In my post previous there
was some examples contained matrices or other data structures of
higher dimensionality—just one-dimensional vectors. To understand
how NumPy treats objects with dimensions greater than one, we need to
develop at least a superficial understanding for the way NumPy is
implemented. It is misleading to think of NumPy as a “matrix
package for Python” (although it’s commonly used as such). I find
it more helpful to think of NumPy as a wrapper and access layer for
underlying C buffers. These buffers are contiguous blocks of C
memory, which—by their nature—are one-dimensional data
structures. All elements in those data structures must be of the same
size, and we can specify almost any native C type (including C
structs) as the type of the individual elements. The default type
corresponds to a C double and that is what we use in the examples
that follow, but keep in mind that other choices are possible. All
operations that apply to the data overall are performed in C…

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