Python can be used for many things, and is mainly known for the shell scripts people wrote. Shai Vangast proposes using the langage for data analysis and visualization.

Content and opinions

After a small introduction where all elements of the book are used for a simple example, the author tells us how to install Python and the different modules that will be needed for our task. Then, the langage itself is fastly introduced. If you don’t know any object-oriented langage or Python, you may find the introduction too light, so you may need to read a book dedicated to the Python langage if you want to master it. It’s obviously not the goal of the book.

Two chapters deal with data management and extracting it from a file. Why can data be stored? How to parse and retrieve what we need? The answer to these questions are in these chapters (even if the more complex formats as XML are not deal with, you’ll have to dig in the documentation) in a simple and efficient way.

Display is of course the key element here. It’s the Matplotlib module which has this responsibility. It’s a complex module, but it is explained so that is is easy to use. Then Numpy, Scipy and PIL have each their chapter. They cannot be fully presented, only with a data analysis orientation. For more advanced functions, you will once again have to use the associated documentation.

The last chapter is dedicated to useful but not mandatory modules, like Pickle. This chapter can be skipped at first, but then the modules will help optimizing your time for more complex visualizations.

Conclusion

The author took the challenge of only showing the data analysis and visualization part of Python. The text is simple and goes straight to the point. By reading the book, one is able to do 99% of data analysis and quality controls (QCs). The challenge is thus a success.