NOTE: This loose-leaf, three-hole punched version of the textbook gives students the flexibility to take only what they need to class and add their own notes – all at an affordable price.

For courses in Java programming.

A layered, back-to-basics approach to Python programming

The authors of the long successful title, Building Java Programs, bring their proven and class-tested, back-to-basics strategy to teaching Python programming for the first time in Building Python Programs . Their signature layered approach introduces programming fundamentals first, with new syntax and concepts added over multiple chapters. Object-oriented programming is discussed only after students have developed a basic understanding of Python programming. This newly published textfocuses on problem solving with an emphasis on algorithmic thinking and is appropriate for the two-semester sequence in introductory computer science.

Stuart Reges is a Principal Lecturer in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. He manages the introductory programming classes, participates in the design of undergraduate curricula, and is involved in K-12 outreach. He held similar positions at Stanford University and the University of Arizona in a career spanning 30 years. In 1985 he won the Dinkelspiel Award for Outstanding Service to Undergraduate Education at Stanford. In 2012 he won the Distinguished Teaching Award which is the highest award given by UW for teaching.

Allison Obourn holds a position as Senior Lecturer in the Computer Science Department at the University of Arizona. She worked as a Lecturer at the University of Washington Computer Science & Engineering Department prior to moving to the University of Arizona. At both schools, she has specialized in teaching introductory programming and web programming. She holds a masters degree from the University of Washington Computer Science & Engineering Department.

Chapter 1 Introduction to Python Programming

1.1 Basic Computing Concepts

Why Programming?

Hardware and Software

The Digital Realm

The Process of Programming

Why Python?

The Python Programming Environment

1.2 And Now: Python

Printing Output

String Literals (Strings)

Escape Sequences

Printing a Complex Figure

Comments, Whitespace, and Readability

1.3 Program Errors

Syntax Errors

Logic Errors (Bugs)

1.4 Procedural Decomposition

Functions

Flow of Control

Identifiers and Keywords

Functions That Call Other Functions

An Example Runtime Error

1.5 Case Study: Drawing Figures

Structured Version

Final Version without Redundancy

Analysis of Flow of Execution

Chapter 2 Data and Definite Loops

2.1 Basic Data Concepts

Types

Expressions

Literals

Arithmetic Operators

Precedence

Mixing and Converting Types

2.2 Variables

A Program with Variables

Increment/Decrement Operators

Printing Multiple Values

2.3 The for Loop

Using a Loop Variable

Details about Ranges

String Multiplication and Printing Partial Lines

Nested for Loops

2.4 Managing Complexity

Scope

Pseudocode

Constants

2.5 Case Study: Hourglass Figure

Problem Decomposition and Pseudocode

Initial Structured Version

Adding a Constant

Chapter 3 Parameters and Graphics

3.1 Parameters

The Mechanics of Parameters

Limitations of Parameters

Multiple Parameters

Parameters versus Constants

Optional Parameters

3.2 Returning Values

The math Module

The random Module

Defining Functions That Return Values

Returning Multiple Values

3.3 Interactive Programs

Sample Interactive Program

3.4 Graphics

Introduction to DrawingPanel

Drawing Lines and Shapes

Colors

Drawing with Loops

Text and Fonts

Images

Procedural Decomposition with Graphics

3.5 Case Study: Projectile Trajectory

Unstructured Solution

Structured Solution

Graphical Version

Chapter 4 Conditional Execution

4.1 if/else Statements

Relational Operators

Nested if/else Statements

Factoring if/else Statements

Testing Multiple Conditions

4.2 Cumulative Algorithms

Cumulative Sum

Min/Max Loops

Cumulative Sum with if

Roundoff Errors

4.3 Functions with Conditional Execution

Preconditions and Postconditions

Raising Exceptions

Revisiting Return Values

Reasoning about Paths

4.4 Strings

String Methods

Accessing Characters by Index

Converting between Letters and Numbers

Cumulative Text Algorithms

4.5 Case Study: Basal Metabolic Rate

One-Person Unstructured Solution

Two-Person Unstructured Solution

Two-Person Structured Solution

Procedural Design Heuristics

Chapter 5 Program Logic and Indefinite Loops

5.1 The while Loop

A Loop to Find the Smallest Divisor

Loop Priming

5.2 Fencepost Algorithms

Fencepost with if

Sentinel Loops

Sentinel with Min/Max

5.3 Boolean Logic

Logical Operators

Boolean Variables and Flags

Predicate Functions

Boolean Zen

Short-Circuited Evaluation

5.4 Robust Programs

The try/except Statement

Handling User Errors

5.5 Assertions and Program Logic

Reasoning about Assertions

A Detailed Assertions Example

5.6 Case Study: Number Guessing Game

Initial Version without Hinting

Randomized Version with Hinting

Final Robust Version

Chapter 6 File Processing

6.1 File-Reading Basics

Data and Files

Reading a File in Python

Line-Based File Processing

Structure of Files and Consuming Input

Prompting for a File

6.2 Token-Based Processing

Numeric Input

Handling Invalid Input

Mixing Lines and Tokens

Handling Varying Numbers of Tokens

Complex Input Files

6.3 Advanced File Processing

Multi-Line Input Records

File Output

Reading Data from the Web

6.4 Case Study: ZIP Code Lookup

Chapter 7 Lists

7.1 List Basics

Creating Lists

Accessing List Elements

Traversing a List

A Complete List Program

Random Access

List Methods

7.2 List-Traversal Algorithms

Lists as Parameters

Searching a List

Replacing and Removing Values

Reversing a List

Shifting Values in a List

Nested Loop Algorithms

List Comprehensions

7.3 Reference Semantics

Values and References

Modifying a List Parameter

The Value None

Mutability

Tuples

7.4 Multidimensional Lists

Rectangular Lists

Jagged Lists

Lists of Pixels

7.5 Case Study: Benford’s Law

Tallying Values

Completing the Program

Chapter 8 Dictionaries and Sets

8.1 Dictionary Basics

Creating a Dictionary

Dictionary Operations

Looping Over a Dictionary

Dictionary Ordering

8.2 Advanced Dictionary Usage

Dictionary for Tallying

Nested Collections

Dictionary Comprehensions

8.3 Sets

Set Basics

Set Operations

Set Efficiency

Set Example: Lottery

Chapter 9 Recursion

9.1 Thinking Recursively

A Nonprogramming Example

Iteration to Recursion

Structure of Recursive Solutions

Reversing a File

The Recursive Call Stack

9.2 Recursive Functions and Data

Integer Exponentiation

Greatest Common Divisor

Directory Crawler

9.3 Recursive Graphics

Cantor Set

Sierpinski Triangle

9.4 Recursive Backtracking

Traveling North/East

Eight Queens Puzzle

Stopping after One Solution

9.5 Case Study: Prefix Evaluator

Infix, Prefix, and Postfix Notation

Evaluating Prefix Expressions

Complete Program

Chapter 10 Searching and Sorting

10.1 Searching and Sorting Libraries

Binary Search

Sorting

Shuffling

10.2 Program Complexity

Empirical Analysis

Complexity Classes

10.3 Implementing Searching and Sorting Algorithms

Sequential Search

Binary Search

Recursive Binary Search

Selection Sort

10.4 Case Study: Implementing Merge Sort

Splitting and Merging lists

Recursive Merge Sort

Runtime Performance

Hybrid Approach

Chapter 11 Classes and Objects

11.1 Object-Oriented Programming

Classes and Objects

Date Objects

11.2 Object State and Behavior

Data Attributes

Initializers

Methods

Accessors and Mutators

Making Objects Printable

Object Equality and Ordering

11.3 Encapsulation

Motivation for Encapsulation

Private Attributes and Properties

Class Invariants

11.4 Case Study: Designing a Stock Class

Object-Oriented Design Heuristics

Stock Attributes and Method Headers

Stock Method and Property Implementation

Chapter 12 Functional Programming

12.1 Functional Programming Concepts

Side Effects

First-Class Functions

Higher-Order Functions

Lambda Expressions

12.2 Functional Operations on Collections

Using Map

Using Filter

Using Reduce

List Comprehensions

12.3 Function Closures

Generator Functions

Lazy Evaluation

Iterable Objects

Generator Expressions

12.4 Case Study: Perfect Numbers

Computing Sums

The Fifth Perfect Number

Leveraging Concurrency

Appendix A: Python Summary

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