SciPy User Guide#

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.

Subpackages#

SciPy is organized into subpackages covering different scientific computing domains. These are summarized in the following table:

Subpackage

Description

cluster

Clustering algorithms

constants

Physical and mathematical constants

fftpack

Fast Fourier Transform routines

integrate

Integration and ordinary differential equation solvers

interpolate

Interpolation and smoothing splines

io

Input and Output

linalg

Linear algebra

ndimage

N-dimensional image processing

odr

Orthogonal distance regression

optimize

Optimization and root-finding routines

signal

Signal processing

sparse

Sparse matrices and associated routines

spatial

Spatial data structures and algorithms

special

Special functions

stats

Statistical distributions and functions

SciPy subpackages need to be imported separately, for example:

>>> from scipy import linalg, optimize

Below, you can find the complete user guide organized by subpackages.

Executable tutorials#

Below you can also find tutorials in MyST Markdown format. These can be opened as Jupyter Notebooks with the help of the Jupytext extension.

Executable tutorials