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(38 reviews)
Author: Visit Amazon's Wes McKinney Page
ISBN : 1449319793
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Format: PDF
Direct download links available Free Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython Paperback from with Mediafire Link Download Link
About the Author
Wes McKinney is the main author of pandas, the popular open source Python library for data analysis. Wes is an active speaker and participant in the Python and open source communities. He worked as a quantitative analyst at AQR Capital Management and Python consultant before founding DataPad, a data analytics company, in 2013. He graduated from MIT with an S.B. in Mathematics.
Books with free ebook downloads available Free Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython Paperback
- Paperback: 470 pages
- Publisher: O'Reilly Media (October 29, 2012)
- Language: English
- ISBN-10: 1449319793
- ISBN-13: 978-1449319793
- Product Dimensions: 7 x 1.2 x 9.2 inches
- Shipping Weight: 1.8 pounds (View shipping rates and policies)
Free Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
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).
I pre-ordered this book from Amazon and also added the PDF version from O'Reilly. This book was widely anticipated after the author barnstormed Python meetings giving tutorials on the pandas library he created.
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.