Book Image

Practical Data Science Cookbook

By : Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta
Book Image

Practical Data Science Cookbook

By: Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta

Overview of this book

<p>As increasing amounts of data is generated each year, the need to analyze and operationalize it is more important than ever. Companies that know what to do with their data will have a competitive advantage over companies that don't, and this will drive a higher demand for knowledgeable and competent data professionals.</p> <p>Starting with the basics, this book will cover how to set up your numerical programming environment, introduce you to the data science pipeline (an iterative process by which data science projects are completed), and guide 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 in the two most popular programming languages for data analysis—R and Python.</p>
Table of Contents (18 chapters)
Practical Data Science Cookbook
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Visualizing graphs


Throughout this chapter, we have been visualizing social networks to help develop our understanding and intuition around graphs. In this recipe, we dig a little bit deeper into graph visualization.

Getting ready

Ensure that you have networkx and matplotlib installed.

How to do it...

Complete this list of steps to gain a better understanding of graph visualization in Python:

  1. NetworkX wraps matplotlib or graphviz to draw simple graphs using the same charting library we saw in the previous chapter. This is effective for smaller-size graphs, but with larger graphs, memory can quickly be consumed. To draw a small graph, simply use the networkx.draw function, and then use pyplot.show to display it:

    >>> import networkx as nx
    >>> import matplotlib.pyplot as plt
    >>> nx.draw(graph)
    >>> plt.show()
    
  2. There is, however, a rich drawing library underneath that lets you customize how the graph looks and is laid out with many different layout algorithms. Let...