Data Structures and Algorithms in Python

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This book is based upon the book Data Structures and Algorithms in Java by Goodrich and Tamassia, and the related Data Structures and Algorithms in C++ by Goodrich, Tamassia, and Mount. However, this book is not simply a translation of those other books to Python. In adapting the material for this b
Data Structures and Algorithms in Python Michael T. Goodrich Department of computer Science University of California, Irvine Roberto tamassia Department of Computer Science Brown University Michael H. Goldwasser Department of Mathematics and Computer Science Saint louis university WILEY vp Publisher Don Fowley EXECUTIVE EDITOR Beth lang golub EDITORIAL PROGRAM ASSISTANT Katherine willis MARKETING MANAGER Christopher ruel DESIGNER Kenji ngieng SENIOR PRODUCTION MANAGER Janis soo ASSOCIATE PRODUCTION MANAGER Joyce Poh This book was set in LA tEX by the authors. Printed and bound by courier Westford The cover was printed by courier westford This book is printed on acid free paper Founded in 1807, John Wiley sons, InC. has been a valued source of knowledge and understanding for more than 200 years, helping people around the world meet their needs and fulfill their aspirations. 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No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the publisher, or authorization through payment of the appropriate per- copy fee to the Copyright Clearance Center, InC. 222 RosewoodDrive,Danvers,Mao1923,websitewww.copyright.comRequeststothePublisherforpermission should be addressed to the Permissions Department, John Wiley sons, Inc, 1ll River Street, Hoboken, Nj07030-5774,(201)748-6011,fax(201)748-6008,websitehttp:/ Evaluation copies are provided to qualified academics and professionals for review purposes only, for use in their courses during the next academic year. These copies are licensed and may not be sold or transferred to a third party. Upon completion of the review period, please return the evaluation copy to Wiley. Return have chosen to adopt this textbook for use in your course, please accept this book as your complimentary desk copy. Outside of the United States, please contact your local sales representative Printed in the United States of america 10987654321 To Karen Paul. anna and jack Michael. goodrich To Isabel Roberto tamassia To Susan, Calista, and maya Michael h. goldwasser Preface The design and analysis of efficient data structures has long been recognized as a vital subject in computing and is part of the core curriculum of computer science and computer engineering undergraduate degrees. Data Structures and algorithms in Python provides an introduction to data structures and algorithms, including their design, analysis, and implementation. This book is designed for use in a beginning- level data structures course, or in an intermediate-level introduction to algorithms course. We discuss its use for such courses in more detail later in this preface To promote the development of robust and reusable software, we have tried to take a consistent object-oriented viewpoint throughout this text. One of the main ideas of the object-oriented approach is that data should be presented as being en apsulated with the methods that access and modify them. That is, rather than simply viewing data as a collection of bytes and addresses, we think of data ob jects as instances of an abstract data type(ADt), which includes a repertoire of methods for performing operations on data objects of this type. We then empha- size that there may be several different implementation strategies for a particular ADT, and explore the relative pros and cons of these choices. We provide complete Python implementations for almost all data structures and algorithms discussed and we introduce important object-oriented design patterns as means to organize those implementations into reusable components Desired outcomes for readers of our book include that They have knowledge of the most common abstractions for data collections (e.g., stacks, queues, lists, trees, maps) They understand algorithmic strategies for producing efficient realizations of common data structures They can analyze algorithmic performance, both theoretically and experi mentally, and recognize common trade-offs between competing strategies They can wisely use existing data structures and algorithms found in modern programming language libraries They have experience working with concrete implementations for most foun dational data structures and algorithms e They can apply data structures and algorithms to solve complex problems In support of the last goal, we present many example applications of data structures throughout the book, including the processing of file systems, matching of tags in structured formats such as HTML, simple cryptography, text frequency analy- sis, automated geometric layout, Huffman coding, DNA sequence alignment, and search engine indexing Preface Book features This book is based upon the book Data Structures and Algorithms in Java by Goodrich and Tamassia, and the related Data Structures and algorithms in C++ by Goodrich, Tamassia, and Mount. However, this book is not simply a translation of those other books to python In adapting the material for this book, we have significantly redesigned the organization and content of the book as follows e The code base has been entirely redesigned to take advantage of the features of python, such as use of generators for iterating elements of a collection e Many algorithms that were presented as pseudo-code in the Java and C++ versions are directly presented as complete Python code. e In general, adts are defined to have consistent interface with pythons built- in data types and those in pythons collections module Chapter 5 provides an in-depth exploration of the dynamic array-based un- derpinnings of pythons built-in list tuple and str classes New appendix a serves as an additional reference regarding the functionality of the str class e Over 450 illustrations have been created or revised e New and revised exercises bring the overall total number to 750 Online resources This book is accompanied by an extensive set of online resources, which can be found at the following Web site Students are encouraged to use this site along with the book, to help with exer- cises and increase understanding of the subject. Instructors are likewise welcome to use the site to help plan, organize, and present their course materials. Included on this Web site is a collection of educational aids that augment the topics of this book. for both students and instructors because of their added value. some of these online resources are pass word protected For all readers, and especially for students, we include the following resources All the python source code presented in this book PDF handouts of Powerpoint slides(four-per-page) provided to instructors a database of hints to all exercises, indexed by problem number For instructors using this book, we include the following additional teaching aids e Solutions to hundreds of the book's exercises e Color versions of all figures and illustrations from the book Slides in Powerpoint and PDF(one-per-page)format The slides are fully editable, so as to allow an instructor using this book full free- dom in customizing his or her presentations. All the online resources are provided at no extra charge to any instructor adopting this book for his or her course Presa Contents and Organization The chapters for this book are organized to provide a pedagogical path that starts with the basics of python programming and object-oriented design We then add foundational techniques like algorithm analysis and recursion. In the main portion of the book, we present fundamental data structures and algorithms, concluding with a discussion of memory management(that is, the architectural underpinnings of data structures). Specifically, the chapters for this book are organized as follows 1. Python Primer 2. Object-Oriented Programming 3. Algorithm Analysis 4. Recursion 5. Array-Based Sequences 6. Stacks, Queues, and deques 7. Linked Lists 8. Trees 9. Priority Queues 10. Maps, Hash Tables, and skip lists 11. Search trees 12. Sorting and selection 13. Text Processing 14. Graph algorithms 15. Memory Management and B-Trees A. Character Strings in Python B. Useful mathematical facts A more detailed table of contents follows this preface, beginning on page xi Prerequisites We assume that the reader is at least vaguely familiar with a high-level program ming language such as c, c++, python or Java, and that he or she understands the main constructs from such a high-level language, including e Variables and expressions Decision structures(such as if-statements and switch-statements) Iteration structures(for loops and while loops) Functions(whether stand-alone or object-oriented methods) For readers who are familiar with these concepts, but not with how they are ex pressed in Python, we provide a primer on the python language in Chapter l Still this book is primarily a data structures book, not a Python book; hence, it does not give a comprehensive treatment of python vl Preface We delay treatment of object-oriented programming in Python until Chapter 2 This chapter is useful for those new to Python, and for those who may be familiar with Python, yet not with object-oriented programming In terms of mathematical back ground, we assume the reader is somewhat famil iar with topics from high-school mathematics. Even So, in Chapter 3, we discuss the seven most-important functions for algorithm analysis. In fact, sections that use something other than one of these seven functions are considered optional, and are indicated with a star(*). We give a summary of other useful mathematical facts, including elementary probability, in Appendix b Relation to Computer Science curriculum To assist instructors in designing a course in the context of the IEEE/ACM 2013 Computing Curriculum, the following table describes curricular knowledge units that are covered within this book Knowledge Unit Relevant material AL/Basic analysis Chapter 3 and Sections 4.2& 12.2.4 AL/Algorithmic Strategies Sections12.2.1,13.2.1,13.3,&13.4.2 AL/Fundamental Data Structures Sections 4.1.3, 5.5.2, 9.4.1, 9.3, 10.2, 11.1, 13.2, and algorithms Chapter 12 much of Chapter 14 AL/Advanced data Structures Sections5.3,10.4,11.2 through11.6,12.3.1, 13.5,14.5.1,&15.3 AR/Memory System Organization Chapter 15 and Architecture DS/Sets, Relations and Functions Sections 10.5. 1, 10.5.2, &9.4 DS/Proof Techniques Sections3.4,4.2,53.2,9.3.6,&12.4.1 DS/Basics of Counting Sections 2.4.2, 6.2.2, 12.2.4, 8.2.2& appendix b DS/Graphs and Trees Much of Chapters 8 and 14 DS/Discrete Probability Sections1. PL/ObJect-Oriented Programming Sectio. of the book, yet especially Chapter 2 and PL/Functional Programming Section 1.10 SDF/Algorithms and Design Sections 2.1, 3.3, &12.2.1 SDF/Fundamental Programming Chapters 1 &4 Concepts SDF/Fundamental Data Structures Chapters 6& 7, appendix a, and Sections 1. 2.1 5.2,5.4,9.1,&10.1 SDF/Developmental methods Sections 1.7& 2.2 SE/Software Design Sections 2.1 & 2.1.3 Mapping IEEE/ACM 2013 Computing Curriculum knowledge units to coverage in this book

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