[chapter]

| 3 Think Python

| How to Think Like a Computer Scientist

2nd Edition, Version 2.2.14

| 3 Think Python

| How to Think Like a Computer Scientist

2nd Edition, Version 2.2.14

Allen Downey

Green Tea Press

Needham, Massachusetts

Copyright © 2015 Allen Downey.

| Green Tea Press

| 9 Washburn Ave

| Needham MA 02492

Permission is granted to copy, distribute, and/or modify this document

under the terms of the Creative Commons Attribution-NonCommercial 3.0

Unported License, which is available at

http://creativecommons.org/licenses/by-nc/3.0/.

The original form of this book is LaTeX source code. Compiling this

LaTeX source has the effect of generating a device-independent

representation of a textbook, which can be converted to other formats

and printed.

The LaTeX source for this book is available from

http://www.thinkpython2.com

Think Python: How to Think Like a Computer Scientist

Allen B. Downey

2nd Edition, Version 2.2.14

Preface

=======

The strange history of this book

--------------------------------

In January 1999 I was preparing to teach an introductory programming

class in Java. I had taught it three times and I was getting frustrated.

The failure rate in the class was too high and, even for students who

succeeded, the overall level of achievement was too low.

One of the problems I saw was the books. They were too big, with too

much unnecessary detail about Java, and not enough high-level guidance

about how to program. And they all suffered from the trap door effect:

they would start out easy, proceed gradually, and then somewhere around

Chapter 5 the bottom would fall out. The students would get too much new

material, too fast, and I would spend the rest of the semester picking

up the pieces.

Two weeks before the first day of classes, I decided to write my own

book. My goals were:

- Keep it short. It is better for students to read 10 pages than not

read 50 pages.

- Be careful with vocabulary. I tried to minimize jargon and define

each term at first use.

- Build gradually. To avoid trap doors, I took the most difficult

topics and split them into a series of small steps.

- Focus on programming, not the programming language. I included the

minimum useful subset of Java and left out the rest.

I needed a title, so on a whim I chose *How to Think Like a Computer

Scientist*.

My first version was rough, but it worked. Students did the reading, and

they understood enough that I could spend class time on the hard topics,

the interesting topics and (most important) letting the students

practice.

I released the book under the GNU Free Documentation License, which

allows users to copy, modify, and distribute the book.

What happened next is the cool part. Jeff Elkner, a high school teacher

in Virginia, adopted my book and translated it into Python. He sent me a

copy of his translation, and I had the unusual experience of learning

Python by reading my own book. As Green Tea Press, I published the first

Python version in 2001.

In 2003 I started teaching at Olin College and I got to teach Python for

the first time. The contrast with Java was striking. Students struggled

less, learned more, worked on more interesting projects, and generally

had a lot more fun.

Since then I’ve continued to develop the book, correcting errors,

improving some of the examples and adding material, especially

exercises.

The result is this book, now with the less grandiose title *Think

Python*. Some of the changes are:

- I added a section about debugging at the end of each chapter. These

sections present general techniques for finding and avoiding bugs,

and warnings about Python pitfalls.

- I added more exercises, ranging from short tests of understanding to

a few substantial projects. Most exercises include a link to my

solution.

- I added a series of case studies—longer examples with exercises,

solutions, and discussion.

- I expanded the discussion of program development plans and basic

design patterns.

- I added appendices about debugging and analysis of algorithms.

The second edition of *Think Python* has these new features:

- The book and all supporting code have been updated to Python 3.

- I added a few sections, and more details on the web, to help

beginners get started running Python in a browser, so you don’t have

to deal with installing Python until you want to.

- For Chapter [turtle] I switched from my own turtle graphics package,

called Swampy, to a more standard Python module, turtle, which is

easier to install and more powerful.

- I added a new chapter called “The Goodies”, which introduces some

additional Python features that are not strictly necessary, but

sometimes handy.

I hope you enjoy working with this book, and that it helps you learn to

program and think like a computer scientist, at least a little bit.

| Allen B. Downey

| Olin College

Acknowledgments

---------------

Many thanks to Jeff Elkner, who translated my Java book into Python,

which got this project started and introduced me to what has turned out

to be my favorite language.

Thanks also to Chris Meyers, who contributed several sections to *How to

Think Like a Computer Scientist*.

Thanks to the Free Software Foundation for developing the GNU Free

Documentation License, which helped make my collaboration with Jeff and

Chris possible, and Creative Commons for the license I am using now.

Thanks to the editors at Lulu who worked on *How to Think Like a

Computer Scientist*.

Thanks to the editors at O’Reilly Media who worked on *Think Python*.

Thanks to all the students who worked with earlier versions of this book

and all the contributors (listed below) who sent in corrections and

suggestions.

Contributor List

----------------

More than 100 sharp-eyed and thoughtful readers have sent in suggestions

and corrections over the past few years. Their contributions, and

enthusiasm for this project, have been a huge help.

If you have a suggestion or correction, please send email to

feedback@thinkpython.com. If I make a change based on your feedback, I

will add you to the contributor list (unless you ask to be omitted).

If you include at least part of the sentence the error appears in, that

makes it easy for me to search. Page and section numbers are fine, too,

but not quite as easy to work with. Thanks!

- Lloyd Hugh Allen sent in a correction to Section 8.4.

- Yvon Boulianne sent in a correction of a semantic error in Chapter 5.

- Fred Bremmer submitted a correction in Section 2.1.

- Jonah Cohen wrote the Perl scripts to convert the LaTeX source for

this book into beautiful HTML.

- Michael Conlon sent in a grammar correction in Chapter 2 and an

improvement in style in Chapter 1, and he initiated discussion on the

technical aspects of interpreters.

- Benoit Girard sent in a correction to a humorous mistake in Section

5.6.

- Courtney Gleason and Katherine Smith wrote horsebet.py, which was

used as a case study in an earlier version of the book. Their program

can now be found on the website.

- Lee Harr submitted more corrections than we have room to list here,

and indeed he should be listed as one of the principal editors of the

text.

- James Kaylin is a student using the text. He has submitted numerous

corrections.

- David Kershaw fixed the broken catTwice function in Section 3.10.

- Eddie Lam has sent in numerous corrections to Chapters 1, 2, and 3.

He also fixed the Makefile so that it creates an index the first time

it is run and helped us set up a versioning scheme.

- Man-Yong Lee sent in a correction to the example code in Section 2.4.

- David Mayo pointed out that the word \`\`unconsciously“ in Chapter 1

needed to be changed to \`\`subconsciously”.

- Chris McAloon sent in several corrections to Sections 3.9 and 3.10.

- Matthew J. Moelter has been a long-time contributor who sent in

numerous corrections and suggestions to the book.

- Simon Dicon Montford reported a missing function definition and

several typos in Chapter 3. He also found errors in the increment

function in Chapter 13.

- John Ouzts corrected the definition of \`\`return value" in Chapter

3.

- Kevin Parks sent in valuable comments and suggestions as to how to

improve the distribution of the book.

- David Pool sent in a typo in the glossary of Chapter 1, as well as

kind words of encouragement.

- Michael Schmitt sent in a correction to the chapter on files and

exceptions.

- Robin Shaw pointed out an error in Section 13.1, where the printTime

function was used in an example without being defined.

- Paul Sleigh found an error in Chapter 7 and a bug in Jonah Cohen’s

Perl script that generates HTML from LaTeX.

- Craig T. Snydal is testing the text in a course at Drew University.

He has contributed several valuable suggestions and corrections.

- Ian Thomas and his students are using the text in a programming

course. They are the first ones to test the chapters in the latter

half of the book, and they have made numerous corrections and

suggestions.

- Keith Verheyden sent in a correction in Chapter 3.

- Peter Winstanley let us know about a longstanding error in our Latin

in Chapter 3.

- Chris Wrobel made corrections to the code in the chapter on file I/O

and exceptions.

- Moshe Zadka has made invaluable contributions to this project. In

addition to writing the first draft of the chapter on Dictionaries,

he provided continual guidance in the early stages of the book.

- Christoph Zwerschke sent several corrections and pedagogic

suggestions, and explained the difference between *gleich* and

*selbe*.

- James Mayer sent us a whole slew of spelling and typographical

errors, including two in the contributor list.

- Hayden McAfee caught a potentially confusing inconsistency between

two examples.

- Angel Arnal is part of an international team of translators working

on the Spanish version of the text. He has also found several errors

in the English version.

- Tauhidul Hoque and Lex Berezhny created the illustrations in Chapter

1 and improved many of the other illustrations.

- Dr. Michele Alzetta caught an error in Chapter 8 and sent some

interesting pedagogic comments and suggestions about Fibonacci and

Old Maid.

- Andy Mitchell caught a typo in Chapter 1 and a broken example in

Chapter 2.

- Kalin Harvey suggested a clarification in Chapter 7 and caught some

typos.

- Christopher P. Smith caught several typos and helped us update the

book for Python 2.2.

- David Hutchins caught a typo in the Foreword.

- Gregor Lingl is teaching Python at a high school in Vienna, Austria.

He is working on a German translation of the book, and he caught a

couple of bad errors in Chapter 5.

- Julie Peters caught a typo in the Preface.

- Florin Oprina sent in an improvement in makeTime, a correction in

printTime, and a nice typo.

- D. J. Webre suggested a clarification in Chapter 3.

- Ken found a fistful of errors in Chapters 8, 9 and 11.

- Ivo Wever caught a typo in Chapter 5 and suggested a clarification in

Chapter 3.

- Curtis Yanko suggested a clarification in Chapter 2.

- Ben Logan sent in a number of typos and problems with translating the

book into HTML.

- Jason Armstrong saw the missing word in Chapter 2.

- Louis Cordier noticed a spot in Chapter 16 where the code didn’t

match the text.

- Brian Cain suggested several clarifications in Chapters 2 and 3.

- Rob Black sent in a passel of corrections, including some changes for

Python 2.2.

- Jean-Philippe Rey at Ecole Centrale Paris sent a number of patches,

including some updates for Python 2.2 and other thoughtful

improvements.

- Jason Mader at George Washington University made a number of useful

suggestions and corrections.

- Jan Gundtofte-Bruun reminded us that “a error” is an error.

- Abel David and Alexis Dinno reminded us that the plural of “matrix”

is “matrices”, not “matrixes”. This error was in the book for years,

but two readers with the same initials reported it on the same day.

Weird.

- Charles Thayer encouraged us to get rid of the semi-colons we had put

at the ends of some statements and to clean up our use of “argument”

and “parameter”.

- Roger Sperberg pointed out a twisted piece of logic in Chapter 3.

- Sam Bull pointed out a confusing paragraph in Chapter 2.

- Andrew Cheung pointed out two instances of “use before def”.

- C. Corey Capel spotted the missing word in the Third Theorem of

Debugging and a typo in Chapter 4.

- Alessandra helped clear up some Turtle confusion.

- Wim Champagne found a brain-o in a dictionary example.

- Douglas Wright pointed out a problem with floor division in arc.

- Jared Spindor found some jetsam at the end of a sentence.

- Lin Peiheng sent a number of very helpful suggestions.

- Ray Hagtvedt sent in two errors and a not-quite-error.

- Torsten Hübsch pointed out an inconsistency in Swampy.

- Inga Petuhhov corrected an example in Chapter 14.

- Arne Babenhauserheide sent several helpful corrections.

- Mark E. Casida is is good at spotting repeated words.

- Scott Tyler filled in a that was missing. And then sent in a heap of

corrections.

- Gordon Shephard sent in several corrections, all in separate emails.

- Andrew Turner spotted an error in Chapter 8.

- Adam Hobart fixed a problem with floor division in arc.

- Daryl Hammond and Sarah Zimmerman pointed out that I served up

math.pi too early. And Zim spotted a typo.

- George Sass found a bug in a Debugging section.

- Brian Bingham suggested Exercise [exrotatepairs].

- Leah Engelbert-Fenton pointed out that I used tuple as a variable

name, contrary to my own advice. And then found a bunch of typos and

a “use before def”.

- Joe Funke spotted a typo.

- Chao-chao Chen found an inconsistency in the Fibonacci example.

- Jeff Paine knows the difference between space and spam.

- Lubos Pintes sent in a typo.

- Gregg Lind and Abigail Heithoff suggested Exercise [checksum].

- Max Hailperin has sent in a number of corrections and suggestions.

Max is one of the authors of the extraordinary *Concrete

Abstractions*, which you might want to read when you are done with

this book.

- Chotipat Pornavalai found an error in an error message.

- Stanislaw Antol sent a list of very helpful suggestions.

- Eric Pashman sent a number of corrections for Chapters 4–11.

- Miguel Azevedo found some typos.

- Jianhua Liu sent in a long list of corrections.

- Nick King found a missing word.

- Martin Zuther sent a long list of suggestions.

- Adam Zimmerman found an inconsistency in my instance of an “instance”

and several other errors.

- Ratnakar Tiwari suggested a footnote explaining degenerate triangles.

- Anurag Goel suggested another solution for ``is_abecedarian`` and

sent some additional corrections. And he knows how to spell Jane

Austen.

- Kelli Kratzer spotted one of the typos.

- Mark Griffiths pointed out a confusing example in Chapter 3.

- Roydan Ongie found an error in my Newton’s method.

- Patryk Wolowiec helped me with a problem in the HTML version.

- Mark Chonofsky told me about a new keyword in Python 3.

- Russell Coleman helped me with my geometry.

- Wei Huang spotted several typographical errors.

- Karen Barber spotted the the oldest typo in the book.

- Nam Nguyen found a typo and pointed out that I used the Decorator

pattern but didn’t mention it by name.

- Stéphane Morin sent in several corrections and suggestions.

- Paul Stoop corrected a typo in ``uses_only``.

- Eric Bronner pointed out a confusion in the discussion of the order

of operations.

- Alexandros Gezerlis set a new standard for the number and quality of

suggestions he submitted. We are deeply grateful!

- Gray Thomas knows his right from his left.

- Giovanni Escobar Sosa sent a long list of corrections and

suggestions.

- Alix Etienne fixed one of the URLs.

- Kuang He found a typo.

- Daniel Neilson corrected an error about the order of operations.

- Will McGinnis pointed out that polyline was defined differently in

two places.

- Swarup Sahoo spotted a missing semi-colon.

- Frank Hecker pointed out an exercise that was under-specified, and

some broken links.

- Animesh B helped me clean up a confusing example.

- Martin Caspersen found two round-off errors.

- Gregor Ulm sent several corrections and suggestions.

- Dimitrios Tsirigkas suggested I clarify an exercise.

- Carlos Tafur sent a page of corrections and suggestions.

- Martin Nordsletten found a bug in an exercise solution.

- Lars O.D. Christensen found a broken reference.

- Victor Simeone found a typo.

- Sven Hoexter pointed out that a variable named input shadows a

build-in function.

- Viet Le found a typo.

- Stephen Gregory pointed out the problem with cmp in Python 3.

- Matthew Shultz let me know about a broken link.

- Lokesh Kumar Makani let me know about some broken links and some

changes in error messages.

- Ishwar Bhat corrected my statement of Fermat’s last theorem.

- Brian McGhie suggested a clarification.

- Andrea Zanella translated the book into Italian, and sent a number of

corrections along the way.

- Many, many thanks to Melissa Lewis and Luciano Ramalho for excellent

comments and suggestions on the second edition.

- Thanks to Harry Percival from PythonAnywhere for his help getting

people started running Python in a browser.

- Xavier Van Aubel made several useful corrections in the second

edition.

The way of the program

======================

The goal of this book is to teach you to think like a computer

scientist. This way of thinking combines some of the best features of

mathematics, engineering, and natural science. Like mathematicians,

computer scientists use formal languages to denote ideas (specifically

computations). Like engineers, they design things, assembling components

into systems and evaluating tradeoffs among alternatives. Like

scientists, they observe the behavior of complex systems, form

hypotheses, and test predictions.

The single most important skill for a computer scientist is **problem

solving**. Problem solving means the ability to formulate problems,

think creatively about solutions, and express a solution clearly and

accurately. As it turns out, the process of learning to program is an

excellent opportunity to practice problem-solving skills. That’s why

this chapter is called, “The way of the program”.

On one level, you will be learning to program, a useful skill by itself.

On another level, you will use programming as a means to an end. As we

go along, that end will become clearer.

What is a program?

------------------

A **program** is a sequence of instructions that specifies how to

perform a computation. The computation might be something mathematical,

such as solving a system of equations or finding the roots of a

polynomial, but it can also be a symbolic computation, such as searching

and replacing text in a document or something graphical, like processing

an image or playing a video.

The details look different in different languages, but a few basic

instructions appear in just about every language:

input:

Get data from the keyboard, a file, the network, or some other

device.

output:

Display data on the screen, save it in a file, send it over the

network, etc.

math:

Perform basic mathematical operations like addition and

multiplication.

conditional execution:

Check for certain conditions and run the appropriate code.

repetition:

Perform some action repeatedly, usually with some variation.

Believe it or not, that’s pretty much all there is to it. Every program

you’ve ever used, no matter how complicated, is made up of instructions

that look pretty much like these. So you can think of programming as the

process of breaking a large, complex task into smaller and smaller

subtasks until the subtasks are simple enough to be performed with one

of these basic instructions.

Running Python

--------------

One of the challenges of getting started with Python is that you might

have to install Python and related software on your computer. If you are

familiar with your operating system, and especially if you are

comfortable with the command-line interface, you will have no trouble

installing Python. But for beginners, it can be painful to learn about

system administration and programming at the same time.

To avoid that problem, I recommend that you start out running Python in

a browser. Later, when you are comfortable with Python, I’ll make

suggestions for installing Python on your computer.

There are a number of web pages you can use to run Python. If you

already have a favorite, go ahead and use it. Otherwise I recommend

PythonAnywhere. I provide detailed instructions for getting started at

http://tinyurl.com/thinkpython2e.

There are two versions of Python, called Python 2 and Python 3. They are

very similar, so if you learn one, it is easy to switch to the other. In

fact, there are only a few differences you will encounter as a beginner.

This book is written for Python 3, but I include some notes about Python

2.

The Python **interpreter** is a program that reads and executes Python

code. Depending on your environment, you might start the interpreter by

clicking on an icon, or by typing python on a command line. When it

starts, you should see output like this:

::

Python 3.4.0 (default, Jun 19 2015, 14:20:21)

[GCC 4.8.2] on linux

Type "help", "copyright", "credits" or "license" for more information.

>>>

The first three lines contain information about the interpreter and the

operating system it’s running on, so it might be different for you. But

you should check that the version number, which is 3.4.0 in this

example, begins with 3, which indicates that you are running Python 3.

If it begins with 2, you are running (you guessed it) Python 2.

The last line is a **prompt** that indicates that the interpreter is

ready for you to enter code. If you type a line of code and hit Enter,

the interpreter displays the result:

::

>>> 1 + 1

2

Now you’re ready to get started. From here on, I assume that you know

how to start the Python interpreter and run code.

The first program

-----------------

Traditionally, the first program you write in a new language is called

“Hello, World!” because all it does is display the words “Hello,

World!”. In Python, it looks like this:

::

>>> print('Hello, World!')

This is an example of a **print statement**, although it doesn’t

actually print anything on paper. It displays a result on the screen. In

this case, the result is the words

::

Hello, World!

The quotation marks in the program mark the beginning and end of the

text to be displayed; they don’t appear in the result.

The parentheses indicate that print is a function. We’ll get to

functions in Chapter [funcchap].

In Python 2, the print statement is slightly different; it is not a

function, so it doesn’t use parentheses.

::

>>> print 'Hello, World!'

This distinction will make more sense soon, but that’s enough to get

started.

Arithmetic operators

--------------------

After “Hello, World”, the next step is arithmetic. Python provides

**operators**, which are special symbols that represent computations

like addition and multiplication.

The operators +, -, and perform addition, subtraction, and

multiplication, as in the following examples:

::

>>> 40 + 2

42

>>> 43 - 1

42

>>> 6 * 7

42

The operator / performs division:

::

>>> 84 / 2

42.0

You might wonder why the result is 42.0 instead of 42. I’ll explain in

the next section.

Finally, the operator \* performs exponentiation; that is, it raises a

number to a power:

::

>>> 6**2 + 6

42

In some other languages, ``^`` is used for exponentiation, but in Python

it is a bitwise operator called XOR. If you are not familiar with

bitwise operators, the result will surprise you:

::

>>> 6 ^ 2

4

I won’t cover bitwise operators in this book, but you can read about

them at http://wiki.python.org/moin/BitwiseOperators.

Values and types

----------------

A **value** is one of the basic things a program works with, like a

letter or a number. Some values we have seen so far are 2, 42.0, and

``'Hello, World!'``.

These values belong to different **types**: 2 is an **integer**, 42.0 is

a **floating-point number**, and ``'Hello, World!'`` is a **string**,

so-called because the letters it contains are strung together.

If you are not sure what type a value has, the interpreter can tell you:

::

>>> type(2)

<class 'int'>

>>> type(42.0)

<class 'float'>

>>> type('Hello, World!')

<class 'str'>

In these results, the word “class” is used in the sense of a category; a

type is a category of values.

Not surprisingly, integers belong to the type int, strings belong to str

and floating-point numbers belong to float.

What about values like ``'2'`` and ``'42.0'``? They look like numbers,

but they are in quotation marks like strings.

::

>>> type('2')

<class 'str'>

>>> type('42.0')

<class 'str'>

They’re strings.

When you type a large integer, you might be tempted to use commas

between groups of digits, as in 1,000,000. This is not a legal *integer*

in Python, but it is legal:

::

>>> 1,000,000

(1, 0, 0)

That’s not what we expected at all! Python interprets 1,000,000 as a

comma-separated sequence of integers. We’ll learn more about this kind

of sequence later.

Formal and natural languages

----------------------------

**Natural languages** are the languages people speak, such as English,

Spanish, and French. They were not designed by people (although people

try to impose some order on them); they evolved naturally.

**Formal languages** are languages that are designed by people for

specific applications. For example, the notation that mathematicians use

is a formal language that is particularly good at denoting relationships

among numbers and symbols. Chemists use a formal language to represent

the chemical structure of molecules. And most importantly:

**Programming languages are formal languages that have been designed

to express computations.**

Formal languages tend to have strict **syntax** rules that govern the

structure of statements. For example, in mathematics the statement

:math:`3 + 3 = 6` has correct syntax, but :math:`3 + = 3 \$ 6` does not.

In chemistry :math:`H_2O` is a syntactically correct formula, but

:math:`_2Zz` is not.

Syntax rules come in two flavors, pertaining to **tokens** and

structure. Tokens are the basic elements of the language, such as words,

numbers, and chemical elements. One of the problems with

:math:`3 += 3 \$ 6` is that :math:` \$ ` is not a legal token in

mathematics (at least as far as I know). Similarly, :math:`_2Zz` is not

legal because there is no element with the abbreviation :math:`Zz`.

The second type of syntax rule pertains to the way tokens are combined.

The equation :math:`3 += 3` is illegal because even though :math:`+` and

:math:`=` are legal tokens, you can’t have one right after the other.

Similarly, in a chemical formula the subscript comes after the element

name, not before.

This is @ well-structured Engli$h sentence with invalid t\*kens in it.

This sentence all valid tokens has, but invalid structure with.

When you read a sentence in English or a statement in a formal language,

you have to figure out the structure (although in a natural language you

do this subconsciously). This process is called **parsing**.

Although formal and natural languages have many features in

common—tokens, structure, and syntax—there are some differences:

ambiguity:

Natural languages are full of ambiguity, which people deal with by

using contextual clues and other information. Formal languages are

designed to be nearly or completely unambiguous, which means that

any statement has exactly one meaning, regardless of context.

redundancy:

In order to make up for ambiguity and reduce misunderstandings,

natural languages employ lots of redundancy. As a result, they are

often verbose. Formal languages are less redundant and more concise.

literalness:

Natural languages are full of idiom and metaphor. If I say, “The

penny dropped”, there is probably no penny and nothing dropping

(this idiom means that someone understood something after a period

of confusion). Formal languages mean exactly what they say.

Because we all grow up speaking natural languages, it is sometimes hard

to adjust to formal languages. The difference between formal and natural

language is like the difference between poetry and prose, but more so:

Poetry:

Words are used for their sounds as well as for their meaning, and

the whole poem together creates an effect or emotional response.

Ambiguity is not only common but often deliberate.

Prose:

The literal meaning of words is more important, and the structure

contributes more meaning. Prose is more amenable to analysis than

poetry but still often ambiguous.

Programs:

The meaning of a computer program is unambiguous and literal, and

can be understood entirely by analysis of the tokens and structure.

Formal languages are more dense than natural languages, so it takes

longer to read them. Also, the structure is important, so it is not

always best to read from top to bottom, left to right. Instead, learn to

parse the program in your head, identifying the tokens and interpreting

the structure. Finally, the details matter. Small errors in spelling and

punctuation, which you can get away with in natural languages, can make

a big difference in a formal language.

Debugging

---------

Programmers make mistakes. For whimsical reasons, programming errors are

called **bugs** and the process of tracking them down is called

**debugging**.

Programming, and especially debugging, sometimes brings out strong

emotions. If you are struggling with a difficult bug, you might feel

angry, despondent, or embarrassed.

There is evidence that people naturally respond to computers as if they

were people. When they work well, we think of them as teammates, and

when they are obstinate or rude, we respond to them the same way we

respond to rude, obstinate people (Reeves and Nass, *The Media Equation:

How People Treat Computers, Television, and New Media Like Real People

and Places*).

Preparing for these reactions might help you deal with them. One

approach is to think of the computer as an employee with certain

strengths, like speed and precision, and particular weaknesses, like

lack of empathy and inability to grasp the big picture.

Your job is to be a good manager: find ways to take advantage of the

strengths and mitigate the weaknesses. And find ways to use your

emotions to engage with the problem, without letting your reactions

interfere with your ability to work effectively.

Learning to debug can be frustrating, but it is a valuable skill that is

useful for many activities beyond programming. At the end of each

chapter there is a section, like this one, with my suggestions for

debugging. I hope they help!

Glossary

--------

problem solving:

The process of formulating a problem, finding a solution, and

expressing it.

high-level language:

A programming language like Python that is designed to be easy for

humans to read and write.

low-level language:

A programming language that is designed to be easy for a computer to

run; also called “machine language” or “assembly language”.

portability:

A property of a program that can run on more than one kind of

computer.

interpreter:

A program that reads another program and executes it

prompt:

Characters displayed by the interpreter to indicate that it is ready

to take input from the user.

program:

A set of instructions that specifies a computation.

print statement:

An instruction that causes the Python interpreter to display a value

on the screen.

operator:

A special symbol that represents a simple computation like addition,

multiplication, or string concatenation.

value:

One of the basic units of data, like a number or string, that a

program manipulates.

type:

A category of values. The types we have seen so far are integers

(type int), floating-point numbers (type float), and strings (type

str).

integer:

A type that represents whole numbers.

floating-point:

A type that represents numbers with fractional parts.

string:

A type that represents sequences of characters.

natural language:

Any one of the languages that people speak that evolved naturally.

formal language:

Any one of the languages that people have designed for specific

purposes, such as representing mathematical ideas or computer

programs; all programming languages are formal languages.

token:

One of the basic elements of the syntactic structure of a program,

analogous to a word in a natural language.

syntax:

The rules that govern the structure of a program.

parse:

To examine a program and analyze the syntactic structure.

bug:

An error in a program.

debugging:

The process of finding and correcting bugs.

Exercises

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It is a good idea to read this book in front of a computer so you can

try out the examples as you go.

Whenever you are experimenting with a new feature, you should try to

make mistakes. For example, in the “Hello, world!” program, what happens

if you leave out one of the quotation marks? What if you leave out both?

What if you spell print wrong?

This kind of experiment helps you remember what you read; it also helps

when you are programming, because you get to know what the error

messages mean. It is better to make mistakes now and on purpose than

later and accidentally.

#. In a print statement, what happens if you leave out one of the

parentheses, or both?

#. If you are trying to print a string, what happens if you leave out