Book Image

Practical Data Analysis - Second Edition

By : Hector Cuesta, Dr. Sampath Kumar
Book Image

Practical Data Analysis - Second Edition

By: Hector Cuesta, Dr. Sampath Kumar

Overview of this book

Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you’ll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark.
Table of Contents (21 chapters)
Practical Data Analysis - Second Edition
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface

Degree distribution


The degree of a node is the number of connections (links) with other nodes. In the case of directed graphs, each node has two degrees—the out-degree and the in-degree. In the undirected graph, the relationship is mutual; then we just have a single degree for each node. In the code listed here, we get the source node and target node references from the links.csv file. Then, we create a single list merging the two lists (target and source). Finally, we get a dictionary (dic) of how many times each node appears in the list, and we plot the result in a bar char using matplotlib.

The links.csv file will look like this:

edgedef>node1 VARCHAR,node2 VARCHAR 
23917067,35702006 
23917067,629395837 
23917067,747343482 
23917067,755605075 
23917067,1186286815 
.  .  . 
import numpy as np 
import matplotlib.pyplot as plt 
 
links = np.genfromtxt("links.csv", 
                       dtype=str, 
                       delimiter...