scipy/scipy

Health v2 · maintenance only

Adoption not part of the score

14954 stars · 5881 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

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

library · maturity stable

math data-science machine-learning image-processing simulation data-science mathematics developer-tools python cross-platform windows scientific-computing numerical-methods numpy optimization signal-processing statistics sparse-matrices algorithms linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="scipy/scipy")