On top of the efficient data structures of NumPy, SciPy offers a magnitude of algorithms for working on those arrays. Whatever numerical heavy algorithm you take from current books on numerical recipes, you will most likely find support for them in SciPy in one way or another. Whether it is matrix manipulation, linear algebra, optimization, clustering, spatial operations, or even fast Fourier transformation, the toolbox is readily filled. Therefore, it is a good habit to always inspect the scipy module before you start implementing a numerical algorithm.

For convenience, the complete namespace of NumPy is also accessible via SciPy. So, from now on, we will use NumPy's machinery via the SciPy namespace. You can check this by easily comparing the function references of any base function, such as the following:

>>> import scipy, numpy
>>> scipy.version.full_version 
1.0.0
>>> scipy.dot is numpy.dot 
True

The diverse algorithms are grouped into the following toolboxes:

The toolboxes that are most pertinent to our goals are scipy.stats, scipy.interpolate, scipy.cluster, and scipy.signal. For the sake of brevity, we will briefly explore some features of the stats package and explain the others when they show up in the individual chapters.