Python数据分析第二版：使用Pandas、NumPy和IPython进行数据处理

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Python for Data Analysis Data 2nd Edition Python for Data Analysis是数据分析领域的经典教材，由Wes McKinney所著，第二版。该书籍涵盖了数据处理、数据分析、数据可视化等方面的知识，涵盖了Python编程语言、Pandas、NumPy、IPython等技术。该书籍的主要内容包括： 1. 数据处理：介绍了数据处理的基本概念和技术，包括数据清洁、数据转换、数据合并等。 2. 数据分析：介绍了数据分析的基本概念和技术，包括数据探索、数据可视化、数据建模等。 3. 数据可视化：介绍了数据可视化的基本概念和技术，包括Matplotlib、Seaborn、Plotly等。 4. Pandas：详细介绍了Pandas库的使用，包括数据结构、数据操作、数据分析等。 5. NumPy：详细介绍了NumPy库的使用，包括数组操作、矩阵操作、随机数生成等。 6. IPython：介绍了IPython的使用，包括交互式计算、数据探索、数据可视化等。该书籍适合数据分析、科学计算、人工智能等领域的专业人士和学生，帮助他们快速掌握Python数据分析的知识和技能。 Pandas是一个流行的Python库，用于数据处理和数据分析。Pandas提供了强大的数据结构和数据操作功能，包括数据帧、系列、索引、选取、合并等。Pandas库的主要特点包括： * 高效的数据处理能力 * 强大的数据结构和数据操作功能 * 广泛应用于数据科学和科学计算领域 NumPy是一个流行的Python库，用于科学计算和数据分析。NumPy提供了强大的数组操作和矩阵操作功能，包括数组索引、数组切片、矩阵乘法等。NumPy库的主要特点包括： * 高效的数组操作能力 * 强大的矩阵操作功能 * 广泛应用于科学计算和数据分析领域 IPython是一个交互式计算环境，用于数据探索、数据可视化和数据分析。IPython提供了强大的交互式计算功能，包括代码补全、代码执行、数据可视化等。IPython的主要特点包括： * 交互式计算环境 * 强大的代码补全和代码执行功能 * 广泛应用于数据科学和科学计算领域该书籍提供了系统的Python数据分析知识和技能，涵盖了数据处理、数据分析、数据可视化等方面的知识，适合数据分析、科学计算、人工智能等领域的专业人士和学生。

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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