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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>ThinkPython</title>
<link rel="stylesheet" href="https://stackedit.io/style.css" />
</head>
<body class="stackedit">
<div class="stackedit__left">
<div class="stackedit__toc">
<ul>
<li>
<ul>
<li></li>
</ul>
</li>
<li><a href="#preface">Preface</a>
<ul>
<li><a href="#the-strange-history-of-this-book">The strange history of this book</a></li>
<li><a href="#contents">Contents</a></li>
<li><a href="#chapter-1">Chapter 1</a></li>
</ul>
</li>
<li><a href="#the-way-of-the-program">The way of the program</a>
<ul>
<li></li>
<li><a href="#chapter-2">Chapter 2</a></li>
</ul>
</li>
<li><a href="#variables-expressions-and">Variables, expressions and</a></li>
<li><a href="#statements">statements</a>
<ul>
<li></li>
<li><a href="#operators-and-operands">2.4 Operators and operands</a></li>
</ul>
</li>
<li><a href="#compute-the-percentage-of-the-hour-that-has-elapsed">compute the percentage of the hour that has elapsed</a>
<ul>
<li></li>
<li><a href="#chapter-3">Chapter 3</a></li>
</ul>
</li>
<li><a href="#functions">Functions</a>
<ul>
<li></li>
<li><a href="#chapter-4">Chapter 4</a></li>
</ul>
</li>
<li><a href="#case-study-interface-design">Case study: interface design</a>
<ul>
<li></li>
<li><a href="#chapter-5">Chapter 5</a></li>
</ul>
</li>
<li><a href="#conditionals-and-recursion">Conditionals and recursion</a>
<ul>
<li></li>
<li><a href="#chapter-6">Chapter 6</a></li>
</ul>
</li>
<li><a href="#fruitful-functions">Fruitful functions</a>
<ul>
<li></li>
<li><a href="#chapter-7">Chapter 7</a></li>
</ul>
</li>
<li><a href="#iteration">Iteration</a>
<ul>
<li></li>
<li><a href="#chapter-8">Chapter 8</a></li>
</ul>
</li>
<li><a href="#strings">Strings</a>
<ul>
<li></li>
<li><a href="#chapter-9">Chapter 9</a></li>
</ul>
</li>
<li><a href="#case-study-word-play">Case study: word play</a>
<ul>
<li></li>
<li><a href="#chapter-10">Chapter 10</a></li>
</ul>
</li>
<li><a href="#lists">Lists</a>
<ul>
<li></li>
<li><a href="#chapter-11">Chapter 11</a></li>
</ul>
</li>
<li><a href="#dictionaries">Dictionaries</a>
<ul>
<li></li>
<li><a href="#chapter-12">Chapter 12</a></li>
</ul>
</li>
<li><a href="#tuples">Tuples</a>
<ul>
<li></li>
</ul>
</li>
</ul>
</div>
</div>
<div class="stackedit__right">
<div class="stackedit__html">
<p>Think Python</p>
<h3 id="how-to-think-like-a-computer-scientist">How to Think Like a Computer Scientist</h3>
<pre><code>Version 2.0.
</code></pre>
<p>Think Python</p>
<h3 id="how-to-think-like-a-computer-scientist-1">How to Think Like a Computer Scientist</h3>
<pre><code>Version 2.0.
</code></pre>
<h3 id="allen-downey">Allen Downey</h3>
<h3 id="green-tea-press">Green Tea Press</h3>
<pre><code>Needham, Massachusetts
</code></pre>
<p>Copyright © 2012 Allen Downey.</p>
<p>Green Tea Press<br>
9 Washburn Ave<br>
Needham MA 02492</p>
<p>Permission is granted to copy, distribute, and/or modify this document under the terms of the<br>
Creative Commons Attribution-NonCommercial 3.0 Unported License, which is available athttp:<br>
<a href="//creativecommons.org/licenses/by-">//creativecommons.org/licenses/by-</a> nc/3.0/.</p>
<p>The original form of this book is LATEX source code. Compiling this LATEX source has the effect of gen-<br>
erating a device-independent representation of a textbook, which can be converted to other formats<br>
and printed.</p>
<p>The LATEX source for this book is available from <a href="http://www.thinkpython.com">http://www.thinkpython.com</a></p>
<h1 id="preface">Preface</h1>
<h2 id="the-strange-history-of-this-book">The strange history of this book</h2>
<p>In January 1999 I was preparing to teach an introductory programming class in Java. I had<br>
taught it three times and I was getting frustrated. The failure rate in the class was too high<br>
and, even for students who succeeded, the overall level of achievement was too low.</p>
<p>One of the problems I saw was the books. They were too big, with too much unnecessary<br>
detail about Java, and not enough high-level guidance about how to program. And they all<br>
suffered from the trap door effect: they would start out easy, proceed gradually, and then<br>
somewhere around Chapter 5 the bottom would fall out. The students would get too much<br>
new material, too fast, and I would spend the rest of the semester picking up the pieces.</p>
<p>Two weeks before the first day of classes, I decided to write my own book. My goals were:</p>
<ul>
<li>Keep it short. It is better for students to read 10 pages than not read 50 pages.</li>
<li>Be careful with vocabulary. I tried to minimize the jargon and define each term at<br>
first use.</li>
<li>Build gradually. To avoid trap doors, I took the most difficult topics and split them<br>
into a series of small steps.</li>
<li>Focus on programming, not the programming language. I included the minimum<br>
useful subset of Java and left out the rest.</li>
</ul>
<p>I needed a title, so on a whim I choseHow to Think Like a Computer Scientist.</p>
<p>My first version was rough, but it worked. Students did the reading, and they understood<br>
enough that I could spend class time on the hard topics, the interesting topics and (most<br>
important) letting the students practice.</p>
<p>I released the book under the GNU Free Documentation License, which allows users to<br>
copy, modify, and distribute the book.</p>
<p>What happened next is the cool part. Jeff Elkner, a high school teacher in Virginia, adopted<br>
my book and translated it into Python. He sent me a copy of his translation, and I had the<br>
unusual experience of learning Python by reading my own book. As Green Tea Press, I<br>
published the first Python version in 2001.</p>
<p>In 2003 I started teaching at Olin College and I got to teach Python for the first time. The<br>
contrast with Java was striking. Students struggled less, learned more, worked on more<br>
interesting projects, and generally had a lot more fun.</p>
<p><strong>vi Chapter 0. Preface</strong></p>
<p>Over the last nine years I continued to develop the book, correcting errors, improving some<br>
of the examples and adding material, especially exercises.</p>
<p>The result is this book, now with the less grandiose titleThink Python. Some of the changes<br>
are:</p>
<ul>
<li>I added a section about debugging at the end of each chapter. These sections present<br>
general techniques for finding and avoiding bugs, and warnings about Python pit-<br>
falls.</li>
<li>I added more exercises, ranging from short tests of understanding to a few substantial<br>
projects. And I wrote solutions for most of them.</li>
<li>I added a series of case studies—longer examples with exercises, solutions, and<br>
discussion. Some are based on Swampy, a suite of Python programs I wrote for<br>
use in my classes. Swampy, code examples, and some solutions are available from<br>
<a href="http://thinkpython.com.">http://thinkpython.com.</a></li>
<li>I expanded the discussion of program development plans and basic design patterns.</li>
<li>I added appendices about debugging, analysis of algorithms, and UML diagrams<br>
with Lumpy.</li>
</ul>
<p>I hope you enjoy working with this book, and that it helps you learn to program and think,<br>
at least a little bit, like a computer scientist.</p>
<p>Allen B. Downey<br>
Needham MA</p>
<p>Allen Downey is a Professor of Computer Science at the Franklin W. Olin College of Engi-<br>
neering.</p>
<h3 id="acknowledgments">Acknowledgments</h3>
<p>Many thanks to Jeff Elkner, who translated my Java book into Python, which got this<br>
project started and introduced me to what has turned out to be my favorite language.</p>
<p>Thanks also to Chris Meyers, who contributed several sections toHow to Think Like a Com-<br>
puter Scientist.</p>
<p>Thanks to the Free Software Foundation for developing the GNU Free Documentation Li-<br>
cense, which helped make my collaboration with Jeff and Chris possible, and Creative<br>
Commons for the license I am using now.</p>
<p>Thanks to the editors at Lulu who worked onHow to Think Like a Computer Scientist.</p>
<p>Thanks to all the students who worked with earlier versions of this book and all the con-<br>
tributors (listed below) who sent in corrections and suggestions.</p>
<pre><code>vii
</code></pre>
<h3 id="contributor-list">Contributor List</h3>
<p>More than 100 sharp-eyed and thoughtful readers have sent in suggestions and corrections<br>
over the past few years. Their contributions, and enthusiasm for this project, have been a<br>
huge help.</p>
<p>If you have a suggestion or correction, please send email <a href="mailto:[email protected]">[email protected]</a>.<br>
If I make a change based on your feedback, I will add you to the contributor list (unless<br>
you ask to be omitted).</p>
<p>If you include at least part of the sentence the error appears in, that makes it easy for me to<br>
search. Page and section numbers are fine, too, but not quite as easy to work with. Thanks!</p>
<ul>
<li>Lloyd Hugh Allen sent in a correction to Section 8.4.</li>
<li>Yvon Boulianne sent in a correction of a semantic error in Chapter 5.</li>
<li>Fred Bremmer submitted a correction in Section 2.1.</li>
<li>Jonah Cohen wrote the Perl scripts to convert the LaTeX source for this book into beautiful<br>
HTML.</li>
<li>Michael Conlon sent in a grammar correction in Chapter 2 and an improvement in style in<br>
Chapter 1, and he initiated discussion on the technical aspects of interpreters.</li>
<li>Benoit Girard sent in a correction to a humorous mistake in Section 5.6.</li>
<li>Courtney Gleason and Katherine Smith <a href="http://wrotehorsebet.py">wrotehorsebet.py</a>, which was used as a case study<br>
in an earlier version of the book. Their program can now be found on the website.</li>
<li>Lee Harr submitted more corrections than we have room to list here, and indeed he should be<br>
listed as one of the principal editors of the text.</li>
<li>James Kaylin is a student using the text. He has submitted numerous corrections.</li>
<li>David Kershaw fixed the brokencatTwicefunction in Section 3.10.</li>
<li>Eddie Lam has sent in numerous corrections to Chapters 1, 2, and 3. He also fixed the Makefile<br>
so that it creates an index the first time it is run and helped us set up a versioning scheme.</li>
<li>Man-Yong Lee sent in a correction to the example code in Section 2.4.</li>
<li>David Mayo pointed out that the word “unconsciously" in Chapter 1 needed to be changed to<br>
“subconsciously".</li>
<li>Chris McAloon sent in several corrections to Sections 3.9 and 3.10.</li>
<li>Matthew J. Moelter has been a long-time contributor who sent in numerous corrections and<br>
suggestions to the book.</li>
<li>Simon Dicon Montford reported a missing function definition and several typos in Chapter 3.<br>
He also found errors in theincrementfunction in Chapter 13.</li>
<li>John Ouzts corrected the definition of “return value" in Chapter 3.</li>
<li>Kevin Parks sent in valuable comments and suggestions as to how to improve the distribution<br>
of the book.</li>
<li>David Pool sent in a typo in the glossary of Chapter 1, as well as kind words of encouragement.</li>
<li>Michael Schmitt sent in a correction to the chapter on files and exceptions.</li>
</ul>
<p><strong>viii Chapter 0. Preface</strong></p>
<ul>
<li>Robin Shaw pointed out an error in Section 13.1, where the printTime function was used in an<br>
example without being defined.</li>
<li>Paul Sleigh found an error in Chapter 7 and a bug in Jonah Cohen’s Perl script that generates<br>
HTML from LaTeX.</li>
<li>Craig T. Snydal is testing the text in a course at Drew University. He has contributed several<br>
valuable suggestions and corrections.</li>
<li>Ian Thomas and his students are using the text in a programming course. They are the first ones<br>
to test the chapters in the latter half of the book, and they have made numerous corrections and<br>
suggestions.</li>
<li>Keith Verheyden sent in a correction in Chapter 3.</li>
<li>Peter Winstanley let us know about a longstanding error in our Latin in Chapter 3.</li>
<li>Chris Wrobel made corrections to the code in the chapter on file I/O and exceptions.</li>
<li>Moshe Zadka has made invaluable contributions to this project. In addition to writing the first<br>
draft of the chapter on Dictionaries, he provided continual guidance in the early stages of the<br>
book.</li>
<li>Christoph Zwerschke sent several corrections and pedagogic suggestions, and explained the<br>
difference betweengleichandselbe.</li>
<li>James Mayer sent us a whole slew of spelling and typographical errors, including two in the<br>
contributor list.</li>
<li>Hayden McAfee caught a potentially confusing inconsistency between two examples.</li>
<li>Angel Arnal is part of an international team of translators working on the Spanish version of<br>
the text. He has also found several errors in the English version.</li>
<li>Tauhidul Hoque and Lex Berezhny created the illustrations in Chapter 1 and improved many<br>
of the other illustrations.</li>
<li>Dr. Michele Alzetta caught an error in Chapter 8 and sent some interesting pedagogic com-<br>
ments and suggestions about Fibonacci and Old Maid.</li>
<li>Andy Mitchell caught a typo in Chapter 1 and a broken example in Chapter 2.</li>
<li>Kalin Harvey suggested a clarification in Chapter 7 and caught some typos.</li>
<li>Christopher P. Smith caught several typos and helped us update the book for Python 2.2.</li>
<li>David Hutchins caught a typo in the Foreword.</li>
<li>Gregor Lingl is teaching Python at a high school in Vienna, Austria. He is working on a Ger-<br>
man translation of the book, and he caught a couple of bad errors in Chapter 5.</li>
<li>Julie Peters caught a typo in the Preface.</li>
<li>Florin Oprina sent in an improvement inmakeTime, a correction inprintTime, and a nice typo.</li>
<li>D. J. Webre suggested a clarification in Chapter 3.</li>
<li>Ken found a fistful of errors in Chapters 8, 9 and 11.</li>
<li>Ivo Wever caught a typo in Chapter 5 and suggested a clarification in Chapter 3.</li>
<li>Curtis Yanko suggested a clarification in Chapter 2.</li>
</ul>
<pre><code>ix
</code></pre>
<ul>
<li>Ben Logan sent in a number of typos and problems with translating the book into HTML.</li>
<li>Jason Armstrong saw the missing word in Chapter 2.</li>
<li>Louis Cordier noticed a spot in Chapter 16 where the code didn’t match the text.</li>
<li>Brian Cain suggested several clarifications in Chapters 2 and 3.</li>
<li>Rob Black sent in a passel of corrections, including some changes for Python 2.2.</li>
<li>Jean-Philippe Rey at Ecole Centrale Paris sent a number of patches, including some updates<br>
for Python 2.2 and other thoughtful improvements.</li>
<li>Jason Mader at George Washington University made a number of useful suggestions and cor-<br>
rections.</li>
<li>Jan Gundtofte-Bruun reminded us that “a error” is an error.</li>
<li>Abel David and Alexis Dinno reminded us that the plural of “matrix” is “matrices”, not “ma-<br>
trixes”. This error was in the book for years, but two readers with the same initials reported it<br>
on the same day. Weird.</li>
<li>Charles Thayer encouraged us to get rid of the semi-colons we had put at the ends of some<br>
statements and to clean up our use of “argument” and “parameter”.</li>
<li>Roger Sperberg pointed out a twisted piece of logic in Chapter 3.</li>
<li>Sam Bull pointed out a confusing paragraph in Chapter 2.</li>
<li>Andrew Cheung pointed out two instances of “use before def.”</li>
<li>C. Corey Capel spotted the missing word in the Third Theorem of Debugging and a typo in<br>
Chapter 4.</li>
<li>Alessandra helped clear up some Turtle confusion.</li>
<li>Wim Champagne found a brain-o in a dictionary example.</li>
<li>Douglas Wright pointed out a problem with floor division inarc.</li>
<li>Jared Spindor found some jetsam at the end of a sentence.</li>
<li>Lin Peiheng sent a number of very helpful suggestions.</li>
<li>Ray Hagtvedt sent in two errors and a not-quite-error.</li>
<li>Torsten Hübsch pointed out an inconsistency in Swampy.</li>
<li>Inga Petuhhov corrected an example in Chapter 14.</li>
<li>Arne Babenhauserheide sent several helpful corrections.</li>
<li>Mark E. Casida is is good at spotting repeated words.</li>
<li>Scott Tyler filled in a that was missing. And then sent in a heap of corrections.</li>
<li>Gordon Shephard sent in several corrections, all in separate emails.</li>
<li>Andrew Turnerspotted an error in Chapter 8.</li>
<li>Adam Hobart fixed a problem with floor division inarc.</li>
</ul>
<p><strong>x Chapter 0. Preface</strong></p>
<ul>
<li>Daryl Hammond and Sarah Zimmerman pointed out that I served upmath.pitoo early. And<br>
Zim spotted a typo.</li>
<li>George Sass found a bug in a Debugging section.</li>
<li>Brian Bingham suggested Exercise 11.10.</li>
<li>Leah Engelbert-Fenton pointed out that I usedtupleas a variable name, contrary to my own<br>
advice. And then found a bunch of typos and a “use before def.”</li>
<li>Joe Funke spotted a typo.</li>
<li>Chao-chao Chen found an inconsistency in the Fibonacci example.</li>
<li>Jeff Paine knows the difference between space and spam.</li>
<li>Lubos Pintes sent in a typo.</li>
<li>Gregg Lind and Abigail Heithoff suggested Exercise 14.4.</li>
<li>Max Hailperin has sent in a number of corrections and suggestions. Max is one of the authors<br>
of the extraordinaryConcrete Abstractions, which you might want to read when you are done<br>
with this book.</li>
<li>Chotipat Pornavalai found an error in an error message.</li>
<li>Stanislaw Antol sent a list of very helpful suggestions.</li>
<li>Eric Pashman sent a number of corrections for Chapters 4–11.</li>
<li>Miguel Azevedo found some typos.</li>
<li>Jianhua Liu sent in a long list of corrections.</li>
<li>Nick King found a missing word.</li>
<li>Martin Zuther sent a long list of suggestions.</li>
<li>Adam Zimmerman found an inconsistency in my instance of an “instance” and several other<br>
errors.</li>
<li>Ratnakar Tiwari suggested a footnote explaining degenerate triangles.</li>
<li>Anurag Goel suggested another solution foris_abecedarianand sent some additional correc-<br>
tions. And he knows how to spell Jane Austen.</li>
<li>Kelli Kratzer spotted one of the typos.</li>
<li>Mark Griffiths pointed out a confusing example in Chapter 3.</li>
<li>Roydan Ongie found an error in my Newton’s method.</li>
<li>Patryk Wolowiec helped me with a problem in the HTML version.</li>
<li>Mark Chonofsky told me about a new keyword in Python 3.</li>
<li>Russell Coleman helped me with my geometry.</li>
<li>Wei Huang spotted several typographical errors.</li>
<li>Karen Barber spotted the the oldest typo in the book.</li>
</ul>
<pre><code>xi
</code></pre>
<ul>
<li>Nam Nguyen found a typo and pointed out that I used the Decorator pattern but didn’t men-<br>
tion it by name.</li>
<li>Stéphane Morin sent in several corrections and suggestions.</li>
<li>Paul Stoop corrected a typo inuses_only.</li>
<li>Eric Bronner pointed out a confusion in the discussion of the order of operations.</li>
<li>Alexandros Gezerlis set a new standard for the number and quality of suggestions he submit-<br>
ted. We are deeply grateful!</li>
<li>Gray Thomas knows his right from his left.</li>
<li>Giovanni Escobar Sosa sent a long list of corrections and suggestions.</li>
<li>Alix Etienne fixed one of the URLs.</li>
<li>Kuang He found a typo.</li>
<li>Daniel Neilson corrected an error about the order of operations.</li>
<li>Will McGinnis pointed out thatpolylinewas defined differently in two places.</li>
<li>Swarup Sahoo spotted a missing semi-colon.</li>
<li>Frank Hecker pointed out an exercise that was under-specified, and some broken links.</li>
<li>Animesh B helped me clean up a confusing example.</li>
<li>Martin Caspersen found two round-off errors.</li>
<li>Gregor Ulm sent several corrections and suggestions.</li>
<li>Dimitrios Tsirigkas suggested I clarify an exercise.</li>
<li>Carlos Tafur sent a page of corrections and suggestions.</li>
<li>Martin Nordsletten found a bug in an exercise solution.</li>
<li>Lars O.D. Christensen found a broken reference.</li>
<li>Victor Simeone found a typo.</li>
<li>Sven Hoexter pointed out that a variable namedinputshadows a built-in function.</li>
<li>Viet Le found a typo.</li>
<li>Stephen Gregory pointed out the problem withcmpin Python 3.</li>
<li>Matthew Shultz let me know about a broken link.</li>
<li>Lokesh Kumar Makani let me know about some broken links and some changes in error mes-<br>
sages.</li>
<li>Ishwar Bhat corrected my statement of Fermat’s last theorem.</li>
<li>Brian McGhie suggested a clarification.</li>
<li>Andrea Zanella translated the book into Italian, and sent a number of corrections along the<br>
way.</li>
</ul>
<p><strong>xii Chapter 0. Preface</strong></p>
<h2 id="contents">Contents</h2>
<ul>
<li>1 The way of the program Preface v
<ul>
<li>1.1 The Python programming language</li>
<li>1.2 What is a program?</li>
<li>1.3 What is debugging?</li>
<li>1.4 Formal and natural languages</li>
<li>1.5 The first program</li>
<li>1.6 Debugging</li>
<li>1.7 Glossary</li>
<li>1.8 Exercises</li>
</ul>
</li>
<li>2 Variables, expressions and statements
<ul>
<li>2.1 Values and types</li>
<li>2.2 Variables</li>
<li>2.3 Variable names and keywords</li>
<li>2.4 Operators and operands</li>
<li>2.5 Expressions and statements</li>
<li>2.6 Interactive mode and script mode</li>
<li>2.7 Order of operations</li>
<li>2.8 String operations</li>
<li>2.9 Comments</li>
<li>2.10 Debugging</li>
<li>2.11 Glossary</li>
<li>2.12 Exercises</li>
</ul>
</li>
<li>3 Functions xiv Contents
<ul>
<li>3.1 Function calls</li>
<li>3.2 Type conversion functions</li>
<li>3.3 Math functions</li>
<li>3.4 Composition</li>
<li>3.5 Adding new functions</li>
<li>3.6 Definitions and uses</li>
<li>3.7 Flow of execution</li>
<li>3.8 Parameters and arguments</li>
<li>3.9 Variables and parameters are local</li>
<li>3.10 Stack diagrams</li>
<li>3.11 Fruitful functions and void functions</li>
<li>3.12 Why functions?</li>
<li>3.13 Importing withfrom</li>
<li>3.14 Debugging</li>
<li>3.15 Glossary</li>
<li>3.16 Exercises</li>
</ul>
</li>
<li>4 Case study: interface design
<ul>
<li>4.1 TurtleWorld</li>
<li>4.2 Simple repetition</li>
<li>4.3 Exercises</li>
<li>4.4 Encapsulation</li>
<li>4.5 Generalization</li>
<li>4.6 Interface design</li>
<li>4.7 Refactoring</li>
<li>4.8 A development plan</li>
<li>4.9 docstring</li>
<li>4.10 Debugging</li>
<li>4.11 Glossary</li>
<li>4.12 Exercises</li>
</ul>
</li>
<li>5 Conditionals and recursion Contents xv
<ul>
<li>5.1 Modulus operator</li>
<li>5.2 Boolean expressions</li>
<li>5.3 Logical operators</li>
<li>5.4 Conditional execution</li>
<li>5.5 Alternative execution</li>
<li>5.6 Chained conditionals</li>
<li>5.7 Nested conditionals</li>
<li>5.8 Recursion</li>
<li>5.9 Stack diagrams for recursive functions</li>
<li>5.10 Infinite recursion</li>
<li>5.11 Keyboard input</li>
<li>5.12 Debugging</li>
<li>5.13 Glossary</li>
<li>5.14 Exercises</li>
</ul>
</li>
<li>6 Fruitful functions
<ul>
<li>6.1 Return values</li>
<li>6.2 Incremental development</li>
<li>6.3 Composition</li>
<li>6.4 Boolean functions</li>
<li>6.5 More recursion</li>
<li>6.6 Leap of faith</li>
<li>6.7 One more example</li>
<li>6.8 Checking types</li>
<li>6.9 Debugging</li>
<li>6.10 Glossary</li>
<li>6.11 Exercises</li>
</ul>
</li>
<li>7 Iteration xvi Contents
<ul>
<li>7.1 Multiple assignment</li>
<li>7.2 Updating variables</li>
<li>7.3 Thewhilestatement</li>
<li>7.4 break</li>
<li>7.5 Square roots</li>
<li>7.6 Algorithms</li>
<li>7.7 Debugging</li>
<li>7.8 Glossary</li>
<li>7.9 Exercises</li>
</ul>
</li>
<li>8 Strings
<ul>
<li>8.1 A string is a sequence</li>
<li>8.2 len</li>
<li>8.3 Traversal with aforloop</li>
<li>8.4 String slices</li>
<li>8.5 Strings are immutable</li>
<li>8.6 Searching</li>
<li>8.7 Looping and counting</li>
<li>8.8 String methods</li>
<li>8.9 Theinoperator</li>
<li>8.10 String comparison</li>
<li>8.11 Debugging</li>
<li>8.12 Glossary</li>
<li>8.13 Exercises</li>
</ul>
</li>
<li>9 Case study: word play
<ul>
<li>9.1 Reading word lists</li>
<li>9.2 Exercises</li>
<li>9.3 Search</li>
<li>9.4 Looping with indices</li>
<li>9.5 Debugging</li>
<li>9.6 Glossary</li>
<li>9.7 Exercises</li>
</ul>
</li>
<li>10 Lists Contents xvii
<ul>
<li>10.1 A list is a sequence</li>
<li>10.2 Lists are mutable</li>
<li>10.3 Traversing a list</li>
<li>10.4 List operations</li>
<li>10.5 List slices</li>
<li>10.6 List methods</li>
<li>10.7 Map, filter and reduce</li>
<li>10.8 Deleting elements</li>
<li>10.9 Lists and strings</li>
<li>10.10 Objects and values</li>
<li>10.11 Aliasing</li>
<li>10.12 List arguments</li>
<li>10.13 Debugging</li>
<li>10.14 Glossary</li>
<li>10.15 Exercises</li>
</ul>
</li>
<li>11 Dictionaries
<ul>
<li>11.1 Dictionary as a set of counters</li>
<li>11.2 Looping and dictionaries</li>
<li>11.3 Reverse lookup</li>
<li>11.4 Dictionaries and lists</li>
<li>11.5 Memos</li>
<li>11.6 Global variables</li>
<li>11.7 Long integers</li>
<li>11.8 Debugging</li>
<li>11.9 Glossary</li>
<li>11.10 Exercises</li>
</ul>
</li>
<li>12 Tuples xviii Contents
<ul>
<li>12.1 Tuples are immutable</li>
<li>12.2 Tuple assignment</li>
<li>12.3 Tuples as return values</li>
<li>12.4 Variable-length argument tuples</li>
<li>12.5 Lists and tuples</li>
<li>12.6 Dictionaries and tuples</li>
<li>12.7 Comparing tuples</li>
<li>12.8 Sequences of sequences</li>
<li>12.9 Debugging</li>
<li>12.10 Glossary</li>
<li>12.11 Exercises</li>
</ul>
</li>
<li>13 Case study: data structure selection
<ul>
<li>13.1 Word frequency analysis</li>
<li>13.2 Random numbers</li>
<li>13.3 Word histogram</li>
<li>13.4 Most common words</li>
<li>13.5 Optional parameters</li>
<li>13.6 Dictionary subtraction</li>
<li>13.7 Random words</li>
<li>13.8 Markov analysis</li>
<li>13.9 Data structures</li>
<li>13.10 Debugging</li>
<li>13.11 Glossary</li>
<li>13.12 Exercises</li>
</ul>
</li>
<li>14 Files
<ul>
<li>14.1 Persistence</li>
<li>14.2 Reading and writing</li>
<li>14.3 Format operator</li>
<li>14.4 Filenames and paths</li>
<li>14.5 Catching exceptions Contents xix</li>
<li>14.6 Databases</li>
<li>14.7 Pickling</li>
<li>14.8 Pipes</li>
<li>14.9 Writing modules</li>
<li>14.10 Debugging</li>
<li>14.11 Glossary</li>
<li>14.12 Exercises</li>
</ul>
</li>
<li>15 Classes and objects
<ul>
<li>15.1 User-defined types</li>
<li>15.2 Attributes</li>
<li>15.3 Rectangles</li>
<li>15.4 Instances as return values</li>
<li>15.5 Objects are mutable</li>
<li>15.6 Copying</li>
<li>15.7 Debugging</li>
<li>15.8 Glossary</li>
<li>15.9 Exercises</li>
</ul>
</li>
<li>16 Classes and functions
<ul>
<li>16.1 Time</li>
<li>16.2 Pure functions</li>
<li>16.3 Modifiers</li>
<li>16.4 Prototyping versus planning</li>
<li>16.5 Debugging</li>
<li>16.6 Glossary</li>
<li>16.7 Exercises</li>
</ul>
</li>
<li>17 Classes and methods xx Contents
<ul>
<li>17.1 Object-oriented features</li>
<li>17.2 Printing objects</li>
<li>17.3 Another example</li>
<li>17.4 A more complicated example</li>
<li>17.5 The init method</li>
<li>17.6 The__str__method</li>
<li>17.7 Operator overloading</li>
<li>17.8 Type-based dispatch</li>
<li>17.9 Polymorphism</li>
<li>17.10 Debugging</li>
<li>17.11 Interface and implementation</li>
<li>17.12 Glossary</li>
<li>17.13 Exercises</li>
</ul>
</li>
<li>18 Inheritance
<ul>
<li>18.1 Card objects</li>
<li>18.2 Class attributes</li>
<li>18.3 Comparing cards</li>
<li>18.4 Decks</li>
<li>18.5 Printing the deck</li>
<li>18.6 Add, remove, shuffle and sort</li>
<li>18.7 Inheritance</li>
<li>18.8 Class diagrams</li>
<li>18.9 Debugging</li>
<li>18.10 Data encapsulation</li>
<li>18.11 Glossary</li>
<li>18.12 Exercises</li>
</ul>
</li>
</ul>
<p><strong>Contents xxi</strong></p>
<p><strong>19 Case study: Tkinter 179</strong></p>
<pre><code>19.1 GUI......................................... 179
</code></pre>
<pre><code>19.2 Buttons and callbacks............................... 180
</code></pre>
<pre><code>19.3 Canvas widgets.................................. 181
</code></pre>
<pre><code>19.4 Coordinate sequences............................... 182
</code></pre>
<pre><code>19.5 More widgets................................... 182
</code></pre>
<pre><code>19.6 Packing widgets.................................. 183
</code></pre>
<pre><code>19.7 Menus and Callables............................... 185
</code></pre>
<pre><code>19.8 Binding....................................... 186
</code></pre>
<pre><code>19.9 Debugging..................................... 188
</code></pre>
<pre><code>19.10 Glossary...................................... 189
</code></pre>
<pre><code>19.11 Exercises...................................... 190
</code></pre>
<p><strong>A Debugging 193</strong></p>
<pre><code>A.1 Syntax errors.................................... 193
</code></pre>
<pre><code>A.2 Runtime errors................................... 195
</code></pre>
<pre><code>A.3 Semantic errors.................................. 198
</code></pre>
<p><strong>B Analysis of Algorithms 201</strong></p>
<pre><code>B.1 Order of growth.................................. 202
</code></pre>
<pre><code>B.2 Analysis of basic Python operations...................... 204
</code></pre>
<pre><code>B.3 Analysis of search algorithms.......................... 205
</code></pre>
<pre><code>B.4 Hashtables..................................... 206
</code></pre>
<p><strong>C Lumpy 211</strong></p>
<pre><code>C.1 State diagram................................... 211
</code></pre>
<pre><code>C.2 Stack diagram................................... 212
</code></pre>
<pre><code>C.3 Object diagrams.................................. 213
</code></pre>
<pre><code>C.4 Function and class objects............................ 215
</code></pre>
<pre><code>C.5 Class Diagrams.................................. 216
</code></pre>
<p><strong>xxii Contents</strong></p>
<h2 id="chapter-1">Chapter 1</h2>
<h1 id="the-way-of-the-program">The way of the program</h1>
<p>The goal of this book is to teach you to think like a computer scientist. This way of think-<br>
ing combines some of the best features of mathematics, engineering, and natural science.<br>
Like mathematicians, computer scientists use formal languages to denote ideas (specifi-<br>
cally computations). Like engineers, they design things, assembling components into sys-<br>
tems and evaluating tradeoffs among alternatives. Like scientists, they observe the behav-<br>
ior of complex systems, form hypotheses, and test predictions.</p>
<p>The single most important skill for a computer scientist is <strong>problem solving</strong>. Problem solv-<br>
ing means the ability to formulate problems, think creatively about solutions, and express<br>
a solution clearly and accurately. As it turns out, the process of learning to program is an<br>
excellent opportunity to practice problem-solving skills. That’s why this chapter is called,<br>
“The way of the program.”</p>
<p>On one level, you will be learning to program, a useful skill by itself. On another level, you<br>
will use programming as a means to an end. As we go along, that end will become clearer.</p>
<h3 id="the-python-programming-language">1.1 The Python programming language</h3>
<p>The programming language you will learn is Python. Python is an example of a <strong>high-level<br>
language</strong> ; other high-level languages you might have heard of are C, C++, Perl, and Java.</p>
<p>There are also <strong>low-level languages</strong> , sometimes referred to as “machine languages” or “as-<br>
sembly languages.” Loosely speaking, computers can only run programs written in low-<br>
level languages. So programs written in a high-level language have to be processed before<br>
they can run. This extra processing takes some time, which is a small disadvantage of<br>
high-level languages.</p>
<p>The advantages are enormous. First, it is much easier to program in a high-level language.<br>
Programs written in a high-level language take less time to write, they are shorter and<br>
easier to read, and they are more likely to be correct. Second, high-level languages are<br>
<strong>portable</strong> , meaning that they can run on different kinds of computers with few or no modi-<br>
fications. Low-level programs can run on only one kind of computer and have to be rewrit-<br>
ten to run on another.</p>
<p><strong>2 Chapter 1. The way of the program</strong></p>
<pre><code>SOURCE
CODE
</code></pre>
<pre><code>INTERPRETER OUTPUT
</code></pre>
<p>Figure 1.1: An interpreter processes the program a little at a time, alternately reading lines<br>
and performing computations.</p>
<pre><code>CODE
</code></pre>
<pre><code>OBJECT EXECUTOR
CODE
</code></pre>
<pre><code>SOURCE COMPILER OUTPUT
</code></pre>
<p>Figure 1.2: A compiler translates source code into object code, which is run by a hardware<br>
executor.</p>
<p>Due to these advantages, almost all programs are written in high-level languages. Low-<br>
level languages are used only for a few specialized applications.</p>
<p>Two kinds of programs process high-level languages into low-level languages: <strong>interpreters</strong><br>
and <strong>compilers</strong>. An interpreter reads a high-level program and executes it, meaning that it<br>
does what the program says. It processes the program a little at a time, alternately reading<br>
lines and performing computations. Figure 1.1 shows the structure of an interpreter.</p>
<p>A compiler reads the program and translates it completely before the program starts run-<br>
ning. In this context, the high-level program is called the <strong>source code</strong> , and the translated<br>
program is called the <strong>object code</strong> or the <strong>executable</strong>. Once a program is compiled, you<br>
can execute it repeatedly without further translation. Figure 1.2 shows the structure of a<br>
compiler.</p>
<p>Python is considered an interpreted language because Python programs are executed by an<br>
interpreter. There are two ways to use the interpreter: <strong>interactive mode</strong> and <strong>script mode</strong>.<br>
In interactive mode, you type Python programs and the interpreter displays the result:</p>
<blockquote>
<blockquote>
<blockquote>
<p>1 + 1<br>
2</p>
</blockquote>
</blockquote>
</blockquote>
<p>The chevron,>>>, is the <strong>prompt</strong> the interpreter uses to indicate that it is ready. If you type<br>
1 + 1, the interpreter replies 2.</p>
<p>Alternatively, you can store code in a file and use the interpreter to execute the contents of<br>
the file, which is called a <strong>script</strong>. By convention, Python scripts have names that end with<br>
.py.</p>
<p>To execute the script, you have to tell the interpreter the name of the file. If you have a<br>
script nameddinsdale.pyand you are working in a UNIX command window, you type<br>
python <a href="http://dinsdale.py">dinsdale.py</a>. In other development environments, the details of executing scripts<br>
are different. You can find instructions for your environment at the Python websitehttp:<br>
<a href="//python.org">//python.org</a>.</p>
<p>Working in interactive mode is convenient for testing small pieces of code because you can<br>
type and execute them immediately. But for anything more than a few lines, you should<br>
save your code as a script so you can modify and execute it in the future.</p>
<p><strong>1.2. What is a program? 3</strong></p>
<h3 id="what-is-a-program">1.2 What is a program?</h3>
<p>A <strong>program</strong> is a sequence of instructions that specifies how to perform a computation. The<br>
computation might be something mathematical, such as solving a system of equations or<br>
finding the roots of a polynomial, but it can also be a symbolic computation, such as search-<br>
ing and replacing text in a document or (strangely enough) compiling a program.</p>
<p>The details look different in different languages, but a few basic instructions appear in just<br>
about every language:</p>
<p><strong>input:</strong> Get data from the keyboard, a file, or some other device.</p>
<p><strong>output:</strong> Display data on the screen or send data to a file or other device.</p>
<p><strong>math:</strong> Perform basic mathematical operations like addition and multiplication.</p>
<p><strong>conditional execution:</strong> Check for certain conditions and execute the appropriate code.</p>
<p><strong>repetition:</strong> Perform some action repeatedly, usually with some variation.</p>
<p>Believe it or not, that’s pretty much all there is to it. Every program you’ve ever used,<br>
no matter how complicated, is made up of instructions that look pretty much like these.<br>
So you can think of programming as the process of breaking a large, complex task into<br>
smaller and smaller subtasks until the subtasks are simple enough to be performed with<br>
one of these basic instructions.</p>
<p>That may be a little vague, but we will come back to this topic when we talk about <strong>algo-<br>
rithms</strong>.</p>
<h3 id="what-is-debugging">1.3 What is debugging?</h3>
<p>Programming is error-prone. For whimsical reasons, programming errors are called <strong>bugs</strong><br>
and the process of tracking them down is called <strong>debugging</strong>.</p>
<p>Three kinds of errors can occur in a program: syntax errors, runtime errors, and semantic<br>
errors. It is useful to distinguish between them in order to track them down more quickly.</p>
<p><strong>1.3.1 Syntax errors</strong></p>
<p>Python can only execute a program if the syntax is correct; otherwise, the interpreter dis-<br>
plays an error message. <strong>Syntax</strong> refers to the structure of a program and the rules about that<br>
structure. For example, parentheses have to come in matching pairs, so(1 + 2)is legal,<br>
but8)is a <strong>syntax error</strong>.</p>
<p>In English, readers can tolerate most syntax errors, which is why we can read the poetry<br>
of e. e. cummings without spewing error messages. Python is not so forgiving. If there<br>
is a single syntax error anywhere in your program, Python will display an error message<br>
and quit, and you will not be able to run your program. During the first few weeks of your<br>
programming career, you will probably spend a lot of time tracking down syntax errors.<br>
As you gain experience, you will make fewer errors and find them faster.</p>
<p><strong>4 Chapter 1. The way of the program</strong></p>
<p><strong>1.3.2 Runtime errors</strong></p>
<p>The second type of error is a runtime error, so called because the error does not appear until<br>
after the program has started running. These errors are also called <strong>exceptions</strong> because they<br>
usually indicate that something exceptional (and bad) has happened.</p>
<p>Runtime errors are rare in the simple programs you will see in the first few chapters, so it<br>
might be a while before you encounter one.</p>
<p><strong>1.3.3 Semantic errors</strong></p>
<p>The third type of error is the <strong>semantic error</strong>. If there is a semantic error in your program, it<br>
will run successfully in the sense that the computer will not generate any error messages,<br>
but it will not do the right thing. It will do something else. Specifically, it will do what you<br>
told it to do.</p>
<p>The problem is that the program you wrote is not the program you wanted to write. The<br>
meaning of the program (its semantics) is wrong. Identifying semantic errors can be tricky<br>
because it requires you to work backward by looking at the output of the program and<br>
trying to figure out what it is doing.</p>
<p><strong>1.3.4 Experimental debugging</strong></p>
<p>One of the most important skills you will acquire is debugging. Although it can be frus-<br>
trating, debugging is one of the most intellectually rich, challenging, and interesting parts<br>
of programming.</p>
<p>In some ways, debugging is like detective work. You are confronted with clues, and you<br>
have to infer the processes and events that led to the results you see.</p>
<p>Debugging is also like an experimental science. Once you have an idea about what is going<br>
wrong, you modify your program and try again. If your hypothesis was correct, then you<br>
can predict the result of the modification, and you take a step closer to a working program.<br>
If your hypothesis was wrong, you have to come up with a new one. As Sherlock Holmes<br>
pointed out, “When you have eliminated the impossible, whatever remains, however im-<br>
probable, must be the truth.” (A. Conan Doyle,The Sign of Four)</p>
<p>For some people, programming and debugging are the same thing. That is, programming<br>
is the process of gradually debugging a program until it does what you want. The idea is<br>
that you should start with a program that doessomethingand make small modifications,<br>
debugging them as you go, so that you always have a working program.</p>
<p>For example, Linux is an operating system that contains thousands of lines of code, but<br>
it started out as a simple program Linus Torvalds used to explore the Intel 80386 chip.<br>
According to Larry Greenfield, “One of Linus’s earlier projects was a program that would<br>
switch between printing AAAA and BBBB. This later evolved to Linux.” (The Linux Users’<br>
GuideBeta Version 1).</p>
<p>Later chapters will make more suggestions about debugging and other programming prac-<br>
tices.</p>
<p><strong>1.4. Formal and natural languages 5</strong></p>
<h3 id="formal-and-natural-languages">1.4 Formal and natural languages</h3>
<p><strong>Natural languages</strong> are the languages people speak, such as English, Spanish, and French.<br>
They were not designed by people (although people try to impose some order on them);<br>
they evolved naturally.</p>
<p><strong>Formal languages</strong> are languages that are designed by people for specific applications. For<br>
example, the notation that mathematicians use is a formal language that is particularly<br>
good at denoting relationships among numbers and symbols. Chemists use a formal lan-<br>
guage to represent the chemical structure of molecules. And most importantly:</p>
<pre><code>Programming languages are formal languages that have been designed to
express computations.
</code></pre>
<p>Formal languages tend to have strict rules about syntax. For example, 3+ 3 = 6 is a<br>
syntactically correct mathematical statement, but 3+ =3$6 is not. H 2 Ois a syntactically<br>
correct chemical formula, but 2 Zzis not.</p>
<p>Syntax rules come in two flavors, pertaining to <strong>tokens</strong> and structure. Tokens are the basic<br>
elements of the language, such as words, numbers, and chemical elements. One of the<br>
problems with 3+ =3$6 is that $ is not a legal token in mathematics (at least as far as I<br>
know). Similarly, 2 Zzis not legal because there is no element with the abbreviationZz.</p>
<p>The second type of syntax rule pertains to the structure of a statement; that is, the way the<br>
tokens are arranged. The statement 3+ =3 is illegal because even though+and=are<br>
legal tokens, you can’t have one right after the other. Similarly, in a chemical formula the<br>
subscript comes after the element name, not before.<br>
<strong>Exercise 1.1.</strong> Write a well-structured English sentence with invalid tokens in it. Then write an-<br>
other sentence with all valid tokens but with invalid structure.</p>
<p>When you read a sentence in English or a statement in a formal language, you have to<br>
figure out what the structure of the sentence is (although in a natural language you do this<br>
subconsciously). This process is called <strong>parsing</strong>.</p>
<p>For example, when you hear the sentence, “The penny dropped,” you understand that<br>
“the penny” is the subject and “dropped” is the predicate. Once you have parsed a sen-<br>
tence, you can figure out what it means, or the semantics of the sentence. Assuming that<br>
you know what a penny is and what it means to drop, you will understand the general<br>
implication of this sentence.</p>
<p>Although formal and natural languages have many features in common—tokens, struc-<br>
ture, syntax, and semantics—there are some differences:</p>
<p><strong>ambiguity:</strong> Natural languages are full of ambiguity, which people deal with by using con-<br>
textual clues and other information. Formal languages are designed to be nearly or<br>
completely unambiguous, which means that any statement has exactly one meaning,<br>
regardless of context.</p>
<p><strong>redundancy:</strong> In order to make up for ambiguity and reduce misunderstandings, natural<br>
languages employ lots of redundancy. As a result, they are often verbose. Formal<br>
languages are less redundant and more concise.</p>
<p><strong>6 Chapter 1. The way of the program</strong></p>
<p><strong>literalness:</strong> Natural languages are full of idiom and metaphor. If I say, “The penny<br>
dropped,” there is probably no penny and nothing dropping (this idiom means that<br>
someone realized something after a period of confusion). Formal languages mean<br>
exactly what they say.</p>
<p>People who grow up speaking a natural language—everyone—often have a hard time ad-<br>
justing to formal languages. In some ways, the difference between formal and natural<br>
language is like the difference between poetry and prose, but more so:</p>
<p><strong>Poetry:</strong> Words are used for their sounds as well as for their meaning, and the whole poem<br>
together creates an effect or emotional response. Ambiguity is not only common but<br>
often deliberate.</p>
<p><strong>Prose:</strong> The literal meaning of words is more important, and the structure contributes more<br>
meaning. Prose is more amenable to analysis than poetry but still often ambiguous.</p>
<p><strong>Programs:</strong> The meaning of a computer program is unambiguous and literal, and can be<br>
understood entirely by analysis of the tokens and structure.</p>
<p>Here are some suggestions for reading programs (and other formal languages). First, re-<br>
member that formal languages are much more dense than natural languages, so it takes<br>
longer to read them. Also, the structure is very important, so it is usually not a good idea<br>
to read from top to bottom, left to right. Instead, learn to parse the program in your head,<br>
identifying the tokens and interpreting the structure. Finally, the details matter. Small er-<br>
rors in spelling and punctuation, which you can get away with in natural languages, can<br>
make a big difference in a formal language.</p>
<h3 id="the-first-program">1.5 The first program</h3>
<p>Traditionally, the first program you write in a new language is called “Hello, World!” be-<br>
cause all it does is display the words “Hello, World!”. In Python, it looks like this:</p>
<p>print ‘Hello, World!’</p>
<p>This is an example of a <strong>print statement</strong> , which doesn’t actually print anything on paper. It<br>
displays a value on the screen. In this case, the result is the words</p>
<p>Hello, World!</p>
<p>The quotation marks in the program mark the beginning and end of the text to be dis-<br>
played; they don’t appear in the result.</p>
<p>In Python 3, the syntax for printing is slightly different:</p>
<p>print(‘Hello, World!’)</p>
<p>The parentheses indicate thatprintis a function. We’ll get to functions in Chapter 3.</p>
<p>For the rest of this book, I’ll use the print statement. If you are using Python 3, you will<br>
have to translate. But other than that, there are very few differences we have to worry<br>
about.</p>
<p><strong>1.6. Debugging 7</strong></p>
<h3 id="debugging">1.6 Debugging</h3>
<p>It is a good idea to read this book in front of a computer so you can try out the examples as<br>
you go. You can run most of the examples in interactive mode, but if you put the code in a<br>
script, it is easier to try out variations.</p>
<p>Whenever you are experimenting with a new feature, you should try to make mistakes.<br>
For example, in the “Hello, world!” program, what happens if you leave out one of the<br>
quotation marks? What if you leave out both? What if you spellprintwrong?</p>
<p>This kind of experiment helps you remember what you read; it also helps with debugging,<br>
because you get to know what the error messages mean. It is better to make mistakes now<br>
and on purpose than later and accidentally.</p>
<p>Programming, and especially debugging, sometimes brings out strong emotions. If you<br>
are struggling with a difficult bug, you might feel angry, despondent or embarrassed.</p>
<p>There is evidence that people naturally respond to computers as if they were people. When<br>
they work well, we think of them as teammates, and when they are obstinate or rude, we<br>
respond to them the same way we respond to rude, obstinate people (Reeves and Nass,<br>
The Media Equation: How People Treat Computers, Television, and New Media Like Real People<br>
and Places).</p>
<p>Preparing for these reactions might help you deal with them. One approach is to think of<br>
the computer as an employee with certain strengths, like speed and precision, and partic-<br>
ular weaknesses, like lack of empathy and inability to grasp the big picture.</p>
<p>Your job is to be a good manager: find ways to take advantage of the strengths and mitigate<br>
the weaknesses. And find ways to use your emotions to engage with the problem, without<br>
letting your reactions interfere with your ability to work effectively.</p>
<p>Learning to debug can be frustrating, but it is a valuable skill that is useful for many ac-<br>
tivities beyond programming. At the end of each chapter there is a debugging section, like<br>
this one, with my thoughts about debugging. I hope they help!</p>
<h3 id="glossary">1.7 Glossary</h3>
<p><strong>problem solving:</strong> The process of formulating a problem, finding a solution, and express-<br>
ing the solution.</p>
<p><strong>high-level language:</strong> A programming language like Python that is designed to be easy for<br>
humans to read and write.</p>
<p><strong>low-level language:</strong> A programming language that is designed to be easy for a computer<br>
to execute; also called “machine language” or “assembly language.”</p>
<p><strong>portability:</strong> A property of a program that can run on more than one kind of computer.</p>
<p><strong>interpret:</strong> To execute a program in a high-level language by translating it one line at a time.</p>