more-itertools
Python library providing 100+ additional iterator utilities beyond Python's stdlib itertools. Includes chunked, windowed, batched, flatten, zip_broadcast, side_effect, seek_after, and many more. Essential for data processing pipelines, ETL scripts, and functional-style Python code. Docs-driven design with extensive usage examples.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Pure Python iteration library — no network access, no security concerns.
⚡ Reliability
Best When
Processing Python iterables in data pipelines where stdlib itertools doesn't have the combinator you need.
Avoid When
Working with NumPy arrays or needing parallel/async iteration — use appropriate array or async libraries.
Use Cases
- • Process large iterables in fixed-size batches: more_itertools.chunked(iterable, n=100) for batch API calls
- • Create sliding windows over sequences: more_itertools.windowed(items, n=3) for time-series analysis
- • Flatten nested iterables of arbitrary depth: more_itertools.flatten(nested_list) without recursion
- • Interleave multiple iterators: more_itertools.roundrobin(*iterables) for fair scheduling
- • Split iterables based on predicates: more_itertools.split_when(iterable, pred) for grouping
Not For
- • NumPy array operations — more-itertools works with Python iterables, not NumPy arrays; use NumPy for numeric array processing
- • Parallel iteration — more-itertools is single-threaded; use concurrent.futures or asyncio for parallel processing
- • Streaming data with backpressure — more-itertools is synchronous; use async generators or reactive streams for async data
Interface
Authentication
Pure Python library — no auth needed.
Pricing
MIT licensed open source library.
Agent Metadata
Known Gotchas
- ⚠ Most more-itertools functions return generators (lazy) — the iterable is not consumed until iterated; call list() to materialize if needed
- ⚠ chunked() returns a list of lists — each chunk is a list, not a generator; for lazy chunking use chunked_even() or batched()
- ⚠ Python 3.12+ stdlib has itertools.batched() — for simple batching on Python 3.12+, consider avoiding the dependency
- ⚠ windowed() returns None for missing values at edges — filter or use fillvalue parameter to handle edge windows
- ⚠ side_effect() doesn't change the iterator but executes a function — its purpose is for debugging; don't use for critical logic in side effects
- ⚠ more-itertools has frequent additions — version pin your dependency to avoid behavior changes when upgrading
Alternatives
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Scores are editorial opinions as of 2026-03-07.