numpy/numpy
The fundamental package for scientific computing with Python. observed · 2026-08-28
Health v2 · maintenance only
94/100
- Activity 99
- Release rhythm 84
- Longevity 100
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 31
- age_days: 5833
- days_rel: 24
- days_push: 7
- n_releases_24m: 26
Adoption not part of the score
32607 stars · 12679 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
NumPy is the fundamental open-source Python library for scientific computing, providing a powerful N-dimensional array object, vectorized operations, broadcasting, and routines for linear algebra, Fourier transforms, and random number generation. Its optimized C core delivers compiled-code speed with Python's ease of use, and it underpins nearly the entire scientific Python ecosystem.
Use cases
- perform fast numerical computations on large arrays in Python
- do linear algebra and matrix operations
- generate random numbers for simulations
- compute Fourier transforms and signal processing
- manipulate and reshape multidimensional data
- build a foundation for pandas, scikit-learn, or PyTorch workflows
When to choose
- you need fast, vectorized N-dimensional array operations in Python
- you are doing scientific computing, data analysis, or machine learning in Python
- you want a battle-tested, widely supported numerical foundation with C-level performance
When to avoid
- you only need simple lists and don't care about numerical performance
- you need GPU-native arrays with automatic differentiation - consider JAX or PyTorch instead
- you need sparse or distributed arrays beyond NumPy's interoperability layer
Facets
library · maturity stable
math data-science machine-learning sdk data-science machine-learning developer-tools python cross-platform n-dimensional-arrays linear-algebra fourier-transform random-number-generation vectorization broadcasting numerical-computing scientific-computing algorithms gpu
6 sources
- readme: https://github.com/numpy/numpy · fetched 2026-08-28 · a7bbb51112e7
- homepage: https://numpy.org · fetched 2026-08-29 · c2b55e52e196
- site_page: https://numpy.org/install · fetched 2026-08-29 · 5bb274d45325
- site_page: https://numpy.org/about · fetched 2026-08-29 · 6ce6061ad12b
- site_page: https://numpy.org/doc/stable · fetched 2026-08-29 · 65daaf3683f4
- site_page: https://numpy.org/news · fetched 2026-08-29 · 20409c65ba21
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| numpy/numpy | main | 94 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem