Itertools

Python · Reference cheat sheet

📋 Overview

itertools offers fast, memory-efficient building blocks for iterators: infinite streams, combinatorics, and chaining. Compose small tools instead of materializing huge lists.

🔧 Core concepts

Function	Role
`count` `cycle` `repeat`	Infinite / repeated
`chain` `chain.from_iterable`	Concatenate
`islice`	Slice an iterator
`tee`	Split into n iterators
`groupby`	Group consecutive keys
`accumulate`	Running totals
`product` `permutations` `combinations`	Combinatorics
`zip_longest`	Zip with fill
`batched`	Chunks (3.12+)

All return iterators — consume with for, list, next, or islice.

💡 Examples

islice and count:

from itertools import count, islice

print(list(islice(count(10, 2), 5)))  # [10, 12, 14, 16, 18]

chain and groupby:

from itertools import chain, groupby

print(list(chain([1, 2], (3, 4))))

rows = [("a", 1), ("a", 2), ("b", 3)]
for key, group in groupby(rows, key=lambda r: r[0]):
    print(key, list(group))

Combinatorics:

from itertools import combinations, product

print(list(combinations("ABC", 2)))
print(list(product([0, 1], repeat=2)))

batched (3.12+) / chunk recipe:

from itertools import batched, islice
from collections.abc import Iterator

print(list(batched(range(10), 3)))

def chunked(it: Iterator[int], n: int) -> Iterator[list[int]]:
    while True:
        block = list(islice(it, n))
        if not block:
            return
        yield block

⚠️ Pitfalls

groupby requires sorted (or already consecutive) keys.
tee can buffer a lot if iterators diverge — prefer re-iterating sources.
Infinite iterators need a stop condition.
Combinatorial functions explode in size — bound inputs.
Exhausting one tee branch while another lags costs memory.

Itertools

Itertools

📋 Overview

🔧 Core concepts

💡 Examples

⚠️ Pitfalls

🔗 Related

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