Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition
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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.
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.
- Publisher : O'Reilly Media; 2nd edition (October 31, 2017)
- Language : English
- Paperback : 550 pages
- ISBN-10 : 1491957662
- ISBN-13 : 978-1491957660
- Item Weight : 2.07 pounds
- Dimensions : 7 x 1.3 x 9.1 inches
- Best Sellers Rank: #8,133 in Books (See Top 100 in Books)
- Customer Reviews:
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Top reviews from the United States
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This book's problem is the classic curse of knowledge. The author does not know what it's like to get started with pandas and what are the difficulties users will have.
Overall, this book provides a jumping off point in understanding the capabilities of pandas as well as its strengths, but it wasn't terribly useful in even basic data science workflow and concepts. For that, I highly recommend something like Hadley Wickham's "R for Data Science," which is much more approachable and rewarding in its use of example datasets, its more personable writing style, and its outlining of good practices for data science.
This book primarily focuses on the pandas Python library, which is awesome at processing and organizing data (Python pandas is like MS Excel times 100. This is not an exaggeration). It also introduces the reader into numpy (lower level number crunching and arrays), matplotlib (data visualizations), scikitlearn (machine learning), and other useful data science libraries. The book contains other book recommendations for continuing education.
Although this would be a challenging book for a brand new Python user, I would still recommend it, especially if you are currently doing a lot of work in MS Excel and/ or exporting data from databases. I had a few false starts learning Python, and my biggest stumbling block was lack of application in what I was learning. This book puts practical tools in the reader's hands very quickly. I personally don't have time to make goofy games etc. that other books have used as practice examples. Despite other reviews criticizing the use of random data throughout the book, I found the examples easy to follow and useful. I would also argue that learning how to generate random data is useful in itself (thus the purpose of the numpy random library), and that there are practical examples throughout the book. Chapter 14 devoted to real-world data analysis examples.
I am almost finished with my second time through the book, this time working through every example. This book has been well worth the hours spent in it. For context, I previously relied on Excel, SQL, and some AutoHotKey. This book has significantly improved how I work.
Thanks, Wes and team.
The book mainly deals with introducing you to Numpy and Pandas libraries used for data analysis, such cleaning, manipulating wrangling, processing and visualisation.
Its a great book to have as a reference and learning data analysis techniques. There are plenty of code examples. So worth the purchase.
Only negative I wish there were mini projects to learn from.
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.