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Python for Informatics
Exploring Information
Version 2.7.0
Charles Severance
Copyright © 2009- Charles Severance.
Printing history:
May 2014:
Editorial pass thanks to Sue Blumenberg.
October 2013:
Major revision to Chapters 13 and 14 to switch to JSON and use OAuth.
Added new chapter on Visualization.
September 2013:
Published book on Amazon CreateSpace
January 2010:
Published book using the University of Michigan Espresso Book ma-
chine.
December 2009:
Major revision to chapters 2-10 from
Think Python: How to Think Like
a Computer Scientist
and writing chapters 1 and 11-15 to produce
Python for In-
formatics: Exploring Information
June 2008:
Major revision, changed title to
Think Python: How to Think Like a Com-
puter Scientist.
August 2007:
Major revision, changed title to
How to Think Like a (Python) Program-
mer.
April 2002:
First edition of
How to Think Like a Computer Scientist.
This work is licensed under a Creative Common Attribution-NonCommercial-ShareAlike
3.0 Unported License. This license is available at
creativecommons.org/licenses/
by-nc-sa/3.0/.
You can see what the author considers commercial and non-commercial
uses of this material as well as license exemptions in the Appendix titled Copyright Detail.
A
The LTEX source for the
Think Python: How to Think Like a Computer Scientist
version
of this book is available from
http://www.thinkpython.com.
Preface
Python for Informatics: Remixing an Open Book
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 “remixing”
the book titled
Think Python: How to Think Like a Computer Scientist
written by
Allen B. Downey, Jeff Elkner, and others.
In December of 2009, I was preparing to teach
SI502 - Networked Programming
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 lifelong data
handling skills using Python. Few of my students were planning to be professional
computer programmers. Instead, they planned to be librarians, managers, lawyers,
biologists, economists, etc., who happened to want to skillfully use technology in
their chosen field.
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
Think Python
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.
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.
Chapters 2–10 are similar to the
Think Python
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 needed to build increasingly
sophisticated data analysis solutions. Some topics like
try
and
except
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
than introduced as an early lesson in abstraction. Nearly all user-defined functions
iv
Chapter 0. Preface
have been removed from the example code and exercises outside of Chapter 4.
The word “recursion”
1
does not appear in the book at all.
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 expres-
sions 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.
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.
Students who find this book interesting and want to further explore should look
at Allen B. Downey’s
Think Python
book. Because there is a lot of overlap be-
tween the two books, students will quickly pick up skills in the additional areas of
technical programming and algorithmic thinking that are covered in
Think Python.
And given that the books have a similar writing style, they should be able to move
quickly through
Think Python
with a minimum of effort.
As the copyright holder of
Think Python,
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 Attri-
bution — Share Alike license. This follows a general shift in open documentation
licenses moving from the GFDL to the CC-BY-SA (e.g., 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.
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
our
collective efforts.
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.
Charles Severance
www.dr-chuck.com
Ann Arbor, MI, USA
September 9, 2013
Charles Severance is a Clinical Associate Professor at the University of Michigan
School of Information.
1
Except,
of course, for this line.
Contents
Preface
1 Why should you learn to write programs?
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
1.10
1.11
1.12
1.13
Creativity and motivation . . . . . . . . . . . . . . . . . . . . .
Computer hardware architecture . . . . . . . . . . . . . . . . .
Understanding programming . . . . . . . . . . . . . . . . . . .
Words and sentences . . . . . . . . . . . . . . . . . . . . . . .
Conversing with Python . . . . . . . . . . . . . . . . . . . . . .
Terminology: interpreter and compiler . . . . . . . . . . . . . .
Writing a program . . . . . . . . . . . . . . . . . . . . . . . . .
What is a program? . . . . . . . . . . . . . . . . . . . . . . . .
The building blocks of programs . . . . . . . . . . . . . . . . .
What could possibly go wrong? . . . . . . . . . . . . . . . . . .
The learning journey . . . . . . . . . . . . . . . . . . . . . . .
Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
iii
1
2
3
4
5
6
8
10
11
12
13
14
15
16
19
19
20
21
21
2 Variables, expressions, and statements
2.1
2.2
2.3
2.4
Values and types . . . . . . . . . . . . . . . . . . . . . . . . . .
Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Variable names and keywords . . . . . . . . . . . . . . . . . . .
Statements . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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