McKinney - Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition
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Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. Its 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 Jupyter 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 groupby 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 examplesMcKinney: author's other books
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Second Edition
Data Wrangling with Pandas, NumPy, and IPython
Wes McKinney
by Wes McKinney
Copyright 2018 William McKinney. All rights reserved.
Printed in the United States of America.
Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.
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- October 2012: First Edition
- October 2017: Second Edition
- 2017-09-25: First Release
See http://oreilly.com/catalog/errata.csp?isbn=9781491957660 for release details.
The OReilly logo is a registered trademark of OReilly Media, Inc. Python for Data Analysis, the cover image, and related trade dress are trademarks of OReilly Media, Inc.
While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.
978-1-491-95766-0
[LSI]
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. Ive 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.
The following typographical conventions are used in this book:
Constant widthConstant width boldConstant width italicThis element signifies a tip or suggestion.
This element signifies a general note.
This element indicates a warning or caution.
You can find data files and related material for each chapter is available in this books GitHub repository at http://github.com/wesm/pydata-book.
This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless youre reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from OReilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your products documentation does require permission.
We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: Python for Data Analysis by Wes McKinney (OReilly). Copyright 2017 Wes McKinney, 978-1-491-95766-0.
If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at .
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