pydata/bottleneck
Fast NumPy array functions written in C observed · 2026-08-28
Health v2 · maintenance only
66/100
- Activity 96
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 5758
- days_rel: n/a
- days_push: 24
- n_releases_24m: 0
Adoption not part of the score
1181 stars · 114 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Bottleneck is a collection of fast NumPy array functions implemented as C extensions, covering NaN-aware reductions like nanmean and fast moving-window statistics. It is a drop-in performance accelerator for common NumPy operations.
Use cases
- speed up nanmean, nanstd and other NaN-aware NumPy reductions
- compute moving window mean, sum, std on large arrays quickly
- replace slow NumPy calls in a pandas or scientific pipeline
- benchmark NumPy vs Bottleneck performance
- fill forward values with push on time series arrays
When to choose
- you need faster NaN-aware reductions or moving-window statistics than NumPy provides
- your workload spends significant time in these specific array operations
- you want a lightweight C extension with no heavy dependencies beyond NumPy
When to avoid
- you need operations Bottleneck does not implement
- your arrays are small and NumPy overhead is negligible
- you need GPU acceleration or distributed arrays
Facets
library · maturity active
math benchmarking data-science performance python cross-platform numpy c-extension moving-window nan-handling array-functions algorithms
1 source
- readme: https://github.com/pydata/bottleneck · fetched 2026-08-28 · 0ad59d859023
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| pydata/bottleneck | main | 66 |
For agents
markdown · JSON · MCP: product_card(name="pydata/bottleneck")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem