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Data Structures and Algorithms in Python

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Efnisyfirlit

  • Title Page
  • Copyright Page
  • Preface
  • Contents
  • 1 Python Primer
    • 1.1 Python Overview
      • 1.1.1 The Python Interpreter
      • 1.1.2 Preview of a Python Program
    • 1.2 Objects in Python
      • 1.2.1 Identifiers, Objects, and the Assignment Statement
      • 1.2.2 Creating and Using Objects
      • 1.2.3 Python’s Built-In Classes
    • 1.3 Expressions, Operators, and Precedence
      • 1.3.1 Compound Expressions and Operator Precedence
    • 1.4 Control Flow
      • 1.4.1 Conditionals
      • 1.4.2 Loops
    • 1.5 Functions
      • 1.5.1 Information Passing
      • 1.5.2 Python’s Built-In Functions
    • 1.6 Simple Input and Output
      • 1.6.1 Console Input and Output
      • 1.6.2 Files
    • 1.7 Exception Handling
      • 1.7.1 Raising an Exception
      • 1.7.2 Catching an Exception
    • 1.8 Iterators and Generators
    • 1.9 Additional Python Conveniences
      • 1.9.1 Conditional Expressions
      • 1.9.2 Comprehension Syntax
      • 1.9.3 Packing and Unpacking of Sequences
    • 1.10 Scopes and Namespaces
    • 1.11 Modules and the Import Statement
      • 1.11.1 Existing Modules
    • 1.12 Exercises
  • 2 Object-Oriented Programming
    • 2.1 Goals, Principles, and Patterns
      • 2.1.1 Object-Oriented Design Goals
      • 2.1.2 Object-Oriented Design Principles
      • 2.1.3 Design Patterns
    • 2.2 Software Development
      • 2.2.1 Design
      • 2.2.2 Pseudo-Code
      • 2.2.3 Coding Style and Documentation
      • 2.2.4 Testing and Debugging
    • 2.3 Class Definitions
      • 2.3.1 Example: CreditCard Class
      • 2.3.2 Operator Overloading and Python’s Special Methods
      • 2.3.3 Example: Multidimensional Vector Class
      • 2.3.4 Iterators
      • 2.3.5 Example: Range Class
    • 2.4 Inheritance
      • 2.4.1 Extending the CreditCard Class
      • 2.4.2 Hierarchy of Numeric Progressions
      • 2.4.3 Abstract Base Classes
    • 2.5 Namespaces and Object-Orientation
      • 2.5.1 Instance and Class Namespaces
      • 2.5.2 Name Resolution and Dynamic Dispatch
    • 2.6 Shallow and Deep Copying
    • 2.7 Exercises
  • 3 Algorithm Analysis
    • 3.1 Experimental Studies
      • 3.1.1 Moving Beyond Experimental Analysis
    • 3.2 The Seven Functions Used in This Book
      • 3.2.1 Comparing Growth Rates
    • 3.3 Asymptotic Analysis
      • 3.3.1 The “Big-Oh” Notation
      • 3.3.2 Comparative Analysis
      • 3.3.3 Examples of Algorithm Analysis
    • 3.4 Simple Justification Techniques
      • 3.4.1 By Example
      • 3.4.2 The “Contra” Attack
      • 3.4.3 Induction and Loop Invariants
    • 3.5 Exercises
  • 4 Recursion
    • 4.1 Illustrative Examples
      • 4.1.1 The Factorial Function
      • 4.1.2 Drawing an English Ruler
      • 4.1.3 Binary Search
      • 4.1.4 File Systems
    • 4.2 Analyzing Recursive Algorithms
    • 4.3 Recursion Run Amok
      • 4.3.1 Maximum Recursive Depth in Python
    • 4.4 Further Examples of Recursion
      • 4.4.1 Linear Recursion
      • 4.4.2 Binary Recursion
      • 4.4.3 Multiple Recursion
    • 4.5 Designing Recursive Algorithms
    • 4.6 Eliminating Tail Recursion
    • 4.7 Exercises
  • 5 Array-Based Sequences
    • 5.1 Python’s Sequence Types
    • 5.2 Low-Level Arrays
      • 5.2.1 Referential Arrays
      • 5.2.2 Compact Arrays in Python
    • 5.3 Dynamic Arrays and Amortization
      • 5.3.1 Implementing a Dynamic Array
      • 5.3.2 Amortized Analysis of Dynamic Arrays
      • 5.3.3 Python’s List Class
    • 5.4 Efficiency of Python’s Sequence Types
      • 5.4.1 Python’s List and Tuple Classes
      • 5.4.2 Python’s String Class
    • 5.5 Using Array-Based Sequences
      • 5.5.1 Storing High Scores for a Game
      • 5.5.2 Sorting a Sequence
      • 5.5.3 Simple Cryptography
    • 5.6 Multidimensional Data Sets
    • 5.7 Exercises
  • 6 Stacks, Queues, and Deques
    • 6.1 Stacks
      • 6.1.1 The Stack Abstract Data Type
      • 6.1.2 Simple Array-Based Stack Implementation
      • 6.1.3 Reversing Data Using a Stack
      • 6.1.4 Matching Parentheses and HTML Tags
    • 6.2 Queues
      • 6.2.1 The Queue Abstract Data Type
      • 6.2.2 Array-Based Queue Implementation
    • 6.3 Double-Ended Queues
      • 6.3.1 The Deque Abstract Data Type
      • 6.3.2 Implementing a Deque with a Circular Array
      • 6.3.3 Deques in the Python Collections Module
    • 6.4 Exercises
  • 7 Linked Lists
    • 7.1 Singly Linked Lists
      • 7.1.1 Implementing a Stack with a Singly Linked List
      • 7.1.2 Implementing a Queue with a Singly Linked List
    • 7.2 Circularly Linked Lists
      • 7.2.1 Round-Robin Schedulers
      • 7.2.2 Implementing a Queue with a Circularly Linked List
    • 7.3 Doubly Linked Lists
      • 7.3.1 Basic Implementation of a Doubly Linked List
      • 7.3.2 Implementing a Deque with a Doubly Linked List
    • 7.4 The Positional List ADT
      • 7.4.1 The Positional List Abstract Data Type
      • 7.4.2 Doubly Linked List Implementation
    • 7.5 Sorting a Positional List
    • 7.6 Case Study: Maintaining Access Frequencies
      • 7.6.1 Using a Sorted List
      • 7.6.2 Using a List with the Move-to-Front Heuristic
    • 7.7 Link-Based vs. Array-Based Sequences
    • 7.8 Exercises
  • 8 Trees
    • 8.1 General Trees
      • 8.1.1 Tree Definitions and Properties
      • 8.1.2 The Tree Abstract Data Type
      • 8.1.3 Computing Depth and Height
    • 8.2 Binary Trees
      • 8.2.1 The Binary Tree Abstract Data Type
      • 8.2.2 Properties of Binary Trees
    • 8.3 Implementing Trees
      • 8.3.1 Linked Structure for Binary Trees
      • 8.3.2 Array-Based Representation of a Binary Tree
      • 8.3.3 Linked Structure for General Trees
    • 8.4 Tree Traversal Algorithms
      • 8.4.1 Preorder and Postorder Traversals of General Trees
      • 8.4.2 Breadth-First Tree Traversal
      • 8.4.3 Inorder Traversal of a Binary Tree
      • 8.4.4 Implementing Tree Traversals in Python
      • 8.4.5 Applications of Tree Traversals
      • 8.4.6 Euler Tours and the Template Method Pattern
    • 8.5 Case Study: An Expression Tree
    • 8.6 Exercises
  • 9 Priority Queues
    • 9.1 The Priority Queue Abstract Data Type
      • 9.1.1 Priorities
      • 9.1.2 The Priority Queue ADT
    • 9.2 Implementing a Priority Queue
      • 9.2.1 The Composition Design Pattern
      • 9.2.2 Implementation with an Unsorted List
      • 9.2.3 Implementation with a Sorted List
    • 9.3 Heaps
      • 9.3.1 The Heap Data Structure
      • 9.3.2 Implementing a Priority Queue with a Heap
      • 9.3.3 Array-Based Representation of a Complete Binary Tree
      • 9.3.4 Python Heap Implementation
      • 9.3.5 Analysis of a Heap-Based Priority Queue
      • 9.3.6 Bottom-Up Heap Construction
      • 9.3.7 Python’s heapq Module
    • 9.4 Sorting with a Priority Queue
      • 9.4.1 Selection-Sort and Insertion-Sort
      • 9.4.2 Heap-Sort
    • 9.5 Adaptable Priority Queues
      • 9.5.1 Locators
      • 9.5.2 Implementing an Adaptable Priority Queue
    • 9.6 Exercises
  • 10 Maps, Hash Tables, and Skip Lists
    • 10.1 Maps and Dictionaries
      • 10.1.1 The Map ADT
      • 10.1.2 Application: Counting Word Frequencies
      • 10.1.3 Python’s MutableMapping Abstract Base Class
      • 10.1.4 Our MapBase Class
      • 10.1.5 Simple Unsorted Map Implementation
    • 10.2 Hash Tables
      • 10.2.1 Hash Functions
      • 10.2.2 Collision-Handling Schemes
      • 10.2.3 Load Factors, Rehashing, and Efficiency
      • 10.2.4 Python Hash Table Implementation
    • 10.3 Sorted Maps
      • 10.3.1 Sorted Search Tables
      • 10.3.2 Two Applications of Sorted Maps
    • 10.4 Skip Lists
      • 10.4.1 Search and Update Operations in a Skip List
      • 10.4.2 Probabilistic Analysis of Skip Lists
    • 10.5 Sets, Multisets, and Multimaps
      • 10.5.1 The Set ADT
      • 10.5.2 Python’s MutableSet Abstract Base Class
      • 10.5.3 Implementing Sets, Multisets, and Multimaps
    • 10.6 Exercises
  • 11 Search Trees
    • 11.1 Binary Search Trees
      • 11.1.1 Navigating a Binary Search Tree
      • 11.1.2 Searches
      • 11.1.3 Insertions and Deletions
      • 11.1.4 Python Implementation
      • 11.1.5 Performance of a Binary Search Tree
    • 11.2 Balanced Search Trees
      • 11.2.1 Python Framework for Balancing Search Trees
    • 11.3 AVL Trees
      • 11.3.1 Update Operations
    • 11.3.2 Python Implementation
    • 11.4 Splay Trees
      • 11.4.1 Splaying
      • 11.4.2 When to Splay
      • 11.4.3 Python Implementation
      • 11.4.4 Amortized Analysis of Splaying
    • 11.5 (2,4) Trees
      • 11.5.1 Multiway Search Trees
      • 11.5.2 (2,4)-Tree Operations
    • 11.6 Red-Black Trees
      • 11.6.1 Red-Black Tree Operations
      • 11.6.2 Python Implementation
    • 11.7 Exercises
  • 12 Sorting and Selection
    • 12.1 Why Study Sorting Algorithms?
    • 12.2 Merge-Sort
      • 12.2.1 Divide-and-Conquer
      • 12.2.2 Array-Based Implementation of Merge-Sort
      • 12.2.3 The Running Time of Merge-Sort
      • 12.2.4 Merge-Sort and Recurrence Equations
      • 12.2.5 Alternative Implementations of Merge-Sort
    • 12.3 Quick-Sort
      • 12.3.1 Randomized Quick-Sort
      • 12.3.2 Additional Optimizations for Quick-Sort
    • 12.4 Studying Sorting through an Algorithmic Lens
      • 12.4.1 Lower Bound for Sorting
      • 12.4.2 Linear-Time Sorting: Bucket-Sort and Radix-Sort
    • 12.5 Comparing Sorting Algorithms
    • 12.6 Python’s Built-In Sorting Functions
      • 12.6.1 Sorting According to a Key Function
    • 12.7 Selection
      • 12.7.1 Prune-and-Search
      • 12.7.2 Randomized Quick-Select
      • 12.7.3 Analyzing Randomized Quick-Select
    • 12.8 Exercises
  • 13 Text Processing
    • 13.1 Abundance of Digitized Text
      • 13.1.1 Notations for Strings and the Python str Class
    • 13.2 Pattern-Matching Algorithms
      • 13.2.1 Brute Force
      • 13.2.2 The Boyer-Moore Algorithm
      • 13.2.3 The Knuth-Morris-Pratt Algorithm
    • 13.3 Dynamic Programming
      • 13.3.1 Matrix Chain-Product
      • 13.3.2 DNA and Text Sequence Alignment
    • 13.4 Text Compression and the Greedy Method
      • 13.4.1 The Huffman Coding Algorithm
      • 13.4.2 The Greedy Method
    • 13.5 Tries
      • 13.5.1 Standard Tries
      • 13.5.2 Compressed Tries
      • 13.5.3 Suffix Tries
      • 13.5.4 Search Engine Indexing
    • 13.6 Exercises
  • 14 Graph Algorithms
    • 14.1 Graphs
      • 14.1.1 The Graph ADT
    • 14.2 Data Structures for Graphs
      • 14.2.1 Edge List Structure
      • 14.2.2 Adjacency List Structure
      • 14.2.3 Adjacency Map Structure
      • 14.2.4 Adjacency Matrix Structure
      • 14.2.5 Python Implementation
    • 14.3 Graph Traversals
      • 14.3.1 Depth-First Search
      • 14.3.2 DFS Implementation and Extensions
      • 14.3.3 Breadth-First Search
    • 14.4 Transitive Closure
    • 14.5 Directed Acyclic Graphs
      • 14.5.1 Topological Ordering
    • 14.6 Shortest Paths
      • 14.6.1 Weighted Graphs
      • 14.6.2 Dijkstra’s Algorithm
    • 14.7 Minimum Spanning Trees
      • 14.7.1 Prim-Jarník Algorithm
      • 14.7.2 Kruskal’s Algorithm
      • 14.7.3 Disjoint Partitions and Union-Find Structures
    • 14.8 Exercises
  • 15 Memory Management and B-Trees
    • 15.1 Memory Management
      • 15.1.1 Memory Allocation
      • 15.1.2 Garbage Collection
      • 15.1.3 Additional Memory Used by the Python Interpreter
    • 15.2 Memory Hierarchies and Caching
      • 15.2.1 Memory Systems
      • 15.2.2 Caching Strategies
    • 15.3 External Searching and B-Trees
      • 15.3.1 (a,b) Trees
      • 15.3.2 B-Trees
    • 15.4 External-Memory Sorting
      • 15.4.1 Multiway Merging
    • 15.5 Exercises
  • A Character Strings in Python
  • B Useful Mathematical Facts
  • Bibliography
  • Index

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Vörumerki: John Wiley
Tilboði lýkur 26.03.2019
Vörunúmer: 9781118549582
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Data Structures and Algorithms in Python

Vörumerki: John Wiley
Tilboði lýkur 26.03.2019
Vörunúmer: 9781118549582
Rafbók

Veldu vöru

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5.090 kr. 4.934 kr.
Get the product now
-3%
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