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Decorators in Python

  • Difficulty Level : Medium
  • Last Updated : 01 Mar, 2021

Decorators are very powerful and useful tool in Python since it allows programmers to modify the behavior of function or class. Decorators allow us to wrap another function in order to extend the behavior of the wrapped function, without permanently modifying it. But before diving deep into decorators let us understand some concepts that will come in handy in learning the decorators.

First Class Objects

In Python, functions are first class objects that means that functions in Python can be used or passed as arguments.

Properties of first class functions:

  • A function is an instance of the Object type.
  • You can store the function in a variable.
  • You can pass the function as a parameter to another function.
  • You can return the function from a function.
  • You can store them in data structures such as hash tables, lists, …

Consider the below examples for better understanding.

Example 1: Treating the functions as objects.






# Python program to illustrate functions 
# can be treated as objects 
def shout(text): 
    return text.upper() 
  
print(shout('Hello')) 
  
yell = shout 
  
print(yell('Hello')) 

Output:

HELLO
HELLO

In the above example, we have assigned the function shout to a variable. This will not call the function instead it takes the function object referenced by shout and creates a second name pointing to it, yell.

Example 2: Passing the function as argument




# Python program to illustrate functions 
# can be passed as arguments to other functions 
def shout(text): 
    return text.upper() 
  
def whisper(text): 
    return text.lower() 
  
def greet(func): 
    # storing the function in a variable 
    greeting = func("""Hi, I am created by a function passed as an argument."""
    print (greeting) 
  
greet(shout) 
greet(whisper) 

Output:

HI, I AM CREATED BY A FUNCTION PASSED AS AN ARGUMENT.
hi, i am created by a function passed as an argument.

In the above example, the greet function takes another function as a parameter (shout and whisper in this case). The function passed as argument is then called inside the function greet.

Example 3: Returning functions from another functions.




# Python program to illustrate functions 
# Functions can return another function 
  
def create_adder(x): 
    def adder(y): 
        return x+
  
    return adder 
  
add_15 = create_adder(15
  
print(add_15(10)) 

Output:

25

In the above example, we have created a function inside of another function and then have returned the function created inside.



The above three examples depict the important concepts that are needed to understand decorators. After going through them let us now dive deep into decorators.

Decorators

As stated above the decorators are used to modify the behavior of function or class. In Decorators, functions are taken as the argument into another function and then called inside the wrapper function.

Syntax for Decorator:

@gfg_decorator
def hello_decorator():
    print("Gfg")

'''Above code is equivalent to -

def hello_decorator():
    print("Gfg")
    
hello_decorator = gfg_decorator(hello_decorator)'''

In the above code, gfg_decorator is a callable function, will add some code on the top of some another callable function, hello_decorator function and return the wrapper function.

Decorator can modify the behavior:




# defining a decorator
def hello_decorator(func):
  
    # inner1 is a Wrapper function in 
    # which the argument is called
      
    # inner function can access the outer local
    # functions like in this case "func"
    def inner1():
        print("Hello, this is before function execution")
  
        # calling the actual function now
        # inside the wrapper function.
        func()
  
        print("This is after function execution")
          
    return inner1
  
  
# defining a function, to be called inside wrapper
def function_to_be_used():
    print("This is inside the function !!")
  
  
# passing 'function_to_be_used' inside the
# decorator to control its behavior
function_to_be_used = hello_decorator(function_to_be_used)
  
  
# calling the function
function_to_be_used()

Output:

Hello, this is before function execution
This is inside the function !!
This is after function execution

 

Let’s see the behavior of the above code how it runs step by step when the “function_to_be_used” is called.


 
Let’s jump to another example where we can easily find out the execution time of a function using a decorator.






# importing libraries
import time
import math
  
# decorator to calculate duration
# taken by any function.
def calculate_time(func):
      
    # added arguments inside the inner1,
    # if function takes any arguments,
    # can be added like this.
    def inner1(*args, **kwargs):
  
        # storing time before function execution
        begin = time.time()
          
        func(*args, **kwargs)
  
        # storing time after function execution
        end = time.time()
        print("Total time taken in : ", func.__name__, end - begin)
  
    return inner1
  
  
  
# this can be added to any function present,
# in this case to calculate a factorial
@calculate_time
def factorial(num):
  
    # sleep 2 seconds because it takes very less time
    # so that you can see the actual difference
    time.sleep(2)
    print(math.factorial(num))
  
# calling the function.
factorial(10)

Output:

3628800
Total time taken in :  factorial 2.0061802864074707

 

What if a function returns something or an argument is passed to the function?

In all the above examples the functions didn’t return anything so there wasn’t any issue, but one may need the returned value.




def hello_decorator(func):
    def inner1(*args, **kwargs):
          
        print("before Execution")
          
        # getting the returned value
        returned_value = func(*args, **kwargs)
        print("after Execution")
          
        # returning the value to the original frame
        return returned_value
          
    return inner1
  
  
# adding decorator to the function
@hello_decorator
def sum_two_numbers(a, b):
    print("Inside the function")
    return a + b
  
a, b = 1, 2
  
# getting the value through return of the function
print("Sum =", sum_two_numbers(a, b))

Output:

before Execution
Inside the function
after Execution
Sum = 3

In the above example, you may notice a keen difference in the parameters of the inner function. The inner function takes the argument as *args and **kwargs which means that a tuple of positional arguments or a dictionary of keyword arguments can be passed of any length. This makes it a general decorator that can decorate a function having any number of arguments.

Chaining Decorators

In simpler terms chaining decorators means decorating a function with multiple decorators.

Example:




# code for testing decorator chaining 
def decor1(func): 
    def inner(): 
        x = func() 
        return x *
    return inner 
  
def decor(func): 
    def inner(): 
        x = func() 
        return 2 *
    return inner 
  
@decor1
@decor
def num(): 
    return 10
  
print(num()) 

Output:

400

The above example is similar to calling the function as –

decor1(decor(num))

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