I got a review copy of NumPy Beginner’s Guide – Second Edition from Packt Publishing. The book is relatively thick, a bit over 300 pages and packed with content.
As usual with Packt books, it starts by introducing the tools and giving detailed instructions on installing them, before diving into actual subject. The book starts easy, teaching how to create arrays and manipulate vectors. Soon more concepts are introduced starting from slicing and ending to SciPy. There is even a chapter about testing, which I found especially interesting to read.
I liked how there are pop quizes to help the reader to check if he understood what he just read. They aren’t really hard, but still quite fun. Layout of the book is clear and makes the books easy to read. There are plenty of examples and graphs in the book that help to explain the concepts.
The book is very suited for a person who is not familiar with NumPy and wants to learn it. It covers lot of ground in sufficient detail. I felt that reading this book was good investment of time and enjoyed it.
Recently I got a copy of NumPy Cookbook to read and review from Packt Publishing and I must say that I was positively surprised. Focus is of course NumPy, but the book touches SciPy too.
The book is laid out nicely and is generally easy to read and digest. I really loved how instead of doing examples as Python programs or even interpreter commands, they chose to use IPython, which is like regular Python shell, but in steroids (as my colleague eloquently put it). IPython makes experimenting and sharing the experiments with others fun and easy.
My only experience with NumPy before was from time when I was writing a simple ray tracer with Python. I knew that the library had lot to offer besides simple things I was doing, but did not really have good way to dig in into it. This book has over 70 different ways of using NumPy, SciPy, PIL among other libraries that can be used to analyze and manipulate data. It also briefly touches subject of quality assurance, which of course is very close to my heart.
Focus is all the time in showing how to do things with brief examples. This suited very well for me, since I’m more about learning by doing than learning by reading type of person. While the book is relatively thin (around 200 pages + index), it has quite lot in it. For seasoned NumPy / SciPy user it probably does not offer that much new, but for a person not familiar with the libraries it offers a fast way getting started.