Data Structures and Algorithms in Python
Sample code from the book Data Structures and Algorithms in Python
GPL license by Michael T. Goodrich, Roberto Tamassia, and Michael H. Goldwasser
Data Structures and Algorithms in Python
- Preface v
- 1 Python Primer 1
- 1.1 Python Overview.2
- 1.2 Objects in Python.4
- 1.3 Expressions, Operators, and Precedence.12
- 1.4 Control Flow 18
- 1.5 Functions 23
- 1.6 Simple Input and Output 30
- 1.7 Exception Handling.33
- 1.8 Iterators and Generators 39
- 1.9 Additional Python Conveniences 42
- 1.10 Scopes and Namespaces 46
- 1.11 Modules and the Import Statement 48
- 1.12 Exercises 51
- 2 Object-Oriented Programming 56
- 2.1 Goals, Principles, and Patterns 57
- 2.2 Software Development 62
- 2.3 Class Definitions.69
- 2.4 Inheritance 82
- 2.5 Namespaces and Object-Orientation. 96
- 2.6 Shallow and Deep Copying101
- 2.7 Exercises 103
- 3 Algorithm Analysis 109
- 3.1 Experimental Studies 111
- 3.1.1 Moving Beyond Experimental Analysis.113
- 3.2 The Seven Functions Used in This Book.115
- 3.3 Asymptotic Analysis.123
- 3.4 Simple Justification Techniques 137
- 3.5 Exercises 141
- 4 Recursion 148
- 4.1 Illustrative Examples 150
- 4.2 Analyzing Recursive Algorithms 161
- 4.3 Recursion Run Amok 165
- 4.4 Further Examples of Recursion169
- 4.5 Designing Recursive Algorithms 177
- 4.6 Eliminating Tail Recursion178
- 4.7 Exercises 180
- 5 Array-Based Sequences 183
- 5.1 Python’s Sequence Types 184
- 5.2 Low-Level Arrays.185
- 5.3 Dynamic Arrays and Amortization 192
- 5.4 Efficiency of Python’s Sequence Types. 202
- 5.5 Using Array-Based Sequences210
- 5.6 Multidimensional Data Sets219
- 5.7 Exercises 224
- 6 Stacks, Queues, and Deques 228
- 6.1 Stacks.229
- 6.2 Queues.239
- 6.3 Double-Ended Queues 247
- 6.4 Exercises 250
- 7 Linked Lists 255
- 7.1 Singly Linked Lists.256
- 7.2 Circularly Linked Lists 266
- 7.3 Doubly Linked Lists.270
- 7.4 The Positional List ADT 277
- 7.5 Sorting a Positional List 285
- 7.6 Case Study: Maintaining Access Frequencies 286
- 7.7 Link-Based vs Array-Based Sequences. 292
- 7.8 Exercises 294
- 8 Trees 299
- 8.1 General Trees 300
- 8.2 Binary Trees 311
- 8.3 Implementing Trees.317
- 8.4 Tree Traversal Algorithms328
- 8.5 Case Study: An Expression Tree 348
- 8.6 Exercises 352
- 9 Priority Queues 362
- 9.1 The Priority Queue Abstract Data Type.363
- 9.2 Implementing a Priority Queue 365
- 9.3 Heaps.370
- 9.4 Sorting with a Priority Queue385
- 9.5 Adaptable Priority Queues390
- 9.6 Exercises 395
- 10 Maps, Hash Tables, and Skip Lists 401
- 10.1 Maps and Dictionaries 402
- 10.2 Hash Tables 410
- 10.3 Sorted Maps 427
- 10.4 Skip Lists 437
- 10.5 Sets, Multisets, and Multimaps 446
- 10.6 Exercises 452
- 11 Search Trees 459
- 11.1 Binary Search Trees.460
- 11.2 Balanced Search Trees 475
- 11.2.1 Python Framework for Balancing Search Trees 478
- 11.3 AVL Trees 481
- 11.4 Splay Trees 490
- 11.5 (2,4) Trees 502
- 11.6 Red-Black Trees.512
- 11.7 Exercises 528
- 12 Sorting and Selection 536
- 12.1 Why Study Sorting Algorithms? 537
- 12.2 Merge-Sort 538
- 12.3 Quick-Sort 550
- 12.4 Studying Sorting through an Algorithmic Lens 562
- 12.5 Comparing Sorting Algorithms567
- 12.6 Python’s Built-In Sorting Functions 569
- 12.7 Selection 571
- 12.8 Exercises 574
- 13 Text Processing 581
- 13.1 Abundance of Digitized Text582
- 13.2 Pattern-Matching Algorithms584
- 13.3 Dynamic Programming 594
- 13.4 Text Compression and the Greedy Method.601
- 13.5 Tries.604
- 13.6 Exercises 613
- 14 Graph Algorithms 619
- 14.1 Graphs.620
- 14.2 Data Structures for Graphs627
- 14.3 Graph Traversals.638
- 14.4 Transitive Closure.651
- 14.5 Directed Acyclic Graphs 655
- 14.6 Shortest Paths659
- 14.7 Minimum Spanning Trees 670
- 14.8 Exercises 686
- 15 Memory Management and B-Trees 697
- 15.1 Memory Management 698
- 15.2 Memory Hierarchies and Caching 705
- 15.3 External Searching and B-Trees 711
- 15.4 External-Memory Sorting 715
- 15.5 Exercises 717
- A Character Strings in Python 721
- B Useful Mathematical Facts 725
- Bibliography 732
- Index 737