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Top (max 10) reviews: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython.

4.1 out of 5.0    156 total reviews.

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5.0 out of 5.0 -

by Sanjana on Sept. 22, 2016

Must read for anyone starting out to use Python for Data Science / Data Analysis. The first few chapters are very basic so you can skip them if you have some background in python.

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by mypenname2126 on Dec. 30, 2014

This book has changed the way I, as a doctoral student/ scientist, analyze data. No one needs to be paying out the nose for software like matlab or prism graphpad, or put up with the headache that is Perl. With the instruction from this book and open access to Python/Pandas & Ipython notebook you can get all your data analysis done for free. The book provides salient examples that you can follow along with even if you have minimal python experience. That being said, it does not go over some super- basic stuff about setting up your IDE and downloading files, so you may need to supplement this book with a basic python programming book or already be familiar with that stuff to really get going. However, the python basics it goes over in one of the chapters can help you through most of the necessary syntax. I would HIGHLY recommend this book to anyone who has data to analyze. It's practically worth it's weight in gold to any scientist or aspiring data analyst.

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by Dr. Howard B. Bandy on July 7, 2014

Wes McKinney is the primary author of pandas, a python library for data analysis. This book is a description of pandas and their use.
Pandas extends the data analysis capabilities of python beyond that provided by numpy and scipy with a set of data structures (primarily the DataFrame) and functions designed to handle time series data. There are functions for cleaning, aligning, transforming, and other data munging tasks -- all described and discussed with excellent examples.
While this book has no references to machine learning, this book is required reading and a key reference for anyone working with machine learning, financial time series, and python.

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by christopher j. parrish on Sept. 26, 2017

Limited amount of useful info.

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by Tony George on April 21, 2016

This is a good book for a beginners

3.0 out of 5.0 -

by J. Yoon on June 27, 2016

Pretty good resource for a beginner to Python's data analysis libraries. This book is not for a Python beginner. Although Wes McKinney wrote Pandas, I feel that the Pandas part of the book is somewhat outdated or redundant in 2016. There is an excellent online resource maintained by Wes and other key contributors that is up-to-date, and, in my opinion, has better content for beginners learning Pandas. Because of several different contributors' perspectives, I find the examples and explanations better than those in the book. In 2012, when this book was published, that online resource may not have been as good. I found the Pandas Time Series and Financial Applications chapters interesting, but they are also replicated on the website. So the book was improved upon by the author's own website (with the help of other contributors). :-)
See[...]
In particular, see sections: Tutorials, Intro to Data Structures - Series and DataFrame, and Essential Basic Functionality.
The remaining 1/4 of the book had very useful concentrated intro to NumPy, Advanced NumPy, and Python Essentials reference. This book does not cover the newer development of R function calls from Python. In my opinion, R is winning the R vs Pandas argument due to ggplot2 and statistical learning professors publishing code first in R. Since R is now easy to use from within Python, Pandas might not get as much use. But it's still useful to know how to use Pandas as part of a data analyst's toolkit.
I also want to warn buyers about faint printing on several physical copies of this book. I bought from Amazon AND directly from O'Reilly Media in trying to get a physical book that had good, solid printing on all pages. This was not possible. The physical book from O'Reilly had even fainter/worse printing than the version I got from Amazon. Better to save your money and just get with the eBook version if you are OK with that, which you can usually find cheaper online. O'Reilly puts on excellent conferences, but may be getting out of the printed book business. I guess most programmers buy eBooks now. I just find eBooks difficult to deal with when it comes to dense, technical books. I am fine with eBooks for fiction or more narrative non-fiction such as economics, popular science, or history.

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by Amazon Customer on Feb. 28, 2017

very good quality and fast delivery

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by Amazon Customer on June 21, 2016

Really a great book. Helped me a lot in my data scientist internship. A lot of examples in this book are very useful!

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by Dr. J on Oct. 17, 2016

expected more of a beginners tutorial - great material but not for the newbie