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Python Data Science Handbook: Essential Tools for Working with Data
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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.
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Product details
Paperback: 548 pages
Publisher: O'Reilly Media; 1 edition (December 10, 2016)
Language: English
ISBN-10: 9781491912058
ISBN-13: 978-1491912058
ASIN: 1491912057
Product Dimensions:
7 x 1.2 x 10 inches
Shipping Weight: 1.8 pounds (View shipping rates and policies)
Average Customer Review:
4.6 out of 5 stars
44 customer reviews
Amazon Best Sellers Rank:
#4,146 in Books (See Top 100 in Books)
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.
I am currently taking a Machine Learning course from Udacity and this book has proven to be a great reference guide for several projects and quizes. Although it does not go in depth in regards to machine learning (although almost half of the book is dedicated to it), it does give an understanding of essential concepts. For those interested in machine learning I would recommend bying "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Geron as well as this book.There is no one book for data science, and this one is no exception. Just keep that in mind before buying it.Other than that, I am really happy with my purchase.P.S. For those complaining about black and white graphs and diagrams - check the author's GitHub.
This is an excellent reference book for people working with data science. Remember, 80% of the effort in machine learning, data analysis or data science in general is about processing data and understanding data. This book is for that purpose and I think it's the best book out there about data processing, analysis and visualization using python. If you look for hardcore machine learning, go for other books. Highly recommended!
I have used R for a few years and this was my first book that covered Python for data science. Even though it does not go into super great depth in any area, it is definitely a super book. It covers everything from Pandas, Matplotlib, and scikit-learn. I would highly recommend it for anyone that is new to Python and/or data science. The book is written with Jupyter Notebooks so it is easy to follow along and try code from the book in your own notebook.
When I first received this book, I was surprised that it didn't get to scikit-learn until the last third of the book. The first third is about numpy and pandas, and the middle third is about matplotlib. Now that I've been applying it at work, however, I've found that the items covered in the first two thirds were really essential. I wouldn't be nearly as productive if I had just jumped straight to the sections on scikit-learn. The author does an excellent job covering broad terrain with enough detail that you are able to apply it to your problems. You will find yourself going back to use this book as a reference.
I really enjoyed this book. I had not much experience with python prior to reading the book however I was able to pick it up quickly. Before long I was plotting distributions of real time statistics and prototyped a predictive modeling micro service. I consider this a must have book for any aspiring data scientist.
This book is well written and easy to follow. It's saved me from spending hours searching the internet to get acquainted with the standard libraries.I have used it extensively for the intro to ML at Berkeley and for now the book belongs to my short list of desk reference books.
I love the presentation style and the treatment of the subject in this book. This is a must have for experienced programmers breaking into the Data Science/ Machine Learning in Python. The book could have been organized better into more chapters instead of five.
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