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Python Data Science Essentials

You're reading from   Python Data Science Essentials A practitioner's guide covering essential data science principles, tools, and techniques

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Product type Paperback
Published in Sep 2018
Publisher Packt
ISBN-13 9781789537864
Length 472 pages
Edition 3rd Edition
Languages
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Authors (2):
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Luca Massaron Luca Massaron
Author Profile Icon Luca Massaron
Luca Massaron
Alberto Boschetti Alberto Boschetti
Author Profile Icon Alberto Boschetti
Alberto Boschetti
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Toc

Table of Contents (11) Chapters Close

Preface 1. First Steps 2. Data Munging FREE CHAPTER 3. The Data Pipeline 4. Machine Learning 5. Visualization, Insights, and Results 6. Social Network Analysis 7. Deep Learning Beyond the Basics 8. Spark for Big Data 9. Strengthen Your Python Foundations 10. Other Books You May Enjoy

Graph algorithms

To get insights from graphs, many algorithms have been developed. In this chapter, we'll use a well-known graph in NetworkX, that is, the Krackhardt Kite graph. It is a dummy graph containing 10 nodes, and it is typically used to proof graph algorithms. David Krackhardt is the creator of the structure, which has the shape of a kite. It's composed of two different zones. In the first zone (composed of nodes 0 to 6), the nodes are interlinked; in the other zone (nodes 7 to 9), they are connected as a chain:

In: G = nx.krackhardt_kite_graph()
nx.draw_networkx(G)
plt.show()

In the following plot, you can examine the Krackhardt Kite's graph structure:

Let's start with connectivity. Two nodes of a graph are connected if there is at least a path (that is, a sequence of nodes) between them.

If at least one path exists, the shortest path between...

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