فهرست مطالب

• Table of Contents
• About the Author
• About the Technical Reviewer
• Chapter 1: Introduction
• Models of the World and Formalizing Problems
• What Is Computational Thinking?
• Computational Thinking in a Broader Context
• What Is to Come
• Chapter 2: Introducing Python Programming
• Obtaining Python
• Running Python
• Expressions in Python
• Logical (or Boolean) Expressions
• Variables
• Working with Strings
• Lists
• Tuples
• Sets and Dictionaries
• Input and Output
• Conditional Statements (if Statements)
• Loops (for and while)
• Using Modules
• Chapter 3: Introduction to Algorithms
• Designing Algorithms
• Exercises for Sequential Algorithms
• Exercises on Lists
• Chapter 4: Algorithmic Efficiency
• The RAM Model of a Computer and Its Primitive Operations
• Types of Efficiency
• Asymptotic Running Time and Big-Oh Notation
• Empirically Validating Algorithms’ Running Time
• Chapter 5: Searching and Sorting
• Searching
• Sorting
• Generalizing Searching and Sorting
• How Computers Represent Numbers
• Chapter 6: Functions
• Parameters and Local and Global Variables
• Side Effects
• Returning from a Function
• Higher-Order Functions
• Functions vs. Function Instances
• Default Parameters and Keyword Arguments
• Generalizing Parameters
• Exceptions
• Writing Your Own Python Modules
• Chapter 7: Inner Functions
• A Comparison Function for a Search Algorithm
• Counter Function
• Apply
• Currying Functions
• Function Composition
• Thunks and Lazy Evaluation
• Lambda Expressions
• Decorators
• Efficiency
• Chapter 8: Recursion
• Definitions of Recursion
• Recursive Functions
• Recursion Stacks
• Recursion and Iteration
• Tail Calls
• Continuations
• Continuations, Thunks, and Trampolines
• Chapter 9: Divide and Conquer and Dynamic Programming
• Merge Sort
• Quick Sort
• Divide and Conquer Running Times
• Dynamic Programming
• Representing Floating-Point Numbers
• Chapter 10: Hidden Markov Models
• Probabilities
• Conditional Probabilities and Dependency Graphs
• Markov Models
• Hidden Markov Models
• Forward Algorithm
• Viterbi Algorithm
• Chapter 11: Data Structures, Objects, and Classes
• Classes
• Exceptions and Classes
• Methods
• Polymorphism
• Abstract Data Structures
• Magical Methods
• Class Variables
• Attributes (The Simple Story)
• Objects, Classes, and Meta-classes
• Getting Attributes
• Setting Attributes
• Properties
• Descriptors
• Return of the Decorator
• Chapter 12: Class Hierarchies and Inheritance
• Inheritance and Code Reuse
• Multiple Inheritance
• Mixins
• Chapter 13: Sequences
• Sequences
• Linked Lists Sequences
• Doubly Linked Lists
• A Word on Garbage Collection
• Iterators
• Python Iterators and Other Interfaces
• Generators
• Chapter 14: Sets
• Sets with Built-In Lists
• Linked Lists Sets
• Search Trees
• Hash Table
• Dictionaries
• Chapter 15: Red-Black Search Trees
• A Persistent Recursive Solution
• An Iterative Solution
• Chapter 16: Stacks and Queues
• Building Stacks and Queues from Scratch
• Expression Stacks and Stack Machines
• Quick Sort and the Call Stack
• Writing an Iterator for a Search Tree
• Merge Sort with an Explicit Stack
• Breadth-First Tree Traversal and Queues
• Chapter 17: Priority Queues
• A Tree Representation for a Heap
• Leftist Heaps
• Binomial Heaps
• Binary Heaps
• Adding Keys and Values
• Comparisons
• Huffman Encoding
• Chapter 18: Conclusions
• Where to Go from Here
• Index