itertools

Q: accumulate custom func?

accumulate(values, func=operator.mul) for running product etc.

Q: pairwise availability?

stdlib 3.10+ - else zip(items, items[1:]) pattern.

Q: starmap?

Like map but unpacks tuple args - starmap(pow, [(2,3), (3,2)]).

itertools provides fast, memory-efficient iterator building blocks - chaining, slicing, combinatorics, and grouping without intermediate lists.

Recipe

from itertools import chain, islice, product
 
flat = chain([1, 2], [3, 4])
first_three = list(islice(range(10), 3))
pairs = list(product(["A", "B"], [1, 2]))

When to reach for this:

Flattening nested iterables
Pagination via islice on infinite counter
Grouping sorted log lines by key
Cartesian products for test case generation

Working Example

from itertools import chain, groupby, islice, accumulate
 
def group_sorted(rows: list[tuple[str, int]]):
    for key, group in groupby(rows, key=lambda r: r[0]):
        yield key, sum(v for _, v in group)
 
def paginate(iterator, page_size: int):
    page = 0
    while True:
        chunk = list(islice(iterator, page_size))
        if not chunk:
            break
        yield page, chunk
        page += 1
 
def running_totals(values):
    return accumulate(values)
 
if __name__ == "__main__":
    rows = [("a", 1), ("a", 2), ("b", 3)]
    print(list(group_sorted(sorted(rows))))
    print(list(paginate(iter(range(7)), 3)))
    print(list(running_totals([1, 2, 3, 4])))

What this demonstrates:

groupby needs sort by same key function first
islice takes from any iterator without len()
paginate pattern for streaming APIs
accumulate yields running reductions (default sum)

Deep Dive

How It Works

Iterator algebra - Functions consume iterables, return iterators.
groupby - Groups consecutive equal keys only - not SQL GROUP BY.
tee - Splits iterator into independent iterators with buffered storage.
cycle/repeat/count - Infinite iterators - always bound consumer loop.
combinations/permutations - Combinatorial generation lazily.

Frequently Used

Function	Purpose
`chain`	Concatenate iterables
`islice`	Slice iterator
`groupby`	Consecutive grouping
`product`	Cartesian product
`zip_longest`	Zip unequal lengths

Python Notes

from itertools import pairwise  # 3.10+
 
for a, b in pairwise([1, 2, 3]):
    print(a, b)

Gotchas

groupby without sort - Splits groups incorrectly when keys not contiguous. Fix: sorted(data, key=key) first.
tee memory - Buffers values for secondary iterators. Fix: Use sparingly on large streams.
infinite product - product(count(), repeat=2) never ends. Fix: islice bound.
chain.from_iterable - Flattens one level - deeper nesting needs recursion or nested chain.
zip vs zip_longest - zip stops at shortest; zip_longest needs fillvalue for padding.

Alternatives

Alternative	Use When	Don't Use When
nested for loops	Two small iterables	Many combinations - itertools clearer
list comprehensions	Materialize small result	Large cross product
pandas groupby	DataFrame analytics	Plain Python tuples
more-itertools	Extra recipes (chunked)	Stdlib-only constraint

FAQs

Why sort before groupby?

groupby only groups adjacent equal keys - sorting clusters equal keys together.

islice vs slicing list?

islice works on iterators without random access or len().

chain vs yield from?

chain for known iterables at once; yield from for generator delegation with protocol.

product memory?

Lazy - generates tuples on demand. Materializing huge product explodes memory.

accumulate custom func?

accumulate(values, func=operator.mul) for running product etc.

pairwise availability?

stdlib 3.10+ - else zip(items, items[1:]) pattern.

starmap?

Like map but unpacks tuple args - starmap(pow, [(2,3), (3,2)]).

filterfalse?

Opposite of filter - elements where pred false.

tee when?

Need two passes over iterator without storing all - pays buffer memory.

combinations vs permutations?

combinations order-insensitive subsets; permutations order matters.

Generators & yield - custom iterators
functools - reduce related to accumulate
Comprehensions & Generator Expressions - genexp
collections Module - Counter with iterables

Stack versions: This page was written for Python 3.14.0 (stable 3.14, maintenance 3.13), FastAPI 0.115+, Django 5.2, Flask 3.1, Pydantic 2, PyTorch 2.6+, pandas 2.2+, Polars 1.x, ruff 0.9+, and uv 0.6+.

Recipe

from itertools import chain, islice, product
 
flat = chain([1, 2], [3, 4])
first_three = list(islice(range(10), 3))
pairs = list(product(["A", "B"], [1, 2]))

When to reach for this:

Flattening nested iterables
Pagination via islice on infinite counter
Grouping sorted log lines by key
Cartesian products for test case generation

Working Example

from itertools import chain, groupby, islice, accumulate
 
def group_sorted(rows: list[tuple[str, int]]):
    for key, group in groupby(rows, key=lambda r: r[0]):
        yield key, sum(v for _, v in group)
 
def paginate(iterator, page_size: int):
    page = 0
    while True:
        chunk = list(islice(iterator, page_size))
        if not chunk:
            break
        yield page, chunk
        page += 1
 
def running_totals(values):
    return accumulate(values)
 
if __name__ == "__main__":
    rows = [("a", 1), ("a", 2), ("b", 3)]
    print(list(group_sorted(sorted(rows))))
    print(list(paginate(iter(range(7)), 3)))
    print(list(running_totals([1, 2, 3, 4])))

What this demonstrates:

groupby needs sort by same key function first
islice takes from any iterator without len()
paginate pattern for streaming APIs
accumulate yields running reductions (default sum)

Deep Dive

How It Works

Iterator algebra - Functions consume iterables, return iterators.
groupby - Groups consecutive equal keys only - not SQL GROUP BY.
tee - Splits iterator into independent iterators with buffered storage.
cycle/repeat/count - Infinite iterators - always bound consumer loop.
combinations/permutations - Combinatorial generation lazily.

Frequently Used

Function	Purpose
`chain`	Concatenate iterables
`islice`	Slice iterator
`groupby`	Consecutive grouping
`product`	Cartesian product
`zip_longest`	Zip unequal lengths

Python Notes

from itertools import pairwise  # 3.10+
 
for a, b in pairwise([1, 2, 3]):
    print(a, b)

Gotchas

groupby without sort - Splits groups incorrectly when keys not contiguous. Fix: sorted(data, key=key) first.
tee memory - Buffers values for secondary iterators. Fix: Use sparingly on large streams.
infinite product - product(count(), repeat=2) never ends. Fix: islice bound.
chain.from_iterable - Flattens one level - deeper nesting needs recursion or nested chain.
zip vs zip_longest - zip stops at shortest; zip_longest needs fillvalue for padding.

Alternatives

Alternative	Use When	Don't Use When
nested for loops	Two small iterables	Many combinations - itertools clearer
list comprehensions	Materialize small result	Large cross product
pandas groupby	DataFrame analytics	Plain Python tuples
more-itertools	Extra recipes (chunked)	Stdlib-only constraint

FAQs

Why sort before groupby?

groupby only groups adjacent equal keys - sorting clusters equal keys together.

islice vs slicing list?

islice works on iterators without random access or len().

chain vs yield from?

chain for known iterables at once; yield from for generator delegation with protocol.

product memory?

Lazy - generates tuples on demand. Materializing huge product explodes memory.

accumulate custom func?

accumulate(values, func=operator.mul) for running product etc.

pairwise availability?

stdlib 3.10+ - else zip(items, items[1:]) pattern.

starmap?

Like map but unpacks tuple args - starmap(pow, [(2,3), (3,2)]).

filterfalse?

Opposite of filter - elements where pred false.

tee when?

Need two passes over iterator without storing all - pays buffer memory.

combinations vs permutations?

combinations order-insensitive subsets; permutations order matters.

Generators & yield - custom iterators
functools - reduce related to accumulate
Comprehensions & Generator Expressions - genexp
collections Module - Counter with iterables

Stack versions: This page was written for Python 3.14.0 (stable 3.14, maintenance 3.13), FastAPI 0.115+, Django 5.2, Flask 3.1, Pydantic 2, PyTorch 2.6+, pandas 2.2+, Polars 1.x, ruff 0.9+, and uv 0.6+.

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