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.
Preface
Free Chapter
Python Data Types and Structures
Introduction to Algorithm Design
Algorithm Design Techniques and Strategies
Stacks and Queues
Trees
Heaps and Priority Queues
Hash Tables
Graphs and Algorithms
Searching
Sorting
Selection Algorithms
String Matching Algorithms
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Index

Summary

In this chapter, we have discussed an important data structure, in other words, the heap data structure. We also discussed heap properties for `min-heap` and `max-heap`. We have seen the implementation of several operations that can be applied to the heap data structure, such as heapifying, and the insertion and deletion of a data element from the heap. We have also discussed two of the important applications of the heap—heap sort and a priority queue. The heap is an important data structure since it has many applications, such as sorting, selecting minimum and maximum values in a list, graph algorithms, and priority queues. Moreover, the heap can also be useful when we have to repeatedly remove a data object with the highest or lowest priority values.

In the next chapter, we will discuss the concepts of Hashing and Symbol Tables.