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Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython Kindle Edition
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|Length: 885 pages||Enhanced Typesetting: Enabled||Page Flip: Enabled|
About the Author
Wes McKinney is a New York?based software developer and entrepreneur. After finishing his undergraduate degree in mathematics at MIT in 2007, he went on to do quantitative finance work at AQR Capital Management in Greenwich, CT. Frustrated by cumbersome data analysis tools, he learned Python and started building what would later become the pandas project. He's now an active member of the Python data community and is an advocate for the use of Python in data analysis, finance, and statistical computing applications.
Wes was later the co-founder and CEO of DataPad, whose technology assets and team were acquired by Cloudera in 2014. He has since become involved in big data technology, joining the Project Management Committees for the Apache Arrow and Apache Parquet projects in the Apache Software Foundation. In 2016, he joined Two Sigma Investments in New York City, where he continues working to make data analysis faster and easier through open source software.--This text refers to the paperback edition.
- ASIN : B075X4LT6K
- Publisher : O'Reilly Media; 2 edition (25 September 2017)
- Language : English
- File size : 21870 KB
- Simultaneous device usage : Unlimited
- Text-to-Speech : Enabled
- Enhanced typesetting : Enabled
- X-Ray : Not Enabled
- Word Wise : Not Enabled
- Print length : 885 pages
- Best Sellers Rank: 132,388 in Kindle Store (See Top 100 in Kindle Store)
- Customer Reviews:
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Top reviews from Australia
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If you are looking for more in depth graphical representation of plots using pandas and skitlearn then maybe look at another book as this one is more of a back ground tools kind-a-thing.
Top reviews from other countries
Probably my favourite aspect of this book is that you can just read it- every single concept is demonstrated in code, on the paper, with the full input and outputs. The only time I've opened my editor is to play around with concepts I wanted to clarify- the rest has been just a good solid read with everything clearly demonstrated. It's well structured and builds concepts as you progress but is also an excellent reference book I can see myself dipping back into time and again.
I think this is essential foundational material for starting your journey into data analysis and/or machine learning with Python.
Look for a book that takes a project based approach to learning if you are looking to get into python data analysis.