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Practical Data Science Cookbook, Second Edition

You're reading from   Practical Data Science Cookbook, Second Edition Data pre-processing, analysis and visualization using R and Python

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787129627
Length 434 pages
Edition 2nd Edition
Languages
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Authors (5):
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Anthony Ojeda Anthony Ojeda
Author Profile Icon Anthony Ojeda
Anthony Ojeda
Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Author Profile Icon Prabhanjan Narayanachar Tattar
Prabhanjan Narayanachar Tattar
ABHIJIT DASGUPTA ABHIJIT DASGUPTA
Author Profile Icon ABHIJIT DASGUPTA
ABHIJIT DASGUPTA
Sean P Murphy Sean P Murphy
Author Profile Icon Sean P Murphy
Sean P Murphy
Bhushan Purushottam Joshi Bhushan Purushottam Joshi
Author Profile Icon Bhushan Purushottam Joshi
Bhushan Purushottam Joshi
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Table of Contents (12) Chapters Close

Preface 1. Preparing Your Data Science Environment 2. Driving Visual Analysis with Automobile Data with R FREE CHAPTER 3. Creating Application-Oriented Analyses Using Tax Data and Python 4. Modeling Stock Market Data 5. Visually Exploring Employment Data 6. Driving Visual Analyses with Automobile Data 7. Working with Social Graphs 8. Recommending Movies at Scale (Python) 9. Harvesting and Geolocating Twitter Data (Python) 10. Forecasting New Zealand Overseas Visitors 11. German Credit Data Analysis

Exploring the characteristics of entire networks


In the next set of recipes, we will characterize our social network as a whole, rather than from the perspective of individual actors. This task is usually secondary to getting a feel of the most important nodes, but it is a chicken and an egg problem; determining the techniques to analyze and splitting the whole graph can be informed by key player analyses, and vice versa.

Getting ready

If you completed the previous recipes, you will be ready to proceed with this one.

How to do it...

The following steps will walk us through our first exploration of graph characteristics at the level of the whole graph:

  1. Let's compute both the density of the entire network and that of the ego graphs:
>>> nx.density(graph)
0.00810031232554
>>> ego = nx.ego_graph(graph, "LONGBOW/AMELIA GREER")
>>> nx.density(ego)
0.721014492754

As you can see, our heroic social network is not very dense; it's not very cliquish as a whole. However, Longbow...

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