- Paperback: 548 pages
- Publisher: O'Reilly Media; 1 edition (December 10, 2016)
- Language: English
- ISBN-10: 1491912057
- ISBN-13: 978-1491912058
- Product Dimensions: 7 x 1.2 x 10 inches
- Shipping Weight: 1.8 pounds (View shipping rates and policies)
- Average Customer Review: 33 customer reviews
- Amazon Best Sellers Rank: #13,312 in Books (See Top 100 in Books)
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Python Data Science Handbook: Essential Tools for Working with Data 1st Edition
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Python Data Science Handbook
|What You Need to Know about Data Mining and Data-Analytic Thinking||First Principles with Python||Straight Talk from the Frontline||Visualize, Model, Transform, Tidy, and Import Data||Facing the Future with Time-Tested Tools||Tools and Techniques for Developers|
About the Author
Jake VanderPlas is a long-time user and developer of the Python scientific stack. He currently works as an interdisciplinary research director at the University of Washington, conducts his own astronomy research, and spends time advising and consulting with local scientists from a wide range of fields.
Top customer reviews
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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.
You will learn a lot.
In addition to Data Science, the author also covers Ipython and ipynb (Ipython notebook).
The author also provides a list of references at the end of each chapter, which I find very useful to delve into topics that goes beyond the scope of this book.