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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

Graph visualization with D3.js

D3.js provides us with the d3.layout.force() function, which uses the Force Atlas layout algorithm and helps us to visualize our graph. See Chapter 3, Getting to Grips with Visualization, for instructions of how to create D3.js visualizations.

First, we need to define the CSS style for the nodes, links, and node labels:

<style> 
 
.link { 
  fill: none; 
  stroke: #666; 
  stroke-width: 1.5px; 
} 
 
.node circle { 
  fill: steelblue; 
  stroke: #fff; 
  stroke-width: 1.5px; 
} 
 
.node text { 
  pointer-events: none; 
  font: 10px sans-serif; 
} 
</style> 

Then, we need to refer to the d3.js library:

<script src="http://d3js.org/d3.v3.min.js"></script> 

Then, we need to define the width and height for the svg container and include it in the body tag:

var width = 1100, 
    height = 800 
 
var svg = d3.select("body").append("svg") 
    .attr("width", width) 
    .attr("height", height); ...
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