Map Lambda Function in Python

Python Lambda Functions are anonymous function means that the function is without a name. As we already know that the def keyword is used to define a normal function in Python. Similarly, the lambda keyword is used to define an anonymous function in Python.

Python Lambda Function Syntax
Syntax: lambda arguments: expression

This function can have any number of arguments but only one expression, which is evaluated and returned.
One is free to use lambda functions wherever function objects are required.
You need to keep in your knowledge that lambda functions are syntactically restricted to a single expression.
It has various uses in particular fields of programming, besides other types of expressions in functions.
Python Lambda Function Example

str1 = 'GeeksforGeeks'

lambda returns a function object

rev_upper = lambda string: string.upper()[::-1]
print(rev_upper(str1))
Output:

SKEEGROFSKEEG
Explanation: In the above example, we defined a lambda function(rev_upper) to convert a string to it’s upper-case and reverse it.

Use of Lambda Function in Python
Example 1: Condition Checking Using Python lambda function

format_numeric = lambda num: f"{num:e}" if isinstance(num, int) else f"{num:,.2f}"

print("Int formatting:", format_numeric(1000000))
print("float formatting:", format_numeric(999999.789541235))
Output:

Int formatting: 1.000000e+06
float formatting: 999,999.79
Example 2: Difference Between Lambda functions and def defined function
def cube(y):
return y*y*y

lambda_cube = lambda y: y*y*y

using function defined

using def keyword

print("Using function defined with def keyword, cube:", cube(5))

using the lambda function

print("Using lambda function, cube:", lambda_cube(5))
Output:

Using function defined with def keyword, cube: 125
Using lambda function, cube: 125
As we can see in the above example, both the cube() function and lambda_cube() function behave the same and as intended. Let’s analyze the above example a bit more:

With lambda function Without lambda function
Supports single line statements that returns some value. Supports any number of lines inside a function block
Good for performing short operations/data manipulations. Good for any cases that require multiple lines of code.
Using lambda function can sometime reduce the readability of code. We can use comments and function descriptions for easy readability.
Practical Uses of Python lambda function
Example 1: Python Lambda Function with List Comprehension
In this example, we will use the lambda function with list comprehension.

is_even_list = [lambda arg=x: arg * 10 for x in range(1, 5)]

iterate on each lambda function

and invoke the function to get the calculated value

for item in is_even_list:
print(item())
Output:

10
20
30
40
Explanation: On each iteration inside the list comprehension, we are creating a new lambda function with default argument of x (where x is the current item in the iteration). Later, inside the for loop, we are calling the same function object having the default argument using item() and getting the desired value. Thus, is_even_list stores the list of lambda function objects.

Example 2: Python Lambda Function with if-else
Here we are using Max lambda function to find the maximum of two integers.

Example of lambda function using if-else

Max = lambda a, b : a if(a > b) else b

print(Max(1, 2))
Output:

2
Example 3: Python Lambda with Multiple statements
Lambda functions does not allow multiple statements, however, we can create two lambda functions and then call the other lambda function as a parameter to the first function. Let’s try to find the second maximum element using lambda.

List = [[2,3,4],[1, 4, 16, 64],[3, 6, 9, 12]]

Sort each sublist

sortList = lambda x: (sorted(i) for i in x)

Get the second largest element

secondLargest = lambda x, f : [y[len(y)-2] for y in f(x)]
res = secondLargest(List, sortList)

print(res)
Output:

[3, 16, 9]
Explanation: In the above example, we have created a lambda function that sorts each sublist of the given list. Then this list is passed as the parameter to the second lambda function, which returns the n-2 element from the sorted list, where n is the length of the sublist.

Lambda functions can be used along with built-in functions like filter(), map() and reduce().

Using lambda() Function with filter()
The filter() function in Python takes in a function and a list as arguments. This offers an elegant way to filter out all the elements of a sequence “sequence”, for which the function returns True. Here is a small program that returns the odd numbers from an input list:

Example 1: Filter out all odd numbers using filter() and lambda function
Here, lambda x: (x % 2 != 0) returns True or False if x is not even. Since filter() only keeps elements where it produces True, thus it removes all odd numbers that generated False.

li = [5, 7, 22, 97, 54, 62, 77, 23, 73, 61]

final_list = list(filter(lambda x: (x % 2 != 0), li))
print(final_list)
Output:

[5, 7, 97, 77, 23, 73, 61]
Example 2: Filter all people having age more than 18, using lambda and filter() function

Python 3 code to people above 18 yrs

ages = [13, 90, 17, 59, 21, 60, 5]

adults = list(filter(lambda age: age > 18, ages))

print(adults)
Output:

[90, 59, 21, 60]
Using lambda() Function with map()
The map() function in Python takes in a function and a list as an argument. The function is called with a lambda function and a list and a new list is returned which contains all the lambda modified items returned by that function for each item. Example:

Example 1: Multiply all elements of a list by 2 using lambda and map() function

Python code to illustrate

map() with lambda()

to get double of a list.

li = [5, 7, 22, 97, 54, 62, 77, 23, 73, 61]

final_list = list(map(lambda x: x*2, li))
print(final_list)
Output:

[10, 14, 44, 194, 108, 124, 154, 46, 146, 122]
Example 2: Transform all elements of a list to upper case using lambda and map() function

Python program to demonstrate

use of lambda() function

with map() function

animals = ['dog', 'cat', 'parrot', 'rabbit']

here we intend to change all animal names

to upper case and return the same

uppered_animals = list(map(lambda animal: animal.upper(), animals))

print(uppered_animals)
Output:

['DOG', 'CAT', 'PARROT', 'RABBIT']
Using lambda() Function with reduce()
The reduce() function in Python takes in a function and a list as an argument. The function is called with a lambda function and an iterable and a new reduced result is returned. This performs a repetitive operation over the pairs of the iterable. The reduce() function belongs to the functools module.

Example 1: Sum of all elements in a list using lambda and reduce() function

Python code to illustrate

reduce() with lambda()

to get sum of a list

from functools import reduce
li = [5, 8, 10, 20, 50, 100]
sum = reduce((lambda x, y: x + y), li)
print(sum)
Output:

193
Here the results of the previous two elements are added to the next element and this goes on till the end of the list like (((((5+8)+10)+20)+50)+100).

Example 2: Find the maximum element in a list using lambda and reduce() function

python code to demonstrate working of reduce()

with a lambda function

importing functools for reduce()

import functools

initializing list

lis = [1, 3, 5, 6, 2, ]

using reduce to compute maximum element from list

print("The maximum element of the list is : ", end="")
print(functools.reduce(lambda a, b: a if a > b else b, lis))
Output:

The maximum element of the list is : 6