Statistical software · SciPy developers (NumFOCUS-sponsored open-source project)
SciPy
Open-source Python library of numerical algorithms — optimization, integration, statistics, linear algebra — underlying the scientific Python stack.
SciPy is a free, open-source Python library that supplies the numerical building blocks used throughout the scientific Python ecosystem, built on top of NumPy arrays. Its scipy.stats module provides over a hundred probability distributions and statistical tests, and the library as a whole covers optimization, numerical integration, linear algebra, signal and image processing, and interpolation. In analytics work it typically sits underneath higher-level tools: pandas for data handling, statsmodels for inferential statistics, and scikit-learn for machine learning all depend on SciPy's numerical routines, and analysts call it directly for tasks like curve fitting, distribution testing, or optimization that these higher-level libraries don't expose. It has no commercial edition or vendor; it is maintained by an open-source community and fiscally sponsored by NumFOCUS.
At a glance
| Vendor | SciPy developers (NumFOCUS-sponsored open-source project) |
|---|---|
| Pricing model | Open source + paid options |
| Free tier | Yes |
| Deployment | Self-hosted |
| Open source | Yes (BSD-3-Clause) |
| Best for | Python developers who need numerical and statistical primitives underneath higher-level analytics tools. |
Pricing
Free and open source; no commercial tiers.
Pricing has not been verified yet — see the vendor's site.
Features
- scipy.stats module with 100+ probability distributions and statistical tests
- Numerical optimization and root-finding
- Linear algebra and sparse matrix routines
- Signal and image processing
- Numerical integration and ODE solvers
- Interpolation and Fourier transforms
- N-dimensional array operations built on NumPy
Integrations
Profile last reviewed September 21, 2026