I became aware recently of three books that are related to data-analysis, modelling, and statistics in a fairly broad sense.
They are pictured below, from left to right:
- “Python for Data Analysis” by Wes McKinney (of pandas fame) published by O’Reilly
- “NumPy Cookbook” by Ivan Idris published by Packt Publishing
- “NumPy 1.5 Beginner’s Guide” by Ivan Idris published by Packt Publishing
Python for Data Analysis
This is the most in-depth book. It covered the most important python modules: iPython, NumPy, Pandas, Matplotlib. Additionally, it has chapters containing examples on practical issues with data (aggregation, data with a time-stamp, sorting). I really just started diving into it. However, it already led me to upgrade to iPython 13.1. It seems like it is well suited for my level of understanding of programming: Having some experience, trying to learn more existing tools
The title already gives it away: The book is organised in sections with “recipes”. Mostly, these recipes are self-containing. The focus is clearly on NumPy, even though Matplotlib, iPython, and also Pandas are covered to some extent. I enjoyed browsing through it, most of the examples are interesting (resizing images, playing with PythonAnywhere (like I’ve done before), f.ex). Generally, I think this is a great resource to have.
Despite being by the same author (Ivan Idris), there is positively little overlap between his two books. “NumPy 1.5” covered NumPy in great detail, and is as such mostly useful for beginners who try to use python for some numerical analysis. When I read this book, I also was reminded, that the webpage that lists NumPy functions is a very valuable resource (which I tend to spend too little time with).
It is interesting to see that people realise that there is a market for books explaining open source tools. And I do think those books complement available documentation nicely.