Python map filter and reduce

map() — Transform Every Item

map(function, iterable) applies a function to every item in an iterable and returns a map object (wrap it with list() to see the results). It is a quick way to transform data without writing an explicit loop.

nums = [1, 2, 3, 4]
doubled = list(map(lambda x: x * 2, nums))
print(doubled)   # [2, 4, 6, 8]

filter() — Keep Matching Items

filter(function, iterable) keeps only the items for which the function returns True, throwing away everything else. The function passed in should return a boolean.

nums = [1, 2, 3, 4, 5, 6, 7, 8]
evens = list(filter(lambda x: x % 2 == 0, nums))
print(evens)   # [2, 4, 6, 8]

reduce() — Combine Into One Value

reduce(), found in the functools module (not built-in like map and filter), repeatedly applies a function to pairs of items until the whole iterable collapses into a single value, such as a running total or product.

from functools import reduce

nums = [1, 2, 3, 4]
total = reduce(lambda acc, x: acc + x, nums)
print(total)   # 10 -> ((1+2)+3)+4

Chaining Them Together

These three tools can be combined: filter out unwanted items, transform what remains with map, then collapse the result with reduce.

from functools import reduce

nums = [1, 2, 3, 4, 5, 6]
result = reduce(lambda a, b: a + b,
                map(lambda x: x * x,
                    filter(lambda x: x % 2 == 0, nums)))
print(result)   # squares of evens (4+16+36) = 56

map/filter vs List Comprehensions

Most Python developers prefer list comprehensions over map and filter for everyday tasks, because they read more like plain English and avoid extra lambda syntax. map and filter are still useful for functional-style pipelines or when passing an existing named function.