Python可视化入门：打造数据转换脚本的艺术

需积分: 0 170 浏览量更新于2024-07-17 收藏 2.19MB PDF 举报

《Python可视化入门：构建视觉转换脚本的艺术》是一本由Shai Vaingast撰写的专业书籍，于2009年出版，专为初学者设计，旨在帮助读者掌握Python在数据可视化领域的基础和高级技巧。本书内容丰富，分为十个章节，从数据可视化世界的探索开始，逐步深入到图形和图表、文本文件处理、数学游戏、科学可视化、图像处理以及高级文件处理等多个主题。第1章"导航数据可视化世界"将引导读者理解可视化的基本概念和其在数据分析中的重要性，帮助读者建立起对可视化工具和技术的初步认识。随后的章节逐渐深化，如第二章"环境设置"介绍所需的Python环境和库，确保读者有一个稳固的开发平台。对于编程背景的读者来说，第三章"Python编程"会回顾Python基础知识，并强调其在数据处理中的优势，使非专业背景的读者也能快速上手。数据组织是数据可视化的基石，第四章"数据组织"探讨如何有效地存储和管理数据，以便后续操作。第五章和第六章是核心内容，分别涵盖了文本文件处理与图形和图表的创建，包括如何使用Python的Pandas和Matplotlib等库来生成各种可视化效果。通过这些章节，读者可以学习到如何将数据转化为易于理解的视觉表达。第七章引入了数学游戏元素，通过有趣的练习让读者在实践中熟悉数学模型与可视化之间的联系，提高解决问题的能力。第八章"科学与可视化"聚焦于科学应用中的数据可视化，展示如何将科学理论与实际数据结合，进行深度分析。第九章"图像处理"则探讨如何利用Python处理和增强图像，这在许多领域，如计算机视觉和图像分析中至关重要。最后，第十章"高级文件处理"深入讲解更复杂的文件操作和数据流处理，帮助读者扩展他们的技能。本书还附有详细的索引和额外的源代码列表，便于读者查找和实践所学知识。版权方面，所有内容受法律保护，未经许可禁止任何形式的复制或传播。此外，书中使用商标名称遵循了编辑性引用原则，尊重了知识产权。《Python可视化入门：构建视觉转换脚本的艺术》是一本既适合初学者系统学习，又可作为专业人士参考的手册，通过一步步的指导，帮助读者掌握如何用Python创造丰富的视觉表达，从而提升数据分析和沟通的能力。

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.

CHAPTER 1 N NAVIGATING THE WORLD OF DATA VISUALIZATION

 s &UNDAMENTALSOFTHE0YTHONPROGRAMMINGLANGUAGEREQUIREDFORDATAANALYSISAND

visualization

 s $ATAFILESFORMATANDORGANIZATIONASWELLASMETHODSANDGUIDELINESFORSELECTINGFILE

formats and storing and organizing data to enable fast, efficient data processing

 s 2EADILYAVAILABLE0YTHONPACKAGESFORNUMERICALANALYSISSIGNALANDIMAGEPROCESSING

graphing and plotting, and more

Gathering Data

We spend a considerable time recording and analyzing data. Data is stored in various formats

depending on the tools used to collect it, the nature of the data (e.g., pictures vs. sampled

analog data), the application that will later process the data, and personal preferences. Data

files are of varying sizes; some are very large, others are smaller but in larger quantities. Data

ORGANIZATIONADDSANOTHERLEVELOFCOMPLEXITY&ILESCANBESTOREDINDIRECTORIESACCORDINGTO

date, grouped together in one big directory or in a database, or adhere to a different scheme

altogether. Typically, the number of data files or the amount of data per file is too large to

allow skimming or browsing with an editor or viewer. Methods and tools are required to find

the data and analyze it to produce meaningful results.

Case Study: GPS Data

You just got a USB GPS receiver for your birthday! You’d like to analyze GPS data and find out

how often you exceed the speed limit and how much time you spend in traffic. You’d like to

track data over a year, or even longer.

Some hardware background: most USB GPS receivers behave as serial ports (this is also

true for Bluetooth GPS devices). What this means is that once a GPS is connected, and assum-

ing it’s installed properly, reading GPS data is as simple as opening the COM port associated

with the GPS and reading the values. GPS values are typically clear text values: numbers and

text. Of course, if you’re planning on recording data from your car, it would make a lot of sense

to hook it up to a laptop rather than a desktop.

7EWOULDLIKETORECORDANALYZEANDVISUALIZETHE'03DATAIN0YTHON&IRSTTHINGSFIRST

recording GPS data.

NNote If you wish to follow along with the remainder of the chapter by means of issuing the commands

yourself and viewing the results, you might first want to refer to Chapter 2 and set up Python on your system.

That being said, it’s not necessary, and you can follow along to get an understanding of the book and its

purpose. In fact, I encourage you to come back to this chapter and read it again after you’ve had more expe-

rience with Python.

Python is an interpreted programming language. What this means is each command

is first read and then executed, in contrast to compiled programming languages, where the

entire program is evaluated (compiled) and then executed. One of the important features of

剩余377页未读，继续阅读

drjiachen

粉丝: 172
资源: 2138

Python可视化入门：打造数据转换脚本的艺术

Beginning-Python-Visualization-Crafting-Visual-Transformation-Scripts.pdf.pdf

Learning Python Data Visualization

Beginning Python Visualization - Crafting Visual Transformation Scripts.pdf

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

Beginning_Python_Visualization__Crafting_Visual_Transformation_Scripts.pdf

英文原版-Beginning Python Visualization 1st Edition

3d Linear Transformation Visualization:可视化3d线性变换。-开源

Python可视化入门：构建视觉转换脚本

Python可视化入门：塑造数据转换脚本

Python视觉入门：打造数据可视化转译脚本

最新资源