Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 1st Edition

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.

Wes McKinney provides an introduction to the most popular and critical libraries for doing data analysis with the Python language. The book does not delve into much for advanced data analysis (statistical methods for example), but provides an excellent starting point for understanding the main tools and a strong tour to what can be done with Python in the data analysis field.The text focuses strongly on the pandas library which is used for the actual data manipulation, but provides a strong introduction to NumPy, matplotlib, and the IPython environment which is used by most of the Python data analysis community. I would have liked to see stronger coverage of SciPy and at least a chapter devoted to the statsmodels library, both of which are mentioned, but not discussed in great detail. These libraries deal with statistical methods and advanced analysis, whereas the focus of this text seems to be more on preparing data for these sorts of topics. A sequel covering these advanced topics would be greatly welcome, whereas they are probably beyond the scope of this particular text, and would add too much length.There have been some api changes since the book was published which do affect some of the examples provided but they all seem to be identified in the book's errata. The only issue that I didn't find identified is a change in how pandas' DataFrame objects are displayed in an interactive environment. This means that some of the outputs will look different than what is shown in the book, but pandas does provide an option to restore the older behavior shown.Readers should already be familiar with the Python language. Some negative reviews stress that the book does not teach Python, but that is not really it's intent. An appendix introducing the Python language is provided, but the language cannot be taught in that short of space. The purpose of the text is to introduce the reader to a specific use of Python, and does well in that case.This text was my first introduction to these libraries and I have always used R for any data analysis work. I was impressed with how similar these are to using R, and many of the libraries feel strongly like R written in Python. Readers already familiar with R will have no problem following along in the examples and will likely pick up the material very quickly. For readers new to data analysis there will likely be a steeper learning curve, but McKinney does provide excellent and detailed examples that should allow those readers to pick up the material quickly as well.Other than the api changes, which always will present an unavoidable issue with any text in the subject (especially as pandas seems to be evolving very quickly), and the lack of coverage of some essential data analysis libraries, the book is strongly recommended for anyone wishing to start using the Python language for data analysis.(I received an electronic copy of the book as part of the O'Reilly reader review program, but was impressed enough to purchase a printed copy.)

This is an excellent book, assuming you like the author's approach to computation/data. This is in a sense also a review for pandas. I should emphasize I am NOT a programmer, in the proper sense. I am a (computational) physicist and have transitioned all of my data storage/analysis to pandas, for reasons I'll explain.A large portion of my work is "exploratory", where I try out many different ideas, hoping something sticks. I've wasted a large amount of time hacking away trying to piece together a somewhat complicated calculation on fairly abstract data sets, only to eventually lose track of what physics I'm trying to do because of how sloppy things get. Again, I am not a programmer! Computation is a tool to me, and time spent trying to make a tool work is time away from the actual job.Though I have only used it in earnest for a few months now, Pandas has increased my productivity tremendously. The organization/philosophy behind the program is amazing. Often (though less and less, thankfully) I find myself reverting to my old habits of working with a sloppy mixture of dicts,np.arrays, and classes, because I feel like I can do it faster/easier than setting it up in pandas. I am never right. Once I set up the problem in pandas, everything I could possibly want to do flows naturally.If you work with physical data and perform relatively complex calculations/transformations on it, I strongly recommend pandas and this book. Regarding the book, I will only say that by reading the author's (of the code and book!) perspective you quickly gain an appreciation for how powerful pandas can be.