After some days spent with the book Python Data Analytics, which was kindly provided to me by Apress for a review, now its time to make it.
So, the book is named “Python Data Analytics” and it provides exactly what it says. It shows you how to use Python step-by-step for Data Analytics. The important libraries in Python – NumPy, Pandas, MatPlotLib are presented in literally step-by-step introduction – from their installation to about 70-80 % of their properties and functions. The step-by-step approach really shows you what to write in the Python shell and what to expect. Furthermore, some mathematical operations (e.g. matrix multiplication) are well explained with pictures.
What a lot of people may not like is the way the book is written – one should copy and paste the code from the book to his PC manually. It is already 21. century outside, if you are writing a programming book, please share your code for everyone. The code, that was shared in the Apress.Com site was really just a tiny part of the one in the book and it was mainly some useless outputs. 🙁 Why did you share it at all? Who needs output of sample tables?
Furthermore, the first chapter looks like a copy-paste from an introduction of a mediocre master thesis. It reminded me of mine. 🙂 At the end of the hate section – there is a whole appendix with Mathematical Expressions with LaTeX, containing 10 pages. This would have been really useful … 25 years ago (I am repeating myself – its 2015 outside, anyone who uses LaTeX probably can use search engines as well). The time when the books were ending at the end with copied libraries must be over. Or I have thought so.
So, at the end of the review, let’s summarize. Although the hate section is quite larger than the review one, I should ask myself whether I learnt something from the book. The answer is positive – the chapters with NumPy, Pandas, Reading and Writing Data and MatPlotLib were useful for me (these are more than 80% of the book, which is quite ok). The example with the meteorological data was nice to see. And the step-by-step approach in the building of these chapters was useful. If only the code of all these was available online, my experience would have been better. And if the first chapter was reduced by 80% and the LaTeX appendix was deleted.
The verdict – if you need Python for data analysis this book is quite ok. With step by step approach, you will become fluent in what you write (that is probably the only bonus from the fact that you do not get the code available online).