Ebook The practice of computing using python (2nd edition) Part 1

Boston Columbus Indianapolis New York San Francisco Upper Saddle River
Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto
Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo


Editorial Director, ECS: Marcia Horton
Editor-in-Chief: Michael Hirsch
Acquisitions Editor: Matt Goldstein
Editorial Assistant: Chelsea Kharakozova
Director of Marketing: Patrice Jones
Marketing Manager: Yezan Alayan
Marketing Coordinator: Kathryn Ferranti
Director of Production: Vince O’Brien

Managing Editor: Jeff Holcomb
Manufacturing Buyer: Lisa McDowell
Cover Designer: Anthony Gemmellaro
Media Editor: Daniel Sandin
Media Project Manager: John Cassar
Full-Service Project Management: Peggy Kellar,
Aptara® Corporation
Composition: Aptara® Corporation

Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook appear
on appropriate page within text. Reprinted with permission.
Unless otherwise noted, Screenshot by Microsoft. Copyright © 2011 by the Microsoft Corporation. Reprinted with
permission./Screenshot by Python. Copyright © 2001–2010 by Python Software Foundation. All Rights Reserved.
Reprinted with permission.
Cover Photo Credit: LanaN./Shutterstock.com, Stephen Aaron Rees/Shutterstock.com, Fotonic/Shutterstock.com,
Robert Adrian Hillman/Shutterstock.com, dmiskv/Shutterstock.com, Dan Ionut Popescu/Shutterstock.com,

AlexRoz/Shutterstock.com, Irin-K/Shutterstock.com, S.Borisov/Shutterstock.com, © UK History/Alamy
The programs and applications presented in this book have been included for their instructional value. They have been
tested with care but are not guaranteed for any particular purpose. The publisher does not offer any warranty or
representation, nor does it accept any liabilities with respect to the programs or applications.

Copyright © 2013, 2011 Pearson Education, Inc., publishing as Addison-Wesley. All rights reserved. Printed in the
United States of America. This publication is protected by Copyright, and permission should be obtained from the
publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any
means, electronic, mechanical, photocopying, recording, or likewise. To obtain permission(s) to use material from this
work, please submit a written request to Pearson Education, Inc., Permissions Department, One Lake Street, Upper
Saddle River, New Jersey 07458, or you may fax your request to 201-236-3290.
Many of the designations by manufacturers and sellers to distinguish their products are claimed as trademarks. Where
those designations appear in this book, and the publisher was aware of a trademark claim, the designations have been
printed in initial caps or all caps.
Library of Congress Cataloging-in-Publication Data available upon request.

10

9 8 7 6 5 4 3 2 1
ISBN 10: 0-13-280557-X
ISBN 13: 978-0-13-280557-5


To our long-suffering spouses, Laurie and Wendy;
our kids, Zach, Alex, Abby, Carina, and Erik;
and our parents.
We love you and couldn’t have done this
without your love and support.

This page intentionally left blank


•

B R I E F
C O N T E N T S

PREFACE

xxiii

PART 1 THINKING ABOUT COMPUTING

Chapter 0

The Study of Computer Science 3

PART 2 STARTING TO PROGRAM

Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5

1

35

Beginnings 37
Control 81
Algorithms and Program Development 153
Working with Strings 179
Files and Exceptions I 227

PART 3 FUNCTIONS AND DATA STRUCTURES

Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10

255

Functions—QuickStart 257
Lists and Tuples 283
More on Functions 357
Dictionaries and Sets 383
More Program Development 437

PART 4 CLASSES, MAKING YOUR OWN DATA STRUCTURES
AND ALGORITHMS 475

Chapter 11 Introduction to Classes 477
Chapter 12 More on Classes 517
Chapter 13 Program Development with Classes 561
PART 5 BEING A BETTER PROGRAMMER

589

Chapter 14 Files and Exceptions II 591
Chapter 15 Testing 631

v


vi

BRIEF

CONTENTS

Chapter 16 Recursion: Another Control Mechanism 649
Chapter 17 Other Fun Stuff with Python 667
Chapter 18 The End, or Perhaps the Beginning 695
APPENDICES

697

Appendix A
Appendix B
Appendix C
Appendix D
Appendix E
Appendix F
Appendix G
INDEX

749

Getting and Using Python 697
Simple Drawing with Turtle Graphics 709
Plotting and Numeric Tools: A Quick Survey 721
Table of UTF-8 One-Byte Encodings 735
Precedence 737
Naming Conventions 739
Check Yourself Solutions 743


•

C O N T E N T S

PREFACE

xxiii

PART 1 THINKING ABOUT COMPUTING

Chapter 0

1

The Study of Computer Science

3

0.1

Why Computer Science? 3
0.1.1 Importance of Computer Science 3
0.1.2 Computer Science Around You 4
0.1.3 Computer “Science” 4
0.1.4 Computer Science Through Computer Programming 6

0.2

The Difficulty and Promise of Programming 6
0.2.1 Difficulty 1: Two Things at Once 6
0.2.2 Difficulty 2: What Is a Good Program? 9
0.2.3 The Promise of a Computer Program 10

0.3

Choosing a Computer Language 11
0.3.1 Different Computer Languages 11
0.3.2 Why Python? 11
0.3.3 Is Python the Best Language? 13

0.4

What Is Computation?

0.5

What Is a Computer? 13
0.5.1 Computation in Nature 14
0.5.2 The Human Computer 17

0.6

The Modern, Electronic Computer
0.6.1 It’s the Switch! 18
0.6.2 The Transistor 19

0.7

A High-Level Look at a Modern Computer 24

0.8

Representing Data 26
0.8.1 Binary Data 26
0.8.2 Working with Binary 27

13

18

vii


viii

CONTENTS

0.8.3
0.8.4

0.8.5
0.8.6
0.8.7
0.8.8
0.9

Limits 28
Representing Letters 29
Representing Other Data 30
What Does a Number Represent? 31
How to Talk About Quantities of Data 32
Quantities of Data 32

Overview of Coming Chapters

PART 2 STARTING TO PROGRAM

Chapter 1

Beginnings

34

35

37

1.1

Practice, Practice, Practice 37

1.2

QuickStart, the Circumference Program 38
1.2.1 Examining the Code 40

1.3

An Interactive Session

1.4

Parts of a Program 43
1.4.1 Modules 43
1.4.2 Statements and Expressions 43
1.4.3 Whitespace 45
1.4.4 Comments 46
1.4.5 Special Python Elements: Tokens 46
1.4.6 Naming Objects 48
1.4.7 Recommendations on Naming 49

1.5

Variables 49
1.5.1 Variable Creation and Assignment

1.6

1.7

42

Objects and Types 53
1.6.1 Numbers 55
1.6.2 Other Built-in Types 57
1.6.3 Object Types: Not Variable Types
1.6.4 Constructing New Values 59

50

58

Operators 61
1.7.1 Integer Operators 61
1.7.2 Floating-Point Operators 63
1.7.3 Mixed Operations 63
1.7.4 Order of Operations and Parentheses 64
1.7.5 Augmented Assignment Operators: A Shortcut!

1.8

Your First Module, Math

67

1.9

Developing an Algorithm 68
1.9.1 New Rule, Testing 72

1.10 Visual Vignette: Turtle Graphics 73

65


CONTENTS

Chapter 2

Control 81
2.1

Chapter 3

The Selection Statement for Decisions: if 81
2.1.1 Booleans for Decisions 83
2.1.2 The if Statement 83
2.1.3 Example: What Lead Is Safe in Basketball?
2.1.4 Repetition 90
2.1.5 Example: Finding Perfect Numbers 94
2.1.6 Example: Classifying Numbers 99

2.2

In-Depth Control 103
2.2.1 True and False: Booleans 103
2.2.2 Boolean Variables 104
2.2.3 Relational Operators 104
2.2.4 Boolean Operators 109
2.2.5 Precedence 110

2.2.6 Boolean Operators Example 111
2.2.7 Another Word on Assignments 114
2.2.8 The Selection Statement for Decisions 116
2.2.9 More on Python Decision Statements 116
2.2.10 Repetition: The while Statement 120
2.2.11 Sentinel Loop 130
2.2.12 Summary of Repetition 130
2.2.13 More on the for Statement 131
2.2.14 Nesting 134
2.2.15 Hailstone Sequence Example 136

2.3

Visual Vignette: Plotting Data with Pylab 137
2.3.1 First Plot and Using a List 138
2.3.2 More Interesting Plot: A Sine Wave 140

2.4

Computer Science Perspectives 142
2.4.1 Minimal Universal Computing 142

Algorithms and Program Development

153

3.1

What Is an Algorithm? 153
3.1.1 Example Algorithms 154

3.2

Algorithm Features 155
3.2.1 Algorithm Versus Program 155
3.2.2 Qualities of an Algorithm 157
3.2.3 Can We Really Do All That? 159

3.3

What Is a Program? 159
3.3.1 Readability 159
3.3.2 Robustness 163
3.3.3 Correctness 164

86

ix


x

CONTENTS

Chapter 4

3.4

Strategies for Program Design 165
3.4.1 Engage and Commit 165

3.4.2 Understand, then Visualize 166
3.4.3 Think Before You Program 167
3.4.4 Experiment 167
3.4.5 Simplify 167
3.4.6 Stop and Think 169
3.4.7 Relax: Give Yourself a Break 169

3.5

A Simple Example 169
3.5.1 Build the Skeleton 170
3.5.2 Output 170
3.5.3 Input 171
3.5.4 Doing the Calculation 173

Working with Strings

179

4.1

The String Type 180
4.1.1 The Triple-Quote String 180
4.1.2 Non-Printing Characters 181
4.1.3 String Representation 181
4.1.4 Strings as a Sequence 182
4.1.5 More Indexing and Slicing 183
4.1.6 Strings Are Iterable 187

4.2

String Operations 189
4.2.1 Concatenation (+) and Repetition (*) 189
4.2.2 Determining when + Indicates Addition or
Concatenation 190
4.2.3 Comparison Operators 191
4.2.4 The in Operator 192
4.2.5 String Collections Are Immutable 193

4.3

A Preview of Functions and Methods 194
4.3.1 First Cut: What Is a Function? 194
4.3.2 A String Method 195
4.3.3 Determining Method Names and Method
Arguments 198
4.3.4 String Methods 200
4.3.5 String Functions 201

4.4

Formatted Output for Strings 201
4.4.1 Descriptor Codes 202
4.4.2 Width and Alignment Descriptors 203
4.4.3 Floating-Point Precision Descriptor 204

4.5

Control and Strings

205


CONTENTS

4.6

Chapter 5

Working with Strings 208
4.6.1 Example: Reordering a Person’s Name
4.6.2 Palindromes 210

4.7

More String Formatting

4.8

Computer Science Perspectives: Unicode

Files and Exceptions I

213

5.1

What Is a File?

5.2

Accessing Files: Reading Text Files 227
5.2.1 What’s Really Happening? 228

5.3

Accessing Files: Writing Text Files

5.4

Reading and Writing Text Files in a Program 230

5.5

File Creation and Overwriting

5.6

First Cut, Handling Errors 232
5.6.1 Error Names 233
5.6.2 The try-except Construct 233
5.6.3 try-except Flow of Control 234
5.6.4 Exception Example 235

5.7

Example: Counting Poker Hands 238
5.7.1 Program to Count Poker Hands 241

227

Functions—QuickStart
6.1

229

231

255

257

What Is a Function? 257
6.1.1 Why Have Functions?

258

6.2

Python Functions

6.3

Flow of Control with Functions 262
6.3.1 Function Flow in Detail 263
6.3.2 Parameter Passing 263
6.3.3 Another Function Example 265
6.3.4 Function Example: Word Puzzle 267
6.3.5 Functions Calling Functions 273
6.3.6 When to Use a Function 273

6.3.7 What if There Is No Return Statement? 274
6.3.8 What if There Are Multiple Return Statements?

6.4
Chapter 7

216

227

PART 3 FUNCTIONS AND DATA STRUCTURES

Chapter 6

208

Visual Vignette: Turtle Flag 276

Lists and Tuples
7.1

259

283

What Is a List?

283

275

xi


xii

CONTENTS

7.2

What You Already Know How to Do with Lists
7.2.1 Iteration 286
7.2.2 Indexing and Slicing 286
7.2.3 Operators 287
7.2.4 Functions 289
7.2.5 List Iteration 290

7.3

Lists Are Different than Strings 290
7.3.1 Lists Are Mutable 290
7.3.2 List Methods 292

7.4

Old and New Friends: Split and Other Functions and Methods
7.4.1 Split and Multiple Assignment 295
7.4.2 List to String and Back Again, Using join 296
7.4.3 The sorted Function 297

7.5

Working with Some Examples 299
7.5.1 Anagrams 299
7.5.2 Example: File Analysis 305

7.6

Mutable Objects and References 310
7.6.1 Shallow vs. Deep Copy 316
7.6.2 Mutable vs. Immutable 320

7.7

Tuples 321
7.7.1 Tuples from Lists 323
7.7.2 Why Tuples? 324

7.8

Lists: The Data Structure 324
7.8.1 Example Data Structure 325
7.8.2 Other Example Data Structures

7.9

285

326

Algorithm Example: U.S. EPA Automobile
Mileage Data 327

7.10 Python Diversion: List Comprehension 337
7.10.1 Comprehensions, Expressions, and the
Ternary Operator 339
7.11 Visual Vignette: More Plotting 340
7.11.1 NumPy Arrays 340
7.11.2 Plotting Trigonometric Functions
Chapter 8

More on Functions
8.1

342

357

Scope: A First Cut 357
8.1.1 Arguments, Parameters, and Namespaces
8.1.2 Passing Mutable Objects 361
8.1.3 Returning a Complex Object 363
8.1.4 Refactoring evens 365

359

295


CONTENTS

8.2
8.3

Chapter 9

Default Values and Parameters 365
8.2.1 Example: Default Values and Parameter Keywords
Functions as Objects 369
8.3.1 Function Annotations
8.3.2 Docstrings 371

Example: Determining a Final Grade 371
8.4.1 The Data 372
8.4.2 The Design 372
8.4.3 Function: weighted grade 372
8.4.4 Function: parse line 373
8.4.5 Function: main 374
8.4.6 Example Use 374

8.5

Esoterica: “by value" or “by reference" 375

9.1

383

Dictionaries 383
9.1.1 Dictionary Example 384

9.1.2 Python Dictionaries 385
9.1.3 Dictionary Indexing and Assignment
9.1.4 Operators 386

385

9.2

Word Count Example 391
9.2.1 Count Words in a String 392
9.2.2 Word Frequency for the Gettysburg Address 393
9.2.3 Output and Comments 396

9.3

Periodic Table Example 398
9.3.1 Working with CSV Files 398
9.3.2 Algorithm Overview 400
9.3.3 Functions for Divide and Conquer

9.4

Sets
9.4.1
9.4.2
9.4.3
9.4.4
9.4.5

366

370

8.4

Dictionaries and Sets

xiii

401

404
History 405
What’s in a Set? 405
Python Sets 405
Methods, Operators, and Functions for Python Sets
Set Methods 407

406

9.5

Set Applications 411
9.5.1 Relationship Between Words of Different Documents 412
9.5.2 Output and Comments 415

9.6

Scope: The Full Story 416
9.6.1 Namespaces and Scope 416

9.6.2 Search Rule for Scope 416


xiv

CONTENTS

9.7
9.8
9.9

9.6.3 Local 417
9.6.4 Global 418
9.6.5 Built-ins 422
9.6.6 Enclosed 422
Python Pointer: Using zip to Create Dictionaries 424
Python Diversion: Dictionary and Set Comprehension 424
Visual Vignette: Bar Graph of Word Frequency 425
9.9.1 Getting the Data Right 426
9.9.2 Labels and the xticks Command 426
9.9.3 Plotting 427

Chapter 10 More Program Development

437

10.1 Introduction 437
10.2 Divide and Conquer 437
10.2.1 Top-Down Refinement

438

10.3 The Breast Cancer Classifier 438
10.3.1 The Problem 438
10.3.2 The Approach: Classification 439
10.3.3 Training and Testing the Classifier 439
10.3.4 Building the Classifier 439
10.4 Designing the Classifier Algorithm 441
10.4.1 Divided, Now Conquer 444
10.4.2 Data Structures 445
10.4.3 File Format 445
10.4.4 The make training set Function 446
10.4.5 The make test set Function 450
10.4.6 The train classifier Function 451
10.4.7 train classifier, Round 2 453
10.4.8 Testing the Classifier on New Data 456
10.4.9 The report results Function 460
10.5 Running the Classifier on Full Data 462
10.5.1 Training Versus Testing 462
10.6 Other Interesting Problems 466
10.6.1 Tag Clouds 466
10.6.2 S&P 500 Predictions 467
10.6.3 Predicting Religion with Flags

470

PART 4 CLASSES, MAKING YOUR OWN DATA STRUCTURES
AND ALGORITHMS 475

Chapter 11 Introduction to Classes 477

11.0.5 Simple Student Class

477


CONTENTS

11.1 Object-Oriented Programming 478
11.1.1 Python Is Object Oriented! 478
11.1.2 Characteristics of OOP 479
11.2 Working with Object-Oriented Programming
11.2.1 Class and Instance 479

479

11.3 Working with Classes and Instances 481
11.3.1 Built-in Class and Instance 481
11.3.2 Our First Class 482
11.3.3 Changing Attributes 484
11.3.4 The Special Relationship Between an Instance and Class:
instance-of 485
11.4 Object Methods 488
11.4.1 Using Object Methods 488
11.4.2 Writing Methods 489
11.4.3 The Special Argument self 491
11.4.4 Methods Are the Interface to a Class Instance

492

11.5 Fitting into the Python Class Model 493

11.5.1 Making Programmer-Defined Classes 493
11.5.2 A Student Class 493
11.5.3 Python Standard Methods 494
11.5.4 Now There Are Three: Class Designer, Programmer,
and User 498
11.6 Example: Point Class 499
11.6.1 Construction 500
11.6.2 Distance 501
11.6.3 Summing Two Points 501
11.6.4 Improving the Point Class 502
11.7 Python and OOP 506
11.7.1 Encapsulation 506
11.7.2 Inheritance 506
11.7.3 Polymorphism 507
11.8 An Aside: Python and Other OOP Languages
11.8.1 Public Versus Private 507
11.8.2 Indicating Privacy Using Double
Underscores ( ) 508
11.8.3 Python’s Philosophy 509
11.8.4 Modifying an Instance 509

507

Chapter 12 More on Classes 517
12.1 More About Class Properties 517
12.1.1 Rational Number (Fraction) Class Example 518

xv

xvi

CONTENTS

12.2 How Does Python Know? 520
12.2.1 Classes, Types, and Introspection 520
12.2.2 Remember Operator Overloading 523
12.3 Creating Your Own Operator Overloading 523
12.3.1 Mapping Operators to Special Methods 524
12.4 Building the Rational Number Class 526
12.4.1 Making the Class 527
12.4.2 Review Fraction Addition 529
12.4.3 Back to Adding Fractions 532
12.4.4 Equality and Reducing Rationals 536
12.4.5 Divide and Conquer at Work 539
12.5 What Doesn’t Work (Yet) 539
12.5.1 Introspection 540
12.5.2 Repairing int + Rational Errors 542
12.6 Inheritance 544
12.6.1 The “Find the Attribute” Game 544
12.6.2 Using Inheritance 548
12.6.3 Example: The Standard Model 549
Chapter 13 Program Development with Classes

561

13.1 Predator-Prey Problem 561
13.1.1 The Rules 562
13.1.2 Simulation Using Object-Oriented Programming
13.2 Classes 563

13.2.1 Island Class 563
13.2.2 Predator and Prey, Kinds of Animals 565
13.2.3 Predator and Prey Classes 568
13.2.4 Object Diagram 569
13.2.5 Filling the Island 569
13.3 Adding Behavior 572
13.3.1 Refinement: Add Movement 572
13.3.2 Refinement: Time Simulation Loop 575
13.4 Refinement: Eating, Breeding, and Keeping Time 576
13.4.1 Improved Time Loop 577
13.4.2 Breeding 580
13.4.3 Eating 582
13.4.4 The Tick of the Clock 583
13.5 Refinements 584
13.5.1 Refinement: How Many Times to Move? 584
13.5.2 Refinement: Graphing Population Size 585

563


CONTENTS

PART 5 BEING A BETTER PROGRAMMER

Chapter 14 Files and Exceptions II

589

591

14.1 More Details on Files 591
14.1.1 Other File Access Methods, Reading 593
14.1.2 Other File Access Methods, Writing 595
14.1.3 Universal New Line Format 596
14.1.4 Moving Around in a File 597
14.1.5 Closing a File 599
14.1.6 The with Statement 599
14.2 CSV Files 601
14.2.1 csv Module 601
14.2.2 CSV Reader 602
14.2.3 CSV Writer 603
14.2.4 Example: Update Some Grades

603

14.3 Module: os 606
14.3.1 Directory (Folder) Structure 606
14.3.2 os Module Functions 608
14.3.3 os Module Example 609
14.4 More on Exceptions 612
14.4.1 Basic Exception Handling 612
14.4.2 A Simple Example 613
14.4.3 Events 616
14.4.4 A Philosophy Concerning Exceptions

616

14.5 Exception: else and finally 618
14.5.1 finally and with 618
14.5.2 Example: Refactoring the Reprompting of a

File Name 618
14.6 More on Exceptions 620
14.6.1 Raise 620
14.6.2 Create Your Own 621
14.7 Example: Password Manager
Chapter 15 Testing

622

631

15.1 Why Testing? 631
15.1.1 Kinds of Errors 631
15.1.2 “Bugs” and Debugging 632
15.2 Kinds of Testing 633
15.2.1 Testing Is Hard! 634
15.2.2 Importance of Testing 635

xvii


xviii

CONTENTS

15.3 Example Problem 636
15.3.1 NBA Efficiency 636
15.3.2 Basic Algorithm 636
15.4 Incorporating Testing 639
15.4.1 Catching User Errors 639

15.4.2 Catching Developer Errors 641
15.5 Automation of Testing 643
15.5.1 doctest 643
15.5.2 Other Kinds of Testing 647
Chapter 16 Recursion: Another Control Mechanism 649
16.1 What Is Recursion? 649
16.2 Mathematics and Rabbits 651
16.3 Let’s Write Our Own: Reversing a String 654
16.4 How Does Recursion Actually Work? 656
16.4.1 Stack Data Structure 657
16.4.2 Stacks and Function Calls 659
16.5 Recursion in Figures 661
16.5.1 Recursive Tree 661
16.5.2 Sierpinski Triangles 663
16.6 Recursion to Nonrecursion 664
Chapter 17 Other Fun Stuff with Python 667
17.1 Function Stuff 667
17.1.1 Having a Varying Number of Parameters 668
17.1.2 Iterators and Generators 671
17.1.3 Other Functional Programming Ideas 676
17.1.4 Some Functional Tools: map, reduce, filter
17.1.5 Decorators: Functions Calling Functions 678
17.2 Classes 684
17.2.1 Properties 684
17.2.2 Serializing an Instance: pickle 688
17.2.3 Random Numbers 691
17.3 Other Things in Python 693
17.3.1 Data Types 693
17.3.2 Built-in Modules 693
17.3.3 Modules on the Internet 694

Chapter 18 The End, or Perhaps the Beginning 695
APPENDICES

697
Appendix A Getting and Using Python 697
A.1 About Python 697

677


CONTENTS

A.1.1
A.1.2
A.1.3
A.1.4
A.1.5
A.1.6

History 697
Python 3 697
Python Is Free and Portable 698
Starting Python Up 699
Working with Python 700
Making a Program 702

A.2 Some Conventions for This Book 706
A.2.1 Interactive Code 706
A.2.2 Program: Written Code 707
A.2.3 Combined Program and Output 707

A.3 Summary 707
Appendix B Simple Drawing with Turtle Graphics 709
B.1.1 What Is a Turtle? 709
B.1.2 Motion 711
B.1.3 Drawing 711
B.1.4 Color 714
B.1.5 Drawing with Color 715
B.1.6 Other Commands 717
B.2 Tidbits 719
B.2.1 Keeping the Window Open 719
B.2.2 Working Nicely with IDLE 720
Appendix C Plotting and Numeric Tools: A Quick Survey 721
C.1 Matplotlib 721
C.1.1 Getting matplotlib 722
C.2 Working with matplotlib 727
C.2.1 plot Command 727
C.2.2 Plot Properties 728
C.2.3 Tick Labels 729
C.2.4 Bar Graphs 729
C.2.5 Histograms 730
C.2.6 Pie Charts 731
C.3 Numeric Python (NumPy) 732
C.3.1 Arrays Are Not Lists 732
C.3.2 Creating a NumPy Array 732
C.3.3 Manipulating Arrays 732
Appendix D Table of UTF-8 One-Byte Encodings 735
Appendix E Precedence 737
Appendix F Naming Conventions 739
F.1 Python-Style Elements

740

xix


xx

CONTENTS

F.2
F.3

Naming Conventions 740
F.2.1 Our Added Naming Conventions
Other Python Conventions

741

741

Appendix G Check Yourself Solutions 743
Chapter 1 743
Variables and Assignments 743
Types and Operators 743
Chapter 2 744
Basic Control Check 744
Loop Control Check 744
More Control Check 744
for and range Check 745
Chapter 4 745

Slicing Check 745
String Comparison Check

745

Chapter 5 745
File Check 745
Exception Check 745
Chapter 6 745
Simple Functions Check 745
Function Practice with Strings 746
Chapter 7 746
Basic Lists Check 746
Lists and Strings Check 746
Chapter 8 746
Passing Mutables Check 746
More on Functions Check 747
Chapter 9 747
Dictionary Check
Set Check 747

747

Chapter 11 747
Basic Classes Check 747
Defining Special Methods 747
Chapter 12 748
Check Defining Your Own Operators
Chapter 14 748
Basic File Operations 748

Basic Exception Control 748
INDEX

749

748


•

V I D E O N O T E S

VideoNote 0.1

Getting Python

13

VideoNote 1.1

Simple Arithmetic 63

VideoNote 1.2

Solving Your First Problem 72

VideoNote 2.1

Simple Control

90

VideoNote 2.2

Nested Control

134

VideoNote 3.1

Algorithm Decomposition 169

VideoNote 3.2

Algorithm Development

VideoNote 4.1

Playing with Strings

VideoNote 4.2

String Formatting

VideoNote 5.1

Reading Files

VideoNote 5.2

Simple Exception Handling

VideoNote 6.1

Simple Functions

VideoNote 6.2

Problem Design Using Functions

VideoNote 7.1

List Operations

297

VideoNote 7.2

List Application

320

VideoNote 8.1

More on Parameters 366

VideoNote 9.1

Using a Dictionary 396

VideoNote 9.2

More Dictionaries 424

177

201
204

228
235

263
276

VideoNote 10.1 Program Development: Tag Cloud 466
VideoNote 11.1 Designing a Class 493
VideoNote 11.2 Improving a Class

502

VideoNote 12.1 Augmenting a Class 539
VideoNote 12.2 Create a Class

542

xxi


xxii

VIDEONOTES

VideoNote 13.1 Improve Simulation
VideoNote 14.1 Dictionary Exceptions
VideoNote 15.1 Doctest 643
VideoNote 16.1 Recursion 654
VideoNote 17.1 Properties

684

569
613


P R E F A C E

•

A FIRST COURSE IN COMPUTER SCIENCE IS ABOUT A NEW WAY OF SOLVING PROBLEMS:

computationally. Our goal is that after the course, students when presented with a problem
will think, “Hey, I can write a program to do that!”
The teaching of problem solving is inexorably intertwined with the computer language used. Thus, the choice of language for this first course is very important. We
have chosen Python as the introductory language for beginning programming students—
majors and nonmajors alike—based on our combined 30 years of experience teaching
undergraduate introductory computer science at Michigan State University. Having taught
the course in Pascal, C/C++, and now Python, we know that an introductory programming language should have two characteristics. First, it should be relatively simple to
learn. Python’s simplicity, powerful built-in data structures, and advanced control constructs allow students to focus more on problem solving and less on language issues.
Second, it should be practical. Python supports learning not only fundamental programming issues such as typical programming constructs, a fundamental object-oriented approach, common data structures, and so on, but also more complex computing issues,

such as threads and regular expressions. Finally, Python is “industrial strength,” forming
the backbone of companies such as YouTube, DropBox, Industrial Light and Magic, and
many others.
The main driver for the second edition of this textbook came from requests for a Python
3 version. We began our course with Python 2 because Python 3 hadn’t been released when
we started in 2007 and because we expected that it would take some time for important
open-source packages such as NumPy and matplotlib to transition to Python 3. When
NumPy and matplotlib converted to Python 3 in 2011, we felt comfortable making the
transition. Of course, many other useful modules have also been converted to Python 3—the
default installation now includes thousands of modules. With momentum building behind
Python 3, it was time for us to rewrite our course and this text.
Why Python 3? Python 3 is a major step in the development of Python and is a new and
improved version. Some nagging inconsistencies in the Python 2 branch required fixing,
and the Python community decided that these changes were worth breaking backward

xxiii


xxiv

PREFACE

compatibility. One glaring example is that print acted like a function but didn’t have
standard function syntax. Another important change was moving the default character
encoding to Unicode, recognizing the worldwide adoption of the Python language. In many
ways beyond the introductory level, Python 3 is a better language, and the community is
making the transition to Python 3.
At the introductory level, the transition to Python 3 appears to be relatively small, but
the change resulted in touching nearly every page of the book. With a major rewrite in play,
we made a number of other improvements as well. We tried to incorporate every aspect

of Python 3 that was reasonable for an introductory book. Here are some of the many
changes:
r For Python 3:

-

r
r
r
r
r
r
r
r

print is now a function requiring parenthesis.
We adopted the new string formatting approach.
Unicode UTF-8 has replaced ASCII as the default character encoding.
The “/” operator returns float even for int operands. This has implications for
students moving on to C++/Java.
- Sets and their operations have nearly equivalent binary operators. Both are discussed.
- Comprehensions have been extended to include sets and dictionaries. Though not
emphasized, comprehensions are introduced.
- Many functions now return iterators (e.g., range , map )
- Dictionary methods such as items return dictionary views.
- input has replaced raw input .
- Comparison of dissimilar types results in an error.
- Literal base specifications changed.
- File reading and writing changed with the adoption of Unicode default.
- Some exception code has changed (e.g., as ).

- Use of next function instead of the .next method for iterators.
We redid every program and session so that they all met the Python’s PEP 8 naming
conventions. We introduced some extensions as well to help new students read and
write code better.
Many programs were repaired or wholesale replaced based on the changes to Python 3.
We added a set of nine Rules to guide novice programmers.
Each chapter now has a reference summary to make it easier to refer to syntax and
semantics.
The Quick Check exercises now have answers in the back of the book.
We inserted an early chapter that briefly introduces file reading and exceptions, because
our approach uses file reading throughout. A more detailed coverage occurs later.
We removed as many forward reference elements (talking about a feature before it was
fully presented) as possible.
We added a chapter at the end on Python features that more advanced students will
find useful and interesting.