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Python Programming in Context, 3rd Edition

Name: Python Programming in Context, 3rd Edition
ISBN: 9781284175578

by Bradley N. Miller, David L. Ranum, Julie Anderson

October 2019

Beginner to intermediate

498 pages

14h 13m

English

Jones & Bartlett Learning

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Cover
Title Page
Copyright Page
Dedication
Contents
Preface
1 Introduction to Pythona
1.1 Objectives
1.2 What Is Computer Science?
1.3 Why Study Computer Science?
1.3.1 Everyday Applications of Computer Science1.3.2 Why Computer Science Is Important

1.4 Problem-Solving Strategies
1.5 Python Overview
1.5.1 Primitive Elements1.5.2 Naming Objects1.5.3 Abstraction1.5.4 Repetition
1.6 Summary
Key TermsPython KeywordsProgramming Exercises
2 πthon: Estimating Pi
2.1 Objectives
2.2 What Is Pi?
2.3 More About the math Module
2.4 The Archimedes Approach
2.4.1 The Python Implementation2.4.2 Developing a Function to Compute Pi
2.5 Accumulator Approximations
2.5.1 The Accumulator Pattern2.5.2 Summation of Terms: The Leibniz Formula2.5.3 Product of Terms: The Wallis Formula
2.6 A Monte Carlo Simulation
2.6.1 Boolean Expressions2.6.2 Compound Boolean Expressions and Logical Operators2.6.3 Selection Statements2.6.4 Completing the Implementation2.6.5 Adding Graphics
2.7 Summary
Key TermsPython Keywords, Modules and CommandsProgramming Exercises
3 Codes and Other Secrets
3.1 Objectives
3.2 The String Data Type
3.2.1 Concatenation3.2.2 Repetition3.2.3 Indexing3.2.4 String Slicing3.2.5 String Searching3.2.6 String Methods3.2.7 Character Functions
3.3 Encoding and Decoding Messages
3.4 Transposition Cipher
3.4.1 Encrypting Using Transposition3.4.2 Decrypting a Transposed Message3.4.3 Asking for Input
3.5 Substitution Cipher
3.6 Creating a Key
3.7 The Vigenère Cipher
3.8 Summary
Key TermsNew Python Keywords, Functions, and ConstantsProgramming Exercises
4 Introducing the Python Collections
4.1 Objectives
4.2 What Is Data?
4.3 Storing Data for Processing
4.3.1 Strings Revisited4.3.2 Lists
4.4 Calculating Statistics on Data
4.4.1 Simple Dispersion
4.5 Central Tendency
4.5.1 Mean4.5.2 Median4.5.3 Mode
4.6 Frequency Distribution
4.6.1 Using a Dictionary to Compute a Frequency Table4.6.2 Computing a Frequency Table Without a Dictionary4.6.3 Visualizing a Frequency Distribution
4.7 Dispersion: Standard Deviation
4.8 Summary
Key TermsPython Keywords, Functions, and MethodsProgramming Exercises
5 Bigger Data: File I/O
5.1 Objectives
5.2 Using Files for Large Data Sets
5.2.1 Text Files5.2.2 Iterating over Lines in a File5.2.3 Writing a File5.2.4 String Formatting5.2.5 Alternative File-Reading Methods
5.3 Reading Data from the Internet
5.3.1 Using CSV Files5.3.2 Using a while Loop to Process Data5.3.3 List Comprehension5.3.4 Reading JSON Data from the Internet
5.4 Correlating Data
5.5 Summary
Key TermsPython Keywords and FunctionsProgramming Exercise
6 Image Processing
6.1 Objectives
6.2 What Is Digital Image Processing?
6.2.1 The RGB Color Model6.2.2 The cImage Module
6.3 Basic Image Processing
6.3.1 Negative Images6.3.2 Grayscale6.3.3 A General Solution: The Pixel Mapper
6.4 Parameters, Parameter Passing, and Scope
6.4.1 Call by Assignment Parameter Passing6.4.2 Namespaces6.4.3 Calling Functions and Finding Names6.4.4 Modules and Namespaces
6.5 Advanced Image Processing
6.5.1 Resizing6.5.2 Stretching: A Different Perspective6.5.3 Flipping an Image6.5.4 Edge Detection
6.6 Summary
Key TermsPython Keywords, Functions, and VariablesProgramming Exercises
7 Data Mining: Cluster Analysis
7.1 Objectives
7.2 What Is Data Mining?
7.3 Cluster Analysis: A Simple Example
7.4 Implementing Cluster Analysis on Simple Data
7.4.1 Distance Between Two Points7.4.2 Clusters and Centroids7.4.3 The K-Means Cluster Analysis Algorithm7.4.4 Implementation of K-Means7.4.5 Implementation of K-Means, Continued
7.5 Implementing Cluster Analysis: Earthquakes
7.5.1 File Processing7.5.2 Visualization
7.6 Cluster Analysis Shortcomings and Solutions
7.7 Summary
Key TermsPython KeywordsProgramming Exercises
8 Cryptanalysis
8.1 Objectives
8.2 Introduction
8.3 Cracking the Rail Fence
8.3.1 Checking Our Work with a Dictionary8.3.2 A Brute-Force Solution8.3.3 A Rail Fence Decryption Algorithm
8.4 Cracking the Substitution Cipher
8.4.1 Letter Frequency8.4.2 Ciphertext Frequency Analysis8.4.3 Letter Pair Analysis8.4.4 Word Frequency Analysis8.4.5 Pattern Matching with Partial Words8.4.6 Regular Expression Summary
8.5 Summary
Key TermsPython Functions, Methods, and KeywordsProgramming Exercises
9 Fractals: The Geometry of Nature
9.1 Objectives
9.2 Introduction
9.3 Recursive Programs
9.3.1 Recursive Squares9.3.2 Classic Recursive Functions9.3.3 Drawing a Recursive Tree9.3.4 The Sierpinski Triangle9.3.5 Call Tree for a Sierpinski Triangle
9.4 Snowflakes, Lindenmayer, and Grammars
9.4.1 L-Systems9.4.2 Automatically Expanding Production Rules9.4.3 More Advanced L-Systems
9.5 Summary
Key Terms Programming Exercises
10 Planet Objects
10.1 Objectives
10.2 Introduction
10.2.1 Programming10.2.2 Object-Oriented Programming10.2.3 Python Classes
10.3 Designing and Implementing a Planet Class
10.3.1 Constructor Method10.3.2 Accessor Methods10.3.3 Mutator Methods10.3.4 Special Methods10.3.5 Methods and self10.3.6 Details of Method Storage and Lookup
10.4 Designing and Implementing a Sun Class
10.5 Designing and Implementing a Solar System
10.6 Animating the Solar System
10.6.1 Using Turtles10.6.2 Planetary Orbits10.6.3 Implementation
10.7 Summary
Key TermsPython Keywords and FunctionsProgramming Exercises
11 Simulation
11.1 Objectives
11.2 Bears and Fish
11.3 What Is a Simulation?
11.4 Rules of the Game
11.5 Design
11.6 Implementation
11.6.1 The World Class11.6.2 The Fish Class11.6.3 The Bear Class11.6.4 Main Simulation
11.7 Growing Plants
11.8 A Note on Inheritance
11.9 Summary
Key TermsPython Keywords and FunctionsProgramming Exercises
12 Father Was a Rectangle
12.1 Objectives
12.2 Introduction
12.3 First Design
12.4 Basic Implementation
12.4.1 The Canvas Class12.4.2 The GeometricObject Class12.4.3 The Point Class12.4.4 The Line Class12.4.5 Testing Our Implementation
12.5 Understanding Inheritance
12.6 Limitations
12.7 An Improved Implementation
12.8 Implementing Polygons
12.9 Summary
Key TermsPython Keywords, Methods, and DecoratorProgramming Exercises
13 Video Games
13.1 Objectives
13.2 Introduction
13.2.1 Event-Driven Programming13.2.2 Simulating an Event Loop13.2.3 A Multithreaded Event Loop
13.3 Event-Driven Programming with the Turtle
13.3.1 A Simple Etch-a-Sketch Using Key Presses13.3.2 Placing Turtles Using Mouse Clicks13.3.3 Bouncing Turtles
13.4 Creating Your Own Video Game
13.4.1 The LaserCannon Class13.4.2 The BoundedTurtle Class13.4.3 The Drone Class13.4.4 The Bomb Class13.4.5 Putting All the Pieces Together
13.5 Summary
Key TermsPython Keywords and DecoratorProgramming Exercises
APPENDIX A Installing the Required Software
A.1 Installing PythonA.2 Installing the Python Image Library and cImage
APPENDIX B Python Quick Reference
B.1 Python Reserved WordsB.2 Numeric Data TypesB.3 Built-in FunctionsB.4 Sequence OperatorsB.5 DictionariesB.6 FilesB.7 Formatting OutputB.8 IterationB.9 Boolean ExpressionsB.10 SelectionB.11 Python ModulesB.12 Regular Expression PatternsB.13 Defining FunctionsB.14 Defining ClassesB.15 Deleting ObjectsB.16 Common Error Messages
APPENDIX C turtle Reference
C.1 Basic Move and DrawC.2 Turtle StateC.3 Drawing StateC.4 FillingC.5 More Drawing ControlC.6 Controlling the Shape and AppearanceC.7 Measurement SettingsC.8 Drawing SpeedC.9 ColorC.10 EventsC.11 Miscellaneous
APPENDIX D Answers to Selected “Try It Out” Exercises
INDEX

Content preview from Python Programming in Context, 3rd Edition

5.3 Reading Data from the Internet

In the Introducing the Python Collections chapter, we used statistical functions on a week’s worth of data about earthquakes from the U.S. Geological Survey (USGS) website (usgs.gov). One advantage of using such a small set of values was that we could easily demonstrate the concepts of mean, median, mode, and standard deviation. In particular, we used summary data as our data set: the number of earthquakes per day for a seven-day period. The real power of statistics and other methods of analysis, however, can be realized on larger raw (not summarized) collections of data.

Internet sites often provide large sets of data that can be downloaded for analysis. These data sets are usually made available in several ...

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Python Programming in Context, 3rd Edition

by Bradley N. Miller, David L. Ranum, Julie Anderson

5.3 Reading Data from the Internet

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