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Python Programming in Context, 4th Edition

Name: Python Programming in Context, 4th Edition
ISBN: 9781284283228

by Julie Anderson, Jon Anderson

April 2024

Beginner to intermediate

500 pages

24h 20m

English

Jones & Bartlett Learning

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Cover
Title Page
Copyright Page
Dedication Page
Brief Contents
Contents
Preface
Acknowledgments
CHAPTER 1 Introduction to Python
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 Terms
Programming Exercises
CHAPTER 2 Playing by the Rules
2.1 Objectives
2.2 The Chatbot
2.3 The String Data Type
2.4 Making Decisions
2.4.1 Boolean Expressions2.4.2 Compound Boolean Expressions and Logical Operators2.4.3 Selection Statements
2.5 Implementing the Chatbot
2.6 Playing Games
2.6.1 Guess a Number2.6.2 The while Loop2.6.3 Printing Numbers in Strings2.6.4 Asking for Input
2.7 Completing Guess a Number
2.8 Validating User Input
2.9 Counting the Guesses
2.9.1 The Accumulator Pattern
2.10 The Game of Nim
2.10.1 Random Numbers2.10.2 Returning a Value from a Function
2.11 Summary
Key Terms
Programming Exercises
CHAPTER 3 Codes and Other Secrets
3.1 Objectives
3.2 The String Data Type
3.2.1 Repetition3.2.2 Indexing3.2.3 String Slicing3.2.4 String Searching3.2.5 String Methods3.2.6 Character Functions
3.3 Encrypting and Decrypting Messages
3.4 Transposition Cipher
3.4.1 Encrypting Using Transposition3.4.2 Decrypting a Transposed Message
3.5 Substitution Cipher
3.6 Creating a Key
3.7 The Vigenère Cipher
3.8 Summary
Key Terms
Programming Exercises
CHAPTER 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 Terms
Programming Exercises
CHAPTER 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 Read a File5.3.3 List Comprehension
5.4 Reading and Manipulating Data with pandas
5.4.1 pandas Data Structures5.4.2 Reading CSV Data with pandas5.4.3 Data Manipulation with pandas
5.5 Reading JSON Data from the Internet
5.6 Correlating Data
5.7 Summary
Key Terms
Programming Exercise
CHAPTER 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 Terms
Programming Exercises
CHAPTER 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 Terms
Programming Exercises
CHAPTER 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 Terms
Programming Exercises
CHAPTER 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-Systems and the match Statement9.4.2 Automatically Expanding Production Rules9.4.3 More Advanced L-Systems
9.5 Summary
Key Terms
Programming Exercises
CHAPTER 10 Planet Objects
10.1 Objectives
10.2 Introduction
10.2.1 Focusing on Data10.2.2 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 Implementing the Simulation
10.7 Summary
Key Terms
Programming Exercises
CHAPTER 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 Terms
Programming Exercises
CHAPTER 12 A Hierarchy of Shapes
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 Terms
Programming Exercises
CHAPTER 13 Interactive 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 Drawing 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 Terms
Programming Exercises
Appendix A: Installing the Required Software
A.1 Installing Python
A.2 Installing the Jupyter Notebook IDE
A.3 Installing the Matplotlib and pandas Libraries
A.4 Installing the Python Image Library and cImage
Appendix B: Python Quick Reference
B.1 Python Reserved Words
B.2 Numeric Data Types
B.3 Built-in Functions
B.4 Sequence Operators
B.5 Dictionaries
B.6 Files
B.7 Formatting Output
B.8 Iteration
B.9 Boolean Expressions
B.10 Selection
B.11 Python Modules
B.12 Regular Expression Patterns
B.13 Syntax for Defining a Function
B.14 Syntax for Defining a Class
B.15 Deleting Objects
B.16 matplotlib
B.17 pandas
B.18 Common Error Messages
Appendix C: turtle Reference
C.1 Basic Move and Draw
C.2 Turtle State
C.3 Drawing State
C.4 Filling
C.5 More Drawing Control
C.6 Controlling the Shape and Appearance
C.7 Measurement Settings
C.8 Drawing Speed
C.9 Color
C.10 Events
C.11 Miscellaneous
Appendix D: Answers to Selected Try It Out Exercises
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Glossary
Index

Content preview from Python Programming in Context, 4th Edition

4.5 Central Tendency

One of the most often used measures of a collection of data is known as central tendency—a measure that estimates where the “typical value” of a collection will be found. Three common ways to compute central tendency are (1) the mean, (2) the median, and (3) the mode.

4.5.1 Mean

The most common measure of central tendency is the mean (more commonly called the average). The mean is simply the sum of all the values in the data collection divided by the number of items. As shown in Listing 4.4, to compute the mean, we add up the values in the collection and divide by the length. Specifically, given a list of values, we use the built-in sum function, shown in Table 4.6, to compute a total and the len function shown in Table ...

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Python Programming in Context, 4th Edition

by Julie Anderson, Jon Anderson

4.5 Central Tendency

4.5.1 Mean

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