargs)

    return AddSubclass()

And the __call__ method in the class `Addition` means that its instance is callable. Hence the instance of subclass `AddSubclass()` can run the task and as a result the programmer can avoid calling the run method as

.

Decorators are an excellent tool for wrapping code around functions and classes, which provides an efficient way to use boilerplate code and also contributes to readability. Even though they are modular and explicit, there are drawbacks to this tool. Since it's a tool for wrapping code around defined blocks, debugging becomes more challenging. And also, poorly written decorators may result in an error. After all, a well-written decorator can be treated as an efficient reusable piece of Python functionality for wrapping defined blocks of code.

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