Book Image

Python Data Science Essentials

Book Image

Python Data Science Essentials

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)

Introduction to graph theory


Basically, a graph is a data structure that's able to represent relations in a collection of objects. Under this paradigm, the objects are the graph's nodes and the relations are the graph's links (or edges). The graph is directed if the links have an orientation (conceptually, they're like the one-way streets of a city); otherwise, the graph is undirected. In the following table, examples of well-known graphs are provided:

Graph example

Type

Nodes

Edges

Internet network

Directed

Web pages

Links

Facebook

Undirected

People

Friendship

Twitter

Directed

People

Follower

IP network

Undirected

Hosts

Wires/connections

Navigation systems

Directed

Places/addresses

Streets

Wikipedia

Directed

Pages

Anchor links

Scientific literature

Directed

Papers

Citations

Markov chains

Directed

Status

Emission probability

All the preceding examples can be expressed as relations between nodes like in a traditional RDBMS, such as MySQL or Postgres. Now...