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Preface
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 happ ened 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 ab out to start my new book from scratch over the holiday break, Dr. Atul
Prakash showed me the Think Python bo ok 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
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 expressions
for searching and parsing, automating tasks on your computer, retrieving data
across the network, scraping web pages for data, object-oriented programming,
using web services, parsing XML and JSON data, creating and using databases
using Structured Query Language, and visualizing data.
The ultimate goal of all of these changes is to shift from a Computer Science to an
Informatics focus and to only include topics into a first technology class that can
be useful even if one chooses not to become a professional programmer.
1
Except, of course, for this line.
