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.

User guide

• Special functions (scipy.special)
• Integration (scipy.integrate)
• Optimization (scipy.optimize)
• Interpolation (scipy.interpolate)
• Fourier Transforms (scipy.fft)
• Signal Processing (scipy.signal)
• Linear Algebra (scipy.linalg)
• Sparse Arrays (scipy.sparse)
• Sparse eigenvalue problems with ARPACK
• Compressed Sparse Graph Routines (scipy.sparse.csgraph)
• Spatial data structures and algorithms (scipy.spatial)
• Statistics (scipy.stats)
• Multidimensional image processing (scipy.ndimage)
• File IO (scipy.io)

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

• Interpolate transition guide