scipy
Fundamental algorithms for scientific computing in Python
Decision gist · record as of 2026-08-14
Yes. scipy is a mature, actively maintained library with no known vulnerabilities, permissive BSD-3-Clause licensing, and broad platform support. It is a de facto standard for scientific computing in Python and essential for projects requiring numerical algorithms beyond basic linear algebra. Install friction is moderate but manageable via pre-built wheels.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later; compiled dependencies (OpenBLAS, LAPACK) included in wheels but may require system libraries on some platforms.
- Medium install friction due to compiled dependencies.
- Pre-built wheels available for Python 3.12 and 3.13 on Linux, macOS, Windows, and ARM architectures.
License · maintenance · safety
permissive license (permissive) — BSD-3-Clause primary license permits commercial use and modification with attribution. Bundled dependencies carry BSD-3-Clause and GPL-3.0-or-later licenses; GPL components are runtime libraries with GCC exception.
last release 2026-06-19 (56 days) · last repo commit 2026-08-13 · 14,915 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 448,963,567 downloads/mo, #90 on PyPI
Alternatives
Verify before relying
pip install scipy
import scipy.optimize
result = scipy.optimize.minimize(lambda x: (x - 2)**2, x0=0)- Whether scipy's GPL-licensed runtime components (libgfortran, libgcc) impose redistribution constraints on proprietary applications
What it is and what it does
scipy is a foundational library for numerical and scientific computing in Python. It provides algorithms for optimization, integration, linear algebra, Fourier analysis, signal processing, image processing, and solving differential equations. All routines work directly with numpy arrays, making it a natural extension of numpy for users who need specialized mathematical functionality.
The package is designed for scientists, engineers, and researchers performing complex numerical computations. It bundles high-performance linear algebra libraries (OpenBLAS, LAPACK) for efficient execution. scipy is actively maintained, widely used in production, and available as pre-built wheels for Python 3.12 and 3.13 on all major operating systems.
Use it for
- Solve optimization problems: minimize/maximize functions, constrained optimization, root-finding
- Perform numerical integration and solve systems of ordinary differential equations
- Conduct linear algebra operations: matrix decomposition, eigenvalue problems, sparse matrices
- Apply signal processing techniques: filtering, spectral analysis, convolution
- Process and analyze images: filtering, morphological operations, feature detection
- Compute Fourier transforms and work with frequency-domain representations
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
scipy is a mature, actively maintained library with no known vulnerabilities, permissive BSD-3-Clause licensing, and broad platform support. It is a de facto standard for scientific computing in Python and essential for projects requiring numerical algorithms beyond basic linear algebra. Install friction is moderate but manageable via pre-built wheels.
Install
scipy on PyPI
Before you install
Medium install friction due to compiled dependencies. Pre-built wheels available for Python 3.12 and 3.13 on Linux, macOS, Windows, and ARM architectures. Active maintenance with last commit on 2026-08-13.
Requires Python 3.12 or later; compiled dependencies (OpenBLAS, LAPACK) included in wheels but may require system libraries on some platforms.
License in practice
BSD-3-Clause primary license permits commercial use and modification with attribution. Bundled dependencies carry BSD-3-Clause and GPL-3.0-or-later licenses; GPL components are runtime libraries with GCC exception.
Quickstart
pip install scipy
import scipy.optimize
result = scipy.optimize.minimize(lambda x: (x - 2)**2, x0=0)Verify before relying
- Whether scipy's GPL-licensed runtime components (libgfortran, libgcc) impose redistribution constraints on proprietary applications
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Actively maintained 56 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 448,963,567 / month, #90 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: POSIX :: LinuxOperating System :: UnixProgramming Language :: CProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Software Development :: Libraries |
Evidence: scipy-1.18.0-cp312-cp312-macosx_10_15_x86_64.whl; scipy-1.18.0-cp312-cp312-macosx_12_0_arm64.whl; scipy-1.18.0-cp312-cp312-macosx_14_0_arm64.whl; scipy-1.18.0-cp312-cp312-macosx_14_0_x86_64.whl; scipy-1.18.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; scipy-1.18.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; scipy-1.18.0-cp312-cp312-musllinux_1_2_aarch64.whl; scipy-1.18.0-cp312-cp312-musllinux_1_2_x86_64.whl; scipy-1.18.0-cp312-cp312-win_amd64.whl; scipy-1.18.0-cp312-cp312-win_arm64.whl; scipy-1.18.0-cp313-cp313-macosx_10_15_x86_64.whl; scipy-1.18.0-cp313-cp313-macosx_12_0_arm64.whl; scipy-1.18.0-cp313-cp313-macosx_14_0_arm64.whl; scipy-1.18.0-cp313-cp313-macosx_14_0_x86_64.whl; scipy-1.18.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; scipy-1.18.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; scipy-1.18.0-cp313-cp313-musllinux_1_2_aarch64.whl; scipy-1.18.0-cp313-cp313-musllinux_1_2_x86_64.whl; scipy-1.18.0-cp313-cp313-win_amd64.whl; scipy-1.18.0-cp313-cp313-win_arm64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “numerical optimization integration”
- scipyscipy provides numerical algorithms for mathematics, science, and…
- scsscs is a Python interface to the Splitting Conic Solver, a numerical…
- osqpOSQP is a Python wrapper for the Operator Splitting Quadratic Program…
Give your agent the search over MCP, or paste the wish link into any chat.
More Libraries packages
urllib3 is an HTTP client library that provides thread-safe connection pooling, SSL/TLS verification, multipart file uploads, request retries, compression support, and proxy handling for Python applications.
Requests is a Python HTTP library that simplifies sending HTTP/1.1 requests with automatic handling of headers, authentication, cookies, and response parsing.
Pluggy provides a plugin system that lets you define hook specifications and register implementations to be called in sequence, enabling extensible Python applications without tight coupling.
Install it if you're building an extensible application or framework.
Provides parsing, arithmetic, and recurrence rule computation for dates and times, with timezone support and iCalendar RFC compliance.
Install it if you need to parse flexible date strings, compute relative dates, handle timezones, or work with recurrence rules—it's the de facto choice for these tasks.
Six provides utility functions to write Python code that runs on both Python 2.7 and Python 3.3+, smoothing over language differences between the two versions.
pytest is a testing framework that lets you write test functions using plain assert statements and automatically discovers and runs them, with detailed failure reporting.
See also eigenpy · hankel · numpy · ropt · quantecon · xarray-einstats · scipy-openblas32 · scikit-image · pyroots · gekko