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Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython Paperback – October 29, 2012

ISBN-13: 860-1401323730 ISBN-10: 1449319793 Edition: 1st

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Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython + Learning Python, 5th Edition + Python Pocket Reference (Pocket Reference (O'Reilly))
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Product Details

  • Paperback: 466 pages
  • Publisher: O'Reilly Media; 1 edition (October 29, 2012)
  • Language: English
  • ISBN-10: 1449319793
  • ISBN-13: 978-1449319793
  • Product Dimensions: 7 x 0.9 x 9.2 inches
  • Shipping Weight: 1.8 pounds (View shipping rates and policies)
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (72 customer reviews)
  • Amazon Best Sellers Rank: #6,576 in Books (See Top 100 in Books)

Editorial Reviews

Book Description

Data Wrangling with Pandas, NumPy, and IPython

About the Author

Wes McKinney is the main author of pandas, the popular open sourcePython library for data analysis. Wes is an active speaker andparticipant in the Python and open source communities. He worked as aquantitative analyst at AQR Capital Management and Python consultantbefore founding DataPad, a data analytics company, in 2013. Hegraduated from MIT with an S.B. in Mathematics.


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Customer Reviews

Great book on Pandas with detailed introduction/tutorial on ipython too.
W.Dinesh
The structure of this book is well organized and very easy to follow with simple examples.
Hill Kim
The author does a great job introducing the reader to Pandas, NumPy, and IPython.
Ryan Whitmore

Most Helpful Customer Reviews

67 of 72 people found the following review helpful By Jason Wirth on October 29, 2012
Format: Paperback
Python For Data Analysis is a book about tools. Python is an excellent general purpose language that has developed some niche applications, science being one of them due to some excellent libraries such as NumPy, SciPy, IPython, Matplotlib, and increasingly Pandas -- which Wes created. Collectively these tools form the basis of the "scientific computing stack" and are utilized by anyone who gets their hands dirty with data.

To steal from the book, Wes states, "This book is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical 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 (author's emphasis) an exposition on analytical methods using Python as the implementation language."

This is a book for any level of professional, researcher, or academic working with data. You could be a beginner who wants to get started, a professional coming from discipline rooted in another language like Matlab, or even someone seasoned in data-manipulation with Python who wants to get more work done in less time with greater ease.

While Pandas is the main focus of the book, sections dedicated to IPython (a shell for interactive execution) and NumPy (Matlab-like vectorized arrays) means there is something for everyone. For example, you might already use IPython, but not to its fullest potential. Wes shows how to be more efficient using the interactive debugger.

Amazon limits their ratings to 5-stars, but if I gave a star for every time I learned something new that made my analysis easier this book would be off the charts!
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125 of 147 people found the following review helpful By R. Friesel Jr. on October 22, 2012
Format: Kindle Edition
Wes McKinney's "Python for Data Analysis" (O'Reilly, 2012) is a tour pandas and NumPy (mostly pandas) for folks looking to crunch "big-ish" data with Python. The target audience is not Pythonistas, but rather scientists, educators, statisticians, financial analysts, and the rest of the "non-programmer" cohort that is finding more and more these days that it needs to do a little bit-sifting to get the rest of their jobs done.

First, two warnings:

1. **This book is not an introduction to Python.** While McKinney does not assume that you know *any* Python, he isn't exactly going to hold your hand on the language here. There is an appendix ("Python Language Essentials") that beginners will want to read before getting too far, but otherwise you're on your own. ("Lucky for you Python is executable pseudocode"?)

2. **This book is not about theories of data analysis.** What I mean by that is: if you're looking for a book that is going to tell you the *types* of analyses to do, this is not that book. McKinney assumes that you already know, through your "actual" training, what kinds of analyses you need to perform on your data, and how to go about the computations necessary for those analyses.

That being said: McKinney is the principal author on pandas, a Python package for doing data transformation and statistical analysis. The book is largely about pandas (and NumPy), offering overviews of the utilities in these packages, and concrete examples on how to employ them to great effect. In examining these libraries, McKinney also delves into general methodologies for munging data and performing analytical operations on them (e.g., normalizing messy data and turning it into graphs and tables).
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70 of 81 people found the following review helpful By Richard C. Yeh on March 29, 2013
Format: Paperback Verified Purchase
I think this book is genuinely trying to be helpful, by giving an extended tutorial on the pandas library; but the tutorial covers only selected topics, and needs to be supplemented with a comprehensive function reference. The narrative also needs to be cut with the help of a strict editor.

If you are trying to decide whether to learn to use the pandas library, this book is for you. It starts with an example of how python and the pandas library can make it easy to do some basic analyses of data, and then develops more specialized chapters: summary statistics, data storage, data transformation (merging and joining), plotting, aggregation, time-series, special considerations for financial or economic data, advanced special topics.

Once I decided to use the pandas library, the book suddenly became less useful. The author has a verbose pedagogical style, and the book never departs from its tutorial perspective. Functions are introduced with examples but no definitions, and it's hard to find the rare summaries of functions, function arguments, or discussion suggesting when to use one method instead of another.

If you want to do something very close to what's done in an example, it's easy to follow along. Once you want to do something not emphasized or covered by an example, there is no guidance, no reference or dictionary section to give any hint about where I might search next --- google will probably direct you to stackoverflow.com, or the official pandas documentation site.

For example, suppose you have loaded your data into a DataFrame, and you want to use another column as the index. The book has several pages on the useful reindex() method, but that method is for resampling the data.
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