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

Sep 11, 2025 · Want to build from source rather than use a Python distribution or pre-built SciPy binary? This guide will describe how to set up your build environment, and how to build SciPy itself, including …

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.

SciPy is a collection of mathematical algorithms and convenience functions built on NumPy . It adds significant power to Python by providing the user with high-level commands and classes for …

To clarify which modules these are, we define below what the public API is for SciPy, and give some recommendations for how to import modules/functions/objects from SciPy.

Numpy and Scipy Documentation Welcome! This is the documentation for Numpy and Scipy. For contributors: Numpy developer guide Scipy developer guide Latest releases: Complete Numpy …

The SciPy library is one of the core packages that make up the SciPy stack. It provides many user-friendly and efficient numerical routines, such as routines for numerical integration, interpolation, …

On this base, the SciPy ecosystem includes general and specialised tools for data management and computation, productive experimentation, and high-performance computing.

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 …

To try out SciPy, you don’t even need to install it! You can use SciPy in your browser at https://jupyter.org/try-jupyter/lab/ - just open a Python Notebook, then write import scipy in one of the …