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Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition
There is a newer edition of this item:
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupiter in the process.
Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.
- Use the IPython shell and Jupiter notebook for exploratory computing
- Learn basic and advanced features in NumPy (Numerical Python)
- Get started with data analysis tools in the pandas library
- Use flexible tools to load, clean, transform, merge, and reshape data
- Create informative visualizations with matplotlib
- Apply the pandas group by facility to slice, dice, and summarize datasets
- Analyze and manipulate regular and irregular time series data
- Learn how to solve real-world data analysis problems with thorough, detailed examples.
- ISBN-101491957662
- ISBN-13978-1491957660
- Edition2nd
- PublisherO'Reilly Media
- Publication dateNovember 14, 2017
- LanguageEnglish
- Dimensions7 x 1.11 x 9.19 inches
- Print length547 pages
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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 install instructions for the Anaconda Python Distribution & other 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
- Reorganized since from the first edition to make the book more accessible to newcomers.
Editorial Reviews
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.
Product details
- Publisher : O'Reilly Media; 2nd edition (November 14, 2017)
- Language : English
- Paperback : 547 pages
- ISBN-10 : 1491957662
- ISBN-13 : 978-1491957660
- Item Weight : 1.47 pounds
- Dimensions : 7 x 1.11 x 9.19 inches
- Best Sellers Rank: #128,647 in Books (See Top 100 in Books)
- #42 in Data Modeling & Design (Books)
- #75 in Data Processing
- #164 in Python Programming
- Customer Reviews:
About the author

Since 2007, I have been creating fast, easy-to-use data wrangling and statistical computing tools, mostly in the Python programming language. I am best known for creating the pandas project and writing the book Python for Data Analysis. I am also a contributor to the Apache Arrow, Kudu, and Parquet projects within the Apache Software Foundation. I am currently the CTO and Co-founder of Voltron Data, which builds accelerated computing technologies powered by Apache Arrow. I previously worked for Ursa Labs (within RStudio / Posit), Two Sigma, Cloudera, DataPad, and AQR Capital Management.
Customer reviews
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Learn more how customers reviews work on AmazonReviewed in the United States on January 15, 2020
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Top reviews
Top reviews from the United States
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It's hard work because Wes McKinney often does not articulate why you would need to do something (assuming you are already knowledgeable on the underlying process), and writes like an impatient person who would rather be doing something else. Additionally examples often suffer from being both too long and too short - too long in that almost every example is on a toy dataset created from scratch, too short in that most of those datasets have only 5 or 10 elements and do not always showcase complex operations. Other examples (particularly involving time series) have an overabundance of data that make the critical results hard to spot. Frankly, my first month with Pandas was a miserable one.
But I give the book 5 stars both because I came to love Pandas as I got more familiar with it, and because while McKinney is not fun to read, he does pack the book with useful information and it is (mostly) well organized. If anything it would benefit from being longer and with a more patient treatment of larger and more concrete datasets (eg the Titanic passenger dataset used in the Pandas documentation). The initial chapter on the basics of using Python could go - if you need this book, then you don't want to be trying to learn the rudiments of Python from it. If you can accept that you'll need a lot of bookmarks or margin notes to get through a rather steep learning curve, it will reward your persistence.
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.
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.










