Название: Data Visualization with Python and jаvascript: Scrape, Clean, Explore, and Transform Your Data, 2nd Edition (Final Release)Автор: Kyran Dale
Издательство: O’Reilly Media, Inc.
Год: 2023
Страниц: 569
Язык: английский
Формат: True/Retail PDF EPUB
Размер: 34.7 MB
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 jаvascript 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 jаvascript libraries.
Python provides accessible, powerful, and mature libraries for scraping, cleaning, and processing data. And while jаvascript 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.
Although this book is a big one (that fact is felt most keenly by the author right now), it has had to be very selective, leaving out a lot of really cool Python and jаvascript dataviz tools and focusing on the ones that provide the best building blocks. The number of helpful libraries I couldn’t cover reflects the enormous vitality of the Python and jаvascript data science ecosystems. Even while the book was being written, brilliant new Python and jаvascript libraries were being introduced, and the pace continues.
This book is divided into five parts. The first part introduces a basic Python and jаvascript dataviz toolkit, while the next four show how to retrieve raw data, clean it, explore it, and finally transform it into a modern web visualization.
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
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