map, filter, and reduce

map(), filter(), and reduce() help split data processing into clear steps. They are often used with short lambda functions.

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)

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

Understanding lambda

A lambda creates a short function without a declared name. Its syntax is lambda parameters: expression. Parameters go to the left of the colon; exactly one expression goes to the right. That expression is returned implicitly, so there is no return keyword. For example, lambda x: x ** 2 behaves like a regular function whose body is only return x ** 2.

def square(x):
    return x ** 2

square_lambda = lambda x: x ** 2
print(square(5))
print(square_lambda(5))

A lambda is a function object: you may assign it to a variable or pass it directly to tools such as map, filter, and sorted that expect a function argument. It may also accept multiple parameters, as in lambda x, y: x + y.

Use lambda for a one-rule function that is readable at a glance, such as key=lambda item: item[1]. If the logic needs multiple steps, loops, exception handling, or a descriptive name, use a regular def. Brevity is not the goal; readability is.