Map, Filter & Reduce

map()

map(func, iterable) applies a function to every element, returning a lazy iterator.

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

# Equivalent comprehension:
# [x * 2 for x in nums]

filter()

filter(func, iterable) keeps only items where the function returns truthy.

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

# Equivalent:
# [x for x in nums if x % 2 == 0]

reduce()

reduce(func, iterable, init) folds a sequence into a single value using a binary function.

from functools import reduce

nums = [1, 2, 3, 4]
total = reduce(lambda a, b: a + b, nums)
print(total)   # 10

# Factorial using reduce:
fact5 = reduce(lambda a, b: a * b, range(1, 6), 1)
print(fact5)   # 120

Combining

Chain filter → map → reduce for clean data pipelines.

from functools import reduce

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

# Sum of squares of even numbers
result = reduce(
    lambda a, b: a + b,
    map(lambda x: x * x, filter(lambda x: x % 2 == 0, nums))
)

print(result)   # 56

Best Practices

• Prefer comprehensions for short, simple transformations.
• Use reduce when you naturally “fold” values (sum, product, min/max, joins).
• Keep lambdas very small; move complex logic into named functions and pass those to map/filter.
• Combine filter → map → reduce for functional-style ETL / data-processing pipelines.