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.
Preface
Free Chapter
Python Objects, Types, and Expressions
Python Data Types and Structures
Principles of Algorithm Design
Lists and Pointer Structures
Stacks and Queues
Trees
Hashing and Symbol Tables
Graphs and Other Algorithms
Searching
Sorting
Selection Algorithms
String Algorithms and Techniques
Design Techniques and Strategies
Implementations, Applications, and Tools
Other Books You May Enjoy

Graph traversals

A graph traversal means to visit all the vertices of the graph, while keeping track of which nodes or vertices have already been visited and which ones have not. A graph traversal algorithm is efficient if it traverses all the nodes of the graph in the minimum possible time. A common strategy of graph traversal is to follow a path until a dead end is reached, then traverse back up until there is a point where we meet an alternative path. We can also iteratively move from one node to another in order to traverse the full graph, or part of it. Graph traversal algorithms are very important in answering many fundamental problems—they can be useful to determine how to reach from one vertex to another in a graph, and which path from the A to B vertices in the graph is better than other paths. In the next section, we will discuss two important graph traversal...