Book Image

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

By : Dr. Basant Agarwal, Benjamin Baka
Book Image

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

By: Dr. Basant Agarwal, Benjamin Baka

Overview of this book

Data structures allow you to store and organize data efficiently. They are critical to any problem, provide a complete solution, and act like reusable code. Hands-On Data Structures and Algorithms with Python teaches you the essential Python data structures and the most common algorithms for building easy and maintainable applications. This book helps you to understand the power of linked lists, double linked lists, and circular linked lists. You will learn to create complex data structures, such as graphs, stacks, and queues. As you make your way through the chapters, you will explore the application of binary searches and binary search trees, along with learning common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. In the concluding chapters, you will get to grips with organizing your code in a manageable, consistent, and extendable way. You will also study how to bubble sort, selection sort, insertion sort, and merge sort algorithms in detail. By the end of the book, you will have learned how to build components that are easy to understand, debug, and use in different applications. You will get insights into Python implementation of all the important and relevant algorithms.
Table of Contents (16 chapters)

Heaps

A heap data structure is a specialization of a tree in which the nodes are ordered in a specific way. Heaps are divided into max heaps and min heaps.

In a max heap, each parent node value must always be greater than or equal to its children. It follows that the root node must be the greatest value in the tree. Consider the following diagram for the max heap, where all the nodes have greater values compared to their children:

In a min heap, each parent node must be less than or equal to both its children. As a consequence, the root node holds the lowest value. Consider the following diagram for the min heap, where all the nodes have smaller values compared to their children:

Heaps are used for a number of different things. For one, they are used to implement priority queues. There is also a very efficient sorting algorithm, called heap sort, that uses heaps. We are going...