Python可视化入门：构建视觉转换脚本

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" Beginning Python Visualization: Crafting Visual Transformation Scripts 是一本由 Shai Vaingast 撰写的书籍，专注于介绍如何使用Python进行数据可视化和创建视觉转换脚本。这本书涵盖了Python在数据可视化的应用，旨在帮助初学者掌握这一技能。" 本书详细讲解了Python编程语言在数据可视化领域的应用，适合那些对Python感兴趣并希望提升数据可视化能力的读者。内容可能包括如何使用Python库（如matplotlib、seaborn、plotly等）创建各种图表，如折线图、柱状图、散点图、热力图等，以及如何进行高级的数据可视化技巧，如交互式图表的制作和地图可视化。书中的章节可能涵盖了以下关键知识点： 1. **Python基础知识**：为了进行数据可视化，首先需要了解Python的基本语法和常用的数据结构，如列表、元组、字典等，以及控制流语句和函数的使用。 2. **数据处理**：使用Python的数据处理库，如pandas，学习如何读取、清洗和预处理数据，以便于可视化。 3. **可视化基础**：理解数据可视化的基本原则，包括颜色理论、图形设计原则和有效的信息传达方法。 4. **matplotlib库**：作为Python最基础的绘图库，学习matplotlib的使用，包括创建简单图表、自定义样式和布局等。 5. **其他可视化库**：如seaborn用于更复杂的统计可视化，bokeh和plotly用于交互式图表，以及geopandas和folium用于地理数据的可视化。 6. **高级话题**：可能涉及动画制作、3D可视化、颜色映射以及如何自定义视觉元素以提高可视化效果。 7. **案例研究**：通过实际项目和案例，演示如何将所学应用于解决真实世界的数据可视化问题。 8. **技术审阅者**：C. Titus Brown的专业审阅确保了书中技术内容的准确性和实用性。此书的出版遵循严格的版权规定，未经许可，不得复制或传播其内容。此外，书中可能会提及一些商标名称，这些提及仅限于编辑目的，并无意侵犯任何商标权。总体而言，"Beginning Python Visualization: Crafting Visual Transformation Scripts" 是一本全面且深入的教程，对于希望利用Python进行数据探索、报告撰写或者科学可视化的读者来说，是一份宝贵的资源。通过学习这本书，读者可以提升自己的数据可视化技能，从而更好地理解和解释复杂的数据集。

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

 s 'RADUATEAND0H$STUDENTSINEXACTANDNATURALSCIENCESPHYSICSBIOLOGYANDCHEM-

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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