Python Functions and Decorators

Python Functions and Decorators

Intro to Decoratiors

Reference: https://pythonbasics.org/decorators/

In Python, everything is an object and that applies to functions as well. So, you can assign one function to other.

def hello():
    print ("Hello")

print(type(hello))

greet = hello

print (type(greet))

greet()

hello()

print(id(hello))

print(id(greet))

Here, we have a function named hello and we have assigned this function object to another object named greet. As we can see, greet becomes an object of function class and does the same job as that of the original function hello.

(base) pradeep:~$/usr/local/bin/python3 /Users/pradeep/LearnPython/decorators.py
<class 'function'>
<class 'function'>
Hello
Hello
4372973024
4372973024
(base) pradeep:~$

Both functions return the same. That’s because they refer to the same object, as seen by the id function output.

A function can take another function as input and even return a function.

This is called decorating a function. A decorator takes a function, extends it and returns.

def hello(func):
    def inside():
        print("Hello")
        func()
    return inside

def participant():
    print("Pradeep")

greet = hello(participant)

greet()

(base) pradeep:~$/usr/local/bin/python3 /Users/pradeep/LearnPython/decorators_1.py
Hello
Pradeep
(base) pradeep:~$

In the above example, hello() is a decorator. The function participant() is decorated by the function hello().

It wraps the function in the other function.

def hello(func):
    def inside():
        print("Hello")
        func()
    return inside

def participant():
    print("Pradeep")
                                                                                                                                                                                                    
if __name__ == "__main__":                                                                                  
    greet = hello(participant)
    greet()      

(base) pradeep:~$/usr/local/bin/python3 /Users/pradeep/LearnPython/decorators_1.py
Hello
Pradeep
(base) pradeep:~$

There is no difference between the two ways.

Python can simplify the use of decorators with the @ symbol.

def hello(func):
    def inside():
        print("Hello")
        func()
    return inside

@hello
def participant():
    print("Pradeep")
                                                                                         
                                                                                                            
if __name__ == "__main__":                                                                                  
    participant()

(base) pradeep:~$/usr/local/bin/python3 /Users/pradeep/LearnPython/decorators_1.py
Hello
Pradeep
(base) pradeep:~$

@hello def participant(): is same as greet = hello(participant) greet() but is much simpler and cleaner.

In both cases we apply the decorator to a function.

Parameters can be used with decorators. If you have a funtion that prints the sum a + b, like this

def sum(a, b):
    result = a + b
    print(result)

if __name__ == "__main__":
    sum(5, 3)

(base) pradeep:~$/usr/local/bin/python3 /Users/pradeep/LearnPython/decorators_2.py
8
(base) pradeep:~$

You can decorate this function by calling it in another function

def prettysum(func):
    def pretty(a,b):
        print(str(a) + " + " + str(b) + " is: ", end=" ")
        return func(a,b)
    return pretty

@prettysum
def sum(a, b):
    result = a + b
    print(result)

if __name__ == "__main__":
    sum(5, 3)

(base) pradeep:~$/usr/local/bin/python3 /Users/pradeep/LearnPython/decorators_2.py
5 + 3 is:  8
(base) pradeep:~$

The function sum is wrapped by the function prettysum. This is indicated with the @ symbol above it.

Call the function sum, and see that both the logic of the functions sum and prettysum are run, with parameters.

Here is a realworld example,

import time

def measure_time(func):
    def wrapper (*arg):
        t = time.time()
        res = func(*arg)
        print ("Function took " + str(time.time()-t) + " seconds to execute.")
        return res
    return wrapper

@measure_time
def myFunction(n):
    time.sleep(n)

if __name__ == "__main__":
    myFunction(5)

(base) pradeep:~$/usr/local/bin/python3 /Users/pradeep/LearnPython/time.py
Function took 5.0040247440338135 seconds to execute.
(base) pradeep:~$

This will output the time it took to execute the function myFunction(). By adding one line of code @measure_time we can now measure program execution time.