#### 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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# Big O notation

The letter O in big O notation stands for order, in recognition that rates of growth are defined as the order of a function. It measures the worst-case running time complexity, that is, the maximum time to be taken by the algorithm. We say that one function T(n) is a big O of another function, F(n), and we define this as follows:

The function, g(n), of the input size, n, is based on the observation that for all sufficiently large values of n, g(n) is bounded above by a constant multiple of f(n). The objective is to find the smallest rate of growth that is less than or equal to f(n). We only care what happens at higher values of n. The variable n0 represents the threshold below which the rate of growth is not important. The function T(n) represents the tight upper bound F(n). In the following plot, we can see that T(n) = n2 + 500 = O(n2), with C = 2 and n0 being...