# numpy/numpy

The fundamental package for scientific computing with Python.

Repository: https://github.com/numpy/numpy
Canonical: https://ross.abutalabs.com/products/numpy
Homepage: https://numpy.org
Language: Python
License: NOASSERTION
License Family: other
Topics: numpy, python
Last push: 2026-08-26T19:14:35+00:00

## Health v2 (maintenance only)
Score: 94/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 84, longevity 100
- inputs: {"age_days": 5833, "days_push": 7, "days_rel": 24, "gap_med": 31, "n_releases_24m": 26}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 32607, forks 12679 (observed 2026-08-28T04:11:57.692169+00:00)

## What it is
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
- artifact type: library
- maturity: stable
- function: math, data-science, machine-learning, sdk
- domain: data-science, machine-learning, developer-tools
- platform: python, cross-platform
- tags: n-dimensional-arrays, linear-algebra, fourier-transform, random-number-generation, vectorization, broadcasting, numerical-computing, scientific-computing, algorithms, gpu

## Member repositories
- numpy/numpy (main) score 94

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:57.692169+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:51:55.991139+00:00, confidence not recorded.
  - readme: https://github.com/numpy/numpy (fetched 2026-08-28T04:11:57.692169+00:00, sha a7bbb51112e7)
  - homepage: https://numpy.org (fetched 2026-08-29T07:49:02.822857+00:00, sha c2b55e52e196)
  - site_page: https://numpy.org/install (fetched 2026-08-29T07:49:02.832474+00:00, sha 5bb274d45325)
  - site_page: https://numpy.org/about (fetched 2026-08-29T07:49:02.837036+00:00, sha 6ce6061ad12b)
  - site_page: https://numpy.org/doc/stable (fetched 2026-08-29T07:49:02.835022+00:00, sha 65daaf3683f4)
  - site_page: https://numpy.org/news (fetched 2026-08-29T07:49:02.839194+00:00, sha 20409c65ba21)
- Data as of 2026-08-30T08:39:29.467469+00:00.