Review of Learning IPython for Interactive Computing and Data Visualization

valuable but traditional

May 25, 2013 by Catherine Devlinproduct

Learning IPython for Interactive Computing and Data Visualization

4 stars (of 5)

Packt Publishing recently asked if I could review their new title, Learning IPython for Interactive Computing and Data Visualization. (I got the e-book free for doing the review, but they don't put any conditions on what I say about it.) I don't often do reviews like that, but I couldn't pass one this up because I'm so excited about the IPython Notebook.

It's a mini title, but it does contain a lot of information I was very pleased to see. First and foremost, this is the first book to focus on the IPython Notebook. That's huge. Also:

• The installation section is thorough and goes well beyond the obvious, discussing options like using prepackaged all-in-one Python distributions like Anaconda.
• Some of the improvements IPython can make to a programming workflow are nicely introduced, like the ease of debugging, source code inspection, and profiling with the appropriate magics.
• The section on writing new IPython extensions is extremely valuable - it contains more complete examples than the official documentation does and would have saved me lots of time and excess code if I'd had it when I was writing ipython-sql.
• There are introductions to all the classic uses that scientists doing numerical simulations value IPython for: convenience in array handling, Pandas integration, plotting, parallel computing, image processing, Cython for faster CPU-bound operations, etc. The book makes no claim to go deeply into any of these, but it gives introductory examples that at least give an idea of how the problems are approached and why IPython excels at them.

So what don't I like? Well, I wish for more. It's not fair to ask for more bulk in a small book that was brought to market swiftly, but I can wish for a more forward-looking, imaginative treatment. The IPython Notebook is ready to go far beyond IPython's traditional core usership in the SciPy community, but this book doesn't really make that pitch. It only touches lightly on how easily and beautifully IPython can replace shell scripting. It doesn't get much into the unexplored possibilities that IPython Notebook's rich display capabilities open up. (I'm thinking of IPython Blocks as a great example of things we can do with IPython Notebook that we never imagined at first glance). This book is a good introduction to IPython's uses as traditionally understood, but it's not the manifesto for the upcoming IPython Notebook Revolution.

The power of hybrid documentation/programs for learning and individual and group productivity is one more of IPython Notebook's emerging possibilities that this book only mentions in passing, and passes up a great chance to demonstrate. The sample code is downloadable as IPython Notebook .ipynb files, but the bare code is alone in the cells, with no use of Markdown cells to annotate or clarify. Perhaps this is just because Packt was afraid that more complete Notebook files would be pirated, but it's a shame.

Overall, this is a short book that achieves its modest goal: a technical introduction to IPython in its traditional uses. You should get it, because IPython Notebook is too important to sit around waiting for the ultimate book - you should be using the Notebook today. But save space on your bookshelf for future books, because there's much more to be said on the topic, some of which hasn't even been imagined yet.

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