Overview

For CS1 courses in Python Programming including majors and non-majors alike.   A problem-solving approach to programming with Python.   The Practice of Computing Using Python introduces CS1 students (majors and non-majors) to computational thinking using Python.  With data-manipulation as a theme, students quickly see the value in what they’re learning and leave the course with a set of immediately useful computational skills that can be applied to problems they encounter in future pursuits.  The book takes an “object-use-first” approach–writing classes are covered only after students have mastered using objects.   

Full Product Details

Author:   William F. Punch ,  Richard Enbody
Publisher:   Pearson Education (US)
Imprint:   Pearson
Dimensions:   Width: 23.20cm , Height: 2.40cm , Length: 19.00cm
Weight:   1.020kg
ISBN:  

9780136110675

ISBN 10:   0136110673
Pages:   696
Publication Date:   29 April 2010
Audience:   College/higher education ,  Tertiary & Higher Education
Replaced By:   9780132805575
Format:   Paperback
Publisher's Status:   Out of Print
Availability:   In Print  
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

Table of Contents

"Contents I Thinking About Computing 0 The Study of Computer Science 0.1 Why Computer Science? 0.1.1 Importance of Computer Science 0.1.2 Computer “Science” 0.1.3 Computer Science Through Computer Programming 0.2 The Difficulty and Promise of Programming 0.2.1 Difficulty 1: two things at once 0.2.2 Difficulty 2: What is a good program? 0.2.3 The Promise of a Computer Program 0.3 Choosing a Computer Language 0.3.1 Different Computer Languages 0.3.2 Why Python? 0.3.3 Is Python the Best Language? 0.4 What is Computation? 0.5 What is a computer? 0.5.1 Computation in Nature 0.5.2 The Human Computer 0.6 The Modern, Electronic Computer 0.6.1 It’s the Switch! 0.6.2 The Transistor 0.7 A High Level look at a Modern Computer 0.8 Representing Data 0.8.1 Binary Data 0.8.2 Working with Binary 0.8.3 Limits 0.8.4 Representing Letters 0.8.5 Representing other Data 0.8.6 What does a number represent? 0.8.7 How to talk about quantities of data 0.8.8 How much data is that? 0.9 Overview of Coming Chapters 0.10 Summary II Starting to Program 1 Beginnings 1.1 Practice, Practice, Practice 1.2 QUICKSTART 1, the Circumference Program 1.2.1 Examining the code 1.3 An Interactive Session 1.4 Parts of a Program 1.4.1 Modules 1.4.2 Statements and Expressions 1.4.3 Whitespace 1.4.4 Comments 1.4.5 Special Python Elements: Tokens 1.4.6 Naming Objects 1.5 Variables 1.5.1 Variable Creation and Assignment 1.6 Objects and Types 1.6.1 Numbers 1.6.2 Other Built-in Types 1.6.3 Object, not variable, types 1.6.4 Constructing new values 1.7 Operators 1.7.1 Integer Operators 1.7.2 Floating Point Operators 1.7.3 Mixed Operations 1.7.4 Order of Operations and Parenthesis 1.7.5 Augmented Assignment Operators: a shortcut! 1.8 Our first module, math 1.9 Developing an Algorithm 1.10 Conclusion 1.11 Visual Vignette: Turtle Graphic 1.12 Exercises 1.12.1 Programming Projects 2 Control 2.1 The selection statement for decisions: if 2.1.1 Booleans for decisions 2.1.2 The if statement 2.1.3 Example: what lead is safe in basketball? 2.1.4 Repetition 2.1.5 Example: Finding Perfect Numbers 2.2 In-depth Control 2.2.1 True and False: Booleans 2.2.2 Boolean Variables 2.2.3 Relational Operators 2.2.4 Boolean Operators 2.2.5 Precedence 2.2.6 Boolean Operators Example 2.2.7 Another Word on Assignments 2.2.8 The Selection statement for decisions 2.2.9 More on Python decision statements 2.2.10 Repetition: the while statement 2.2.11 Sentinel Loop 2.2.12 Summary of repetition 2.2.13 More on the for statement 2.2.14 Nesting 2.2.15 Hailstone Sequence example 2.2.16 Summary 2.3 Visual Vignette: Plotting Data with pylab 2.4 Computer Science Perspectives: Minimal Universal Computing 2.4.1 Minimal Universal Computing 2.5 Exercises 2.5.1 Programming Projects 3 Algorithms and Program Development 3.1 What is an algorithm? 3.1.1 Example Algorithms 3.2 Algorithm Features 3.2.1 Algorithm versus program 3.2.2 Detailed 3.2.3 Effective 3.2.4 Specify Behavior 3.2.5 General Purpose 3.2.6 Can we really do all that? 3.3 What is a program? 3.3.1 Readable 3.3.2 Robust 3.3.3 Correct 3.4 Strategies for program design 3.4.1 Engage and Commit 3.4.2 Understand, then visualize 3.4.3 Think before you Program 3.4.4 Try it: Experiment 3.4.5 Simplify 3.4.6 Stop and Think 3.4.7 Relax, give it a break 3.5 A simple example 3.5.1 Build the skeleton 3.5.2 Output 3.5.3 Input 3.5.4 Doing the calculation 3.6 Summary 3.7 Exercises III Organizing: Data Structures and Functions 4 Working with Strings 4.1 The String type 4.1.1 The triple quote string 4.1.2 Non-printing characters 4.1.3 String representation 4.1.4 Strings as a sequence 4.1.5 More Indexing and Slicing 4.2 String operations 4.2.1 Concatenation (+) and Repetition (*) 4.2.2 When is + addition and when is it concatenation? 4.2.3 Comparison operators 4.2.4 The in operator 4.2.5 String collection is immutable 4.3 A preview of functions and methods 4.3.1 What is a function? First cut 4.3.2 A String Method 4.3.3 Determining method names and method arguments 4.3.4 String Methods 4.3.5 String functions 4.4 Formatted Output for Strings 4.4.1 Descriptor codes 4.4.2 Width Descriptor 4.4.3 Floating-point Precision Descriptor 4.5 Control and Strings 4.6 Working with strings 4.6.1 Example, reordering a person’s name 4.6.2 Palindromes 4.7 Example: Counting Poker Hands 4.7.1 Program to Count Poker Hands 4.8 Summary 4.9 Exercises 4.9.1 Programming Projects 5 Functions–QuickStart 5.1 What is a function? 5.1.1 Why have functions? 5.2 Python Functions 5.3 Flow of control with Functions 5.3.1 Function Flow in Detail 5.3.2 Another Function Example 5.3.3 Function Example: Word Puzzle 5.3.4 Functions calling Functions 5.3.5 When to use a Function 5.3.6 What if there is no return statement? 5.3.7 What if there are multiple return statements? 5.4 Conclusion 5.5 Visual Vignette: Turtle Flag 5.6 Exercises 5.6.1 Programming Projects 6 Lists and Tuples 6.1 What is a list? 6.2 What we already know how to do with lists (mostly) 6.2.1 Indexing and Slicing 6.2.2 Operators 6.2.3 Functions 6.2.4 List iteration 6.3 New things in Lists 6.3.1 Lists are Mutable 6.3.2 List Methods 6.4 Some old and new friends: range, split and other functions and methods 6.4.1 range, split and multiple assignment 6.4.2 List to string and back again, using join 6.4.3 The sorted function 6.5 Working with some Examples 6.5.1 Anagrams 6.5.2 Example: file analysis 6.6 Mutable Objects and references 6.6.1 Shallow vs. deep copy 6.6.2 Mutable Implementations 6.7 Tuples 6.7.1 Tuples from Lists 6.7.2 Why tuples? 6.8 Lists, the Data Structure 6.8.1 Example data structure 6.8.2 What are some other example data structures? 6.9 Algorithm Example: US EPA automobile mileage data 6.10 Python Diversion: List Comprehension 6.11 Visual Vignette: More Plotting 6.11.1 Numpy arrays 6.11.2 Plotting Trigonometric Functions 6.12 Summary 6.13 Exercises 6.13.1 Programming Projects 7 More on Functions 7.1 Functions calling Functions 7.2 Scope, a first cut 7.2.1 Arguments, Parameters and Namespaces 7.2.2 Passing Mutable Objects 7.2.3 Returning a complex object. 7.2.4 Refactoring evens() 7.3 Default Values and Parameters as Keywords 7.3.1 Example: Default Values and Parameter Keywords 7.3.2 Issues with Default Values 7.4 Functions as Objects 7.4.1 Doc Strings 7.5 Example: Determining a Final Grade 7.5.1 The Data 7.5.2 The Design 7.5.3 Function: weightedGrade 7.5.4 Function: grade 7.5.5 Function main 7.5.6 Example Use 7.6 Esoterica: ""by value"" or ""by reference"" 7.7 Conclusion 7.8 Exercises 7.8.1 Programming Projects 8 Dictionaries and Sets 8.1 Dictionaries 8.1.1 Dictionary example 8.1.2 Python Dictionaries 8.1.3 Dictionary Indexing and Assignment 8.1.4 Operators 8.2 Word Count Example 8.2.1 Count words in a string 8.2.2 Word Frequency for Gettysburg Address 8.2.3 Output and comments 8.3 Periodic Table Example 8.3.1 Working with CSV files 8.3.2 Algorithm Overview 8.3.3 Functions for Divide and Conquer 8.4 Sets 8.4.1 History 8.4.2 What’s in a set? 8.4.3 Python Sets 8.4.4 Methods, Operators and Functions for Python sets 8.4.5 Set methods 8.5 Set Applications 8.5.1 Relationship betweenWords of Different Documents 8.5.2 Output and Comments 8.6 Scope, the full story 8.6.1 Namespaces and Scope 8.6.2 Search rule for scope 8.6.3 Local 8.6.4 Global 8.6.5 Built-ins 8.6.6 Enclosed 8.7 Python Pointer: Using Zip to Create Dictionaries 8.8 Summary 8.9 Visual Vignette: Bar Graph of Word Frequency 8.9.1 Getting the data right 8.9.2 Labels and the xticks command 8.9.3 Plotting 8.10 Exercises 8.10.1 Programming Projects 9 Files 9.1 What is a file? 9.2 Accessing Files: Reading Text Files 9.2.1 Other File Access Methods 9.2.2 What’s really happening? 9.3 Accessing Files: Writing Text Files 9.4 Reading and Writing Text Files in a Program 9.5 File Creation and Overwriting 9.5.1 Universal New Line Format 9.5.2 Moving around in a file 9.6 Closing a file 9.7 CSV Files 9.7.1 csv module 9.7.2 CSV reader 9.7.3 CSVWriter 9.7.4 Example, Update Some Grades 9.8 Example: Reprompting for a “good” file name 9.9 Module: os 9.9.1 Directory/Folder structure 9.9.2 Module os functions 9.9.3 Module os example 9.10 Summary 9.11 Exercises 9.11.1 Programming Projects 10 More Program Development 10.1 Introduction 10.2 Divide and Conquer 10.2.1 Top-Down Refinement 10.3 The Breast Cancer Classifier 10.3.1 The Problem 10.3.2 The Approach: Classification 10.3.3 Training and testing the classifier 10.3.4 Building the classifier 10.4 Designing the Classifier Algorithm 10.4.1 Divided, now Conquer 10.4.2 Data Structures 10.4.3 File Format 10.4.4 Function: makeTrainingSet 10.5 Continuing the building process 10.5.1 The makeTestSet() function 10.5.2 The trainClassifier() function 10.5.3 trainClassifer(), Round 2 10.5.4 Testing the classifier on new data 10.5.5 The reportResults() function 10.6 Running the Classifier on Full Data 10.6.1 Training versus Testing 10.7 Other interesting problems 10.7.1 Tag Clouds 10.7.2 S&P 500 predictions 10.7.3 Predicting Religion with Flags 10.8 Summary 10.9 Exercises 10.9.1 Programming Projects IV Classes: Making Your Own Data Structures & Algorithms 11 Introduction to Classes 11.0.2 Simple Student Class 11.1 Object-oriented programming 11.1.1 Python is Object Oriented! 11.1.2 Characteristics of OOP 11.2 Working with Object Oriented Programming 11.2.1 Class and Instance 11.3 Working with classes and instances 11.3.1 Built-in Class and Instance 11.3.2 Our First Class 11.3.3 Changing Attributes 11.3.4 The special relationship between instance and class: instance-of 11.4 Object Methods 11.4.1 Using object methods 11.4.2 Writing Methods 11.4.3 The special argument self 11.4.4 Methods are the interface to a class instance 11.5 Fitting into the Python class model 11.5.1 Making Programmer-defined Classes 11.5.2 A Student Class 11.5.3 Python-standard methods 11.5.4 Now there are three: Class Designer, Programmer and User 11.6 Example: Point class 11.6.1 Construction 11.6.2 Distance 11.6.3 Summing two points 11.6.4 Improving the Point Class 11.7 Python and OOP 11.7.1 Encapsulation 11.7.2 Inheritance 11.7.3 Polymorphism 11.8 An aside: Python and other OOP languages 11.8.1 Public vs. Private 11.8.2 Indicating privacy using double underscores ( __ ) 11.8.3 Python’s Philosophy 11.8.4 Modifying an Instance 11.9 Conclusion 11.10 Exercises 11.10.1 Programming Projects 12 More on Classes 12.1 More about Class Properties 12.1.1 Rational Number (Fraction) class example 12.2 How does Python know? 12.2.1 Classes, Types and Introspection 12.2.2 Remember Operator Overloading 12.3 Creating Your Own Operator Overloading 12.3.1 Mapping Operators to Special Methods 12.4 Building the Rational Number Class 12.4.1 Making the Class 12.4.2 Review Fraction Addition 12.4.3 Back to Adding Fractions 12.4.4 Equality and Reducing Fractions 12.4.5 Divide-and-Conquer at work 12.5 What doesn’t work (yet) 12.5.1 Introspection 12.5.2 Repairing int + Rational Errors 12.6 Inheritance 12.6.1 The “Find the Attribute” Game 12.6.2 Using Inheritance 12.6.3 An example, the StandardModel 12.7 Summary 12.8 Exercises 13 Program Development with Classes 13.1 Predator-prey Problem 13.1.1 The rules 13.1.2 Simulation using Object Oriented programming 13.2 Classes 13.2.1 Island Class 13.2.2 Predator and Prey, kinds of Animals 13.2.3 Predator and Prey Class 13.2.4 Object Diagram 13.2.5 Filling the Island 13.3 Adding Behavior 13.3.1 Refinement: AddMovement 13.3.2 Refinement: Time simulation loop 13.4 Refinement: Eating, Breeding and Keeping Time 13.4.1 Improved Time Loop 13.4.2 Breeding 13.4.3 Eating 13.4.4 The tick of the clock 13.5 Refinements 13.5.1 Refinement: How many times to move? 13.5.2 Refinement: Graphing Population Size 13.6 Conclusion 13.7 Exercises V Becoming a Better Programmer 14 Exceptions and Exception Handling 14.1 Introduction 14.2 Basic Exception Handling 14.2.1 A simple example 14.3 A philosophy concerning exceptions 14.4 Exception: else and finally 14.4.1 Example: Refactoring the reprompting of a file name 14.5 Exception usage 14.5.1 Check input 14.5.2 Check file opening 14.6 More on exceptions 14.6.1 Raise 14.6.2 Create your own 14.7 Example: Password Manager 14.8 Summary 14.9 Examples 15 Testing 15.1 Why Testing? 15.1.1 Kinds of errors 15.1.2 “Bugs” and debugging 15.2 Kinds of Testing 15.2.1 Testing is hard! 15.2.2 Importance of Testing 15.3 Example Problem 15.3.1 NBA efficiency 15.3.2 Basic Algorithm 15.4 Incorporating Testing 15.4.1 Catching User Errors 15.4.2 Catching Developer Errors 15.5 Automation of Testing 15.5.1 doctest 15.5.2 Other kinds of testing 15.6 Conclusion 15.7 Exercises 16 Recursion—another control mechanism 16.1 What is recursion? 16.2 Mathematics and Rabbits 16.3 Let’s write our own: reversing a string 16.4 How does recursion actually work? 16.4.1 Stack Data Structure 16.4.2 Stacks and Function Calls 16.5 Recursion in figures 16.5.1 Recursive tree 16.5.2 Sierpinski triangles 16.6 Recursion to Non-recursion 16.7 Conclusion 16.8 Exercises A Getting and Using Python A.1 About Python A.1.1 History A.1.2 Python is free and portable A.1.3 Starting Python Up A.1.4 Working with Python A.1.5 Making a Program A.2 Some Conventions for this Book A.2.1 Interactive Code A.2.2 Program: Written Code A.2.3 Combined Program and Output A.3 Summary B Simple Drawing with Turtle Graphics B.1 Tidbits B.1.1 Keeping the window open B.1.2 Working nicely with IDLE C Plotting and Numeric Tools, a Quick Survey C.1 Matplotlib C.1.1 Getting matplotlib C.2 Working with matplotlib C.2.1 Plot command C.2.2 Plot properties C.2.3 Tick Labels C.2.4 Bar Graphs C.2.5 Histograms C.2.6 Pie Charts C.3 Numeric Python (Numpy) C.3.1 Arrays are not lists C.3.2 Creating a numpy array C.3.3 Manipulating arrays D Python 3.0 D.1 Simpler Built-in Types D.2 Unsurprising Arithmetic D.3 Cleaner Comparisons D.4 Consistent I/O D.5 Iterator Changes D.6 String module D.7 Conversion D.8 Text Examples in Python 3.x D.9 Conclusion E Table of ASCII values F Precedence"

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Author Information

Richard Enbody is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University. Since joining the faculty in 1987, he has served as Acting Chair of the Department, Associate Chair, and as Director of the Computer Engineering Undergraduate Program.   Enbody received his B.A. in Mathematics from Carleton College in 1976, and spent six years teaching high school mathematics in Vermont and New Hampshire. He earned his Ph.D. in Computer Science from the University of Minnesota.   Richard's research interests are in computer security, computer architecture, web-based distance education and parallel processing, especially the application of parallel processing to computational science problems. He has two patents pending on hardware buffer-overflow protection which will prevent most computer worms and viruses.   In 1998 Richard pioneered a CS1 course (first course in Computer Science) over the World Wide Web using RealVideo synchronized with PowerPoint. Students from as far away as Russia and Korea enrolled in the course. When not teaching, Richard plays hockey, squash, canoes, backpacks, as well as a host of family activities.   Bill Punch is an Associate Professor in the Department of Computer Science at Michigan State University as well as the director of Michigan State's High Performance Computing Center.   Punch is co-director of the Genetic Algorithms Research and Applications Group or GARAGe. His main interests are genetic algorithms and genetic programming, including theoretical issues (parallel GA/GP) and application issues (design, layout, scheduling, etc.). He also has conducted active research in data mining, focusing on the use of ontologies such as WordNet and Wikipedia for text search.

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