SciPy
SciPy is an open-source Python library for scientific computing, maintained by a community of volunteers and hosted under the NumFOCUS umbrella.
Profile
SciPy provides a free, open-source Python library for scientific and technical computing, including modules for optimization, integration, interpolation, linear algebra, signal processing, and statistics.
SciPy is an open-source Python library for scientific computing, maintained by a community of volunteers and hosted under the NumFOCUS umbrella. It was first released in 2001 by Travis Oliphant, Pearu Peterson, and Eric Jones as a collection of algorithms for mathematics, science, and engineering. The project has no corporate headquarters, no employees, and no revenue; it is funded entirely through donations, grants, and sponsorships.
As of June 2026, the repository on GitHub has 14,700 stars, 5,700 forks, and over 37,600 commits. The library provides modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers, and more. SciPy does not sell products or services; its primary output is the library itself, distributed under a BSD license.
The project's annual conference, SciPy 2026, will be held July 13–19 at the University of Minnesota, Minneapolis, celebrating 25 years. Sponsors for the 2026 conference include Diamond-level Quansight, Platinum-level Posit and NVIDIA, Gold-level Meta, Thomson Reuters, Anaconda, Curvenote, and others. The project's governance is handled by a steering council elected by contributors.
There is no venture capital funding, no paid sales team, and no formal customer relationships. The library is downloaded millions of times per month via PyPI and conda-forge, and is a dependency for nearly every major Python data science tool, including pandas, scikit-learn, and Matplotlib. Recent development activity includes the removal of the deprecated `scipy.odr` module and ongoing migration of Fortran code to C/C++. The project faces ongoing challenges in maintaining backward compatibility while modernizing its codebase, and relies on a small core of volunteer maintainers.
Who buys this
- Academic researchers and students in STEM fields
- Data scientists and machine learning engineers
- Engineers in aerospace, automotive, and manufacturing
- Government and defense research labs
- Financial analysts and quantitative researchers
Strengths and what to watch
Strengths
- Ubiquitous dependency in the Python scientific ecosystem, used by pandas, scikit-learn, and Matplotlib, ensuring sustained relevance.
- Long-standing community governance under NumFOCUS, with a transparent decision-making process and a 25-year track record of releases.
- Broad functional coverage across optimization, signal processing, and statistics, reducing the need for multiple specialized libraries.
Watch for
- Heavy reliance on a small volunteer maintainer team, creating bus-factor risk and slow response to security or compatibility issues.
- Ongoing technical debt from legacy Fortran code, with gradual migration to C/C++ that may introduce regressions or break existing workflows.
- No formal funding model beyond donations and conference sponsorships, making long-term sustainability dependent on continued community goodwill.
Recent moves
Key Information
- Industry
- Stat Languages
- Founded
- 2001
Frequently Asked Questions
What is SciPy and what is it used for?
SciPy is a free, open-source Python library for scientific and technical computing. It provides modules for optimization, integration, interpolation, linear algebra, signal processing, and statistics, making it essential for researchers, data scientists, and engineers working in STEM fields.
Who maintains the SciPy library and how is it funded?
SciPy is maintained by a community of volunteers under the NumFOCUS umbrella. It has no corporate headquarters or employees. Funding comes entirely from donations, grants, and sponsorships, with no venture capital or paid sales team involved.
What are the main modules included in SciPy?
SciPy includes modules for optimization, linear algebra, integration, interpolation, special functions, fast Fourier transforms, signal and image processing, ordinary differential equation solvers, and statistics. These cover a broad range of scientific computing needs.
How does SciPy relate to other Python data science libraries?
SciPy is a core dependency for nearly every major Python data science tool, including pandas, scikit-learn, and Matplotlib. It provides foundational scientific computing functions that these libraries build upon, making it ubiquitous in the ecosystem.
What are the main challenges facing the SciPy project?
SciPy relies on a small volunteer maintainer team, creating bus-factor risk. It also has technical debt from legacy Fortran code, with ongoing migration to C/C++ that may cause regressions. Funding depends on donations and conference sponsorships, posing sustainability challenges.
When and where is the SciPy 2026 conference taking place?
The SciPy 2026 conference will be held July 13–19 at the University of Minnesota in Minneapolis, celebrating 25 years of open-source scientific computing. Sponsors include Quansight, Posit, NVIDIA, Meta, Thomson Reuters, Anaconda, and Curvenote.
Sources
- github.com — Repository statistics, commit history, and project description.
- www.scipy2026.scipy.org — Conference dates, location, and sponsor list for SciPy 2026.
- scipy.org — Donation and funding model for the SciPy project.
- tidelift.com — Tidelift sponsorship channel for SciPy.