Python数据分析第二版：Pandas, NumPy与IPython实战

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"Python for Data Analysis 2nd Edition 是一本全英文的数据分析入门书籍，作者是 Wes McKinney。本书主要围绕使用 Python、Pandas、NumPy 和 IPython 进行数据清洗和处理展开，适用于想要学习如何利用 Python 进行数据科学工作的读者。" 在 Python for Data Analysis 2nd Edition 中，作者 Wes McKinney 提供了全面的指导，帮助读者掌握使用 Python 在实际数据分析项目中的关键工具和技术。这本书特别关注 Pandas 库，这是一个强大的数据处理框架，广泛应用于数据清洗、转换和分析。 1. **Pandas 深入解析**：书中详细介绍了 Pandas 的核心概念，如 Series（一维数据结构）、DataFrame（二维表格型数据结构）以及 Panel（多维数据结构）。通过实例，读者可以学习如何创建、读取和操作这些对象，以及如何处理缺失值、排序、合并和重塑数据。 2. **NumPy 集成**：NumPy 是 Python 中用于科学计算的基础包，提供了高效的数组操作。书中讲解了如何与 Pandas 结合使用 NumPy，进行数学和统计运算，以及高级数组操作。 3. **IPython 和 Jupyter Notebook**：IPython 提供了一个交互式环境，而 Jupyter Notebook 是基于 IPython 的一个强大工具，支持创建和分享文档，包含代码、解释和结果。书中会介绍如何利用这两个工具来提高数据分析效率，以及如何编写可重复使用的代码和创建交互式报告。 4. **数据清洗**：数据清洗是数据分析的重要步骤，本书会涵盖数据预处理技术，包括数据清洗、异常值处理、数据类型转换等，帮助读者理解如何准备数据以便后续分析。 5. **数据分析和可视化**：书中还会介绍使用 Python 进行探索性数据分析的方法，包括统计分析、时间序列分析，以及使用 Matplotlib 和 Seaborn 进行数据可视化，帮助读者更好地理解和解释数据。 6. **性能优化**：对于大数据集，书中还会讨论如何优化代码以提高处理速度，如利用 C 和 Cython 进行扩展，以及并行计算策略。 7. **实际应用案例**：通过真实世界的数据分析示例，读者可以了解如何将所学知识应用于金融、社会科学、生物信息学等领域，从而增强解决实际问题的能力。 8. **进阶主题**：除了基础内容，书中还涵盖了更高级的主题，如数据分桶、时间序列分析、数据对齐和合并，以及高级数据聚合操作。 Python for Data Analysis 2nd Edition 是一本适合初学者和有一定经验的数据分析师的书籍，它提供了全面的指导，帮助读者熟练掌握使用 Python 进行数据处理和分析的技能。无论是数据科学家、工程师还是研究人员，都能从中受益匪浅。

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

tions@oreilly.com.

For more information about our books, courses, conferences, and news, see our web‐

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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