CHAPTER 1 ■ INTRODUCTION
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can expect and what is expected of you. It also outlines the specific contents of the various chapters to
come in case you want to skip around.
What’s All This, Then?
This is a book about algorithmic problem solving for Python programmers. Just like books on, say,
object-oriented patterns, the problems it deals with are of a general nature—as are the solutions. Your
task as an algorist will, in many cases, be more than simply to implement or execute an existing
algorithm, as you would, for example, in solving an algebra problem. Instead, you are expected to come
up with new algorithms—new general solutions to hitherto unseen, general problems. In this book, you
are going to learn principles for constructing such solutions.
This may not be your typical algorithm book, though. Most of the authoritative books on the subject
(such as the Knuth’s classics or the industry-standard textbook by Cormen et al.) have a heavy formal
and theoretical slant, even though some of them (such as the one by Kleinberg and Tardos) lean more in
the direction of readability. Instead of trying to replace any of these excellent books, I’d like to
supplement them. Building on my experience from teaching algorithms, I try to explain as clearly as
possible how the algorithms work and what common principles underlie many of them. For a
programmer, these explanations are probably enough. Chances are you’ll be able to understand why the
algorithms are correct and how to adapt them to new problems you may come to face. If, however, you
need the full depth of the more formalistic and encyclopedic textbooks, I hope the foundation you get in
this book will help you understand the theorems and proofs you encounter there.
There is another genre of algorithm books as well: the “(Data Structures and) Algorithms in blank”
kind, where the blank is the author’s favorite programming language. There are quite a few of these
(especially for blank = Java, it seems), but many of them focus on relatively basic data structures, to the
detriment of the more meaty stuff. This is understandable if the book is designed to be used in a basic
course on data structures, for example, but for a Python programmer, learning about singly and doubly
linked lists may not be all that exciting (although you will hear a bit about those in the next chapter). And
even though techniques such as hashing are highly important, you get hash tables for free in the form of
Python dictionaries; there’s no need to implement them from scratch. Instead, I focus on more high-
level algorithms. Many important concepts that are available as black-box implementations either in the
Python language itself or in the standard library (such as sorting, searching, and hashing) are explained
more briefly, in special “black box” sidebars throughout the text.
There is, of course, another factor that separates this book from those in the “Algorithms in
Java/C/C++/C#” genre, namely, that the blank is Python. This places the book one step closer to the
language-independent books (such as those by Knuth,
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Cormen et al., and Kleinberg and Tardos, for
example), which often use pseudocode, the kind of fake programming language that is designed to be
readable rather than executable. One of Python’s distinguishing features is its readability; it is, more or
less, executable pseudocode. Even if you’ve never programmed in Python, you could probably decipher
the meaning of most basic Python programs. The code in this book is designed to be readable exactly in
this fashion—you need not be a Python expert to understand the examples (although you might need to
look up some built-in functions and the like). And if you want to pretend the examples are actually
pseudocode, feel free to do so. To sum up …
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Knuth is also well-known for using assembly code for an abstract computer of his own design.
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