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 |
|---|---|
Clustering algorithms |
|
Physical and mathematical constants |
|
Fast Fourier Transform routines |
|
Integration and ordinary differential equation solvers |
|
Interpolation and smoothing splines |
|
Input and Output |
|
Linear algebra |
|
N-dimensional image processing |
|
Orthogonal distance regression |
|
Optimization and root-finding routines |
|
Signal processing |
|
Sparse matrices and associated routines |
|
Spatial data structures and algorithms |
|
Special functions |
|
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