Higher-Order Functions in Python.

Function

It can take one or more functions as parameters.It can be returned as a result of another function.It can be modified.It can be assigned to a variable.

Function as a Return Value

	def square(x):          # a square function
    return x ** 2

def cube(x):            # a cube function
    return x ** 3
	

			
	
			

		
	

Python Decorators

A decorator is a design pattern in Python that allows a user to add new functionality to an existing object without modifying its structure. Decorators call before the definition of a function you want to decorate.

Creating Decorators

To create a decorator function, we need an outer function with an inner wrapper function.

	def welcome():
    return 'Hey Hello'

def makeMeLower(function):
    def cont():
        func = function()
        make_lowercase = func.lower()
        return make_lowercase
    return cont

output = makeMeLower(welcome)
print(output()) # hey hello
	

			
	
			

		
	

Accepting Parameters in Decorator Functions

We need our functions to take parameters most of the time, so we might need to define a decorator that accepts parameters.

	def withParams(function):
    def acceptParams(p1, p2):
        function(p1, p2)
        print("I live in {}".format(p2))
    return acceptParams

@withParams
def printMe(name, country):
    print("I am {} and I Know Coding.".format(
        name, country))

printMe("Adam",'Birmingham')

# I am Adam, and I Know Coding.
# I live in Birmingham
	

			
	
			

		
	

Built-in Higher-Order Functions

• Some of the built-in higher-order functions covered in this part are map(), filter, and reduce.
• Lambda function can be passed as a parameter, and the best use case of lambda functions is in functions like map, filter, and reduce.

Python – Map Function

The map() function is a built-in function that takes a function and is iterable as parameters.

	# syntax
map(function, iterable)
	

			
	
			

		
	

	nums = ['1', '2', '3', '4', '5']
output = map(int, nums)
print(list(output))    # [1, 2, 3, 4, 5]
	

			
	
			

		
	

	names = ['Adam', 'Lovlin', 'Anderson', 'Pope']

def upperMe(name):
    return name.upper()

output = map(upperMe, names)
print(list(output)) # ['ADAM', 'LOVLIN', 'ANDERSON', 'POPE']
	

			
	
			

		
	

Python – Filter Function

• The filter() function calls the specified function, which returns a boolean for each item of the specified iterable (list).
• It filters the items that satisfy the filtering criteria.

	# syntax
filter(function, iterable)
	

			
	
			

		
	

	numbers = [1, 2, 3, 4, 5]  # iterable

def findOdd(num):
    if num % 2 != 0:
        return True
    return False

output = filter(findOdd, numbers)
print(list(output))  # [1, 3, 5]
	

			
	
			

		
	

Python – Reduce Function

• The reduce() function is defined in the functools module, and we should import it from this module.
• Like map and filter, it takes two parameters, a function and an iterable.
• However, it doesn’t return another iterable. Instead it returns a single value.

	from functools import reduce
nums = ['1', '2', '3', '4', '5']  # iterable
def add(x, y):
    return int(x) + int(y)

total = reduce(add, nums)
print(total)    # 15