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Python Data Science Handbook, 2nd Edition 2022版
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This is a book about doing data science with Python, which immediately begs the question: what is data science? It’s a surprisingly hard definition to nail down, especially given how ubiquitous the term has become. Vocal critics have variously dismissed the term as a superfluous label (after all, what science doesn’t involve data?) or a simple buzzword that only exists to salt resumes and catch the eye of overzealous tech recruiters
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Python Data Science Handbook
2ND EDITION
Essential Tools for Working with Data
With Early Release ebooks, you get books in their earliest form—the
author’s raw and unedited content as they write—so you can take
advantage of these technologies long before the official release of these
titles.
Jake VanderPlas
Python Data Science Handbook
by Jake VanderPlas
Copyright © 2022 Jake VanderPlas. All rights reserved.
Printed in the United States of America.
Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North,
Sebastopol, CA 95472.
O’Reilly books may be purchased for educational, business, or sales
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(http://oreilly.com). For more information, contact our
corporate/institutional sales department: 800-998-9938 or
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Acquisitions Editor: Jessica Haberman
Development Editor: Jill Leonard
Production Editor: Daniel Elfanbaum
Interior Designer: David Futato
Cover Designer: Karen Montgomery
Illustrator: Kate Dullea
December 2022: Second Edition
Revision History for the Early Release
2022-01-18: First Release
2022-03-29: Second Release
See http://oreilly.com/catalog/errata.csp?isbn=9781098121228 for release
details.
The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Python
Data Science Handbook, the cover image, and related trade dress are
trademarks of O’Reilly Media, Inc.
The views expressed in this work are those of the author and do not
represent the publisher’s views. 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.
978-1-098-12116-7
[LSI]
Preface
A NOTE FOR EARLY RELEASE READERS
With Early Release ebooks, you get books in their earliest form—the
author’s raw and unedited content as they write—so you can take
advantage of these technologies long before the official release of these
titles.
If you have comments about how we might improve the content and/or
examples in this book, or if you notice missing material within this
chapter, please reach out to the editor at jleonard@oreilly.com.
What Is Data Science?
This is a book about doing data science with Python, which immediately
begs the question: what is data science? It’s a surprisingly hard definition to
nail down, especially given how ubiquitous the term has become. Vocal
critics have variously dismissed the term as a superfluous label (after all,
what science doesn’t involve data?) or a simple buzzword that only exists to
salt resumes and catch the eye of overzealous tech recruiters.
In my mind, these critiques miss something important. Data science, despite
its hype-laden veneer, is perhaps the best label we have for the cross-
disciplinary set of skills that are becoming increasingly important in many
applications across industry and academia. This cross-disciplinary piece is
key: in my mind, the best extisting definition of data science is illustrated
by Drew Conway’s Data Science Venn Diagram, first published on his blog
in September 2010:
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