Book Image

Getting Started with Python

By : Fabrizio Romano, Benjamin Baka, Dusty Phillips
Book Image

Getting Started with Python

By: Fabrizio Romano, Benjamin Baka, Dusty Phillips

Overview of this book

This Learning Path helps you get comfortable with the world of Python. It starts with a thorough and practical introduction to Python. You’ll quickly start writing programs, building websites, and working with data by harnessing Python's renowned data science libraries. With the power of linked lists, binary searches, and sorting algorithms, you'll easily create complex data structures, such as graphs, stacks, and queues. After understanding cooperative inheritance, you'll expertly raise, handle, and manipulate exceptions. You will effortlessly integrate the object-oriented and not-so-object-oriented aspects of Python, and create maintainable applications using higher level design patterns. Once you’ve covered core topics, you’ll understand the joy of unit testing and just how easy it is to create unit tests. By the end of this Learning Path, you will have built components that are easy to understand, debug, and can be used across different applications. This Learning Path includes content from the following Packt products: • Learn Python Programming - Second Edition by Fabrizio Romano • Python Data Structures and Algorithms by Benjamin Baka • Python 3 Object-Oriented Programming by Dusty Phillips
Table of Contents (31 chapters)
Title Page
Copyright and Credits
About Packt
Stacks and Queues
Hashing and Symbol Tables

Graph representation

Graphs can be represented in two main forms. One way is to use an adjacency matrix and the other is to use an adjacency list.

We shall be working with the following figure to develop both types of representation for graphs:

Adjacency list

A simple list can be used to present a graph. The indices of the list will represent the nodes or vertices in the graph. At each index, the adjacent nodes to that vertex can be stored:

The numbers in the box represent the vertices. Index 0 represents vertex A, with its adjacent nodes being B and C.


Using a list for the representation is quite restrictive because we lack the ability to directly use the vertex labels. A dictionary is therefore more suited. To represent the graph in the diagram, we can use the following statements:

    graph = dict() 
    graph['A'] = ['B', 'C'] 
    graph['B'] = ['E','A'] 
    graph['C'] = ['A', 'B', 'E','F'] 
    graph['E'] = ['B', 'C'] 
    graph['F'] = ['C'] 

Now we easy establish that vertex A has the adjacent...