# scipy/scipy

SciPy library main repository

Repository: https://github.com/scipy/scipy
Canonical: https://ross.abutalabs.com/products/scipy
Homepage: https://scipy.org
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: python, scipy, algorithms, closember, scientific-computing
Last push: 2026-08-26T13:16:45+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 86, longevity 100
- inputs: {"age_days": 5656, "days_push": 7, "days_rel": 12, "gap_med": 46, "n_releases_24m": 12}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 14954, forks 5881 (observed 2026-08-28T04:11:08.983584+00:00)

## What it is
SciPy is an open-source Python library providing fundamental algorithms for scientific computing, including optimization, integration, interpolation, linear algebra, statistics, signal processing, and ODE solvers. It extends NumPy with efficient numerical routines implemented in compiled languages like C, C++, and Fortran.

## Use cases
- solve optimization problems in python
- numerical integration of functions
- fit statistical distributions to data
- solve ordinary differential equations
- compute eigenvalues and linear algebra operations
- filter and process signals
- interpolate scattered data
- work with sparse matrices

## When to choose
- you need well-tested numerical algorithms in Python
- you are doing scientific or engineering computing with NumPy arrays
- you need optimization, integration, or statistics routines with a high-level API
- you want performant compiled-code speed with Python flexibility

## When to avoid
- you only need basic array operations that NumPy alone covers
- you need GPU-accelerated deep learning frameworks rather than CPU numerical routines
- you need symbolic mathematics rather than numerical computation

## Facets
- artifact type: library
- maturity: stable
- function: math, data-science, machine-learning, image-processing, simulation
- domain: data-science, mathematics, developer-tools
- platform: python, cross-platform, windows
- tags: scientific-computing, numerical-methods, numpy, optimization, signal-processing, statistics, sparse-matrices, algorithms, linux, macos

## Member repositories
- scipy/scipy (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:08.983584+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 2026-08-29T17:06:47.990864+00:00, confidence not recorded.
  - readme: https://github.com/scipy/scipy (fetched 2026-08-28T04:11:08.983584+00:00, sha 7bbbcfe19f8e)
  - homepage: https://scipy.org (fetched 2026-08-29T08:04:56.869043+00:00, sha 62bd0e273484)
  - site_page: https://scipy.org/install (fetched 2026-08-29T08:04:56.878404+00:00, sha 73df76eb9f2e)
  - site_page: https://docs.scipy.org/doc/scipy (fetched 2026-08-29T08:04:56.881442+00:00, sha c178a02ffa96)
  - site_page: https://scipy.org/about (fetched 2026-08-29T08:04:56.883589+00:00, sha 6630280bf8bf)
  - site_page: https://scipy.org/faq (fetched 2026-08-29T08:04:56.885457+00:00, sha 7cd1cafe2b44)
- Data as of 2026-08-30T08:39:29.467469+00:00.