SciPy - start

https://www.scipy.org/
SciPy. Fundamental algorithms for scientific computing in Python

https://docs.scipy.org/doc/scipy/tutorial/index.html
SciPy User Guide

https://pypi.org/project/scipy/
SciPy: Scientific Library for Python

https://scipy-cookbook.readthedocs.io/
SciPy Cookbook

INSTALLING SCIPY

https://www.scipy.org/install.html

ANACONDA
Use 'conda' or graphical tools.

PIP (in a virtual environment)
python -m pip install numpy scipy matplotlib ipython jupyter pandas sympy nose

APT (for the entire computer)
Debian packages: python-scipy (Py2.7), python3-scipy, and dependencies.
[version 1.1.0 in Debian 10]
[version 1.15.3 in Debian 13]

INTRODUCTION

SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering. These are some of the core packages: NumPy, SciPy library, Matplotlib, IPython, SymPy, pandas, NetworkX, scikit-learn, scikit-image, h5py.

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 sub-packages need to be imported separately, for example:

from scipy import linalg, optimize
import scipy.special as sc   # help(sc)
import scipy.special as special