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Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython Paperback – 1 Nov 2012

4.6 out of 5 stars 30 customer reviews

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  • Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
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Product details

  • Paperback: 466 pages
  • Publisher: O'Reilly Media; 1 edition (1 Nov. 2012)
  • Language: English
  • ISBN-10: 1449319793
  • ISBN-13: 978-1449319793
  • Product Dimensions: 17.8 x 2.3 x 23.3 cm
  • Average Customer Review: 4.6 out of 5 stars  See all reviews (30 customer reviews)
  • Amazon Bestsellers Rank: 4,992 in Books (See Top 100 in Books)
  • See Complete Table of Contents

Product Description

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.


Customer Reviews

4.6 out of 5 stars
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Top Customer Reviews

Format: Paperback Verified Purchase
Python has an especially strong and widespread usage in scientific/engineering/data-analysis computing. Until a few years ago, an important tool that was missing from python was the ability to handle a so-called "data-frame", which in very basic terms is a spreadsheet-like data structure that contains heterogeneous data types in its columns (this type of structure is a main component of, for example, the R programming language for statistical computing). Around 4 years ago, this and related data-structures, and a great big set of tools for working with them, were provided by the pandas library and now pandas is *the* vital component for doing data-analysis in python.
This book is really about pandas (the author is the main author of pandas, after all), and less about either numpy or ipython or other tools. I don't mean that as a criticism. It is precisely as it should be. If you are doing strictly data analysis in python, it is pandas primarily that is center stage, with tools like numpy/ipython etc playing supporting roles. What this book does convey, however, is just how well all these tools work together and how they form a big team for scientific/numerical computing in python.
This book is detailed and extensive. It is entirely focused on well thought out follow-along-yourself code examples, and this makes it a remarkable effective way to learn pandas especially, but also to learn more about numpy/ipython/matplotlib etc.
If you do data analysis in python, this book is a must have. It is highly recommended too for anyone doing scientific/numerical computing in python generally.
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Format: Paperback
I bought this book because I had a time series data set I wanted to work with, and I was interested to try Python. This book takes you through worked examples, which I found to be a great way to quickly get meaningful results whilst learning Python. What really makes it good is that the examples *work*. Often when books take you through code examples there are bugs or differences between the version used in the text and the one you have been able to obtain, or there are just plain old bugs - whether typos or not. So far, all the examples in this book have worked for me, which gives a lot of confidence in learning the Python required.

Well worth the money!
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Format: Paperback
The book focuses on Pandas, but also introduces you to the ecosystem of libraries you'll encounter when doing scientific data analysis in python.

As well as Pandas you'll cover IPython, NumPy and Matplotlib in enough depth to get you started with data analysis and visualization.

You don't need to be a python expert but some python knowledge, and some experience of R, will definitely help.

The book is well structured, breaking down the different topics into well defined chapters which deal with topics such as data wrangling, data loading, time series analysis and more. It has a tutorial feel to it, where you're building your knowledge as you go and applying it all the time - I really enjoyed this approach.

Python For Data Analysis is primarily about doing stuff, about how to manipulate data, clean data and more. And that makes it special to me. If you have spent any amount of time working with data from different sources you'll know that the nitty gritty stuff is something you spend a lot of time on. That's why having such a book and such a framework is golden.

Some of the Pros:
I especially liked the fact that, throughout the whole book, small, self contained examples are given. This allows you to easily generate the data you need and to play with it. This makes the code more accessible and the book more fun.

A detailed appendix on "Python Language Essentials" for people, like me, who have used python in the past but need a bit of a refresher course before reading the book.

Mini-Cons/Things to note:
The book focuses on a slightly older Pandas version, however, the author highlights areas where you may need to do things differently in the future.
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Format: Paperback
I started reading at this particular book being sceptical. Although I most O'Reilly books I've read deliver, this one promises to introduce you to a field that is vast. Python's various usages in data analysis. Does this one deliver? Certainly!

Let me be more specific. In the interest of full disclosure, I should note that I got this book for free via O'Reilly's Blogger Review program. I have some experience in Python and, during the time of my exposure to it, I always read that Python was very powerful in the Data Analysis field, be it Scientific Computing, Financial Computing (up to a point, of course) and others, so naturally, I wanted to read a book to get to study Python's usage in this field. What got me more hooked into reading this book is that this particular one was written by an expert on the field. The author of the book is also the author of the Pandas library. When I finally got through it, here are my comments on it:

- First this book gives you some information on why the data analysis field matters. For instance it refers to an example, using data analysis to come up with data sets to feed a machine learning algorithm.
- The book has short and concise (and above all, easy to follow) code examples that demonstrate the point of the text very quickly.
- The book provides several realistic use cases of the demonstrated content, so that you can get a good idea of what data analysis is all about.
- Covers (in varying degrees) xml parsing, interaction with HTML and databases. It even makes a small reference to MongoDB!
- It also covers string manipulation (including regular expressions) which is very nice!
- Has a whole chapter dedicating to plotting and visualizing.
- Has several chapters on Numpy and Pandas!
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