#### 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
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# Selection by sorting

Items in a list may undergo statistical inquiries such as finding the mean, median, and mode values. Finding the mean and mode values does not require the list to be ordered. However, to find the median in a list of numbers, the list must first be ordered. Finding the median requires you to find the element in the middle position of the ordered list. In addition, this can be used when we want to find the last-smallest item in the list or the first-smallest item in the list. In such situations, selection algorithms can be useful.

To find the ith smallest number in an unordered list of items, the index of where that item occurs is important to obtain. Since the elements of the list are not sorted, it is difficult to know whether the element at index 0 in a list is really the first-smallest number.

A pragmatic and obvious thing to do when dealing with unordered...