Appendix A. Advanced NumPy
In this appendix, I will go deeper into the NumPy library for array computing. This will include more internal details about the ndarray type and more advanced array manipulations and algorithms.
This appendix contains miscellaneous topics and does not necessarily
need to be read linearly. Throughout the chapters, I will generate random
data for many examples that will use the default random number generator in
A.1 ndarray Object Internals
The NumPy ndarray provides a way to interpret a block of homogeneously typed data (either contiguous or strided) as a multidimensional array object. The data type, or dtype, determines how the data is interpreted as being floating point, integer, Boolean, or any of the other types we’ve been looking at.
Part of what makes ndarray flexible is that every array object is a
strided view on a block of data. You might wonder,
for example, how the array view
::-1] does not copy any data. The reason is that the ndarray is
more than just a chunk of memory and a data type; it also has striding
information that enables the array to move through memory with varying
step sizes. More precisely, the ndarray internally consists of the
A pointer to data—that is, a block of data in RAM or in a memory-mapped file
The data type or dtype describing fixed-size value cells in the array
A tuple indicating the array’s shape
A tuple of strides—integers indicating ...
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