Book Image

Hands-On Data Structures and Algorithms with Python - Third Edition

By : Dr. Basant Agarwal
Book Image

Hands-On Data Structures and Algorithms with Python - Third Edition

By: Dr. Basant Agarwal

Overview of this book

Choosing the right data structure is pivotal to optimizing the performance and scalability of applications. This new edition of Hands-On Data Structures and Algorithms with Python will expand your understanding of key structures, including stacks, queues, and lists, and also show you how to apply priority queues and heaps in applications. You’ll learn how to analyze and compare Python algorithms, and understand which algorithms should be used for a problem based on running time and computational complexity. You will also become confident organizing your code in a manageable, consistent, and scalable way, which will boost your productivity as a Python developer. By the end of this Python book, you’ll be able to manipulate the most important data structures and algorithms to more efficiently store, organize, and access data in your applications.
Table of Contents (17 chapters)
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Graph representations

A graph representation technique means how we store the graph in memory, i.e., how we store the vertices, edges, and weights (if the graph is a weighted graph). Graphs can be represented with two methods, i.e. (1) an adjacency list, and (2) an adjacency matrix.

An adjacency list representation is based on a linked list. In this, we represent the graph by maintaining a list of neighbors (also called an adjacent node) for every vertex (or node) of the graph. In an adjacency matrix representation of a graph, we maintain a matrix that represents which node is adjacent to which other node in the graph; i.e., the adjacency matrix has the information of every edge in the graph, which is represented by cells of the matrix.

Either of these two representations can be used; however, our choice depends on the application where we will be using the graph representation. An adjacency list is preferable when we expect that the graph is going to be sparse and we will...