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

How do you turn raw, unprocessed, or malformed data into dynamic, interactive web visualizations? In this practical book, author Kyran Dale shows data scientists and analysts--as well as Python and JavaScript developers--how to create the ideal toolchain for the job. By providing engaging examples and stressing hard-earned best practices, this guide teaches you how to leverage the power of best-of-breed Python and JavaScript libraries. Python provides accessible, powerful, and mature libraries for scraping, cleaning, and processing data. And while JavaScript is the best language when it comes to programming web visualizations, its data processing abilities can't compare with Python's. Together, these two languages are a perfect complement for creating a modern web-visualization toolchain. This book gets you started. You'll learn how to: Obtain data you need programmatically, using scraping tools or web APIs: Requests, Scrapy, Beautiful Soup Clean and process data using Python's heavyweight data processing libraries within the NumPy ecosystem: Jupyter notebooks with pandas+Matplotlib+Seaborn Deliver the data to a browser with static files or by using Flask, the lightweight Python server, and a RESTful API Pick up enough web development skills (HTML, CSS, JS) to get your visualized data on the web Use the data you've mined and refined to create web charts and visualizations with Plotly, D3, Leaflet, and other libraries

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

How do you turn raw, unprocessed, or malformed data into dynamic, interactive web visualizations? In this practical book, author Kyran Dale shows data scientists and analysts--as well as Python and JavaScript developers--how to create the ideal toolchain for the job. By providing engaging examples and stressing hard-earned best practices, this guide teaches you how to leverage the power of best-of-breed Python and JavaScript libraries. Python provides accessible, powerful, and mature libraries for scraping, cleaning, and processing data. And while JavaScript is the best language when it comes to programming web visualizations, its data processing abilities can't compare with Python's. Together, these two languages are a perfect complement for creating a modern web-visualization toolchain. This book gets you started. You'll learn how to: Obtain data you need programmatically, using scraping tools or web APIs: Requests, Scrapy, Beautiful Soup Clean and process data using Python's heavyweight data processing libraries within the NumPy ecosystem: Jupyter notebooks with pandas+Matplotlib+Seaborn Deliver the data to a browser with static files or by using Flask, the lightweight Python server, and a RESTful API Pick up enough web development skills (HTML, CSS, JS) to get your visualized data on the web Use the data you've mined and refined to create web charts and visualizations with Plotly, D3, Leaflet, and other libraries.

You've got data to communicate. But what kind of visualization do you choose, how do you build it, and how do you ensure that it's up to the demands of the Web? In Data Visualization with JavaScript, you'll learn how to use JavaScript, HTML, and CSS to build the most practical visualizations for your data. Step-by-step examples walk you through creating, integrating, and debugging different types of visualizations and will have you building basic visualizations, like bar, line, and scatter graphs, in no time. Then you'll move on to more advanced topics, including how to: Create tree maps, heat maps, network graphs, word clouds, and timelines Map geographic data, and build sparklines and composite charts Add interactivity and retrieve data with AJAX Manage data in the browser and build data-driven web applications Harness the power of the Flotr2, Flot, Chronoline.js, D3.js, Underscore.js, and Backbone.js libraries If you already know your way around building a web page but aren't quite sure how to build a good visualization, Data Visualization with JavaScript will help you get your feet wet without throwing you into the deep end. Before you know it, you'll be well on your way to creating simple, powerful data visualizations.

How do you turn raw, unprocessed, or malformed data into dynamic, interactive web visualizations? In this practical book, author Kyran Dale shows data scientists and analysts--as well as Python and JavaScript developers--how to create the ideal toolchain for the job. By providing engaging examples and stressing hard-earned best practices, this guide teaches you how to leverage the power of best-of-breed Python and JavaScript libraries. Python provides accessible, powerful, and mature libraries for scraping, cleaning, and processing data. And while JavaScript is the best language when it comes to programming web visualizations, its data processing abilities can't compare with Python's. Together, these two languages are a perfect complement for creating a modern web-visualization toolchain. This book gets you started. You'll learn how to: Obtain data you need programmatically, using scraping tools or web APIs: Requests, Scrapy, Beautiful Soup Clean and process data using Python's heavyweight data processing libraries within the NumPy ecosystem: Jupyter notebooks with pandas+Matplotlib+Seaborn Deliver the data to a browser with static files or by using Flask, the lightweight Python server, and a RESTful API Pick up enough web development skills (HTML, CSS, JS) to get your visualized data on the web Use the data you've mined and refined to create web charts and visualizations with Plotly, D3, Leaflet, and other libraries

Author Scott Murray teaches you the fundamental concepts and methods of D3, a JavaScript library that lets you express data visually in a web browser