6.3 map, filter, and reduce

map(), filter(), and reduce() help split data processing into clear steps. They are often used with short lambda functions. A lambda is an anonymous function, useful for a very small transformation or test.

Data Processing Pipeline

Think of a list as data moving through a pipeline:

• filter() decides which elements stay.
• map() transforms each element.
• reduce() combines many values into one result.

Sum of Odd Squares

The program below computes the sum of the squares of all odd numbers from 0 through 9.

from functools import reduce

numbers = list(range(10))
print("numbers =", numbers)

odds = list(filter(lambda x: x % 2 == 1, numbers))
print("odds =", odds)

squares = list(map(lambda x: x ** 2, odds))
print("squares =", squares)

total = reduce(lambda x, y: x + y, squares)
print("total =", total)

Output:

numbers = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
odds = [1, 3, 5, 7, 9]
squares = [1, 9, 25, 49, 81]
total = 165

When to Use lambda

lambda is useful for very short anonymous functions. For example, lambda x: x ** 2 means “take x and return its square.”

If the logic is long, use a normal def function. Do not sacrifice readability just to be shorter.