Building Machine Learning Systems with Python - Richert, Coelho

Chapter 1
However, the speed comes at a price. Using NumPy arrays, we no longer have the
incredible flexibility of Python lists, which can hold basically anything. NumPy
arrays always have only one datatype.
>>> a = np.array([1,2,3])
>>> a.dtype
dtype('int64')
If we try to use elements of different types, NumPy will do its best to coerce them
to the most reasonable common datatype:
>>> np.array([1, "stringy"])
array(['1', 'stringy'], dtype='|S8')
>>> np.array([1, "stringy", set([1,2,3])])
array([1, stringy, set([1, 2, 3])], dtype=object)
Learning SciPy
On top of the efficient data structures of NumPy, SciPy offers a magnitude of
algorithms 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 easily by comparing the function references of any base function;
for example:
>>> import scipy, numpy
>>> scipy.version.full_version
0.11.0
>>> scipy.dot is numpy.dot
True
The diverse algorithms are grouped into the following toolboxes:
SciPy package
cluster
Functionality
Hierarchical clustering (cluster.hierarchy)
Vector quantization / K-Means (cluster.vq)
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