- Paperback: 522 pages
- Publisher: O'Reilly Media, Inc, USA; 2nd ed. edition (3 November 2017)
- Language: English
- ISBN-10: 1491957662
- ISBN-13: 978-1491957660
- Product Dimensions: 17.8 x 3.3 x 23.1 cm
- Boxed-product Weight: 1.2 Kg
- Customer Reviews: 2 customer reviews
- Amazon Bestsellers Rank: 14,232 in Books (See Top 100 in Books)
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Python for Data Analysis, 2e Paperback – 3 Nov 2017
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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.
From the Publisher
What Is This Book About?
This book is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. My goal is to offer a guide to the parts of the Python programming language and its data-oriented library ecosystem and tools that will equip you to become an effective data analyst. While 'data analysis' is in the title of the book, the focus is specifically on Python programming, libraries, and tools as opposed to data analysis methodology. This is the Python programming you need for data analysis.
New for the Second Edition
The first edition of this book was published in 2012, during a time when open source data analysis libraries for Python (such as pandas) were very new and developing rapidly. In this updated and expanded second edition, I have overhauled the chapters to account both for incompatible changes and deprecations as well as new features that have occurred in the last five years. I’ve also added fresh content to introduce tools that either did not exist in 2012 or had not matured enough to make the first cut. Finally, I have tried to avoid writing about new or cutting-edge open source projects that may not have had a chance to mature. I would like readers of this edition to find that the content is still almost as relevant in 2020 or 2021 as it is in 2017.
The major updates in this second edition include:
- All code, including the Python tutorial, updated for Python 3.6 (the first edition used Python 2.7)
- Updated Python installation instructions for the Anaconda Python Distribution and other needed Python packages
- Updates for the latest versions of the pandas library in 2017
- A new chapter on some more advanced pandas tools, and some other usage tips
- A brief introduction to using statsmodels and scikit-learn
- I also reorganized a significant portion of the content from the first edition to make the book more accessible to newcomers.
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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 international reviews
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.
If your looking to get into data science with python this and hands-on machine learning with scikit-learn and tensorflow are solid buys.
For a bit more background and explanations then the Chen book works well with this.