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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries 2. NumPy Arrays FREE CHAPTER 3. Statistics and Linear Algebra 4. pandas Primer 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources
Index

Social network analysis


Social network analysis studies social relations using network theory. Nodes represent participants in a network. Lines between nodes represent relationships. Formally, this is called a graph. Due to the constraints of this book, we will only have a quick look at a simple graph that comes with the popular NetworkX Python library. matplotlib will help with the visualization of the graph.

Install NetworkX with the following commands:

$ pip install networkx
$ pip freeze|grep networkx
networkx==1.9

The import convention for NetworkX is as follows:

import networkx as nx

NetworkX provides a number of sample graphs, which can be listed as follows:

print [s for s in dir(nx) if s.endswith('graph')]

Load the Davis Southern women graph and plot a histogram of the degree of connections:

G = nx.davis_southern_women_graph()
plt.figure(1)
plt.hist(nx.degree(G).values())

The resulting histogram is shown as follows:

Draw the graph with node labels as follows:

plt.figure(2)
pos = nx.spring_layout...
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