Python Data Analytics

by Fabio Nelli

Python Data Analytics will help you tackle the world of data acquisition and analysis using the power of the Python language. At the heart of this book lies the coverage of pandas, an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Author Fabio Nelli expertly shows the strength of the Python programming language when applied to processing, managing and retrieving information. Inside, you will see how intuitive and flexible it is to discover and communicate meaningful patterns of data using Python scripts, reporting systems, and data export. This book examines how to go about obtaining, processing, storing, managing and analyzing data using the Python programming language. You will use Python and other open source tools to wrangle data and tease out interesting and important trends in that data that will allow you to predict future patterns. Whether you are dealing with sales data, investment data (stocks, bonds, etc.), medical data, web page usage, or any other type of data set, Python can be used to interpret, analyze, and glean information from a pile of numbers and statistics. This book is an invaluable reference with its examples of storing and accessing data in a database; it walks you through the process of report generation; it provides three real world case studies or examples that you can take with you for your everyday analysis needs. This book is for the mid to experienced level programmer who already knows the basics of Python programming. It is for programmers who want to know how to use database data and reporting tools to manipulate raw data into coherent useful information.

Genres: Reference, Nonfiction, Computer Science

364 pages, Kindle Edition

First published November 1, 2015

Reviews

[Robert · Rating 2 out of 5]

This book is not terrible but it could have been a lot better. The English is poor (the author is Italian, I think - certainly not a native English speaker, anyway) which makes it harder to follow what's going on - though not impossible. But here's the real problem; I am still spending more time looking things up online than in either of the two books on Python I have. This, to me, means they can't be good. By contrast, Mastering MATLAB 7, is excellent and my copy is showing signs of imminent collapse through heavy use.

What's the difference? Total information content and it's presentation. The Matlab book contains vastly more info and much of it is concentrated in tables. My copy has a profusion of little post-it notes marking pages with reference tables that I use frequently. These are conspicuous by their absence in Python Data Analytics, except in the case of a comprehensive appendix on Latex codes for use in plot annotation.

My search for a decent Python/SciKit reference goes on.

[Ed Terrell · Rating 4 out of 5]

Good overview of the python tool base for understanding data. Matches up well, with the GitHub code that follows along chapter by chapter.

[TN · Rating 3 out of 5]

It is a good book on Python data analytics. It covers a bunch of topics. The examples are ok. However, a bit more details will be more useful.

[Edwarddu · Rating 5 out of 5]

Nice book. Introduce the basics of data analytics with python.