Data Structures and Algorithms in Python

748 Pages·2013·5.62 MB·english

by  Michael T. Goodrich

Data Structures and
Algorithms in Python
Michael T. Goodrich
DepartmentofComputerScience
UniversityofCalifornia, Irvine
Roberto Tamassia
DepartmentofComputerScience
BrownUniversity
Michael H. Goldwasser
DepartmentofMathematicsand ComputerScience
Saint LouisUniversity
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To Karen, Paul,Anna, and Jack
– MichaelT.Goodrich
To Isabel
– RobertoTamassia
To Susan, Calista,and Maya
– MichaelH.Goldwasser

Preface
The design and analysis of efficient data structures has longbeen recognized as a
vital subject in computing and is part of the core curriculum of computer science
andcomputerengineering undergraduate degrees. DataStructures andAlgorithms
inPythonprovidesanintroductiontodatastructuresandalgorithms,includingtheir
design,analysis,andimplementation. Thisbookisdesignedforuseinabeginning-
level data structures course, or in an intermediate-level introduction to algorithms
course. Wediscussitsuseforsuchcoursesinmoredetaillaterinthispreface.
Topromote the development of robust and reusable software,wehave tried to
take a consistent object-oriented viewpoint throughout this text. One of the main
ideas ofthe object-oriented approach isthat data should bepresented as being en-
capsulated 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,andexploretherelativeprosandconsofthesechoices. Weprovidecomplete
Python implementations for almost all data structures and algorithms discussed,
and we introduce important object-oriented design patterns as means to organize
thoseimplementations intoreusablecomponents.
Desiredoutcomesforreaders ofourbookincludethat:
• They have knowledge of the most common abstractions for datacollections
(e.g.,stacks, queues, lists,trees,maps).
• Theyunderstandalgorithmicstrategiesforproducingefficientrealizationsof
commondatastructures.
• They can analyze algorithmic performance, both theoretically and experi-
mentally, andrecognize commontrade-offs betweencompeting strategies.
• Theycanwiselyuseexistingdatastructuresandalgorithmsfoundinmodern
programming language libraries.
• Theyhaveexperienceworkingwithconcreteimplementationsformostfoun-
dational datastructures andalgorithms.
• Theycanapplydatastructures andalgorithms tosolvecomplexproblems.
Insupportofthelastgoal,wepresentmanyexampleapplicationsofdatastructures
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
searchengineindexing.
v
vi 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++
byGoodrich, Tamassia,andMount. However,thisbookisnotsimplyatranslation
of those other books to Python. In adapting the material for this book, we have
significantly redesigned theorganization andcontentofthebookasfollows:
• Thecodebasehasbeenentirelyredesigned totakeadvantageofthefeatures
ofPython,suchasuseofgenerators foriterating elementsofacollection.
• Many algorithms that were presented as pseudo-code in the Java and C++
versions aredirectly presented ascompletePythoncode.
• Ingeneral,ADTsaredefinedtohaveconsistentinterfacewithPython’sbuilt-
indatatypesandthoseinPython’scollectionsmodule.
• Chapter 5 provides an in-depth exploration of the dynamic array-based un-
derpinnings ofPython’sbuilt-inlist,tuple,andstrclasses. NewAppendixA
servesasanadditional reference regardingthefunctionality ofthestrclass.
• Over450illustrations havebeencreatedorrevised.
• Newandrevisedexercises bringtheoveralltotalnumberto750.
Online Resources
This book is accompanied by an extensive set of online resources, which can be
foundatthefollowingWebsite:
www.wiley.com/college/goodrich
Studentsareencouraged tousethissitealongwiththebook,tohelpwithexer-
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,forbothstudentsandinstructors. Becauseoftheiraddedvalue,someofthese
onlineresources arepasswordprotected.
Forallreaders,andespeciallyforstudents,weincludethefollowingresources:
• AllthePythonsourcecodepresented inthisbook.
• PDFhandouts ofPowerpointslides(four-per-page) provided toinstructors.
• Adatabaseofhintstoallexercises,indexedbyproblem number.
Forinstructors usingthisbook,weincludethefollowingadditional teachingaids:
• Solutions tohundreds ofthebook’sexercises.
• Colorversionsofallfiguresandillustrations fromthebook.
• SlidesinPowerpointandPDF(one-per-page) format.
The slides are fully editable, so as to allow an instructor using this book full free-
domincustomizing hisorherpresentations. Alltheonline resources areprovided
atnoextrachargetoanyinstructor adopting thisbookforhisorhercourse.
Preface vii
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 likealgorithm analysis andrecursion. Inthemainportion
of the book, we present fundamental data structures and algorithms, concluding
withadiscussion ofmemorymanagement (that is,thearchitectural underpinnings
ofdatastructures). Specifically,thechaptersforthisbookareorganizedasfollows:
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
Amoredetailedtableofcontents followsthispreface, beginning onpagexi.
Prerequisites
We assume that the reader is at least vaguely familiar with a high-level program-
minglanguage,suchasC,C++,Python,orJava,andthatheorsheunderstands the
mainconstructs fromsuchahigh-level language, including:
• Variablesandexpressions.
• Decisionstructures (suchasif-statements andswitch-statements).
• Iteration structures (forloopsandwhileloops).
• Functions (whetherstand-alone orobject-oriented methods).
For readers who are familiar with these concepts, but not with how they are ex-
pressedinPython,weprovideaprimeronthePythonlanguageinChapter1. Still,
thisbookisprimarily adatastructures book, notaPythonbook;hence, itdoesnot
giveacomprehensive treatmentofPython.
viii Preface
Wedelaytreatment ofobject-oriented programming inPythonuntilChapter2.
This chapter is useful for those new toPython, and forthose who maybe familiar
withPython,yetnotwithobject-oriented programming.
Intermsofmathematicalbackground,weassumethereaderissomewhatfamil-
iar with topics from high-school mathematics. Even so, in Chapter 3, we discuss
thesevenmost-importantfunctionsforalgorithmanalysis. Infact,sectionsthatuse
something otherthanoneofthesesevenfunctions areconsidered optional, andare
(cid:2)
indicated with a star ( ). We give a summary of other useful mathematical facts,
including elementary probability, inAppendixB.
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
thatarecoveredwithinthisbook.
KnowledgeUnit RelevantMaterial
AL/BasicAnalysis Chapter3andSections4.2&12.2.4
AL/AlgorithmicStrategies Sections12.2.1,13.2.1,13.3,&13.4.2
AL/Fundamental Data Structures Sections4.1.3, 5.5.2,9.4.1,9.3, 10.2,11.1, 13.2,
andAlgorithms Chapter12&muchofChapter14
Sections 5.3, 10.4, 11.2 through 11.6, 12.3.1,
AL/AdvancedDataStructures
13.5,14.5.1,&15.3
AR/MemorySystemOrganization Chapter15
andArchitecture
DS/Sets,RelationsandFunctions Sections10.5.1,10.5.2,&9.4
DS/ProofTechniques Sections3.4,4.2,5.3.2,9.3.6,&12.4.1
DS/BasicsofCounting Sections2.4.2,6.2.2,12.2.4,8.2.2&AppendixB
DS/GraphsandTrees MuchofChapters8and14
DS/DiscreteProbability Sections1.11.1,10.2,10.4.2,&12.3.1
Much of the book, yet especially Chapter 2 and
PL/Object-OrientedProgramming
Sections7.4,9.5.1,10.1.3,&11.2.1
PL/FunctionalProgramming Section1.10
SDF/AlgorithmsandDesign Sections2.1,3.3,&12.2.1
SDF/FundamentalProgramming Chapters1&4
Concepts
Chapters6&7,AppendixA,andSections1.2.1,
SDF/FundamentalDataStructures
5.2,5.4,9.1,&10.1
SDF/DevelopmentalMethods Sections1.7&2.2
SE/SoftwareDesign Sections2.1&2.1.3
Mapping IEEE/ACM2013Computing Curriculumknowledge units tocoveragein
thisbook.