[收藏] 20 Fresh JavaScript Data Visualization Libraries

20款JS数据可视化库
本文汇总了20款热门的JavaScript数据可视化库,包括Highcharts、gRaphaël、JavaScriptInfoVisToolkit等,这些工具能帮助开发者高效地创建图表及交互式数据展示。
D3.js 4.x Data Visualization - Third Edition by Andrew Rininsland English | 28 Apr. 2017 | ASIN: B01MG90SSJ | 308 Pages | AZW3 | 6.6 MB Key Features Build interactive and rich graphics and visualization using JavaScript`s powerful library D3.js Learn D3 from the ground up, using the all-new version 4 of the library Gain insight into producing high-quality, extensible charts and visualizations using best practices such as writing testable, extensible code and strong typing Book Description Want to get started with impressive interactive visualizations and implement them in your daily tasks? This book offers the perfect solution-D3.js. It has emerged as the most popular tool for data visualization. This book will teach you how to implement the features of the latest version of D3 while writing JavaScript using the newest tools and technique You will start by setting up the D3 environment and making your first basic bar chart. You will then build stunning SVG and Canvas-based data visualizations while writing testable, extensible code,as accurate and informative as it is visually stimulating. Step-by-step examples walk you through creating, integrating, and debugging different types of visualization and will have you building basic visualizations (such as bar, line, and scatter graphs) in no time. By the end of this book, you will have mastered the techniques necessary to successfully visualize data and will be ready to use D3 to transform any data into an engaging and sophisticated visualization. What you will learn Map data to visual elements using D3's scales Draw SVG elements using D3's shape generators Transform data using D3's collection methods Use D3's various layout patterns to quickly generate various common types of chart Write modern JavaScript using ES2017 and Babel Explore the basics of unit testing D3 visualizations using Mocha and Chai Write and deploy a simple Node.js web service to render charts via HTML Canvas Understand what makes a good data visualization and how to use the tools at your disposal to create accurate charts About the Author Andrew Rininsland is a developer and journalist who has spent much of the last half a decade building interactive content for newspapers such as The Financial Times, The Times, Sunday Times, The Economist, and The Guardian. During his 3 years at The Times and Sunday Times, he worked on all kinds of editorial projects, ranging from obituaries of figures such as Nelson Mandela to high-profile, data-driven investigations such as The Doping Scandal the largest leak of sporting blood test data in history. He is currently a senior developer with the interactive graphics team at the Financial Times. Swizec Teller, author of Data Visualization with d3.js, is a geek with a hat. He founded his first start-up at the age of 21 years and is now looking for the next big idea as a full-stack Web generalist focusing on freelancing for early-stage start-up companies. When he isn't coding, he's usually blogging, writing books, or giving talks at various non-conference events in Slovenia and nearby countries. He is still looking for a chance to speak at a big international conference. In November 2012, he started writing Why Programmers Work At Night, and set out on a quest to improve the lives of developers everywhere. Table of Contents Getting Started with D3, ES2017, and Node.js A Primer on DOM, SVG, and CSS Shape Primitives of D3 Making Data Useful Defining the User Experience - Animation and Interaction Hierarchical Layouts of D3 The Other Layouts D3 on the Server with Canvas, Koa 2, and Node.js Having Confidence in Your Visualizations Designing Good Data Visualizations
Learn how to turn raw data into rich, interactive web visualizations with the powerful combination of Python and JavaScript. With this hands-on guide, author Kyran Dale teaches you how build a basic dataviz toolchain with best-of-breed Python and JavaScript libraries—including Scrapy, Matplotlib, Pandas, Flask, and D3—for crafting engaging, browser-based visualizations. As a working example, throughout the book Dale walks you through transforming Wikipedia’s table-based list of Nobel Prize winners into an interactive visualization. You’ll examine steps along the entire toolchain, from scraping, cleaning, exploring, and delivering data to building the visualization with JavaScript’s D3 library. If you’re ready to create your own web-based data visualizations—and know either Python or JavaScript— this is the book for you. Learn how to manipulate data with Python Understand the commonalities between Python and JavaScript Extract information from websites by using Python’s web-scraping tools, BeautifulSoup and Scrapy Clean and explore data with Python’s Pandas, Matplotlib, and Numpy libraries Serve data and create RESTful web APIs with Python’s Flask framework Create engaging, interactive web visualizations with JavaScript’s D3 library Table of Contents Chapter 1 Development Setup Chapter 2 A Language-Learning Bridge Between Python and JavaScript Chapter 3 Reading and Writing Data with Python Chapter 4 Webdev 101 Chapter 5 Getting Data off the Web with Python Chapter 6 Development Setup Chapter 7 A Language-Learning Bridge Between Python and JavaScript Chapter 8 Reading and Writing Data with Python Chapter 9 Webdev 101 Chapter 10 Getting Data off the Web with Python Chapter 11 Heavyweight Scraping with Scrapy Chapter 12 Introduction to NumPy Chapter 13 Introduction to Pandas Chapter 14 Cleaning Data with Pandas Chapter 15 Visualizing Data with Matplotlib Chapter 16 Exploring Data with Pandas Chapter 17 Delivering the Data Chapter 18 RESTful Data with Flask Chapter 19 Imagining a Nobel Visualization Chapter 20 Building a Visualization Chapter 21 Introducing D3—The Story of a Bar Chart Chapter 22 Visualizing Individual Prizes Chapter 23 Mapping with D3 Chapter 24 Visualizing Individual Winners Chapter 25 The Menu Bar Chapter 26 Conclusion
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