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Preface

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<h1><a name="htoc1">Chapter&nbsp;0</a>&nbsp;&nbsp;Preface</h1>

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<h2>Python for Informatics: Remixing an Open Book</h2>

It is quite natural for academics who are continuously told to

"publish or perish" to want to always create something from scratch

that is their own fresh creation. This book is an

experiment in not starting from scratch, but instead "re-mixing"

the book titled

<em>Think Python: How to Think Like

a Computer Scientist</em>

written by Allen B. Downey, Jeff Elkner and others.<br />

<br />

In December of 2009, I was preparing to teach

<b>SI502 - Networked Programming</b> at the University of Michigan

for the fifth semester in a row and decided it was time

to write a Python textbook that focused on exploring data

instead of understanding algorithms and abstractions.

My goal in SI502 is to teach people life-long data handling

skills using Python. Few of my

students were planning to be be professional

computer programmers. Instead, they

planned be librarians, managers, lawyers, biologists, economists, etc.

who happened to want to skillfully use technology in their chosen field.<br />

<br />

I never seemed to find the perfect data-oriented Python

book for my course so I set out

to write just such a book. Luckily at a faculty meeting three weeks

before I was about to start my new book from scratch over

the holiday break,

Dr. Atul Prakash showed me the <em>Think Python</em> book which he had

used to teach his Python course that semester.

It is a well-written Computer Science text with a focus on

short, direct explanations and ease of learning. <br />

<br />

The overall book structure

has been changed to get to doing data analysis problems as quickly as

possible and have a series of running examples and exercises

about data analysis from the very beginning. <br />

<br />

The chapters 2-10 are similar to the <em>Think Python</em> book

but there have been major changes. Number-oriented examples and

exercises have been replaced with data-oriented exercises.

Topics are presented in the order to needed to build increasingly

sophisticated data analysis solutions. Some topics like <tt>try</tt> and

<tt>except</tt> are pulled forward and presented as part of the chapter

on conditionals. Functions are given very light treatment until

they are needed to handle program complexity rather introduced

as an early lesson in abstraction. Nearly all user-defined functions

have been removed from the example code and exercises outside Chapter 4.

The word "recursion"^{<a name="text1" href="#note1">1</a>}

does not appear in the book at all.<br />

<br />

In chapters 1 and 11-16, all of the material is brand new, focusing

on real-world uses and simple examples of Python for data analysis

including regular expressions for searching and parsing,

automating tasks on your computer, retrieving data across

the network, scraping web pages for data,

using web services, parsing XML and JSON data, and creating

and using databases using Structured Query Language.<br />

<br />

The ultimate goal of all of these changes is a shift from a

Computer Science to an Informatics

focus is to only include topics into a first technology

class that can be useful even if one chooses not to

become a professional programmer.<br />

<br />

Students who find this book interesting and want to further explore

should look at Allen B. Downey's <em>Think Python</em> book. Because there

is a lot of overlap between the two books,

students will quickly pick up skills in the additional

areas of technical programming and algorithmic thinking

that are covered in <em>Think Python</em>.

And given that the books have a similar writing style, you should be

able to move quickly through <em>Think Python</em> with a minimum of effort.<br />

<br />

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As the copyright holder of <em>Think Python</em>,

Allen has given me permission to change the book's license

on the material from his book that remains in this book

from the

GNU Free Documentation License

to the more recent

Creative Commons Attribution --- Share Alike

license.

This follows a general shift in open documentation licenses moving

from the GFDL to the CC-BY-SA (i.e. Wikipedia).

Using the CC-BY-SA license maintains the book's

strong copyleft tradition while making it even more straightforward

for new authors to reuse this material as they see fit.<br />

<br />

I feel that this book serves an example of why open

materials are so important to the future of education,

and want to thank Allen B. Downey and Cambridge University

Press for their forward looking decision to make the book available

under an open Copyright. I hope they are pleased with the

results of my efforts and I hope that you the reader are pleased with

<em>our</em> collective efforts.<br />

<br />

I would like to thank Allen B. Downey and Lauren Cowles for their help,

patience, and guidance in dealing with and resolving the copyright

issues around this book.<br />

<br />

Charles Severance<br />

www.dr-chuck.com<br />

Ann Arbor, MI, USA<br />

September 9, 2013<br />

<br />

Charles Severance is a

Clinical Associate Professor

at the University of Michigan School of Information.<br />

<br />

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<h2>Preface for "Think Python"</h2>

<h3>The strange history of "Think Python"</h3>

(Allen B. Downey)<br />

<br />

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.<br />

<br />

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.<br />

<br />

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

own book.

My goals were:

<ul><li>Keep it short. It is better for students to read 10 pages

than not read 50 pages.<br />

<br />

</li><li>Be careful with vocabulary. I tried to minimize the jargon

and define each term at first use.<br />

<br />

</li><li>Build gradually. To avoid trap doors, I took the most difficult

topics and split them into a series of small steps. <br />

<br />

</li><li>Focus on programming, not the programming language. I included

the minimum useful subset of Java and left out the rest.</li></ul>

I needed a title, so on a whim I chose <em>How to Think Like

a Computer Scientist</em>.<br />

<br />

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.<br />

<br />

I released the book under the GNU Free Documentation License,

which allows users to copy, modify, and distribute the book.<br />

<br />

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<br />

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.<br />

<br />

Jeff and I revised the book, incorporated a case study by

Chris Meyers, and in 2001 we released <em>How to Think Like

a Computer Scientist: Learning with Python</em>, also under

the GNU Free Documentation License.

As Green Tea Press, I published the book and started selling

hard copies through Amazon.com and college book stores.

Other books from Green Tea Press are available at

<tt>greenteapress.com</tt>.<br />

<br />

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.<br />

<br />

Over the last five years I have continued to develop the book,

correcting errors, improving some of the examples and

adding material, especially exercises. In 2008 I started work

on a major revision---at the same time, I was

contacted by an editor at Cambridge University Press who

was interested in publishing the next edition. Good timing!<br />

<br />

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

you learn to program and think, at least a little bit, like

a computer scientist.<br />

<br />

<h3>Acknowledgements for "Think Python"</h3>

(Allen B. Downey)<br />

<br />

First and most importantly, I thank 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.<br />

<br />

I also thank Chris Meyers, who contributed several sections

to <em>How to Think Like a Computer Scientist</em>.<br />

<br />

And I thank the Free Software Foundation for developing

the GNU Free Documentation License, which helped make

my collaboration with Jeff and Chris possible.<br />

<br />

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<br />

I also thank the editors at Lulu who worked on

<em>How to Think Like a Computer Scientist</em>.<br />

<br />

I thank all the students who worked with earlier

versions of this book and all the contributors (listed

in an Appendix) who sent in corrections and suggestions.<br />

<br />

And I thank my wife, Lisa, for her work on this book, and Green

Tea Press, and everything else, too.<br />

<br />

Allen B. Downey <br />

Needham MA<br />

Allen Downey is an Associate Professor of Computer Science at

the Franklin W. Olin College of Engineering.<br />

<br />

<hr width="50%" size="1" /><dl><dt><font size="5"><a name="note1" href="#text1">1</a></font></dt><dd>Except of course for this line.

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