Python map(), filter(), reduce(): A Comprehensive Guide

Introduction to Python map(), filter(), and reduce() Functions

Python's map(), filter(), and reduce() are powerful functional programming constructs that allow us to apply a function to a sequence, filter elements from a sequence, or reduce a sequence to a single value. This tutorial will explore each of these functions through examples and detailed explanations.

Python map() function - The map() function is used to execute a specified function for each item in a iterable.

Python filter() function - The filter() function construct an iterator from those elements of iterable for which function returns true.

Python reduce() function - Apply function of two arguments cumulatively to the items of iterable, from left to right, so as to reduce the iterable to a single value.

Example 1: Using map() to Apply a Function to a List

This example demonstrates using map() to apply a lambda function that squares each number in a list, returning a new list of squared values.

Code:

# A list of numbers
numbers = [1, 2, 3, 4, 5]

# Using map() to square each number in the list
squared_numbers = list(map(lambda x: x ** 2, numbers))

# Output: [1, 4, 9, 16, 25]
print(squared_numbers) 

Explanation:

• 'map()' Function: 'map()' takes two arguments: a function and an iterable (like a list). It applies the function to each element in the iterable.
• Lambda Function: The lambda function ‘lambda x: x ** 2’ squares each number.
• Result: 'map()' returns a map object, which we convert to a list to get the squared numbers.

Example 2: Using map() with a Built-In Function

This example shows how to use 'map()' with a built-in function ('int') to convert a list of strings into a list of integers.

Code:

# A list of strings representing numbers
str_numbers = ['1', '2', '3', '4', '5']

# Using map() to convert each string to an integer
int_numbers = list(map(int, str_numbers))

# Output: [1, 2, 3, 4, 5]
print(int_numbers)

Explanation:

• 'map()' Function: Applies the 'int' function to each element in the 'str_numbers' list.
• Result: Converts a list of strings into a list of integers.

Example 3: Using filter() to Filter Even Numbers

This example uses filter() with a lambda function to extract only even numbers from a list, resulting in a new list of even numbers.

Code:

# A list of numbers
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# Using filter() to keep only even numbers
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))

# Output: [2, 4, 6, 8, 10]
print(even_numbers)

Explanation:

• 'filter()' Function: 'filter()' takes a function and an iterable and returns a new iterable with elements that return 'True' for the function.
• Lambda Function: The lambda function lambda 'x: x % 2 == 0' checks if a number is even.
• Result: Returns a list containing only the even numbers from the original list.

Example 4: Using filter() to Remove Empty Strings

This example demonstrates using filter() to remove empty strings and strings containing only spaces from a list, leaving only non-empty words.

Code:

# A list of strings
words = ["apple", "", "banana", " ", "cherry", ""]

# Using filter() to remove empty strings and strings with only spaces
non_empty_words = list(filter(lambda x: x.strip(), words))

# Output: ['apple', 'banana', 'cherry']
print(non_empty_words)

Explanation:

• 'filter()' Function: Removes elements that are empty or contain only spaces.
• Lambda Function: 'lambda x: x.strip()' removes leading and trailing spaces, and the 'filter()' function filters out empty results.
• Result: Returns a list with only non-empty strings.

Example 5: Using reduce() to Sum a List of Numbers

This example shows how to use 'reduce()' to sum all the elements in a list, demonstrating the reduction of a list to a single cumulative value.

Code:

from functools import reduce

# A list of numbers
numbers = [1, 2, 3, 4, 5]

# Using reduce() to sum the numbers in the list
total_sum = reduce(lambda x, y: x + y, numbers)

# Output: 15
print(total_sum)

Explanation:

• 'reduce()' Function: 'reduce()' takes a function and an iterable and applies the function cumulatively to the items, reducing the iterable to a single value.
• Lambda Function: 'lambda x, y: x + y' adds two numbers together.
• Result: Returns the sum of all numbers in the list.

Example 6: Using ‘reduce()’ to Find the Maximum Value

Code:

from functools import reduce

# A list of numbers
numbers = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]

# Using reduce() to find the maximum value in the list
max_value = reduce(lambda x, y: x if x > y else y, numbers)

# Output: 9
print(max_value)

Explanation:

• 'reduce()' Function: Finds the maximum value by comparing each pair of numbers.
• Lambda Function: 'lambda x, y: x if x > y' else y returns the greater of two numbers.
• Result: Returns the maximum value in the list.

Example 7: Combining 'map()', 'filter()', and 'reduce()'

This example combines 'map()', 'filter()', and 'reduce()' to perform a series of transformations on a list, resulting in the sum of even squares.

Code:

from functools import reduce
# A list of numbers
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
# Using map() to square the numbers, filter() to keep even squares, and reduce() to sum them
result = reduce(lambda x, y: x + y, 
                filter(lambda x: x % 2 == 0, 
                       map(lambda x: x ** 2, numbers)))
# Output: 364 (4 + 16 + 36 + 64 + 100 + 144)
print(result)

Explanation:

• 'map()': Squares each number in the list.
• 'filter()': Keeps only the even squares.
• 'reduce()': Sums the even squares.
• Result: Combines all three functions to produce the sum of the even squares.