Problem Solving with Algorithms and Data Structures using Python 阅读笔记

全书目录

By Brad Miller and David Ranum, Luther College

• Assignments

• 1. Introduction
  • 1.1. Objectives
  • 1.2. Getting Started
  • 1.3. What Is Computer Science?
  • 1.4. What Is Programming?
  • 1.5. Why Study Data Structures and Abstract Data Types?
  • 1.6. Why Study Algorithms?
  • 1.7. Review of Basic Python
  • 1.8. Getting Started with Data
    • 1.8.1. Built-in Atomic Data Types
    • 1.8.2. Built-in Collection Data Types
  • 1.9. Input and Output
    • 1.9.1. String Formatting
  • 1.10. Control Structures
  • 1.11. Exception Handling
  • 1.12. Defining Functions
  • 1.13. Object-Oriented Programming in Python: Defining Classes
    • 1.13.1. A Fraction Class
    • 1.13.2. Inheritance: Logic Gates and Circuits
  • 1.14. Summary
  • 1.15. Key Terms
  • 1.16. Discussion Questions
  • 1.17. Programming Exercises
• 2. Analysis
  • 2.1. Objectives
  • 2.2. What Is Algorithm Analysis?
  • 2.3. Big-O Notation
  • 2.4. An Anagram Detection Example
    • 2.4.1. Solution 1: Checking Off
    • 2.4.2. Solution 2: Sort and Compare
    • 2.4.3. Solution 3: Brute Force
    • 2.4.4. Solution 4: Count and Compare
  • 2.5. Performance of Python Data Structures
  • 2.6. Lists
  • 2.7. Dictionaries
  • 2.8. Summary
  • 2.9. Key Terms
  • 2.10. Discussion Questions
  • 2.11. Programming Exercises
• 3. Basic Data Structures
  • 3.1. Objectives
  • 3.2. What Are Linear Structures?
  • 3.3. What is a Stack?
  • 3.4. The Stack Abstract Data Type
  • 3.5. Implementing a Stack in Python
  • 3.6. Simple Balanced Parentheses
  • 3.7. Balanced Symbols (A General Case)
  • 3.8. Converting Decimal Numbers to Binary Numbers
  • 3.9. Infix, Prefix and Postfix Expressions
    • 3.9.1. Conversion of Infix Expressions to Prefix and Postfix
    • 3.9.2. General Infix-to-Postfix Conversion
    • 3.9.3. Postfix Evaluation
  • 3.10. What Is a Queue?
  • 3.11. The Queue Abstract Data Type
  • 3.12. Implementing a Queue in Python
  • 3.13. Simulation: Hot Potato
  • 3.14. Simulation: Printing Tasks
    • 3.14.1. Main Simulation Steps
    • 3.14.2. Python Implementation
    • 3.14.3. Discussion
  • 3.15. What Is a Deque?
  • 3.16. The Deque Abstract Data Type
  • 3.17. Implementing a Deque in Python
  • 3.18. Palindrome-Checker
  • 3.19. Lists
  • 3.20. The Unordered List Abstract Data Type
  • 3.21. Implementing an Unordered List: Linked Lists
    • 3.21.1. The Node Class
    • 3.21.2. The Unordered List Class
  • 3.22. The Ordered List Abstract Data Type
  • 3.23. Implementing an Ordered List
    • 3.23.1. Analysis of Linked Lists
  • 3.24. Summary
  • 3.25. Key Terms
  • 3.26. Discussion Questions
  • 3.27. Programming Exercises
• 4. Recursion
  • 4.1. Objectives
  • 4.2. What Is Recursion?
  • 4.3. Calculating the Sum of a List of Numbers
  • 4.4. The Three Laws of Recursion
  • 4.5. Converting an Integer to a String in Any Base
  • 4.6. Stack Frames: Implementing Recursion
  • 4.7. Introduction: Visualizing Recursion
  • 4.8. Sierpinski Triangle
  • 4.9. Complex Recursive Problems
  • 4.10. Tower of Hanoi
  • 4.11. Exploring a Maze
  • 4.12. Dynamic Programming
  • 4.13. Summary
  • 4.14. Key Terms
  • 4.15. Discussion Questions
  • 4.16. Glossary
  • 4.17. Programming Exercises
• 5. Sorting and Searching
  • 5.1. Objectives
  • 5.2. Searching
  • 5.3. The Sequential Search
    • 5.3.1. Analysis of Sequential Search
  • 5.4. The Binary Search
    • 5.4.1. Analysis of Binary Search
  • 5.5. Hashing
    • 5.5.1. Hash Functions
    • 5.5.2. Collision Resolution
    • 5.5.3. Implementing the Map Abstract Data Type
    • 5.5.4. Analysis of Hashing
  • 5.6. Sorting
  • 5.7. The Bubble Sort
  • 5.8. The Selection Sort
  • 5.9. The Insertion Sort
  • 5.10. The Shell Sort
  • 5.11. The Merge Sort
  • 5.12. The Quick Sort
  • 5.13. Summary
  • 5.14. Key Terms
  • 5.15. Discussion Questions
  • 5.16. Programming Exercises
• 6. Trees and Tree Algorithms
  • 6.1. Objectives
  • 6.2. Examples of Trees
  • 6.3. Vocabulary and Definitions
  • 6.4. List of Lists Representation
  • 6.5. Nodes and References
  • 6.6. Parse Tree
  • 6.7. Tree Traversals
  • 6.8. Priority Queues with Binary Heaps
  • 6.9. Binary Heap Operations
  • 6.10. Binary Heap Implementation
    • 6.10.1. The Structure Property
    • 6.10.2. The Heap Order Property
    • 6.10.3. Heap Operations
  • 6.11. Binary Search Trees
  • 6.12. Search Tree Operations
  • 6.13. Search Tree Implementation
  • 6.14. Search Tree Analysis
  • 6.15. Balanced Binary Search Trees
  • 6.16. AVL Tree Performance
  • 6.17. AVL Tree Implementation
  • 6.18. Summary of Map ADT Implementations
  • 6.19. Summary
  • 6.20. Key Terms
  • 6.21. Discussion Questions
  • 6.22. Programming Exercises
• 7. Graphs and Graph Algorithms
  • 7.1. Objectives
  • 7.2. Vocabulary and Definitions
  • 7.3. The Graph Abstract Data Type
  • 7.4. An Adjacency Matrix
  • 7.5. An Adjacency List
  • 7.6. Implementation
  • 7.7. The Word Ladder Problem
  • 7.8. Building the Word Ladder Graph
  • 7.9. Implementing Breadth First Search
  • 7.10. Breadth First Search Analysis
  • 7.11. The Knight’s Tour Problem
  • 7.12. Building the Knight’s Tour Graph
  • 7.13. Implementing Knight’s Tour
  • 7.14. Knight’s Tour Analysis
  • 7.15. General Depth First Search
  • 7.16. Depth First Search Analysis
  • 7.17. Topological Sorting
  • 7.18. Strongly Connected Components
  • 7.19. Shortest Path Problems
  • 7.20. Dijkstra’s Algorithm
  • 7.21. Analysis of Dijkstra’s Algorithm
  • 7.22. Prim’s Spanning Tree Algorithm
  • 7.23. Summary
  • 7.24. Key Terms
  • 7.25. Discussion Questions
  • 7.26. Programming Exercises