Python 3.6版《利用Pandas进行数据处理》详解

需积分: 36 183 浏览量更新于2024-07-19 收藏 10.07MB PDF 举报

《Python for Data Analysis》第二版是由Wes McKinney所著的一本权威指南，专为数据分析师和科学家设计，讲述了如何在Python编程环境下，特别是通过Pandas、NumPy以及IPython库来进行高效的数据处理和分析。这本书是针对那些希望提升数据分析技能，并将Python作为主要工具的人群，尤其是在Python 3.6版本下进行操作，相比于第一版的Python 2.7，它适应了新的语言环境和技术发展。本书的核心内容围绕以下几个方面展开： 1. **Python基础与数据结构**：介绍了Python语言的基础知识，包括变量、数据类型、控制结构等，为读者建立起处理数据的基本框架。 2. **Pandas库**：Pandas是本书的核心部分，它提供了强大的数据结构DataFrame，用于高效地存储和操作表格型数据。书中详细讲解了DataFrame的各种功能，如数据清洗、数据重塑、分组、合并、透视表等，使读者能够熟练地进行数据清洗和预处理。 3. **NumPy**：作为科学计算的基础库，NumPy提供了高效的多维数组和矩阵操作。书中展示了如何使用NumPy进行数值计算和数组操作，以及它在数据处理中的应用。 4. **IPython Notebook**：介绍如何利用交互式环境IPython Notebook进行数据分析，演示了如何编写可读性高的代码、执行和展示结果，以及如何进行数据可视化。 5. **实际案例与实践**：书中包含了丰富的实际案例，覆盖金融、经济、社会科学等多个领域，帮助读者将理论知识转化为实际应用能力。 6. **更新与兼容性**：由于是第二版，作者特别提到了对Python 3.6的优化和支持，确保了内容的新鲜度和实用性，同时考虑到Python生态系统的最新进展。《Python for Data Analysis》适合已经掌握了Python基础的读者深入学习数据处理和分析，无论是初学者还是经验丰富的专业人士，都能从中受益匪浅。通过阅读和实践书中的内容，读者不仅能掌握Python在数据科学中的核心工具，还能提升数据分析的效率和质量。对于想要进入或进一步发展数据科学领域的读者来说，这是一本不可多得的参考资料。

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.

Find us on Facebook: http://facebook.com/oreilly

Watch us on YouTube: http://www.youtube.com/oreillymedia

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

剩余540页未读，继续阅读

Gissimo

粉丝: 0
资源: 2

Python 3.6版《利用Pandas进行数据处理》详解

Python for Data Analysis 第二版

《Python For Data Analysis》数据集

python数据分析 第二版 python for data analysis

python for data analysis

python for data analysis 2nd

python for excel oreilly

我想学习Python的numpy和panda包,有没有什么推荐的课程或者书籍或者视频?

近五年内有关python的文献

基于python的数据分析外文文献_python外文文献.doc

有关python大数据分析技术的文献及其作者和出处

最新资源

python数据分析第二版 python for data analysis