Book Image

Python Data Structures and Algorithms

By : Benjamin Baka
Book Image

Python Data Structures and Algorithms

By: Benjamin Baka

Overview of this book

Data structures allow you to organize data in a particular way efficiently. They are critical to any problem, provide a complete solution, and act like reusable code. In this book, you will learn the essential Python data structures and the most common algorithms. With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. You will be able to create complex data structures such as graphs, stacks and queues. We will explore the application of binary searches and binary search trees. You will learn the common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. We will also discuss how to organize your code in a manageable, consistent, and extendable way. The book will explore in detail sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort. By the end of the book, you will learn how to build components that are easy to understand, debug, and use in different applications.
Table of Contents (20 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
5
Stacks and Queues
7
Hashing and Symbol Tables

Amortized analysis


Often we are not so interested in the time complexity of individual operations, but rather the time averaged running time of sequences of operations. This is called amortized analysis. It is different from average case analysis, which we will discuss shortly, in that it makes no assumptions regarding the data distribution of input values. It does, however, take into account the state change of data structures. For example, if a list is sorted it should make any subsequent find operations quicker. Amortized analysis can take into account the state change of data structures because it analyzes sequences of operations, rather then simply aggregating single operations.

Amortized analysis finds an upper bound on runtime by imposing an artificial cost on each operation in a sequence of operations, and then combining each of these costs. The artificial cost of a sequence takes in to account that the initial expensive operations can make subsequent operations cheaper.

When we have...