Python经典：《Python for Data Analysis, 2nd Edition》全面指南

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《Python for Data Analysis, 2nd Edition》是一本由Wes McKinney编著的经典图书，于2018年出版。该书是数据清洗与分析领域的权威指南，特别强调了使用Python语言中的Pandas、Numpy和IPython进行数据处理。它是针对数据分析专业人士和对Python编程感兴趣的读者设计的，旨在帮助他们更有效地进行数据操作、探索和可视化。本书的核心内容包括： 1. **Python基础**：首先介绍Python语言的基础知识，让读者对数据科学工具的底层工作原理有深入理解，如变量、类型、控制结构等。 2. **Pandas库**：Pandas是本书的重点，它提供了强大的数据结构DataFrame和Series，用于高效地处理表格数据，涵盖了数据加载、清洗（如缺失值处理、数据转换）、分组、合并、重塑数据等关键操作。 3. **Numpy库**：作为Python科学计算的基础，Numpy提供高性能的多维数组和矩阵运算，这对于数值计算和数据预处理至关重要。 4. **IPython和Jupyter Notebook**：书中会讲解如何利用交互式环境IPython和可复现的Jupyter Notebook进行数据分析，增强代码的可读性和分享性。 5. **实战案例**：作者通过一系列实际项目，演示如何在各种场景下应用这些工具，包括金融数据、社交网络数据、地理编码等，以便读者掌握实战技巧。 6. **版本更新**：第二版相较于第一版，可能增加了对最新Python版本的支持，以及对新出现的数据分析库和技术的介绍，反映了Python数据科学生态的最新发展。 7. **版权信息**：本书享有William McKinney的版权，适用于教育、商业或销售推广目的。同时，O'Reilly Media提供了在线版本供读者访问，并提供了联系方式供机构购买。这本书对于想要提升Python数据分析技能的读者来说，无论你是初学者还是经验丰富的数据科学家，都是一个不可或缺的参考资源。通过阅读《Python for Data Analysis, 2nd Edition》，读者可以系统地学习如何使用Python进行高效的数据处理，从而推动业务决策和科学研究的效率。

To comment or ask technical questions about this book, send email to bookques‐

tions@oreilly.com.

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site at http://www.oreilly.com.

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Acknowledgments

This work is the product of many years of fruitful discussions, collaborations, and

assistance with and from many people around the world. I’d like to thank a few of

them.

In Memoriam: John D. Hunter (1968–2012)

Our dear friend and colleague John D. Hunter passed away after a battle with colon

cancer on August 28, 2012. This was only a short time after I’d completed the final

manuscript for this book’s first edition.

John’s impact and legacy in the Python scientific and data communities would be

hard to overstate. In addition to developing matplotlib in the early 2000s (a time

when Python was not nearly so popular), he helped shape the culture of a critical gen‐

eration of open source developers who’ve become pillars of the Python ecosystem that

we now often take for granted.

I was lucky enough to connect with John early in my open source career in January

2010, just after releasing pandas 0.1. His inspiration and mentorship helped me push

forward, even in the darkest of times, with my vision for pandas and Python as a

first-class data analysis language.

John was very close with Fernando Pérez and Brian Granger, pioneers of IPython,

Jupyter, and many other initiatives in the Python community. We had hoped to work

on a book together, the four of us, but I ended up being the one with the most free

time. I am sure he would be proud of what we’ve accomplished, as individuals and as

a community, over the last five years.

Acknowledgments for the Second Edition (2017)

It has been five years almost to the day since I completed the manuscript for this

book’s first edition in July 2012. A lot has changed. The Python community has

grown immensely, and the ecosystem of open source software around it has

flourished.

xiv | Preface

This new edition of the book would not exist if not for the tireless efforts of the pan‐

das core developers, who have grown the project and its user community into one of

the cornerstones of the Python data science ecosystem. These include, but are not

limited to, Tom Augspurger, Joris van den Bossche, Chris Bartak, Phillip Cloud,

gfyoung, Andy Hayden, Masaaki Horikoshi, Stephan Hoyer, Adam Klein, Wouter

Overmeire, Jeff Reback, Chang She, Skipper Seabold, Jeff Tratner, and y-p.

On the actual writing of this second edition, I would like to thank the O’Reilly staff

who helped me patiently with the writing process. This includes Marie Beaugureau,

Ben Lorica, and Colleen Toporek. I again had outstanding technical reviewers with

Tom Augpurger, Paul Barry, Hugh Brown, Jonathan Coe, and Andreas Müller contri‐

buting. Thank you.

This book’s first edition has been translated into many foreign languages, including

Chinese, French, German, Japanese, Korean, and Russian. Translating all this content

and making it available to a broader audience is a huge and often thankless effort.

Thank you for helping more people in the world learn how to program and use data

analysis tools.

I am also lucky to have had support for my continued open source development

efforts from Cloudera and Two Sigma Investments over the last few years. With open

source software projects more thinly resourced than ever relative to the size of user

bases, it is becoming increasingly important for businesses to provide support for

development of key open source projects. It’s the right thing to do.

Acknowledgments for the First Edition (2012)

It would have been difficult for me to write this book without the support of a large

number of people.

On the O’Reilly staff, I’m very grateful for my editors, Meghan Blanchette and Julie

Steele, who guided me through the process. Mike Loukides also worked with me in

the proposal stages and helped make the book a reality.

I received a wealth of technical review from a large cast of characters. In particular,

Martin Blais and Hugh Brown were incredibly helpful in improving the book’s exam‐

ples, clarity, and organization from cover to cover. James Long, Drew Conway, Fer‐

nando Pérez, Brian Granger, Thomas Kluyver, Adam Klein, Josh Klein, Chang She,

and Stéfan van der Walt each reviewed one or more chapters, providing pointed feed‐

back from many different perspectives.

I got many great ideas for examples and datasets from friends and colleagues in the

data community, among them: Mike Dewar, Jeff Hammerbacher, James Johndrow,

Kristian Lum, Adam Klein, Hilary Mason, Chang She, and Ashley Williams.

Preface | xv

into a structured form. As an example, a collection of news articles could be pro‐

cessed into a word frequency table, which could then be used to perform sentiment

analysis.

Most users of spreadsheet programs like Microsoft Excel, perhaps the most widely

used data analysis tool in the world, will not be strangers to these kinds of data.

1.2 Why Python for Data Analysis?

For many people, the Python programming language has strong appeal. Since its first

appearance in 1991, Python has become one of the most popular interpreted pro‐

gramming languages, along with Perl, Ruby, and others. Python and Ruby have

become especially popular since 2005 or so for building websites using their numer‐

ous web frameworks, like Rails (Ruby) and Django (Python). Such languages are

often called scripting languages, as they can be used to quickly write small programs,

or scripts to automate other tasks. I don’t like the term “scripting language,” as it car‐

ries a connotation that they cannot be used for building serious software. Among

interpreted languages, for various historical and cultural reasons, Python has devel‐

oped a large and active scientific computing and data analysis community. In the last

10 years, Python has gone from a bleeding-edge or “at your own risk” scientific com‐

puting language to one of the most important languages for data science, machine

learning, and general software development in academia and industry.

For data analysis and interactive computing and data visualization, Python will inevi‐

tably draw comparisons with other open source and commercial programming lan‐

guages and tools in wide use, such as R, MATLAB, SAS, Stata, and others. In recent

years, Python’s improved support for libraries (such as pandas and scikit-learn) has

made it a popular choice for data analysis tasks. Combined with Python’s overall

strength for general-purpose software engineering, it is an excellent option as a pri‐

mary language for building data applications.

Python as Glue

Part of Python’s success in scientific computing is the ease of integrating C, C++, and

FORTRAN code. Most modern computing environments share a similar set of legacy

FORTRAN and C libraries for doing linear algebra, optimization, integration, fast

Fourier transforms, and other such algorithms. The same story has held true for

many companies and national labs that have used Python to glue together decades’

worth of legacy software.

Many programs consist of small portions of code where most of the time is spent,

with large amounts of “glue code” that doesn’t run often. In many cases, the execution

time of the glue code is insignificant; effort is most fruitfully invested in optimizing

2 | Chapter 1: Preliminaries

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