Numpy is central to most scientific Python toolkits, and learning to write effective Numpy code can make your code more readable and faster. While the Numpy documentation is quite comprehensive, books provide a more structured learning path, and since there are not too many books on Numpy, this book hits a sweet spot. The book is aimed at intermediate level Python users. You will gain more from the book if you work out the code examples yourself rather than just read the examples. Also the examples are slightly mathy (its a book about arrays and matrices after all), so you may have to do some reading if you don't remember your linear algebra, for example.
The book covers examples from famous algorithms (Fibonacci, Sieve of Eratosthenes, etc), finance, etc, mainly to show the usage for various NumPy functions, both simple and advanced. There is a full chapter of recipes on Audio and Image processing techniques. There is also discussion of using memory mapped files, sharing data with the Python Image Library (PIL) through the array interface, converting code to Cython for speed, universal functions (none on vectorize() strangely), masking, etc. There is other information, such as interfacing with R using RPy2, running Numpy on Google App Engine and PiCloud (I didn't pay too much attention to these since I didn't anticipate using them).
The format of the recipes were a bit unusual. Generally it tells you what you can do with it (in the title), then gives a quick overview of the approach (in English), followed by the full code to do accomplish the recipe. The recipes in the book flips this around, putting partial code with some explanation first, then the full code, and then the overview. So the reading (or following along with a Python shell) is not linear, reader has to jump back and forth. Not a huge deal once you recognize it, but using a more linear style may enhance the reading and learning experience in future editions.
I read this book after I read "Learning Numpy Arrays" by the same author, and there is quite a bit of overlap in the examples. Perhaps not surprising because the subject is mostly identical. However, I think its still worth purchasing both the books because each book has enough unique content.
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