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Practical Data Analysis

You're reading from   Practical Data Analysis Pandas, MongoDB, Apache Spark, and more

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Product type Paperback
Published in Sep 2016
Publisher
ISBN-13 9781785289712
Length 338 pages
Edition 2nd Edition
Languages
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Authors (2):
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Hector Cuesta Hector Cuesta
Author Profile Icon Hector Cuesta
Hector Cuesta
Dr. Sampath Kumar Dr. Sampath Kumar
Author Profile Icon Dr. Sampath Kumar
Dr. Sampath Kumar
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Toc

Table of Contents (16) Chapters Close

Preface 1. Getting Started 2. Preprocessing Data FREE CHAPTER 3. Getting to Grips with Visualization 4. Text Classification 5. Similarity-Based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Diseases with Cellular Automata 10. Working with Social Graphs 11. Working with Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with Jupyter and Wakari 15. Understanding Data Processing using Apache Spark

Visualizing different types of data


D3 is a project featured by the Stanford Visualization Group developed by Mike Bostock.

D3 provides us with web-based visualization, which is an excellent way to deploy information and illustrate things like proportions, relationships, correlations, patterns, and to discover things previously unknown. A web browser provides us with a very flexible and interactive interface on practically any device, such as a PC, tablet, or smartphone. D3 is an amazing tool for visualization based on data using HTML, JavaScript, SVG, and CSS.

In Chapter 1, Getting Started we talked about the importance of data visualization, but in this chapter, we will present examples for you to understand the use of D3.js. In the following screenshot, we can see the basic structure of an HTML document. D3 is going to be included in a basic script tag or into a JavaScript file (.js).

HTML

Hyper Text Markup Language (HTML) provides the basic skeleton for our visualization. An HTML document...

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