Iteration Superpowers with `itertools`

Standard Library

itertools Recipes

`itertools` provides composable building blocks for iterating over data without materializing huge lists.

Explore recipesApply to streaming data

Recipes

Pattern	Snippet	Use case
itertools.chain	chain(list_a, list_b)	Concatenate iterables lazily
itertools.islice	islice(stream, 10)	Grab first N items
itertools.cycle	cycle(['red', 'blue'])	Repeat pattern
itertools.groupby	see below	Group sorted data
itertools.pairwise (3.10+)	pairwise(points)	Adjacent pairs
itertools.permutations/combinations	combinations(users, 2)	Combinatorics

from itertools import groupby

orders.sort(key=lambda o: o.customer_id)
for cust_id, items in groupby(orders, key=lambda o: o.customer_id):
    process(cust_id, list(items))

Patterns

• Build sliding windows with deque + islice.
• Combine with functools to memoize expensive iterators.
• Offload CPU-heavy combinations to multiprocessing when datasets are large.

Next up in your learning path

functools

Enhance iterator pipelines with caching and partials.

Functional

threading/multiprocessing

Distribute iterator workloads across workers.

Concurrency

List comprehensions

Pair itertools with comprehensions for readability.

Foundation