The figures were generated in color, but printed black and white, so they are often unintelligible. It's hard to tell the red dots from the blue when they are both grey.
Apart from that major oversight, the book is ok. If you want to learn data science, this is not for you; it doesn't get into the fundamentals much at all. If you are an experienced R user looking for how to translate into python, this will get you started. The rest of my review comes from this perspective.
The book spends far too much time on low-level ipython, numpy, and matplotlib functionality (chapters 1, 2, and 4). You are rarely going to use this stuff.
The pandas section (chapter 3) is fine, but I was a little disappointed in the treatment of the grouping/aggregation functions. The book mentions the split-apply-combine paradigm of Hadley Wickham, but doesn't cover the topic in nearly as much detail as the paper of the same name. I was hoping to learn how to translate the dplyr verbs (group_by, filter, select, mutate, summarize, arrange) into pandas, but this book doesn't provide that. You will learn the basics of grouping and aggregation, but your code is going to be a lot more verbose than it was in R.
The machine learning case studies in chapter 5 are pretty nice - probably the only reason I would recommend this book. The chapter provides a good overview of the scikit-learn API and effective patterns for machine learning problems.
ISBN-13: 978-1491912058
ISBN-10: 1491912057
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Customer reviews
Not as easy or straightforward as "Python for Data Analysis " but because it uses Jupyter it's easy on the eyes to read. Highly recommend going to Staples or Office Max and getting the book spiral bound.
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Top reviews from other countries
Mike
This is the introductory book for data programmers
Reviewed in the United Kingdom on August 13, 2018Daniel
Good book for performing data analysis in Python.
Reviewed in the United Kingdom on July 29, 2019123
Standard introduction. Outdated. Zero use as reference.
Reviewed in Germany on June 26, 2021
