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# 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 and User Guides

SciPy is organized into subpackages covering different scientific computing domains. These are summarized in the following table, with their user guide linked in the Description and User Guide column (if available):

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

For guidance on organizing and importing functions from SciPy subpackages, refer to the Guidelines for Importing Functions from SciPy.

For information on support for parallel execution and thread safety, see Parallel execution support in SciPy and Thread Safety in SciPy.