Feb 17, 2025 · SciPy (pronounced “Sigh Pie”) is an open-source software for mathematics, science, and engineering. The user guide provides in-depth information on the key concepts of SciPy with useful background information and explanation. The reference guide contains a detailed description of the SciPy API.

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 manipulating and visualizing data.

This is the documentation for Numpy and Scipy.

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 notebook “cells” and hit play. For next steps, see …

To install SciPy with static type stubs, see Installing with type stubs. Installing type stubs may be required for Integrated Development Environments (IDEs) to provide accurate type hints. Here is a step-by-step guide to setting up a project to use SciPy, with uv, a Python package manager.

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

Getting started with Python for science¶ This part of the Scipy lecture notes is a self-contained introduction to everything that is needed to use Python for science, from the language itself, to numerical computing or plotting.

SciPy, pronounced as Sigh Pi, is a scientific python open source, distributed under the BSD licensed library to perform Mathematical, Scientific and Engineering Computations. The SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. The SciPy library is built to work with NumPy arrays and provides

Feb 16, 2025 · SciPy (pronounced “Sigh Pie”) is an open-source software for mathematics, science, and engineering. It includes modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more.

Jan 24, 2025 · SciPy extends the capabilities of NumPy by offering a wide range of algorithms for optimization, integration, interpolation, linear algebra, signal processing, and more. Whether you are a data scientist, an engineer, or a researcher, SciPy can be an invaluable tool in your toolkit for solving complex computational problems.