Map and Reduce Function

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Python supports functional programming concepts, where functions are treated as first-class objects. The built-in functions filter(), map(), and reduce() help process collections in a clean, concise, and efficient manner.

These functions are commonly used for:

• Data transformation
• Data filtering
• Data aggregation

Overview of Map, Filter, and Reduce

filter()

• Selects elements from an iterable based on a condition
• Keeps only values for which the function returns True
• Returns a filter object

map()

• Transforms each element of an iterable
• Applies the same function to every element independently
• Returns a map object

reduce()

• Combines all elements of an iterable into a single value
• Uses a function that takes two arguments
• Available in the functools module

Real-World Problem Statement

Given a list of numbers:

1. Extract even numbers
2. Double each even number
3. Calculate the sum of the doubled values

nums = [4, 2, 9, 7, 5, 1, 6, 8]

evens = list(filter(lambda n: n % 2 == 0, nums))
print("Evens:", evens)

Evens: [4, 2, 6, 8]

def double_it(n):
    return n * 2

doubles = list(map(double_it, evens))
print("Doubles:", doubles)

doubles = list(map(lambda n: n * 2, evens))
print("Doubles:", doubles)

Doubles: [8, 4, 12, 16]

from functools import reduce

def sum_it(a, b):
    return a + b

total = reduce(sum_it, doubles)
print("Total:", total)

total = reduce(lambda a, b: a + b, doubles)
print("Total:", total)

Total: 40

Complete Example

from functools import reduce

nums = [4, 2, 9, 7, 5, 1, 6, 8]

evens = list(filter(lambda n: n % 2 == 0, nums))
doubles = list(map(lambda n: n * 2, evens))
total = reduce(lambda a, b: a + b, doubles)

print("Evens:", evens)
print("Doubles:", doubles)
print("Total:", total)

Output:

Evens: [4, 2, 6, 8]
Doubles: [8, 4, 12, 16]
Total: 40

Summary

• filter() selects elements from a collection based on a condition, keeping only the values that satisfy the given logic.
• map() transforms each element in a collection by applying a function, producing a new set of modified values.
• reduce() aggregates all elements of a collection into a single result by repeatedly combining values.
• Lambda functions enable compact, one-line function definitions, making filter(), map(), and reduce() more concise and readable.

Together, these functions form a powerful functional programming pipeline for clean, efficient, and expressive data processing in Python.