Information Density: SciPy – Signal Evidence & AI Readability

SciPy
[IMG: SciPy logo. A blue circle with a snake in the shape of the letter]

Fundamental algorithms for scientific computing in Python

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SciPy 1.17.1 released!
2026-02-22

Fundamental algorithms
SciPy provides algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics and many other classes of problems.

Broadly applicable
The algorithms and data structures provided by SciPy are broadly applicable across domains.

Foundational
Extends NumPy providing additional tools for array computing and provides specialized data structures, such as sparse matrices and k-dimensional trees.

Performant
SciPy wraps highly-optimized implementations written in low-level languages like Fortran, C, and C++. Enjoy the flexibility of Python with the speed of compiled code.

Easy to use
SciPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.

Open source
Distributed under a liberal BSD license, SciPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.

[H1] About Us

SciPy is developed in the open on GitHub, through the consensus of the SciPy
and wider scientific Python community. For more information on our governance
approach, please see our
Governance Document.
[H2] Steering Council#
The role of the SciPy Steering Council is to ensure, through working with and
serving the broader SciPy community, the long-term well-being of the project,
both technically and as a community. The SciPy Steering Council currently
consists of the following members (in alphabetical order):
Andrew Nelson
Charles Harris
Christoph Baumgarten
CJ Carey
Eric Larson
Evgeni Burovski
İlhan Polat
Jake Bowhay
Josef Perktold
Lucas Colley
Matt Haberland
Matthew Brett
Nikolay Mayorov
Pauli Virtanen (BDFL)
Ralf Gommers (Chair)
Tyler Reddy (Release manager)
Warren Weckesser
Emeritus:
Anne Archibald
Eric Jones (co-creator of SciPy)
Eric Moore
Eric Quintero
Jaime Fernández del Río
Jarrod Millman
Josh Wilson
Paul van Mulbregt
Pearu Peterson (co-creator of SciPy)
Robert Kern
Stéfan van der Walt
Travis Oliphant (co-creator of SciPy)
[H2] Teams#
The SciPy project is growing; we have teams for
code
website
triage
See the Teams page for individual team members.
[H2] Sponsors#
SciPy receives direct funding from the following sources:

[IMG: Logo of the Chan Zuckerberg Initiative]

[IMG: Logo of Tidelift]

[H2] Institutional Partners#
Institutional Partners are organizations that support the project by employing
people that contribute to SciPy as part of their job. Current Institutional
Partners include:
Quansight (Ralf Gommers, Peter Bell, Melissa Weber Mendonça,
Evgeni Burovski, Albert Steppi)

[IMG: Logo of Quansight]

Los Alamos National Laboratory (Tyler Reddy)
[H2] Donate#
SciPy will always be 100% open source software, free for all to use and
released under the liberal terms of the modified BSD license. While we
have a large number of
contributors
who volunteer their time to improve SciPy, financial resources are
needed to run the project and accelerate its development. If you have
found SciPy useful in your work, research, or company, please consider
making a donation to the project commensurate with your resources. Any
amount helps!
Donations are managed by the NumFOCUS
foundation, which passes your contribution to the SciPy project,
and provides the SciPy development team with basic administrative and
legal services. NumFOCUS is a 501(c)3 non-profit
foundation, so if you are subject to the US Tax law, your contributions
are tax-deductible.

[H2] Acknowledgements#
The SciPy development team would like to thank the following companies
and organizations for providing financial support, services, or
development infrastructure:
JetBrains: licenses of all their
products for all active maintainers
Tidelift:
financial support for SciPy through the Tidelift open source
subscription
CircleCI: continuous integration credit
TravisCI: continuous integration credit
Appveyor: continuous integration credit
Azure: continuous integration credit
Enthought: scipy.org and mailing lists
hosting, holding the SciPy trademark
NumFOCUS: several small development grants,
and a hosted Mac Mini build machine
Google: support for many Google Summer of Code
students
Intel: Intel
MKL licenses
BYU: employed Travis Oliphant while working
on SciPy
Mayo Clinic: employed Travis Oliphant
while working on SciPy
This list is ordered by time (most recent contributions first) and was
last updated in January 2022.
[H2] Social Media#
@scipy on Mastodon
@SciPy_team on X

[H1] Installation

Tip
This page assumes that you are comfortable with using a terminal and happy to learn
how to use a package manager. If you are a beginner and just want to get started
with SciPy as quickly as possible, check out
the beginner installation guide!
The recommended method of installing SciPy depends on your preferred workflow.
The common workflows can roughly be broken down into the following
categories:
Project-based (e.g. uv, pixi) (recommended for new users)
Environment-based (e.g. pip, conda) (the traditional workflow)
System package managers (not recommended)
Building from source (for debugging and development)
To install SciPy with static type stubs,
see Installing with type stubs.
Tip
Installing type stubs may be required for
Integrated Development Environments (IDEs) to provide accurate type hints.

Project Based

Environment Based

Package Manager

Building from Source

[H3] Installing with uv#
Here is a step-by-step guide to setting up a project to use SciPy, with uv, a Python package manager.
Install uv following, the instructions in the uv documentation.
Create a new project in a new subdirectory, by executing the following in a terminal:
uv init try-scipy
cd try-scipy
Hint
The second command changes directory into the directory of your project.
Add SciPy to your project:
uv add scipy
Note
This will automatically install Python if you don’t already have it installed!
Tip
You can install other Python libraries in the same way, e.g.
uv add matplotlib

Try out SciPy!
uv run python
This will launch a Python interpreter session, from which you can import scipy.
See next steps in the SciPy user guide.
Note
After rebooting your computer, you’ll want to navigate to your try-scipy
project directory and execute uv run python to drop back into a Python interpreter
with SciPy importable.
To execute a Python script, you can use uv run myscript.py.
Read more at the uv guide to working on projects.
[H3] Installing with pixi#
If you work with non-Python packages, you may prefer to install SciPy as
a Conda package, so that you can use the same workflow for packages which
are not available on PyPI, the Python Package Index.
Conda can manage packages in any language, so you can use it to install
Python itself, compilers, and other languages.
The steps to install SciPy from conda-forge using the package management
tool pixi are very similar to the steps for uv:
Install pixi, following the instructions in the pixi documentation.
Create a new project in a new subdirectory:
pixi init try-scipy
cd try-scipy
Add SciPy to your project:
pixi add scipy
Try out SciPy!
pixi run python
In project-based workflows, a project is a directory containing a manifest
file describing the project, a lock-file describing the exact dependencies
of the project, and the project’s (potentially multiple) environments.
In contrast,
in environment-based workflows you install packages into an environment,
which you can activate and deactivate from any directory.
These workflows are well-established,
but lack some reproducibility benefits of project-based workflows.
[H3] Installing with pip#
Install Python.
Create and activate a virtual environment with venv.
Hint
See the tutorial in the Python Packaging User Guide.
Install SciPy, using pip:
python -m pip install scipy
[H3] Installing with conda#
Miniforge is the recommended way to install conda and mamba,
two Conda-based environment managers.
After creating an environment, you can install SciPy from conda-forge as follows:
conda install scipy # or
mamba install scipy
[H2] Installing system-wide via a system package manager#
System package managers can install the most common Python packages.
They install packages for the entire computer, often use older versions,
and don’t have as many available versions. They are not the recommended
installation method.
[H3] Ubuntu and Debian#
Using apt-get:
sudo apt-get install python3-scipy
[H3] Fedora#
Using dnf:
sudo dnf install python3-scipy
[H3] macOS#
macOS doesn’t have a preinstalled package manager, but you can install
Homebrew and use it to install SciPy (and Python itself):
brew install scipy
A word of warning: building SciPy from source can be a nontrivial exercise. We
recommend using binaries instead if those are available for your platform
via one of the above methods.
For details on how to build from source, see
the building from source guide in the SciPy docs.
See next steps in the SciPy user guide.
[H2] Installing with Type Stubs#
Static type stubs are available via a separate package, scipy-stubs, on
PyPI and conda-forge.
You can also install SciPy and scipy-stubs as a single package,
via the scipy-stubs[scipy] extra on PyPI, or the scipy-typed
package on conda-forge.
To get a specific version x.y.z of SciPy (such as 1.14.1),
you should install version x.y.z.*, for example:
uv add "scipy-stubs[scipy]==1.14.1.*" # or
pixi add "scipy-typed=1.15.0.*" # or
python -m pip install "scipy-stubs[scipy]" # or
conda install "scipy-typed>=1.14"
Please direct questions about static typing support to
the scipy-stubs GitHub repository.

[H1] Community

SciPy is a community-driven open source project developed by a diverse group of
contributors. The SciPy leadership has made a strong commitment to
creating an open, inclusive, and positive community. Please read the
SciPy Code of Conduct
for guidance on how to interact with others in a way that makes the community
thrive.
We offer several communication channels to learn, share your knowledge and
connect with others within the SciPy community.
[H2] Participate online#
The following are ways to engage directly with the SciPy project and community.
Please note that we encourage users and community members to support each
other for usage questions. Search for an answer first, because someone may
already have found a solution to your problem.
Lastly, maintainers are mostly monitoring the forum and GitHub.
[H3] SciPy community meetings#
SciPy community meetings are ideal to anyone wanting to contribute to SciPy
or just know how current development is going. You can follow
our community calendar from your
preferred calendar manager, or look out for the announcements on our
development forum.
[H3] SciPy new contributor meetings#
Once a month we have special meetings for folks who want to start contributing
or have just started. All are welcome! Check our community calendar for details,
or look out for the announcements on our development forum.
[H3] SciPy development forum#
This space is the main forum for longer-form discussions, like adding new
features to SciPy, making changes to the SciPy Roadmap, and all kinds of
project-wide decision making. Announcements about SciPy, such as for releases,
developer meetings, sprints or conference talks are also made on this forum.
A searchable archive of the old mailing list
is available here.
[H3] SciPy Slack space#
The SciPy team also has a Slack space that you can join. This is not a user
support forum, but you can ask questions about contributing and getting involved
in the community. To join, please follow this invite link.
[H3] Scientific Python Discord#
You can also join the #scipy channel on the Scientific Python discord.
To join, please follow this invite link.
[H3] StackOverflow#
You can ask questions with the scipy tag on
StackOverflow.
[H3] GitHub issue tracker#
For bug reports (e.g. “np.arange(3).shape returns (5,), when it should return (3,)”);
documentation issues (e.g. “I found this section unclear”);
and feature requests (e.g. “I would like to have a new statistical test in scipy.stats”).
Please note that GitHub is not the right place to report a security
vulnerability. If you think you have found a security vulnerability in SciPy,
please report it here.
[H2] Study Groups and Meetups#
If you would like to find a local meetup or study group to learn more about
SciPy and the wider ecosystem of Python packages for data science and
scientific computing, we recommend exploring the
PyData meetups
(150+ meetups, 100,000+ members).
SciPy also organizes in-person sprints for its team and interested contributors
occasionally. These are typically planned several months in advance and will
be announced on the
forum.
[H2] Conferences#
The SciPy project doesn’t organize its own conferences. The conferences that
have traditionally been most popular with SciPy maintainers, contributors and
users are the SciPy and PyData conference series:
SciPy US
EuroSciPy
SciPy Latin America
SciPy India
SciPyData (Japan)
PyData conferences (15-20 events a year spread over many countries)
Many of these conferences include tutorial days that cover SciPy and/or sprints
where you can learn how to contribute to SciPy or related open source projects.
[H2] Join the SciPy community#
To thrive, the SciPy project needs your expertise and enthusiasm. Not a coder?
Not a problem! There are many ways to contribute to SciPy.
If you are interested in becoming a SciPy contributor (yay!) we recommend
checking out our Contribute page.