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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 FREE CHAPTER 2. Data Munging 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 loading, dumping, and sampling

Beyond NetworkX, graphs and networks can be generated and analyzed with other software. One of the best open source multiplatform software that can be used for their analysis is named Gephi. It's a visual tool and it doesn't require programming skills. It's freely available at http://gephi.github.io.

As in machine learning datasets, even graphs have standard formats for storing, loading, and exchanging. This way, you can create a graph with NetworkX, dump it to a file, and then load and analyze it with Gephi.

One of the most frequently used formats is Graph Modeling Language (GML). Now, let's see how we can dump a graph into a GML file:

In: dump_file_base = "dumped_graph"

# Be sure the dump_file file doesn't exist
def remove_file(filename):
import os
if os.path.exists(filename):
...
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