Book Image

Practical Data Science Cookbook, Second Edition - Second Edition

By : Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean Patrick Murphy, ABHIJIT DASGUPTA, Anthony Ojeda
Book Image

Practical Data Science Cookbook, Second Edition - Second Edition

By: Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean Patrick Murphy, ABHIJIT DASGUPTA, Anthony Ojeda

Overview of this book

As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don’t. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface

Finding key players


In the previous recipe, Finding strong ties, we began exploring ego networks and strong ties between individuals in our social network. We started to see that actors with strong ties with other actors created clusters that centered on themselves. This leads to the obvious question: who are the key figures in the graph, and what kind of pull do they have? We'll look at a couple of measures to determine how important a node is or its centrality to try to discover the degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality.

Getting ready

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

How to do it...

The following steps will identify key players in this network of comic book characters:

  1. To find the top ten nodes in the heroes network, we compute the nodes' degree and sort them:
import operator

>>> degrees = sorted(graph.degree().items(), key=operator.itemgetter(1), reverse=True)

>>> for node in...