For someone like me who is a technical programmer but did not study CS, I've seen hints on how to speed up Python numerical code, but I only had a vague understanding of the principles and application. This Early Release version of High Performance Python has examples that demonstrate why certain data structures are faster than others in particular situations, and how to use the various data structures provided.
But what may be unique is the chapter on the ways of speeding up Python through compiled code. There are many ways of using compiled code through Python, C and FORTRAN extensions, Cython and PyPy, and more recently Numba. But this book explains the strengths and limitations for each, along with a number of other ways of using compiled code along with Python that I had not heard of before. There are many references for each of these, but no general overview of this group of resources.
This is still an Early Release stage, so there are some warts. Many of the code examples are raw, and you have to know what you are doing to fill them in and get them to work. A website or Git repository with the source code examples would be very helpful.
Disclaimer: I received a free electronic copy of the Early Release edition of this book as part of the O'Reilly Press Blogger Program.
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