Data Structures and Algorithms in Python - Michael T. Goodrich

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Auteur(s) : Michael T. Goodrich

Editeur : Wiley

Langue : Anglais

Parution : 01/03/2013

Format : Moyen, de 350g à 1kg

Nombre de pages : 768

Expédition : 1298

Dimensions : 24.4 x 19.2 x 3.2

ISBN : 9781118290279

Résumé :

Based on the authors' market leading data structures books in Java and C++, this book offers a comprehensive, definitive introduction to data structures in Python by authoritative authors. Data Structures and Algorithms in Python is the first authoritative object-oriented book available for Python data structures.

Sommaire:
Preface 1 Python Primer 1.1 Python Overview 1.2 Objects in Python 1.3 Expressions, Operators, and Precedence 1.4 Control Flow 1.5 Functions 1.6 Simple Input and Output 1.7 Exception Handling 1.8 Iterators and Generators 1.9 Additional Python Conveniences 1.10 Scopes and Namespaces 1.11 Modules and the Import Statement 1.12 Exercises 2 Object-Oriented Programming 2.1 Goals, Principles, and Patterns 2.2 Software Development 2.3 Class Definitions 2.4 Inheritance 2.5 Namespaces and Object-Orientation 2.6 Shallow and Deep Copying 2.7 Exercises 3 Algorithm Analysis 3.1 Experimental Studies 3.2 The Seven Functions Used in This Book 3.3 Asymptotic Analysis 3.4 Simple Justification Techniques 3.5 Exercises 4 Recursion 4.1 Illustrative Examples 4.2 Analyzing Recursive Algorithms 4.3 Recursion Run Amok 4.4 Further Examples of 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 5.3 Dynamic Arrays and Amortization 5.4 Efficiency of Python's Sequence Types 5.5 Using Array-Based Sequences 5.6 Multidimensional Data Sets 5.7 Exercises 6 Stacks, Queues, and Deques 6.1 Stacks 6.2 Queues 6.3 Double-Ended Queues 6.4 Exercises 7 Linked Lists 7.1 Singly Linked Lists 7.2 Circularly Linked Lists 7.3 Doubly Linked Lists 7.4 The Positional List ADT 7.5 Sorting a Positional List 7.6 Case Study: Maintaining Access Frequencies 7.7 Link-Based vs. Array-Based Sequences 7.8 Exercises 8 Trees 8.1 General Trees 8.2 Binary Trees 8.3 Implementing Trees 8.4 Tree Traversal Algorithms 8.5 Case Study: An Expression Tree 8.6 Exercises 9 Priority Queues 9.1 The Priority Queue Abstract Data Type 9.2 Implementing a Priority Queue 9.3 Heaps 9.4 Sorting with a Priority Queue 9.5 Adaptable Priority Queues 9.6 Exercises 10 Maps, Hash Tables, and Skip Lists 10.1 Maps and Dictionaries 10.2 Hash Tables 10.3 Sorted Maps 10.4 Skip Lists 10.5 Sets, Multisets, and Multimaps 10.6 Exercises 11 Search Trees 11.1 Binary Search Trees 11.2 Balanced Search Trees 11.3 AVL Trees 11.4 Splay Trees 11.5 (2,4) Trees 11.6 Red-Black Trees 11.7 Exercises 12 Sorting and Selection 12.1 Why Study Sorting Algorithms? 12.2 Merge-Sort 12.3 Quick-Sort 12.4 Studying Sorting through an Algorithmic Lens 12.5 Comparing Sorting Algorithms 12.6 Python's Built-In Sorting Functions 12.7 Selection 12.8 Exercises 13 Text Processing 13.1 Abundance of Digitized Text 13.2 Pattern-Matching Algorithms 13.3 Dynamic Programming 13.4 Text Compression and the Greedy Method 13.5 Tries 13.6 Exercises 14 Graph Algorithms 14.1 Graphs 14.2 Data Structures for Graphs 14.3 Graph Traversals 14.4 Transitive Closure 14.5 Directed Acyclic Graphs 14.6 Shortest Paths 14.7 Minimum Spanning Trees 14.8 Exercises 15 Memory Management and B-Trees 15.1 Memory Management 15.2 Memory Hierarchies and Caching 15.3 External Searching and B-Trees 15.4 External-Memory Sorting 15.5 Exercises A Character Strings in Python B Useful Mathematical Facts Bibliography Index