Book Image

Python Data Science Essentials

By : Alberto Boschetti, Luca Massaron
Book Image

Python Data Science Essentials

By: Alberto Boschetti, Luca Massaron

Overview of this book

The book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results. In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Table of Contents (13 chapters)

Graph loading, dumping, and sampling

Beyond NetworkX, graphs and networks can be generated and analyzed with other software. One of the best open source multi-platform 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

As in machine learning datasets, even graphs have standard formats for their storing, loading, and exchanging. In 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 to dump a graph to GML file:

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):

In: G = nx.krackhardt_kite_graph()

# GML format write and read
GML_file = dump_file_base + '.gml'