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Four score and seven years ago our fathers brought forth on this continent, a new nation, conceived in Liberty, and dedicated to the proposition that all men are created equal.
Now we are engaged in a great civil war, testing whether that nation, or any nation so conceived and so dedicated, can long endure. We are met on a great battle-field of that war. We have come to dedicate a portion of that field, as a final resting place for those who here gave their lives that that nation might live. It is altogether fitting and proper that we should do this.
But, in a larger sense, we can not dedicate — we can not consecrate — we can not hallow — this ground. The brave men, living and dead, who struggled here, have consecrated it, far above our poor power to add or detract. The world will little note, nor long remember what we say here, but it can never forget what they did here. It is for us the living, rather, to be dedicated here to the unfinished work which they who fought here have thus far so nobly advanced. It is rather for us to be here dedicated to the great task remaining before us — that from these honored dead we take increased devotion to that cause for which they gave the last full measure of devotion — that we here highly resolve that these dead shall not have died in vain — that this nation, under God, shall have a new birth of freedom — and that government of the people, by the people, for the people, shall not perish from the earth.
Abraham Lincoln
November 19, 1863Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203
second Edition
Fundamentals of Python:
Data STRUCTURES
Kenneth A. Lambert
Australia • Brazil • Mexico • Singapore • United Kingdom • United States
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Fundamentals of Python:
Data ­Structures, Second Edition
Kenneth A. Lambert
SVP, GM Skills & Global Product
Management: Jonathan Lau
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Table of Contents
iii
Pref ace �������������������������������������������������� xi
CHAPTER 1 B as ic
Pyt h o n Programmi ng ��������������������������� 1
Basic Program Elements ��������������������������������������������������� 2
Programs and Modules ��������������������������������������������������� 2
An Example Python Program: Guessing a Number ��������������� 2
Editing, Compiling, and Running Python Programs ��������������� 3
Program Comments ������������������������������������������������������� 4
Lexical Elements ������������������������������������������������������������ 4
Spelling and Naming Conventions ������������������������������������ 4
Syntactic Elements ��������������������������������������������������������� 5
Literals ������������������������������������������������������������������������ 5
Operators and Expressions ��������������������������������������������� 6
Function Calls ��������������������������������������������������������������� 7
The print Function ������������������������������������������������������� 7
The input Function ��������������������������������������������������������� 7
Type Conversion Functions and Mixed-Mode Operations ������ 7
Optional and Keyword Function Arguments ������������������������ 7
Variables and Assignment Statements ������������������������������ 8
Python Data Typing ��������������������������������������������������������� 9
import Statements ������������������������������������������������������� 9
Getting Help on Program Components ������������������������������ 9
Control Statements ���������������������������������������������������������10
Conditional Statements ��������������������������������������������������10
Using if __name__ == “__main__” �����������������������������11
Loop Statements �����������������������������������������������������������12
Strings and Their Operations ��������������������������������������������12
Operators ��������������������������������������������������������������������13
Formatting Strings for Output �����������������������������������������14
Objects and Method Calls �����������������������������������������������15
Built-In Python Collections and Their Operations ��������������������16
Lists ��������������������������������������������������������������������������16
Tuples ������������������������������������������������������������������������17
Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203
Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
contents

Loops Over Sequences ��������������������������������������������������17
Dictionaries �����������������������������������������������������������������18
Searching for a Value �����������������������������������������������������18
Pattern Matching with Collections �����������������������������������18
Creating New Functions ���������������������������������������������������19
Function Definitions ������������������������������������������������������19
Recursive Functions ������������������������������������������������������20
Nested Function Definitions ��������������������������������������������22
Higher-Order Functions ��������������������������������������������������23
Creating Anonymous Functions with lambda ���������������������24
Catching Exceptions ��������������������������������������������������������24
Files and Their Operations ������������������������������������������������25
Text File Output ������������������������������������������������������������26
Writing Numbers to a Text File ���������������������������������������26
Reading Text from a Text File �����������������������������������������27
Reading Numbers from a File �����������������������������������������28
Reading and Writing Objects with pickle ������������������������29
Creating New Classes ������������������������������������������������������30
iv
CHAPTER 2
An Over view o f Col l ecti ons ������������������������� 37
Collection Types ��������������������������������������������������������������38
Linear Collections ��������������������������������������������������������38
Hierarchical Collections ��������������������������������������������������39
Graph Collections ���������������������������������������������������������39
Unordered Collections ���������������������������������������������������40
Sorted Collections ��������������������������������������������������������40
A Taxonomy of Collection Types ��������������������������������������40
Operations on Collections ��������������������������������������������������41
Fundamental Operations on All Collection Types �����������������41
Type Conversion �����������������������������������������������������������43
Cloning and Equality �����������������������������������������������������43
Iterators and Higher-Order Functions �����������������������������������44
Implementations of Collections ������������������������������������������44
CHAPTER 3
Search in g , ­S o r ti ng, and Compl ex i ty Anal y si s ��� 49
Measuring the Efficiency of Algorithms ��������������������������������50
Measuring the Run Time of an Algorithm ��������������������������50
Counting Instructions �����������������������������������������������������53
Measuring the Memory Used by an Algorithm ��������������������55
Complexity Analysis ���������������������������������������������������������55
Orders of Complexity �����������������������������������������������������56
Big-O Notation ��������������������������������������������������������������57
The Role of the Constant of Proportionality �����������������������58
Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203
Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s).
Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.

contents
Search Algorithms �����������������������������������������������������������59
Search for the Minimum ������������������������������������������������59
Sequential Search of a List ��������������������������������������������60
Best-Case, Worst-Case, and Average-Case Performance �����60
Binary Search of a Sorted List ���������������������������������������61
Comparing Data Items ��������������������������������������������������62
Basic Sort Algorithms ������������������������������������������������������64
Selection Sort ��������������������������������������������������������������64
Bubble Sort �����������������������������������������������������������������65
Insertion Sort ��������������������������������������������������������������67
Best-Case, Worst-Case, and Average-Case Performance
Revisited ��������������������������������������������������������������������68
Faster Sorting ��������������������������������������������������������������69
Overview of Quicksort ���������������������������������������������������70
Merge Sort ������������������������������������������������������������������74
An Exponential Algorithm: Recursive Fibonacci ��������������������77
Converting Fibonacci to a Linear Algorithm �����������������������78
CHAPTER 4
v
Ar r ays an d Linked Structures ���������������������� 89
The Array Data Structure ��������������������������������������������������90
Random Access and Contiguous Memory ��������������������������92
Static Memory and Dynamic Memory ��������������������������������93
Physical Size and Logical Size ���������������������������������������94
Operations on Arrays ��������������������������������������������������������94
Increasing the Size of an Array ���������������������������������������95
Decreasing the Size of an Array ��������������������������������������95
Inserting an Item into an Array That Grows �����������������������96
Removing an Item from an Array ��������������������������������������97
Complexity Trade-Off: Time, Space, and Arrays �����������������98
Two-Dimensional Arrays (Grids) �����������������������������������������99
Processing a Grid ������������������������������������������������������� 100
Creating and Initializing a Grid ��������������������������������������� 100
Defining a Grid Class ��������������������������������������������������� 101
Ragged Grids and Multidimensional Arrays ��������������������� 101
Linked Structures ���������������������������������������������������������� 102
Singly Linked Structures and Doubly Linked Structures ���� 103
Noncontiguous Memory and Nodes ������������������������������� 104
Defining a Singly Linked Node Class ������������������������������ 106
Using the Singly Linked Node Class ������������������������������ 106
Operations on Singly Linked Structures ���������������������������� 108
Traversal ������������������������������������������������������������������ 108
Searching ������������������������������������������������������������������ 109
Replacement ������������������������������������������������������������� 110
Inserting at the Beginning ��������������������������������������������� 111
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contents

Inserting at the End ���������������������������������������������������� 111
Removing at the Beginning ������������������������������������������ 112
Removing at the End ��������������������������������������������������� 113
Inserting at Any Position ���������������������������������������������� 114
Removing at Any Position ��������������������������������������������� 116
Complexity Trade-Off: Time, Space, and Singly Linked
Structures ��������������������������������������������������������������� 116
Variations on a Link ������������������������������������������������������� 118
A Circular Linked Structure with a Dummy Header Node ��� 118
Doubly Linked Structures ��������������������������������������������� 119
vi
CHAPTER 5 In t er f aces ,
­I m pl ementati ons,
an d Po lym o r phi sm ���������������������������������� 126
Developing an Interface ������������������������������������������������� 127
Designing the Bag Interface ������������������������������������������ 127
Specifying Arguments and Return Values ������������������������ 129
Constructors and Implementing Classes ��������������������������� 130
Preconditions, Postconditions, Exceptions,
and Documentation ��������������������������������������������������� 131
Coding an Interface in Python ��������������������������������������� 132
Developing an Array-Based Implementation ������������������������ 134
Choose and Initialize the Data Structures ������������������������ 134
Complete the Easy Methods First ��������������������������������� 135
Complete the Iterator ������������������������������������������������� 136
Complete the Methods That Use the Iterator ������������������ 137
The in Operator and the __contains__ Method ������������ 137
Complete the remove Method ��������������������������������������� 138
Developing a Link-Based Implementation ��������������������������� 139
Initialize the Data Structures ���������������������������������������� 139
Complete the Iterator ������������������������������������������������� 140
Complete the Methods clear and add ��������������������������� 140
Complete the Method remove ��������������������������������������� 141
Run-Time Performance of the Two Bag Implementations ������ 142
Testing the Two Bag Implementations ������������������������������� 142
Diagramming the Bag Resource with UML ������������������������� 144
CHAPTER 6
In h er it an ce an d Abstract Cl asses ��������������� 148
Using Inheritance to Customize an Existing Class ��������������� 149
Subclassing an Existing Class ��������������������������������������� 150
Revising the __init__ Method ������������������������������������ 150
Adding a New __contains__ Method ��������������������������� 152
Modifying the Existing add Method ������������������������������� 152
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contents
Modifying the Existing __add__ Method ������������������������ 153
Run-Time Performance of ArraySortedBag ������������������ 153
A Note on Class Hierarchies in Python ��������������������������� 154
Using Abstract Classes to Eliminate Redundant Code ��������� 155
Designing an AbstractBag Class ��������������������������������� 155
Redoing the __init__ Method in AbstractBag ������������ 157
Modifying the Subclasses of AbstractBag ��������������������� 157
Generalizing the __add__ Method in AbstractBag ��������� 158
An Abstract Class for All Collections ��������������������������������� 159
Integrating AbstractCollection into the Collection
Hierarchy ���������������������������������������������������������������� 159
Using Two Iterators in the __eq__ Method ��������������������� 161
A Professional-Quality Framework of Collections ���������������� 162
CHAPTER 7
vii
St ack s ������������������������������������������������� 167
Overview of Stacks ��������������������������������������������������������� 168
Using a Stack ��������������������������������������������������������������� 169
The Stack Interface ���������������������������������������������������� 169
Instantiating a Stack ��������������������������������������������������� 170
Example Application: Matching Parentheses ������������������� 171
Three Applications of Stacks ������������������������������������������� 174
Evaluating Arithmetic Expressions ��������������������������������� 174
Evaluating Postfix Expressions ������������������������������������� 175
Converting Infix to Postfix ������������������������������������������� 176
Backtracking ������������������������������������������������������������� 179
Memory Management ��������������������������������������������������� 181
Implementations of Stacks ��������������������������������������������� 184
Test Driver ���������������������������������������������������������������� 184
Adding Stacks to the Collection Hierarchy ���������������������� 185
Array Implementation ��������������������������������������������������� 186
Linked Implementation ������������������������������������������������ 187
The Role of the Abstract Stack Class ���������������������������� 190
Time and Space Analysis of the Two Implementations ������ 191
C HAPTER 8
Qu eu es ������������������������������������������������� 205
Overview of Queues ������������������������������������������������������� 206
The Queue Interface and Its Use ��������������������������������������� 207
Two Applications of Queues ��������������������������������������������� 210
Simulations ��������������������������������������������������������������� 210
Round-Robin CPU Scheduling ��������������������������������������� 212
Implementations of Queues ��������������������������������������������� 213
A Linked Implementation of Queues ������������������������������� 213
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contents

An Array Implementation ��������������������������������������������� 215
Time and Space Analysis for the Two Implementations ������ 217
Priority Queues ������������������������������������������������������������� 226
CHAPTER 9
viii
Lis t s ���������������������������������������������������� 239
Overview of Lists ���������������������������������������������������������� 240
Using Lists ������������������������������������������������������������������� 240
Index-Based Operations ������������������������������������������������ 241
Content-Based Operations ������������������������������������������� 242
Position-Based Operations ������������������������������������������� 242
Interfaces for Lists ������������������������������������������������������ 247
Applications of Lists ������������������������������������������������������ 249
Heap-Storage Management ������������������������������������������ 249
Organization of Files on a Disk ������������������������������������� 250
Implementation of Other Collections ������������������������������ 252
List Implementations ������������������������������������������������������ 252
The Role of the AbstractList Class ��������������������������� 252
An Array-Based Implementation ������������������������������������ 254
A Linked Implementation ��������������������������������������������� 255
Time and Space Analysis for the Two Implementations ������ 258
Implementing a List Iterator ��������������������������������������������� 260
Role and Responsibilities of a List Iterator ��������������������� 260
Setting Up and Instantiating a List Iterator Class ������������� 261
The Navigational Methods in the List Iterator ������������������ 262
The Mutator Methods in the List Iterator ������������������������ 263
Design of a List Iterator for a Linked List ������������������������ 264
Time and Space Analysis of List Iterator
Implementations ������������������������������������������������������� 265
Recursive List Processing ���������������������������������������������� 270
Basic Operations on a Lisp-Like List ������������������������������ 271
Recursive Traversals of a Lisp-Like List ������������������������� 272
Building a Lisp-Like List ������������������������������������������������ 273
The Internal Structure of a Lisp-Like List ������������������������ 275
Printing Lisp-Like Lists in IDLE with __repr__ ���������������� 276
Lists and Functional Programming ��������������������������������� 277
CHAPTER 10 Trees ���������������������������������������������������� 282
An Overview of Trees ������������������������������������������������������ 283
Tree Terminology �������������������������������������������������������� 283
General Trees and Binary Trees ������������������������������������ 284
Recursive Definitions of Trees ��������������������������������������� 285
Why Use a Tree? ������������������������������������������������������������ 286
The Shape of Binary Trees ���������������������������������������������� 288
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contents
Binary Tree Traversals ��������������������������������������������������� 291
Preorder Traversal ������������������������������������������������������ 291
Inorder Traversal ��������������������������������������������������������� 291
Postorder Traversal ���������������������������������������������������� 292
Level Order Traversal ��������������������������������������������������� 292
Three Common Applications of Binary Trees ���������������������� 293
Heaps ����������������������������������������������������������������������� 293
Binary Search Trees ��������������������������������������������������� 293
Expression Trees ��������������������������������������������������������� 295
Developing a Binary Search Tree ������������������������������������� 297
The Binary Search Tree Interface ���������������������������������� 297
Data Structure for the Linked Implementation ������������������ 299
Complexity Analysis of Binary Search Trees ������������������� 304
Recursive Descent Parsing and Programming
Languages ������������������������������������������������������������������ 304
Introduction to Grammars ��������������������������������������������� 305
Recognizing, Parsing, and Interpreting Sentences
in a Language ���������������������������������������������������������� 306
Lexical Analysis and the Scanner ���������������������������������� 307
Parsing Strategies ������������������������������������������������������ 307
An Array Implementation of Binary Trees ��������������������������� 313
Implementing Heaps ������������������������������������������������������ 315
CHAPTER 11
ix
Set s an d Dict ionari es ������������������������������� 322
Using Sets ������������������������������������������������������������������� 323
The Python Set Class ������������������������������������������������������ 324
A Sample Session with Sets ������������������������������������������ 325
Applications of Sets ��������������������������������������������������� 325
Relationship Between Sets and Bags ������������������������������ 325
Relationship Between Sets and Dictionaries ������������������� 326
Implementations of Sets ���������������������������������������������� 326
Array-Based and Linked Implementations of Sets ��������������� 326
The AbstractSet Class ��������������������������������������������� 327
The ArraySet Class ��������������������������������������������������� 328
Using Dictionaries ��������������������������������������������������������� 329
Array-Based and Linked Implementations of Dictionaries ������ 330
The Entry Class ��������������������������������������������������������� 330
The AbstractDict Class ������������������������������������������� 331
The ArrayDict Class ������������������������������������������������� 333
Complexity Analysis of the Array-Based and Linked
Implementations of Sets and Dictionaries ��������������������� 334
Hashing Strategies ��������������������������������������������������������� 335
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contents

The Relationship of Collisions to Density ������������������������ 336
Hashing with Nonnumeric Keys ������������������������������������� 337
Linear Probing ������������������������������������������������������������ 339
Quadratic Probing ������������������������������������������������������ 340
Chaining ������������������������������������������������������������������� 341
Complexity Analysis ���������������������������������������������������� 342
Hashing Implementation of Sets ��������������������������������������� 349
Hashing Implementation of Dictionaries ���������������������������� 352
Sorted Sets and Dictionaries ������������������������������������������ 354
x
CHAPTER 12
Gr aph s ������������������������������������������������� 359
Why Use Graphs? ���������������������������������������������������������� 360
Graph Terminology ��������������������������������������������������������� 360
Representations of Graphs ��������������������������������������������� 364
Adjacency Matrix ��������������������������������������������������������� 365
Adjacency List ������������������������������������������������������������ 366
Analysis of the Two Representations ������������������������������ 367
Further Run-Time Considerations ���������������������������������� 368
Graph Traversals ������������������������������������������������������������ 369
A Generic Traversal Algorithm ��������������������������������������� 369
Breadth-First and Depth-First Traversals ������������������������� 370
Graph Components ������������������������������������������������������ 372
Trees Within Graphs ������������������������������������������������������� 373
Spanning Trees and Forests ������������������������������������������ 373
Minimum Spanning Tree ����������������������������������������������� 373
Algorithms for Minimum Spanning Trees ������������������������ 373
Topological Sort ������������������������������������������������������������ 376
The Shortest-Path Problem ��������������������������������������������� 377
Dijkstra’s Algorithm ���������������������������������������������������� 377
The Initialization Step ������������������������������������������������� 377
The Computation Step ������������������������������������������������ 379
Representing and Working with Infinity ��������������������������� 380
Analysis ��������������������������������������������������������������������� 380
Floyd’s Algorithm ������������������������������������������������������� 380
Analysis ��������������������������������������������������������������������� 382
Developing a Graph Collection ���������������������������������������� 382
Example Use of the Graph Collection ����������������������������� 383
The Class LinkedDirectedGraph ������������������������������� 384
The Class LinkedVertex ������������������������������������������� 388
The Class LinkedEdge ������������������������������������������������ 390
Glo s s ar y �����������������������������������������������������401
In dex ���������������������������������������������������� 410
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Preface
xi
Welcome to Fundamentals of Python: Data Structures, 2nd Edition. This text is intended
for a second semester course in programming and problem solving with data structures. It
covers the material taught in a typical Computer Science 2 course (CS2) at the undergraduate level. Although this book uses the Python programming language, you need only have a
basic knowledge of programming in a high-level programming language before beginning
Chapter 1.
What You’ll Learn
The book covers four major aspects of computing:
1.
Programming basics—Data types, control structures, algorithm development,
and program design with functions are basic ideas that you need to master to solve
problems with computers. You’ll review these core topics in the Python programming language and employ your understanding of them to solve a wide range of
problems.
2.
Object-Oriented Programming (OOP)—Object-Oriented Programming is the
dominant programming paradigm used to develop large software systems. You’ll
be introduced to the fundamental principles of OOP so that you can apply them
­successfully. Unlike other textbooks, this book helps you develop a professionalquality framework of collection classes to illustrate these principles.
3.
Data structures—Most useful programs rely on data structures to solve problems. At the most concrete level, data structures include arrays and various types
of linked structures. You’ll use these data structures to implement various types of
collection structures, such as stacks, queues, lists, trees, bags, sets, dictionaries, and
graphs. You’ll also learn to use complexity analysis to evaluate the space/time tradeoffs of different implementations of these collections.
4.
Software development life cycle—Rather than isolate software development techniques in one or two chapters, this book deals with them throughout in the context
of numerous case studies. Among other things, you’ll learn that coding a program
is often not the most difficult or challenging aspect of problem solving and software
development.
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P r e fa c e
Why Python?
Why Python?
xii
Computer technology and applications have become increasingly more sophisticated over
the past three decades, and so has the computer science curriculum, especially at the introductory level. Today’s students learn a bit of programming and problem solving and are
then expected to move quickly into topics like software development, complexity analysis,
and data structures that, 30 years ago, were relegated to advanced courses. In addition,
the ascent of object-oriented programming as the dominant paradigm has led instructors
and textbook authors to bring powerful, industrial-strength programming languages such
as C++ and Java into the introductory curriculum. As a result, instead of experiencing the
rewards and excitement of solving problems with computers, beginning computer science
students often become overwhelmed by the combined tasks of mastering advanced concepts as well as the syntax of a programming language.
This book uses the Python programming language as a way of making the second course
in computer science more manageable and attractive for students and instructors alike.
Python has the following pedagogical benefits:
•• Python has simple, conventional syntax. Python statements are very close to those of
pseudocode algorithms, and Python expressions use the conventional notation found
in algebra. Thus, you can spend less time dealing with the syntax of a programming
­language and more time learning to solve interesting problems.
•• Python has safe semantics. Any expression or statement whose meaning violates the
definition of the language produces an error message.
•• Python scales well. It is easy for beginners to write simple programs in Python. Python
also includes all the advanced features of a modern programming language, such as
­support for data structures and object-oriented software development, for use when
they become necessary, especially in the second course in computer science
•• Python is highly interactive. You can enter expressions and statements at an interpreter’s
prompts to try out experimental code and receive immediate feedback. You can also
compose longer code segments and save them in script files to be loaded and run as
modules or stand-alone applications.
•• Python is general purpose. In today’s context, this means that the language includes
resources for contemporary applications, including media computing and web
services.
•• Python is free and is in widespread use in the industry. You can download Python to run
on a variety of devices. There is a large Python user community, and expertise in Python
programming has great resume value.
To summarize these benefits, Python is a comfortable and flexible vehicle for ­expressing
ideas about computation, both for beginners and for experts. If you learn these ideas well
in the first year, you should have no problems making a quick transition to other languages needed for courses later in the curriculum. Most importantly, you will spend less
time staring at a computer screen and more time thinking about interesting problems
to solve.
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Organization of this Book
P r e fa c e
Organization of this Book
The approach in this book is easygoing, with each new concept introduced only when it is
needed.
Chapter 1 provides a review of the features of Python programming that are needed to begin
a second course in programming and problem solving in Python. The content of this chapter
is organized so that you can skim it quickly if you have experience in Python programming,
or you can dig a bit deeper to get up to speed in the language if you are new to Python.
xiii
Chapters 2 through 12 covers the major topics in a typical CS2 course, especially the specification, implementation, and application of abstract data types, with the collection types as the
primary vehicle and focus. Along the way, you will be thoroughly exposed to object-oriented
programming techniques and the elements of good software design. Other important CS2 topics
include recursive processing of data, search and sort algorithms, and the tools used in software
development, such as complexity analysis and graphical notations (UML) to document designs.
Chapter 2 introduces the concept of an abstract data type (ADT) and provides an overview
of various categories of collection ADTs.
Chapters 3 and 4 explore the data structures used to implement most collections and the
tools for analyzing their performance trade-offs. Chapter 3 introduces complexity analysis
with big-O notation. Enough material is presented to enable you to perform simple analyses
of the running time and memory usage of algorithms and data structures, using search and
sort algorithms as examples. Chapter 4 covers the details of processing arrays and linear
linked structures, the concrete data structures used to implement most collections. You’ll
learn the underlying models of computer memory that support arrays and linked structures
and the time/space trade-offs that they entail.
Chapters 5 and 6 shift the focus to the principles of object-oriented design. These principles
are used to organize a professional-quality framework of collection classes that will be covered in detail in later chapters.
Chapter 5 is concerned with the critical difference between interface and implementation.
A single interface and several implementations of a bag collection are developed as a first
example. Emphasis is placed on the inclusion of conventional methods in an interface, to
allow different types of collections to collaborate in applications. For example, one such
method creates an iterator, which allows you to traverse any collection with a simple loop.
Other topics covered in this chapter include polymorphism and information hiding, which
directly stem from the difference between interface and implementation.
Chapter 6 shows how class hierarchies can reduce the amount of redundant code in an objectoriented software system. The related concepts of inheritance, dynamic binding of method
calls, and abstract classes are introduced here and used throughout the remaining chapters.
Armed with these concepts and principles, you’ll then be ready to consider the other major
collection ADTs, which form the subject of Chapters 7 through 12.
Chapters 7 through 9 present the linear collections, stacks, queues, and lists. Each collection is viewed first from the perspective of its users, who are aware only of an interface and
a set of performance characteristics possessed by a chosen implementation. The use of each
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P r e fa c e
Special Features
collection is illustrated with one or more applications, and then several implementations
are developed, and their performance trade-offs are analyzed.
xiv
Chapters 10 through 12 present advanced data structures and algorithms as a transition to
later courses in computer science. Chapter 10 discusses various tree structures, including
binary search trees, heaps, and expression trees. Chapter 11 examines the ­implementation
of the unordered collections, bags, sets, and dictionaries, using hashing strategies.
­Chapter 12 introduces graphs and graph-processing algorithms.
As mentioned earlier, this book is unique in presenting a professional-quality framework of
collection types. Instead of encountering a series of apparently unrelated collections, you
will explore the place of each collection in an integrated whole. This approach allows you
to see what the collection types have in common as well as what makes each one unique.
At the same time, you will be exposed to a realistic use of inheritance and class hierarchies,
topics in object-oriented software design that are difficult to motivate and exemplify at this
level of the curriculum.
Special Features
This book explains and develops concepts carefully, using frequent examples and diagrams.
New concepts are then applied in complete programs to show how they aid in solving problems. The chapters place an early and consistent emphasis on good writing habits and neat,
readable documentation.
The book includes several other important features:
•• Case studies—These present complete Python programs ranging from the simple to the
substantial. To emphasize the importance and usefulness of the software development life
cycle, case studies are discussed in the framework of a user request, followed by analysis,
design, implementation, and suggestions for testing, with well-defined tasks performed at
each stage. Some case studies are extended in end-of-chapter programming projects.
•• Chapter summaries—Each chapter after the first one ends with a summary of the
major concepts covered in the chapter.
•• Key terms—When a new term is introduced in the text, it appears in bold face.
­Definitions of the key terms are also collected in a glossary.
•• Exercises—Most major sections of each chapter after the first one end with exercise
questions that reinforce the reading by asking basic questions about the material in the
section. After Chapter 2, each chapter ends with review questions.
•• Programming projects—Each chapter ends with a set of programming projects of
varying difficulty.
New in this Edition
The most obvious change in this edition is the addition of full color. All program examples
include the color coding used in Python’s IDLE, so students can easily identify program
elements such as keywords, comments, and function, method, and class names. Learning
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Instructor Resources
P r e fa c e
objectives have been added to the beginning of each chapter. Several new figures have been
added to illustrate concepts, and many programming projects have been added or reworked.
A new section on iterators and higher-order functions has been added to Chapter 2. Finally,
a new section on Lisp-like lists, recursive list processing, and functional programming has
been added to Chapter 9.
Instructor Resources
xv
MindTap
MindTap activities for Fundamentals of Python: Data Structures are designed to help students master the skills they need in today’s workforce. Research shows employers need
critical thinkers, troubleshooters, and creative problem-solvers to stay relevant in our
fast-paced, technology-driven world. MindTap helps you achieve this with assignments
and activities that provide hands-on practice and real-life relevance. Students are guided
through assignments that help them master basic knowledge and understanding before
moving on to more challenging problems.
­ ands-on
All MindTap activities and assignments are tied to defined unit learning objectives. H
coding labs provide real-life application and practice. Readings and dynamic visualizations
support the lecture, while a post-course assessment measures exactly how much a class
stands in terms of progress, engagement, and completion rates. Use the content and learning
path as-is, or pick and choose how our materials will wrap around yours. You control what
the students see and when they see it. Learn more at http://www.cengage.com/mindtap/.
Instructor Companion Site
The following teaching tools are available for download at the Companion Site for this text.
Go to instructor.cengage.com and sign in to the instructor account. Search for the textbook
and add the text to the instructor dashboard.
•• Instructor’s Manual: The Instructor’s Manual that accompanies this textbook includes
additional instructional material to assist in class preparation, including items such as
­Overviews, Chapter Objectives, Teaching Tips, Quick Quizzes, Class Discussion Topics,
Additional Projects, Additional Resources, and Key Terms. A sample syllabus is also available.
•• Test Bank: Cengage Testing Powered by Cognero is a flexible, online system that allows
you to:
•• author, edit, and manage test bank content from multiple Cengage solutions
•• create multiple test versions in an instant
•• deliver tests from your LMS, your classroom, or wherever you want
•• PowerPoint Presentations: This text provides PowerPoint slides to accompany each
chapter. Slides may be used to guide classroom presentations, to make available to students for chapter review, or to print as classroom handouts. Files are provided for every
figure in the text. Instructors may use the files to customize PowerPoint slides, illustrate
quizzes, or create handouts.
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P r e fa c e
Dedication
•• Solutions: Solutions to all programming exercises are available. If an input file is
needed to run a programming exercise, it is included with the solution file.
•• Source Code: The source code is available at www.cengage.com. If an input file is
needed to run a program, it is included with the source code.
xvi
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Acknowledgments
I would like to thank my friend, Martin Osborne, for many years of advice, friendly
­criticism, and encouragement on several of my book projects.
I would also like to thank my students in Computer Science 112 at Washington and Lee
University for classroom testing this book over several semesters.
Finally, I would like to thank Kristin McNary, Product Team Manager; Chris Shortt, Product
Manager; Maria Garguilo and Kate Mason, Learning Designers; Magesh Rajagopalan, Senior
Project Manager; Danielle Shaw, Tech Editor; and especially Michelle Ruelos Cannistraci,
Senior Content Manager, for ­handling all the details of producing this edition of the book.
About the Author
Kenneth A. Lambert is a professor of computer science and the chair of that department
at Washington and Lee University. He has taught introductory programming courses for
over 30 years and has been an active researcher in computer science education. Lambert
has authored or coauthored a total of 28 textbooks, including a series of introductory C++
­textbooks with Douglas Nance and Thomas Naps, a series of introductory Java textbooks
with Martin Osborne, and a series of introductory Python textbooks.
Dedication
To Brenda Wilson, with love and admiration.
Kenneth A.­Lambert
Lexington, VA
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Chapter
Basic Python
Programming
1
After completing this chapter, you will be able to:
Write a simple Python program using its basic structure
Perform simple input and output operations
Perform operations with numbers such as arithmetic and
comparisons
Perform operations with Boolean values
Implement an algorithm using the basic constructs of
sequences of statements, selection statements, and loops
Define functions to structure code
Use built-in data structures such as strings, files, lists,
tuples, and dictionaries
Define classes to represent new types of objects
Structure programs in terms of cooperating functions,
data structures, classes, and modules
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Chapter 1
2
Basic Python Programming
This chapter gives a quick overview of Python programming. It is intended to bring those
new to or rusty in Python up to speed, but it does not pretend to be a thorough introduction to computer science or the Python programming language. For a more detailed treatment of programming in Python, see my book Fundamentals of Python: First Programs,
Second Edition (Cengage Learning, 2019). For documentation on the Python programming
language, visit www.python.org.
If your computer already has Python, check the version number by running the python
or python3 command at a terminal prompt. (Linux and Mac users first open a terminal
­window, and Windows users first open a DOS window.) You are best off using the most
current version of Python available. Check for that at www.python.org, and download and
install the latest version if necessary. You will need Python 3.0 or higher to run the programs presented in this book.
Basic Program Elements
Like all contemporary programming languages, Python has a vast array of features and
constructs. However, Python is among the few languages whose basic program elements are quite simple. This section discusses the essentials to get you started in Python
programming.
Programs and Modules
A Python program consists of one or more modules. A module is just a file of Python code,
which can include statements, function definitions, and class definitions. A short Python
program, also called a script, can be contained in one module. Longer, more complex programs typically include one main module and one or more supporting modules. The main
module contains the starting point of program execution. Supporting modules contain
function and class definitions.
An Example Python Program: Guessing a Number
Next, you’ll see a complete Python program that plays a game of guess-the-number with
the user. The computer asks the user to enter the lower and upper bounds of a range of
numbers. The computer then “thinks” of a random number in that range and repeatedly
asks the user to guess this number until the user enters a correct guess. The computer gives
a hint to the user after each guess and displays the total number of guesses at the end of the
process. The program includes several of the types of Python statements to be discussed
later in this chapter, such as input statements, output statements, assignment statements,
loops, and conditional statements. The program also includes a single function definition.
Here is the code for the program, in the file numberguess.py:
“””
Author: Ken Lambert
Plays a game of guess the number with the user.
“””
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Basic Program Elements
import random
def main():
“””Inputs the bounds of the range of numbers
and lets the user guess the computer’s number until
the guess is correct.”””
smaller = int(input(“Enter the smaller number: “))
larger = int(input(“Enter the larger number: “))
myNumber = random.randint(smaller, larger)
count = 0
while True:
count += 1
userNumber = int(input(“Enter your guess: “))
if userNumber < myNumber: print("Too small") elif userNumber > myNumber:
print(“Too large”)
else:
print(“You’ve got it in”, count, “tries!”)
break
3
if __name__ == “__main__”:
main()
Here is a trace of a user’s interaction with the program:
Enter the smaller number: 1
Enter the larger number: 32
Enter your guess: 16
Too small
Enter your guess: 24
Too large
Enter your guess: 20
You’ve got it in 3 tries!
Note that the code and its trace appear in the colors black, blue, orange, and green. Python’s
IDLE uses color coding to help the reader recognize various types of program elements.
The role of each color will be explained shortly.
Editing, Compiling, and Running Python Programs
You can run complete Python programs, including most of the examples presented, by
entering a command in a terminal window. For example, to run the program contained in
the file numberguess.py, enter the following command in most terminal windows:
python3 numberguess.py
To create or edit a Python module, try using Python’s IDLE (short for Integrated
­DeveLopment Environment). To start IDLE, enter the idle or idle3 command at a terminal
prompt or launch its icon if it is available. You can also launch IDLE by double-clicking on
a Python source code file (any file with a .py extension) or by right-clicking on the file and
Copyright 2019 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. WCN 02-200-203
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Chapter 1
Basic Python Programming
selecting Open or Edit with IDLE. Make sure that your system is set to open IDLE when
files of this type are launched (this is the default on macOS but not on Windows).
4
IDLE gives you a shell window for interactively running Python expressions and statements.
Using IDLE, you can move back and forth between editor windows and the shell window to
develop and run complete programs. IDLE also formats your code and color-codes it.
When you open an existing Python file with IDLE, the file appears in an editor window, and
the shell pops up in a separate window. To run a program, move the cursor into the editor
window and press the F5 (function-5) key. Python compiles the code in the editor window
and runs it in the shell window.
If a Python program appears to hang or not quit normally, you can exit by pressing Ctrl+C
or closing the shell window.
Program Comments
A program comment is text ignored by the Python compiler but valuable to the reader as
documentation. An end-of-line comment in Python begins with a # symbol and extends to
the end of the current line. It is color-coded in red. For example:
# This is an end-of-line comment.
A multiline comment is a string enclosed in triple single quotes or triple double quotes.
Such comments, which are colored green, are also called docstrings, to indicate that they
can document major constructs within a program. The numberguess program shown
­earlier includes two doc strings. The first one, at the top of the program file, serves as a
comment for the entire numberguess module. The second one, just below the header of the
main function, describes what this function does. As we shall see shortly, docstrings play a
critical role in giving help to a programmer within the Python shell.
Lexical Elements
The lexical elements in a language are the types of words or symbols used to construct
­sentences. As in all high-level programming languages, some of Python’s basic symbols are
keywords, such as if, while, and def, which are colored orange. Also included among lexical
items are identifiers (names), literals (numbers, strings, and other built-in data structures),
operators, and delimiters (quotation marks, commas, parentheses, square brackets, and
braces). Among the identifiers are the names of built-in functions, which are colored purple.
Spelling and Naming Conventions
Python keywords and names are case-sensitive. Thus, while is a keyword, whereas While
is a programmer-defined name. Python keywords are spelled in lowercase letters and are
color-coded in orange in an IDLE window.
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Basic Program Elements
All Python names, other than those of built-in functions, are color-coded in black, except
when they are introduced as function, class, or method names, in which case they appear
in blue. A name can begin with a letter or an underscore (_), followed by any number of
­letters, underscores, or digits.
In this book, the names of modules, variables, functions, and methods are spelled in lowercase letters. With the exception of modules, when one of these names contains one or more
embedded words, the embedded words are capitalized. The names of classes follow the
same conventions but begin with a capital letter. When a variable names a constant, all the
letters are uppercase, and an underscore separates any embedded words. Table 1-1 shows
examples of these naming conventions.
Type of Name
Examples
Variable
salary, hoursWorked, isAbsent
Constant
ABSOLUTE_ZERO, INTEREST_RATE
Function or method
printResults, cubeRoot, input
Class
BankAccount, SortedSet
Table 1-1
5
Examples of Python Naming Conventions
Use names that describe their role in a program. In general, variable names should
be nouns or adjectives (if they denote Boolean values), whereas function and method
names should be verbs if they denote actions, or nouns or adjectives if they denote values
returned.
Syntactic Elements
The syntactic elements in a language are the types of sentences (expressions, statements,
definitions, and other constructs) composed from the lexical elements. Unlike most highlevel languages, Python uses white space (spaces, tabs, or line breaks) to mark the syntax
of many types of sentences. This means that indentation and line breaks are significant in
Python code. A smart editor like Python’s IDLE can help indent code correctly. The programmer need not worry about separating sentences with semicolons and marking blocks
of sentences with braces. In this book, I use an indentation width of four spaces in all
Python code.
Literals
Numbers (integers or floating-point numbers) are written as they are in other programming languages. The Boolean values True and False are keywords. Some data structures,
such as strings, tuples, lists, and dictionaries, also have literals, as you will see shortly.
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Chapter 1
Basic Python Programming
String Literals
6
You can enclose strings in single quotes, double quotes, or sets of three double quotes or
three single quotes. The last notation is useful for a string containing multiple lines of text.
Character values are single-character strings. The character is used to escape nongraphic
characters such as the newline (n) and the tab (t), or the character itself. The next code
segment, followed by the output, illustrates the possibilities.
print(“Using double quotes”)
print(‘Using single quotes’)
print(“Mentioning the word ‘Python’ by quoting it”)
print(“Embedding anline break with \n”)
print(“””Embedding a
line break with triple quotes”””)
Output:
Using double quotes
Using single quotes
Mentioning the word ‘Python’ by quoting it
Embedding a
line break with n
Embedding a
line break with triple quotes
Operators and Expressions
Arithmetic expressions use the standard operators (+, –, *, /, %) and infix notation. The
/ operator produces a floating-point result with any numeric operands, whereas the //
­operator produces an integer quotient. The + operator means concatenation when used
with collections, such as strings and lists. The ** operator is used for exponentiation.
The comparison operators =, ==, and != work with numbers and strings.
The == operator compares the internal contents of data structures, such as two lists, for
structural equivalence, whereas the is operator compares two values for object identity.
Comparisons return True or False.
The logical operators and, or, and not treat several values, such as 0, None, the empty string,
and the empty list, as False. In contrast, most other Python values count as True.
The subscript operator, [], used with collection objects, will be examined shortly.
The selector operator, ‘ ’, is used to refer to a named item in a module, class, or object.
•
The operators have the standard precedence (selector, function call, subscript, arithmetic,
comparison, logical, assignment). Parentheses are used in the usual manner, to group subexpressions for earlier evaluation.
The ** and = operators are right associative, whereas the others are left associative.
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Basic Program Elements
Function Calls
Functions are called in the usual manner, with the function’s name followed by a parenthesized list of arguments. For example:
min(5, 2)
# Returns 2
Python includes a few standard functions, such as abs and round. Many other functions are
available by import from modules, as you will see shortly.
7
The print Function
The standard output function print displays its arguments on the console. This function
allows a variable number of arguments. Python automatically runs the str function on each
argument to obtain its string representation and separates each string with a space before
output. By default, print terminates its output with a newline.
The input Function
The standard input function input waits for the user to enter text at the keyboard. When the
user presses the Enter key, the function returns a string containing the characters entered.
This function takes an optional string as an argument and prints this string, ­without a line
break, to prompt the user for the input.
Type Conversion Functions and Mixed-Mode Operations
You can use some data type names as type conversion functions. For example, when the
user enters a number at the keyboard, the input function returns a string of digits, not a
numeric value. The program must convert this string to an int or a float before numeric
processing. The next code segment inputs the radius of a circle, converts this string to a
float, and computes and outputs the circle’s area:
radius = float(input(“Radius: “))
print(“The area is”, 3.14 * radius ** 2)
Like most other languages, Python allows operands of different numeric types in arithmetic
expressions. In those cases, the result type is the same type as the most general operand
type. For example, the addition of an int and a float produces a float as the result.
Optional and Keyword Function Arguments
Functions may allow optional arguments, which can be named with keywords when the
function is called. For example, the print function by default outpu…
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