Map Filter Reduce¶

map()¶

1. Transform Elements¶

numbers = [1, 2, 3, 4, 5]
squared = list(map(lambda x: x**2, numbers))
print(squared)  # [1, 4, 9, 16, 25]

2. Multiple Iterables¶

a = [1, 2, 3]
b = [10, 20, 30]
result = list(map(lambda x, y: x + y, a, b))
print(result)  # [11, 22, 33]

3. Lazy Iterator¶

map() returns an iterator, not a list. Values are computed on demand.

numbers = [1, 2, 3, 4]
squares = map(lambda x: x**2, numbers)
print(squares)        # <map object at 0x...>
print(list(squares))  # [1, 4, 9, 16]

filter()¶

1. Select Elements¶

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

2. Lazy Iterator¶

filter() also returns an iterator.

numbers = [1, 2, 3, 4, 5, 6]
evens = filter(lambda x: x % 2 == 0, numbers)
print(evens)        # <filter object at 0x...>
print(list(evens))  # [2, 4, 6]

reduce()¶

1. Accumulate¶

from functools import reduce

numbers = [1, 2, 3, 4, 5]
total = reduce(lambda x, y: x + y, numbers)
print(total)  # 15

2. With Initial¶

product = reduce(lambda x, y: x * y, numbers, 1)
print(product)  # 120

Comprehensions Alternative¶

1. More Pythonic¶

# map equivalent
squared = [x**2 for x in numbers]

# filter equivalent
evens = [x for x in numbers if x % 2 == 0]

Functional Composition¶

Chain filter, map, and reduce for data pipelines.

1. Pipeline Example¶

from functools import reduce

names = ["Alice", "Bob", "Charlie", "David"]

# filter → map → reduce
result = reduce(
    lambda a, b: a + " & " + b,
    map(str.upper, filter(lambda name: len(name) > 4, names))
)

print(result)  # ALICE & CHARLIE & DAVID

2. Step by Step¶

from functools import reduce

numbers = [1, 2, 3, 4, 5, 6]

# Step 1: filter evens
evens = filter(lambda x: x % 2 == 0, numbers)

# Step 2: map to squares
squares = map(lambda x: x ** 2, evens)

# Step 3: reduce to sum
total = reduce(lambda x, y: x + y, squares)

print(total)  # 56 (4 + 16 + 36)

Summary¶

map: transform
filter: select
reduce: accumulate
Comprehensions often better