Mastering Python Data Visualization

Book Description

About This Book Explore various tools and their strengths while building meaningful representations that can make it easier to understand data Packed with computational methods and algorithms in diverse fields of science Written in an easy-to-follow categorical style, this book discusses some niche techniques that will make your code easier to work with and reuse Who This Book Is For If you are a Python developer who performs data visualization and wants to develop your existing Python knowledge, then this book is for you. A basic knowledge level and understanding of Python libraries is assumed. What You Will Learn Gather, cleanse, access, and map data to a visual framework Recognize which visualization method is applicable and learn best practices for data visualization Get acquainted with reader-driven narratives, author-driven narratives, and the principles of perception Understand why Python is an effective tool for numerical computation much like MATLAB, and explore some interesting data structures that come with it Use various visualization techniques to explore how Python can be very useful for financial and statistical computations Compare Python with other visualization approaches using Julia and a JavaScript-based framework such as D3.js Discover how Python can be used in conjunction with NoSQL, such as Hive, to produce results efficiently in a distributed environment In Detail Python has a handful of open source libraries for numerical computations that involve optimization, linear algebra, integration, interpolation, and other special functions using array objects, machine learning, data mining, and plotting. This book offers practical guidance to help you on the journey to effective data visualization. Commencing with a chapter on the data framework, the book covers the complete visualization process, using the most popular Python libraries with working examples. You will learn how to use NumPy, SciPy,

Table of Contents

Chapter 1: A Conceptual Framework for Data Visualization
Chapter 2: Data Analysis and Visualization
Chapter 3: Getting Started with the Python IDE
Chapter 4: Numerical Computing and Interactive Plotting
Chapter 5: Financial and Statistical Models
Chapter 6: Statistical and Machine Learning
Chapter 7: Bioinformatics, Genetics, and Network Models
Chapter 8: Advanced Visualization
Appendix: Go Forth and Explore Visualization

中文：

书名：Mastering Python Data Visualization

关于本书探索各种工具及其优势，同时构建有意义的表示，可以更容易地理解数据与计算方法和算法在科学的不同领域写在一个易于遵循的分类风格，这本书讨论了一些利基技术，将使您的代码更容易使用和重用谁是这本书是谁如果你是一个执行数据可视化的Python开发人员，并想要发展你现有的Python知识，那么这本书是为你。假定您对Python库有基本的知识水平和理解。您将学到的内容收集、清理、访问数据并将其映射到可视化框架了解哪种可视化方法适用并学习数据可视化的最佳实践熟悉读者驱动的叙述、作者驱动的叙述和感知原理理解为什么Python是一种非常有效的数值计算工具，就像MATLAB一样，并探索它附带的一些有趣的数据结构使用各种可视化技术来探索如何对金融和统计计算非常有用将Python与使用Julia和基于Java的框架(如D3.js)的其他可视化方法进行比较发现如何将Python与NoSQL(如Have)结合使用。为了在分布式环境中高效地生成详细的结果，Python提供了几个用于数值计算的开源库，这些库涉及优化、线性代数、积分、内插以及使用数组对象、机器学习、数据挖掘和绘图的其他特殊函数。本书提供了实用的指导，帮助您踏上有效的数据可视化之旅。从数据框架的一章开始，这本书涵盖了完整的可视化过程，使用了最流行的Python库和工作示例。您将学习如何使用NumPy、SciPy、

Table of Contents

第1章：数据可视化的概念框架
Chapter 2: Data Analysis and Visualization
第3章：Python IDE入门
第4章：数值计算和交互绘图
Chapter 5: Financial and Statistical Models
Chapter 6: Statistical and Machine Learning
第7章：生物信息学、遗传学和网络模型
Chapter 8: Advanced Visualization
附录：深入了解可视化