I recently got a review copy of Ivan Idris’ “Learning NumPy Array” from Pact Publishing. I have read his earlier book “NumPy Cookbook” and found that useful, so I had my expectations quite high when I started.

The book is not huge brick, but still has enough content for almost 150 pages. As usual, first chapter is dedicated for installing NumPy, Matplotlib, SciPy, and IPython in various operating systems. While the information is good, I think just pointing to online resources would have been sufficient.

The second chapter is reserved for NumPy basics. This is where things are starting to get interesting if you haven’t worked with NumPy and arrays before. It is a good idea to read this chapter carefully, if you aren’t familiar with NumPy. Later chapters are built on top of the foundation laid here and are easier to understand when you understand the basics.

Starting from the 3rd chapter, the book dives into details of NumPy arrays and tools that are available to work with them. I like the fact the each subsequent chapter is built on a theme (basic data analysis, simple predictive analytics and signal processing techniques) with concrete examples. Mostly examples are built around various kinds of weather data, but there’s a little bit of stocks thrown into the mix too. Mathematical foundations are only explained in briefly because of the limited amount of the pages the book has. There’s enough detail for reader to understand what is going on and more information is readily available on internet.

Near the end of the book, there is short chapter about profiling, debugging and testing. Especially the part about testing I found very brief and not that useful, but this is book about NumPy after all and not about testing. This is probably the weakest part of the book and could have been left out. The pages used for this chapter could have been used to explain NumPy in more detail.

The last chapter of the book touches other related libraries briefly. It’s good to know how NumPy relates to for example SciPy and scikit-learn.

All in all I found the book very enjoyable to read and easy to follow. Sometimes graphics was getting a bit on the way, like when textual output was shown as an image of text instead of text (so font differed just slightly or the output had different coloured background). The author is already working on the next book, called “Learning Python Data Analysis” which also sounds quite interesting and is expected to come out 2015.

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