numpy/numpy
Some data may be outdated — see provenance labels.
The fundamental package for scientific computing with Python. observed · 2026-09-23
Health v2 · maintenance only
96/100
- Activity 96
- Release rhythm 93
- Longevity 100
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.
- gap_med: 29.5
- age_days: 5881
- days_rel: 44
- days_push: 28
- n_releases_24m: 25
Adoption not part of the score
32812 stars · 12834 forks observed · 2026-09-23
What it is AI-extracted, prompt v1, taxonomy v1, 2026-10-05, confidence 0.99
NumPy is a fundamental Python library that provides powerful N-dimensional array objects and a suite of mathematical functions. It enables fast, vectorized computation for scientific, engineering, and data‑analysis workloads.
Use cases
- perform high‑performance numerical calculations on large datasets
- apply linear algebra, Fourier transforms, and statistical operations in Python
- vectorize loops and replace explicit Python for‑loops with fast array operations
- generate random numbers and perform Monte‑Carlo simulations
- integrate C/C++ or Fortran code for custom scientific algorithms
- prepare data for machine‑learning pipelines that require array inputs
When to choose
- You need a mature, well‑tested library for array‑based numerical computing in Python
- Your project relies on a stable API with extensive documentation and community support
- You want seamless integration with the broader scientific Python ecosystem (SciPy, pandas, matplotlib, etc.)
When to avoid
- You require GPU‑accelerated tensor operations beyond what NumPy offers (consider CuPy or PyTorch)
- Your workload involves distributed computing across multiple nodes without additional tooling
- You need a compiled language interface without the overhead of Python
Facets
library · maturity active
data-science math serialization data-science machine-learning windows python cross-platform n-dimensional-array vectorized-operations broadcasting numpy open-source bsd-license fft random scientific-computing linux macos
6 sources
- readme: https://github.com/numpy/numpy · fetched 2026-09-23 · a7bbb51112e7
- homepage: https://numpy.org · fetched 2026-09-21 · c2b55e52e196
- site_page: https://numpy.org/install · fetched 2026-09-21 · 5bb274d45325
- site_page: https://numpy.org/about · fetched 2026-09-21 · 6ce6061ad12b
- site_page: https://numpy.org/news · fetched 2026-09-21 · ba5da2a62479
- site_page: https://numpy.org/doc/stable · fetched 2026-09-21 · 65daaf3683f4
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| numpy/numpy | main | 96 |
For agents
Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem