Lambdas, Map, and Filter in Python: Functional Programming in ActionAn Overview of Anonymous Functions and Functional ProgrammingExploring Lambdas, Map, and Filter in Python: Functional Programming in Action

In Python, a lambda function (also called an anonymous function) is a function that doesn’t have a name. Normally, functions are defined using the def keyword, but lambda functions are defined using the lambda keyword.

Syntax Comparison: Lambda Function vs Regular Function

Lambda Function Syntax:

lambda argument: expression

Regular Function Syntax:

def function_name(argument):
    return expression

Examples of Lambda Functions

1. Sum of Two Numbers with Lambda Function:

sum_of_two_number = lambda x, y: x + y
print(sum_of_two_number(10, 20))

Output:

30

2. Multiplication of Two Numbers with Lambda Function:

multiplication_of_two_number = lambda x, y: x * y
print(multiplication_of_two_number(10, 10))

Output:

100

Explanation:
In the above examples, sum_of_two_number and multiplication_of_two_number These are variables that store the result from the lambda function. lambda is the keyword used to define the anonymous function. x and y are the function’s arguments, and the expression (x + y or x * y) is the result.

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Common Functional Programming Functions in Python

1. map() Function

The map() Function applies a function to each item in an iterable (like a list). It returns an iterable object, which is usually converted into a list.

• Syntax:

map(function, iterable)

You can use both regular functions and lambda functions with map(). Lambda functions are often used for a more Pythonic approach.

With Regular Function:

def sqr(x):
    return x * x

lst_of_digits = [1, 2, 3, 4, 5]
sqr_lst = list(map(sqr, lst_of_digits))
print(sqr_lst)

Output:

[1, 4, 9, 16, 25]

With Lambda Function:

sqr_lst = list(map(lambda item: item * item, [1, 2, 3, 4, 5]))
print(sqr_lst)

Output:

[1, 4, 9, 16, 25]

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2. filter() Function

The filter() function filters items from an iterable based on a function that returns True or False. It returns only the items for which the function returns True.

• Syntax:

filter(function, iterable)

Without Lambda (Find Even Numbers):

def is_even(x):
    return x % 2 == 0

numbers = [2, 5, 4, 22, 13, 122, 34, 25, 21]
even_numbers = list(filter(is_even, numbers))
print(even_numbers)

Output:

[2, 4, 22, 122, 34]

With Lambda (Find Even Numbers):

even_numbers = list(filter(lambda x: x % 2 == 0, [2, 5, 4, 22, 13, 122, 34, 25, 21]))
print(even_numbers)

Output:

[2, 4, 22, 122, 34]

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3. reduce() Function

The reduce() Function applies a function cumulatively to the items in an iterable, reducing it to a single result. To use reduce(), you must import it from the functools module.

• Syntax:

from functools import reduce
reduce(function, iterable)

Example (Sum of Numbers):

from functools import reduce

def add(x, y):
    return x + y

numbers = [1, 2, 3, 4, 5, 6]
total = reduce(add, numbers)
print(total)

Output:

CopyEdit21

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Conclusion

Lambda functions provide a concise way to define small functions for simple tasks. They are especially useful when combined with functional programming tools like map(), filter(), and reduce(), helping to write cleaner, more Pythonic code.