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)

Randomized selection

In the previous chapter, we discussed the quicksort algorithm. The quicksort algorithm allows us to sort an unordered list of items, but has a way of preserving the index of elements as the sorting algorithm runs. Generally speaking, the quicksort algorithm does the following:

  1. Selects a pivot
  2. Partitions the unsorted list around the pivot
  3. Recursively sorts the two halves of the partitioned list using steps 1 and 2

One interesting and important fact is that after every partitioning step the index of the pivot will not change, even after the list has become sorted. This means that after each iteration the selected pivot value will be placed at its correct position in the list. It is this property that enables us to be able to work with a not-so-fully sorted list to obtain the ith smallest number. Because randomized selection is based on the quicksort algorithm...