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Python for Data Analysis

Wes McKinney

Why we're reading this: Python for Data Analysis: is concerned with the nuts and bolts of manipulating, processing, cleaning, and…more
[close] Python for Data Analysis: is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern 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 an exposition on analytical methods using Python as the implementation language.

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Feb 09, 2017 Joshua Hruzik rated it really liked it

Great for Transition
As an R user I always hear people say that one should also learn Python as a secondary language. So I gave this book a shot and it did not disappoint.
Wes McKinney is the creator of Pandas, a framework for working with structured data in Python. That being said, a great deal of the book deals with Pandas and solving classical data science tasks (cleaning and munging your data). His style of writing is very clear and one can easily grasp the concepts by applying them in one's o ...more

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Feb 06, 2014 Nancy rated it it was amazing · review of another edition

Shelves: reference

This book was the perfect set of training wheels for me, especially since my main goal was to operate on economic and financial data. By chapter 4 (practically the beginning of this book), I was able to sample random stocks, run correlations between stocks and commodities. I think that the TimeSeries chapter should be read just before or after chapter 4, to avoid some time groping in the dark with this datatype. Chapter 11 is also very useful with a focus on data munging for financial data.

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