Data Visualization with Python and JavaScript 2e
Scrape, Clean, Explore, and Transform Your Data
Seiten
2022
|
2nd edition
O'Reilly Media (Verlag)
978-1-0981-1187-8 (ISBN)
O'Reilly Media (Verlag)
978-1-0981-1187-8 (ISBN)
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
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
Kyran Dale is a jobbing programmer, ex-research scientist, recreational hacker, independent researcher, occasional entrepreneur, cross-country runner and improving jazz pianist. During 15 odd years as a research scientist he hacked a lot of code, learned a lot of libraries and settled on some favorite tools. These days he finds Python, JavaScript, and a little C++ goes a long way to solving most problems out there. He specializes in fast-prototyping and feasibility studies, with an algorithmic bent but is happy to just build cool things.
| Erscheinungsdatum | 20.12.2022 |
|---|---|
| Verlagsort | Sebastopol |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge | |
| Informatik ► Software Entwicklung ► User Interfaces (HCI) | |
| Mathematik / Informatik ► Informatik ► Web / Internet | |
| ISBN-10 | 1-0981-1187-7 / 1098111877 |
| ISBN-13 | 978-1-0981-1187-8 / 9781098111878 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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