Python可视化入门：《Crafting Visual Transformation Scripts》

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《Python可视化入门》(Beginning Python Visualization)是由Shai Vaingast编著的一本专业书籍，旨在帮助读者掌握Python编程语言在视觉影像处理和数据可视化的应用。这本书不仅提供理论知识，还包含实践性的脚本示例，让学习者能够通过编写实际代码来体验视觉转换的过程。该书的核心内容涵盖了以下几个方面： 1. **Python基础知识**：对于没有接触过Python的读者，作者会首先介绍Python的基础语法、数据类型、控制结构和函数等基础知识，确保读者对编程语言有一个坚实的基础。 2. **数据可视化基础**：书中详细讲解了如何使用Python的数据可视化库，如matplotlib、seaborn、plotly等，进行图表制作，包括线图、柱状图、散点图、热力图等各种常见的可视化形式。 3. **数据处理与预处理**：作者会涉及如何使用pandas等库对数据进行清洗、整理和分析，以便于后续的可视化展示。 4. **交互式可视化**：现代可视化不仅仅是静态图像，本书也会介绍如何创建交互式的图表，让用户可以通过交互式操作探索数据。 5. **实战项目**：每章末尾通常会提供一些实践性的项目，帮助读者将所学知识应用到实际场景中，如数据分析报告、网络图绘制等。 6. **版权和出版信息**：《Beginning Python Visualization》享有严格的版权保护，未经许可，禁止任何形式的复制或传播。书本提供了印刷版和电子版的ISBN号，并注明了出版国别以及编辑、技术审阅者和编辑团队成员。 7. **商标使用规则**：书中提到的商标名仅用于教育目的，并尊重商标所有者的权益，不会侵犯其商标权。《Beginning Python Visualization》是一本适合初学者和有一定编程基础的读者深入理解Python在数据可视化领域的实用教程，通过丰富的实例和指导，帮助读者提升数据洞察和沟通能力。无论你是数据分析师、图形设计师还是希望扩展技能的开发者，这本书都是一个理想的学习资源。

xvii

Acknowledgments

I’d like to thank the following individuals for their contribution to the book (in alphabetical

order): Shai Ayal, C. Titus Brown, Ehud Cohen, Bryan Crouse, Kylie Johnston, Michelle Lowman,

Rich Lundeen, Frank Pohlmann, Ami Saguy, Sam Saguy, Janet Vaingast, Motty Vaingast, Orna

Vaingast, and Arnon Zeira.

xviii

Introduction

I was always drawn to math and computers, ever since I was a kid playing computer games

on my Sinclair ZX81. When I attended university, I had a special interest in numerical analy-

sis, a field that I felt combines math and computers ideally. During my career, I learned of

MATLAB, widely popular for digital signal processing, numerical analysis, and feedback and

control. MATLAB’s strong suits include a high-level programming language, excellent graph-

ing capabilities, and numerous packages from almost every imaginable engineering field. But

I found that MATLAB wasn’t enough. I worked with very large files and needed the ability to

manipulate both text and data. So I combined Perl, AWK, and Bash scripts to write programs

that automate data analysis and visualization. And along the way, I’ve developed practices and

ideas involving the organization of data—for example, ways to ensure file names are unique

and self-explanatory.

With the increasing popularity of the Internet, I learned of GNU/Linux and the open

source movement. I made an effort to use open source software whenever possible, and so I’ve

learned of GNU-Octave and gnuplot, which together provide excellent scientific computing

functionality. That fit well on my Linux machine: Bash scripts, Perl and AWK, GNU-Octave and

gnuplot.

Knowing I was interested in programming languages and open source software, a friend

suggested I give Python a try. My first impression was that it’s just another programming lan-

guage: I can do most anything I need with Perl and Bash, resorting to C/C++ if things got hairy.

And I’d still need GNU-Octave and gnuplot, so what’s to gain? Eventually, I did learn Python

and discovered that it is far better than my collection of tools. Python provides something that

is extremely appealing: it’s a one-stop shop—you can do it all in Python.

I’ve shared my enthusiasm with friends and colleagues. Many who expressed interest with

the ideas of data processing and visualization would ask, “Can you recommend a book that

teaches the ideas you’re preaching?” And I would tell them, “Of course, numerous books cover

this subject!” But they didn’t want numerous books, just one, with information distilled to

focus on data analysis and visualization. I realized there wasn’t such a title, and this was how

the idea for this book originated.

Who This Book Is For

Although this book is about software, the target audience is not necessarily programmers or

computer scientists. The reader’s main line of work is research or R&D, in his or her field of

interest, be it astrophysics, signal and image processing, or biology. The audience includes

NINTRODUCTION

xix

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istry) working on their thesis, dealing with large experimental data sets. The book also

appeals to students working on purely theoretical projects, as they require simulations

and means to analyze the results.

 s 2$ENGINEERSINTHEFIELDSOFELECTRICALENGINEERING%%MECHANICALENGINEERINGAND

chemical engineering: engineers working with large sets of data from multiple sources.

In EE more specifically, signal processing engineers, communication engineers, and

systems engineers will find the book appealing.

 s 0ROGRAMMERSANDCOMPUTERENTHUSIASTSUNFAMILIARWITH0YTHONANDTHE'.5,INUX

world, willing to dive into a new world of tools.

 s (OBBYASTRONOMERSANDOTHERHOBBYISTSWHODEALWITHDATAANDAREINTERESTEDINUSING

Python to support their hobby.

The book can be appealing to these groups for different reasons. For scientists and engi-

neers, the book provides the means to be more productive in their work, without investing a

considerable amount of time learning new tools and programs that constantly change. For

programmers and computer enthusiasts, the book can serve as an appetizer, opening up their

world to Python. And because of the unique approach presented here, they might share the

enthusiasm the author has for this wonderful software world. Perhaps it will even entice them

to be part of the large and growing open source community, sharing their own code.

It is assumed that the reader does have minimal proficiency with a computer; namely he

or she must know how to manipulate files, install applications, view and edit files, and use

applications to generate reports and presentations. Background in numerical analysis, signal

processing, and image processing, as well as programming, is of help, but not required.

This book does not intend to serve as an encyclopedia of programming in Python and the

covered packages; nor does it try to be complete. It serves as an introduction to data analysis

and visualization in Python and covers most of the topics associated with that field.

How This Book Is Structured

The book is designed so that you can easily skip back and forth as you engage topics.

Chapter 1 is a case study introducing the topics discussed throughout the book: data anal-

ysis, data management, and, of course, data visualization. The case study involves reading GPS

data, analyzing it, and plotting it along with relevant annotations (direction of travel, speed,

etc.). A fully functional Python script will be built from the ground up, complemented with lots

of explanations. The fruit of our work will be an eye-catching GPS route.

If you’re new to data analysis and visualization, consider reading Chapter 2 first. The

chapter describes how to set up a development environment to perform the tasks associated

with data analysis and visualization in Python, including the selection of an OS, installing

Python, and installing third-party packages.

If you’re new to Python, your next stop should be Chapter 3. In this chapter, I swiftly

discuss the Python programming language. I won’t be overly rehashing basic programming

paradigms; instead I’ll quickly overview the Python programming building blocks.

NINTRODUCTION

Regardless of your Python programming experience, I highly encourage you to read Chap-

ter 4 before proceeding to the next chapters. Organization is the key to successful data analysis

and visualization. This chapter covers organizing data files, pros and cons of different file

formats, file naming conventions, finding data files, and automating file creation. The ideas in

Chapter 4 are used throughout the book.

From here on out you have several options. If you intend to process text and data files,

proceed to Chapter 5. Chapter 5 covers text files from all aspects: I/O operations, string pro-

cessing, the csv module, regular expressions, and localization and internationalization. If

Chapter 5 leaves you wanting to know more about file processing, proceed to Chapter 10.

Chapter 10 includes advanced file processing topics: binary files, command-line arguments,

file and directory manipulation, and more. Both Chapters 5 and 10 are complemented with

numerous examples.

If graphs and plots are your heart’s desire, skip directly to Chapter 6. In Chapter 6 I exam-

ine matplotlib and explore its capabilities.

If you’re interested in the numerical aspects of data, it is advised you read Chapter 7

first. Chapter 7 discusses the basic building blocks for scientific computing. Chapter 8 builds

on Chapter 7 and includes more advanced topics such as numerical analysis and signal

processing.

Image processing is an important aspect of data processing. Chapter 9 deals with tools

available as part of the Python Imaging Library (PIL) package and shows how to further

expand the package and perform more complex image processing tasks.

Chapter 10 covers advanced file processing topics including binary files and random

access, object serialization, command-line parameters, file compression, and more.

Finally, the Appendix provides additional source code listings used in the book.

Downloading the Code

The source code for this book is available to readers at sss*]lnaoo*_ki in the Source Code sec-

tion of this book’s home page. Please feel free to visit the Apress web site and download all the

code there. You can also check for errata and find related titles from Apress.

Contacting the Author

You can contact me at od]e*r]ejc]op<ci]eh*_ki.

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