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Data Visualization with Python and JavaScript: Scrape, Clean, Explore, and Transform Your Data

Data Visualization with Python and JavaScript: Scrape, Clean, Explore, and Transform Your Data

Data Visualization with Python and JavaScript: Scrape, Clean, Explore, and
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Data Visualization with Python and JavaScript: Scrape, Clean, Explore, and Transform Your Data Papeback - 2015

by Kyran Dale

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2nd edition NO-PA16APR2015-KAP. Papeback. New.
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Details

  • Title Data Visualization with Python and JavaScript: Scrape, Clean, Explore, and Transform Your Data
  • Author Kyran Dale
  • Binding Papeback
  • Condition New
  • Pages 566
  • Volumes 1
  • Language ENG
  • Publisher O'Reilly Media
  • Publication date 2nd edition NO-PA16APR2015-
  • Features Index
  • Bookseller's Inventory # 6395223343
  • ISBN 9781098111878 / 1098111877
  • Weight 1.98 lbs (0.90 kg)
  • Dimensions 9.1 x 6.9 x 1.2 in (23.11 x 17.53 x 3.05 cm)
  • Category Computers - Data Base Management
  • Library of Congress subjects Python (Computer program language), JavaScript (Computer program language)
  • Dewey Decimal Code 001.422
  • Quantity available 3

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From the publisher

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

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