SciPy¶
| Release: | 0.19.1 |
|---|---|
| Date: | June 21, 2017 |
SciPy (pronounced “Sigh Pie”) is open-source software for mathematics, science, and engineering.
Tutorial¶
Tutorials with worked examples and background information for most SciPy submodules.
- SciPy Tutorial
- Introduction
- Basic functions
- Special functions (
scipy.special) - Integration (
scipy.integrate) - Optimization (
scipy.optimize) - Interpolation (
scipy.interpolate) - Fourier Transforms (
scipy.fftpack) - Signal Processing (
scipy.signal) - Linear Algebra (
scipy.linalg) - 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)
Developer’s Guide¶
Explanations of how to start contributing to SciPy, and descriptions of maintenance activities and policies.
To get an overview of where help or new features are desired or planned, see the roadmap:
API Reference¶
The exact API of all functions and classes, as given by the docstrings. The API documents expected types and allowed features for all functions, and all parameters available for the algorithms.
- Clustering package (
scipy.cluster) - Constants (
scipy.constants) - Discrete Fourier transforms (
scipy.fftpack) - Integration and ODEs (
scipy.integrate) - Interpolation (
scipy.interpolate) - Input and output (
scipy.io) - Linear algebra (
scipy.linalg) - Miscellaneous routines (
scipy.misc) - Multi-dimensional image processing (
scipy.ndimage) - Orthogonal distance regression (
scipy.odr) - Optimization and root finding (
scipy.optimize) - Signal processing (
scipy.signal) - Sparse matrices (
scipy.sparse) - Sparse linear algebra (
scipy.sparse.linalg) - Compressed Sparse Graph Routines (
scipy.sparse.csgraph) - Spatial algorithms and data structures (
scipy.spatial) - Special functions (
scipy.special) - Statistical functions (
scipy.stats) - Statistical functions for masked arrays (
scipy.stats.mstats) - Low-level callback functions