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Not sure if it counts, but High Performance Python[0] starts you out from the bottom explaining the hardware and how it has to process your code, and they go from there. It's a really good read, was recommended by a coworker and I haven't had issues reading through it so far. Also you might learn things to consider when writing or refactoring Python code that affect performance (I always suggest never to "solve" a performance issue that isn't there yet but to instead write something that works first, then try to optimize the code once it works).

IanOzsvald · 2015-02-06 · Original thread
Don't forget that the higher level functionality (e.g. the scikit-learn routines Radim uses) are typically wrappers for underlying C/Fortran routines and they're the real bottleneck. The relatively few lines of VM'd Python are 'slow' compared to e.g. C but aren't the bottleneck.

The win with Python (and other dynamic languages) is that you can experiment quickly with ideas when you're formulating a solution, that's a big part of exploratory data science.

If you're curious about high-speed work in Python - Radim did a blog series on how he sped up word2vec to be faster than Google's original C code:

I'll also note [self promo!] that I wrote on book on High Performance Python, if that's your cup of tea (and Radim wrote a section in it):

IanOzsvald · 2013-10-28 · Original thread
I'm working on an O'Reilly book using IPython Notebook, I'm basing my workflow on Olivier Grisel's script (with a couple of tiny fixes): which extracts the Notebook's 'markdown' blocks (which contain asciidoc which obviously won't render in the browser) & code blocks, these get exported as asciidoc for O'Reilly.

It is clunky but I'm hoping we'll get better control as nbconvert evolves, so we're experimenting with this approach.

Most of the code examples are not 'live', they're pasted in along with analysis results (the book is about high performance and parallel computing: ), as lots of the examples are best run from a fresh VM.

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