🌀 itertools Module

Last Updated: 24th August 2025

It provides tools for creating iterators (objects you can loop over) in a very efficient way.

List of Functions:

  • itertools.combinations(iterable, r): Returns an iterator over all combinations of length r in iterable.
import itertools
# example 1

students = list(range(100))
pairs = itertools.combinations(students, 2)  # generator, not list

for p in pairs:
    print(p)

# example 2

features = ["age", "income", "education"]
for combo in itertools.combinations(features, 2):
    print(combo)
  • permutations(iterable, r): Returns an iterator over all permutations of length r in iterable.
from itertools import permutations

models = ["LR", "SVM", "RF"]
for order in permutations(models, 2):
    print(order)
  • product(*iterables, repeat=1): Returns an iterator that computes the cartesian product of multiple iterables.
from itertools import product

colors = ["red", "green", "blue"]
sizes = ["S", "M", "L"]
for color, size in product(colors, sizes):
    print(color, size) # red S, red M, red L, green S, green M, green L, blue S, blue M, blue L
  • cyclic(iterable): Returns an iterator that cycles through the elements of iterable.Repeats the sequence indefinitely.
from itertools import cycle

labels = ["Train", "Test"]
cycler = cycle(labels)

for _ in range(5):
    print(next(cycler))
  • chain(*iterables): Returns an iterator that chains the elements of multiple iterables together.
from itertools import chain

a = [1, 2, 3]
b = [4, 4, 6]
c = [7, 6, 9]
merged = chain(a, b, c)
print(list(merged))  # [1, 2, 3, 4, 4, 6, 7, 6, 9]
  • accumulate(iterable, start=0): Returns an iterator that accumulates the elements of iterable.
from itertools import accumulate

numbers = [1, 2, 3, 4, 5]
cumulative_sum = accumulate(numbers)
print(list(cumulative_sum))  # [1, 3, 6, 10, 15]
  • groupby(iterable, key=None): Returns an iterator that groups elements of iterable based on the key function.
from itertools import groupby

numbers = [1, 2, 3, 4, 5]
grouped = groupby(sorted(numbers, key=lambda x: x % 2), lambda x: x % 2)

for key, group in grouped:
    print(key, list(group))
  • dropwhile(predicate, iterable): Returns an iterator that drops elements from the iterable while the predicate is true.
from itertools import dropwhile

temps = [28, 30, 32, 35, 29, 27]
hot_days = list(dropwhile(lambda t: t < 33, temps))

print(hot_days)
  • takewhile(predicate, iterable): Returns an iterator that takes elements from the iterable while the predicate is true.
from itertools import takewhile

temps = [28, 30, 32, 35, 29, 27]
cool_days = list(takewhile(lambda t: t < 33, temps))

print(cool_days)
  • filterfalse(function, iterable): It is opposite of filter function. Returns an iterator that filters out elements for which the function returns False.
from itertools import filterfalse

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