Data Visualization With Python And Javascript 2nd Edition
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Data Visualization with Python and JavaScript, 2nd Edition
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.
Data Visualization with Python and JavaScript
Python and Javascript are the perfect complement for turning data into rich, interactive web visualizations, in a world that increasingly expects more than a pre-rendered, static image. Developers need to know how to turn raw, unprocessed data, often "dirty" or malformed, into dynamic, interactive web visualizations. Author Kyran Dale teaches you how to leverage the power of best-of-breed Python and Javascript libraries to do so, using engaging examples and stressing hard-earned best-practice. You'll learn how to: Get data programmatically, using scraping tools or web APIs Clean and process data using Python's heavyweight data-processing libraries Deliver data to a browser using a lightweight Python server (Flask) Receive data and use it to create a web visualization, using D3, Canvas, or WebGL
Handbook of Research on Acquiring 21st Century Literacy Skills Through Game-Based Learning
Emerging technologies are becoming more prevalent in global classrooms. Traditional literacy pedagogies are shifting toward game-based pedagogy, addressing 21st century learners. Therefore, within this context there remains a need to study strategies to engage learners in meaning-making with some element of virtual design. Technology supports the universal design learning framework because it can increase the access to meaningful engagement in learning and reduce barriers. The Handbook of Research on Acquiring 21st Century Literacy Skills Through Game-Based Learning provides theoretical frameworks and empirical research findings in digital technology and multimodal ways of acquiring literacy skills in the 21st century. This book gains a better understanding of how technology can support leaner frameworks and highlights research on discovering new pedagogical boundaries by focusing on ways that the youth learn from digital sources such as video games. Covering topics such as elementary literacy learning, indigenous games, and student-worker training, this book is an essential resource for educators in K-12 and higher education, school administrators, academicians, pre-service teachers, game developers, researchers, and libraries.