Media praise for Python for Data Analysis

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"This book is a welcome addition to the Python canon, and anyone who is at all concerned with data analysis would do well to read it."
-- Steve Holden, For Some Value of "Magic"

"There are plenty of tools and programming languages focusing on data analysis and machine learning. I recently discovered that some programming languages and tools, e.g. F# and Julia, can be leveraged through IPython allowing one to work in a single environment instead of having to switch tools for different tasks. " Full Review >
-- Carsten Jorgensen,

"I think this book belongs on the shelf (virtual or otherwise) of anyone who is serious about using python to store, process, or visualize data." Full Review >
-- Cavendish McKay,

"Mea culpa, but I was waiting for THE Pandas book given its author. I believed that pandas deserve a good intro book with decently built examples and learning curve, but I was wrong. This book is a very uneven kind of batched together webreference chapters. Most of the material is not meant for beginners sometimes even can get kind of scary. No pun intended, it smells like good intent, bad execution for me. As a starter Chapter 2 contained non-working code snippets while Chapter 3 exposed such inner workings that could confuse readers. Why is this here, I mean all editors went on strike? Truth to be told the 2nd edition fixed some of the most annoying bugs. Strictly appendix stuff is edited in as the main course. Handle with care "” I will stick to pandas tutorials presented in IPython notebooks." Full Review >
-- Daniel Molnar,

"The book is essentially a reference on how to use the pandas Python library.It is well written and can be used both as a tutorial for learning about a particular aspect of the library and as a reference..." Full Review >
-- Marco Dinacci,

"A great book on statistical analysis with Python utilizing big-data. I was skeptical about this title at first (I'm not much of a mathematician at all) I was overall pleased with the experience and flow of this book. Mostly focusing on Pandas, this book also cameos IPython and Num-Py. Recommended for anyone who's doing some serious number crunching or analysis with large datasets. " Full Review >
-- Mat Powell,

"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." Full Review >
-- Rob Friesel,