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

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.

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 …

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 …

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 …

Large-scale bundle adjustment in scipy demonstrates large-scale capabilities of least_squares and how to efficiently compute finite difference approximation of sparse Jacobian. Robust …

SciPy has many functions for performing hypothesis tests that return a test statistic and a p-value, and several of them return confidence intervals and/or other related information.

For global optimization, other choices of objective function, and other advanced features, consider using SciPy’s Global optimization tools or the LMFIT package.

Optimization and root finding (scipy.optimize) # SciPy optimize provides functions for minimizing (or maximizing) objective functions, possibly subject to constraints.