#### 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

# Deterministic selection

The worst-case performance of a randomized selection algorithm is O(n2). It is possible to improve the section of an element of the randomized selection algorithm to obtain a worst-case performance of O(n). We can obtain the performance of O(n) by using an algorithm, that is, deterministic selection.

Median of the median is an algorithm that provides us with the approximate median value, that is, a value close to the actual median for a given unsorted list of elements. This approximate median is often used as a pivot point in the quickselect algorithm for selecting the ith smallest element from a list. It is due to the fact that the median of median algorithm finds out the estimated median in a linear time, and when this estimated median is used as a pivot point in the quickselect algorithm, the worst-case running time's complexity drastically improves...