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4.7 out of 5 stars
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on 16 May 2014
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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on 8 January 2013
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!
- Has a great chapter focusing on date and time data manipulation and the relevant modules in the Python lib.
- Although this book is better read if you have some Python knowledge already and want to extend your Python knowledge, it also has an appendix which goes through the essential knowledge of the Python programming Language, so even beginners with Python should feel comfortable with it.

Overall, I recommend this book if you want to get a good idea about Python's usage in Data Analysis, whether you are a Python novice or a Python expert.
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on 16 August 2014
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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on 17 December 2014
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.

The book is about "how to do things" not "what to do". It's not the book for you if you are looking for a general "Data Science Book" to give you an overview of different analytic and/or statistical methods showing you when to and where to apply which.

The tutorial feel to the book is something I liked, however, if you're looking for a quick desktop reference it doesn't quite work.

All in all:

I really enjoyed this book, if you've been using R and have some python background then Pandas will feel very natural to you.

If you're building an open source data toolkit then Pandas, and thus this book, has a place in it.

Disclaimer: I received a free pdf copy of this book as part of the O'Reilly Reader Review Program.
However, I would, and actually already did, recommend this book to colleagues.
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on 27 November 2015
I do a lot of analysis of large amounts of data and don't really have the tools to do what I need. I started using Python to do the work and this book has been a great help in getting my head around how it handles data and what you can then do with it. I like the way it's written and the flow worked well for me. I also found it good if you have a specific problem with bite size solutions for many common challenges.
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on 6 December 2012
I'm still ploughing through this book, but I've already found it incredibly useful.

I've heard of the Pandas library for Python before - I heard that it provided similar functionality to the data.frame object in R. I believe this book is written by the author of the Pandas library. It certainly provides an excellent description of the library.

There are plenty of practical examples in this book as well, but it is really the introduction to Pandas that I've found useful. It has revolutionised the way I write my code for data analysis.

One final thing; if you find the Pandas library useful, I'd suggest having a look at the Ramp library for machine learning:

[...]

It says it is a wrapper for various Python machine learning libraries using the Pandas framework. I've used it briefly and it looks very promising.
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on 9 September 2015
Just started working my way through this book and trying to do all the examples, currently on page 95 and I've found quite a few typos in the code examples already, some blatantly obvious, some not. I can only hope they get more accurate
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on 15 March 2013
I already do a lot of this kind of stuff in numpy and scipy but wanted to learn about pandas so I only read the chapters on pandas but they were excellent as you'd expect from the creator of the library.
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on 20 November 2014
Very well done, easily explained. And shows the breadth of Pandas. If you're used to Matlab and think you should be able to do matrix manipulation the same way (find statements, boolean lookups), then Pandas is the only way to go. But you get so much more out of it--dataframes. This isn't the most exciting area really but it is absolutely one of the most useful.
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on 25 April 2014
This book is a great introduction to python pandas. A good practical background in Python is useful before buying this book, although there is a very useful condensed language summary at the back of the book. Most of the examples in the book are practical and real-world. In general, the book is extremely well written and is very concise.
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