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.

These tools handle projects, like SciPy itself, that start to grow larger and more complicated. Separate files can hold frequently used functions, types, variables, and analysis scripts for simpler, more …

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 …

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.

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 nonlinear …

SciPy is a set of open source (BSD licensed) scientific and numerical tools for Python. It currently supports special functions, integration, ordinary differential equation (ODE) solvers, gradient …

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 …