End of 2013 Kindle Update--> Many ebooks (not just Kindle) have problems with math formulas in LaTex. Others (like this) have code or pseudocode, and lots of tables, which are problematic at times. IF you get this book for Kindle in 2014 or late 13, you are in for a treat: not only the online goodies, but the entire ebook itself has been extensively revised for Kindle, including code and tables. They are outstanding!
Our previous Kindle edition wasn't awful, but this is just awesome now. If you're tired of R glitches and complexity, consider the many new (and FREE) features Wes details in this fine text, especially tips for free libraries and APIs, including of course NumPy and others that used to require a lot more math than they do today. Wes even has many plug and plays, and if you have even beginning skills in any oop (Java/C#), this will be easier than starting R from scratch. It has "nearly" the stats of R, and much more on all kinds of big data, not just research data. Highly recommended for my fellow kindlers. The native object- recursion in Python apis is alone worth this compared to R functional workarounds, even though I use both. Prior to this book, you'd be spending a LOTof time putting all this together visiting forums, libraries and APIs online.
IMPORTANT NOTE on previous negative reviews: This new update not only fixes many issues with the tables for Kindle, but as you probably know if you're a Panda person, the online functional documentation for the library has been massively updated between late '12 and late '13. The author (the creator of the API) takes advantage of this with the kindle links. This makes this book a MUCH better reference than the last edition, part due to the new update and part to the value of the community work on functions, types and methods, which of course this author often leads. SO, even if you have an older version of print, many of the deficiencies (that frankly were not this author's fault!) are gone there too, because the links are still active and much better for configuring your code than before.
This still isn't meant to be a "documentation" book, but, with the newly updated links, there are few programs you can't build now, including a LOT more detail on the functions themselves, with good keywords to augment the already fine examples and exercises here. Also, much less "heavy" from a programming view than most O'reilly tomes-- this author obviously understands beginners, and though this is not a how to learn Python book, it IS now a much better how to pick up DA, including pandas, numpy and other plug ins.
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This is by no means a complete list. Even though it may not always be obvious, a large percentage of data sets can be transformed into a structured form that is more suitable for analysis and modeling. If not, it may be possible to extract features from a data set into a structured form.
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This book is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.
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Top reviews from other countries
Suresh
For the technical hands on developer getting into data analysis
Reviewed in India on April 9, 2017
CDev
Excelente libro, merece la pena cada centavo, puesto que contiene una basta informacion necesaria de python.
Reviewed in Mexico on April 25, 2016Trading Central
Great Coding Book on Using Python for Statistical/Data Analytics
Reviewed in Canada on April 11, 2015