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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

Getting started with D3.js


First, download the latest version of D3 from the official website at http://d3js.org/.

To go directly to the latest release, copy this snippet:

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

In our basic examples, we can just open our HTML document in a web browser to view it. But when we need to load external data sources, we need to publish the folder on a web server like Apache, nginx, or IIS. Python provides us with an easy way to run a web server with http.server, so we just need to open the folder where our D3 files are present and execute the following command in the terminal:

$ python3 -m http.server 8000

In Windows, you can use the same command by removing the number 3 from Python:

> python -m http.server 8000

The following examples are based on the Mike Bostock reference gallery, which can be found at https://github.com/mbostock/d3/wiki/Gallery.

All the codes and datasets of this chapter may be found in the author's GitHub repository...

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