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. The purpose of this user guide is to provide an overview of the functionality of each subpackage along with some general application notes.

The following table lists the subpackages SciPy provides. The left column contains the subpackage names, whereas the right column provides a description and links to the corresponding chapter of this User Guide (if available):

Subpackage	Description (link to User Guide chapter)
scipy.cluster	Clustering algorithms
scipy.constants	Physical and mathematical constants
scipy.differentiate	Finite difference differentiation tools
scipy.fft	Discrete Fourier Transforms (scipy.fft)
scipy.fftpack	Fast Fourier Transform routines (legacy)
scipy.integrate	Integration (scipy.integrate)
scipy.interpolate	Interpolation (scipy.interpolate)
scipy.io	File IO (scipy.io)
scipy.linalg	Linear Algebra (scipy.linalg)
scipy.ndimage	Multidimensional Image Processing (scipy.ndimage)
scipy.optimize	Optimization (scipy.optimize)
scipy.signal	Signal Processing (scipy.signal)
scipy.sparse	Sparse Arrays (scipy.sparse)
scipy.spatial	Spatial Data Structures and Algorithms (scipy.spatial)
scipy.special	Special Functions (scipy.special)
scipy.stats	Statistics (scipy.stats)

There are also additional user guides for these topics:

• Sparse eigenvalue problems with ARPACK - Eigenvalue problem solver using iterative methods

• Compressed Sparse Graph Routines (scipy.sparse.csgraph) - Compressed Sparse Graph Routines

• Parallel execution support in SciPy

• Thread Safety in SciPy

• Security