A solid technical book -- and that's not meant as faint praise, since so, so many technical books are poorly written. This one is better than that, but where it falls short, I guess, is in the lack of exercises / projects to get the reader to really engage with the material. There are Jupyter notebook files available for the book (in some cases they've been updated and veer away from the print considerably, which can be confusing if you're not watching carefully), so you can sort of follow along with a live "ok, now execute THAT" sort of way -- which falls a little short of entering code yourself and executing it yourself and dealing with whatever errors you may enounter ... yourself. Good coverage of numpy and pandas. ( )

This has the flavor of an O'Reilly Nutshell book because it's mostly a tour of pandas features. Most of the examples are unmotivated and use random numbers instead of real data. If you're looking for a pandas tutorial this is probably fine. If you're looking for a pandas tutorial plus a primer on data analysis, this falls short of the bar set in the R world by Wickham's R for Data Science. ( )

A better title for this book might be Pandas and NumPy in Action

As the creator of the pandas project, a Python data analysis framework, [a:Wes McKinney|6007417|Wes McKinney|http://www.goodreads.com/assets/nophoto/nophoto-U-50x66-347709e8e0c4cd87940bf10aebee7a1c.jpg] is well placed to write this book. His experience and vision for the pandas framework is clear, and he is able to explain the main function and inner workings of both pandas and another package, NumPy, very well.

Although the title of the book suggests a broad look at the Python language for data analysis, McKinney almost exclusively focuses on an in-depth exploration of pandas. The book started with a great deal of promise, but as McKinney delved into the detail of NumPy and pandas, the ideas and examples of data analysis are replaced with random number datasets.

The book became a tiresome parade of pandas feature after pandas feature. Each example was stripped of meaning without any real world basis. It would have been great to see more real world cases drawn from McKinney's experience as a day to day user of pandas and Python for data analysis.

This book would be ideal if you're using, or thinking about using NumPy or pandas. If you're looking for a broader introduction to Data Analysis with Python, this might not be the book for you. ( )

A great handbook for anyone looking to do break down data sets in Python. This won't teach you what to look for or how to do data analysis, but it will show you all the tools to get it done. ( )

452 p.