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Table of Contents

1.1 Data structures
1.2 Introduction to Algorithms
1.3 Relation between data structures and algorithms
1.4 Abstract data types
1.5 Applications of ADTs
1.6 Algorithm efficiency

2.1 Searching and Algorithms
2.2 Binary search
2.3 Constant time operations
2.4 Growth of functions and complexity
2.5 O notation
2.6 Algorithm analysis
2.7 Recursive definitions
2.8 Recursive algorithms
2.9 Analyzing the time complexity of recursive algorithms

3.1 Sorting: Introduction
3.2 Selection sort
3.3 Insertion sort
3.4 Shell sort
3.5 Quicksort
3.6 Merge sort
3.7 Radix sort
3.8 Overview of fast sorting algorithms
3.9 Python: Sorting with different operators

4.1 List abstract data type (ADT)
4.2 Singly-linked lists
4.3 Singly-linked lists: Search and insert
4.4 Singly-linked lists: Remove
4.5 Doubly-linked lists
4.6 Doubly-linked lists: Search and insert
4.7 Doubly-linked lists: Remove
4.8 Linked list traversal
4.9 Sorting linked lists
4.10 Linked list dummy nodes
4.11 Linked lists: Recursion
4.12 Array-based lists

5.1 Stack abstract data type (ADT)
5.2 Stacks using linked lists
5.3 Array-based stacks
5.4 Queue abstract data type (ADT)
5.5 Queues using linked lists
5.6 Array-based queues
5.7 Deque abstract data type (ADT)
5.8 Using a Python list as a stack
5.9 Python queue class

6.1 Map ADT
6.2 Hash tables
6.3 Chaining
6.4 Linear probing
6.5 Quadratic probing
6.6 Double hashing
6.7 Hash table resizing
6.8 Common hash functions
6.9 Direct hashing
6.10 Hashing Algorithms: Cryptography, Password Hashing
6.11 Python dictionary

7.1 Binary trees
7.2 Applications of trees
7.3 Binary search trees
7.4 BST: Search algorithm
7.5 BST: Insertion
7.6 BST: Removal
7.7 BST: Traversal
7.8 BST: Height and insertion order
7.9 BST: Recursion
7.10 BST: Parent node references
7.11 Set abstract data type (ADT)
7.12 Implementing a set ADT with a BST
7.13 Python set class
7.14 Tries

8.1 AVL: A balanced tree
8.2 AVL rotations
8.3 AVL insertions
8.4 AVL removals
8.5 Red-black tree: A balanced tree
8.6 Red-black tree: Rotations
8.7 Red-black tree: Insertion
8.8 Red-black tree: Removal

9.1 Heaps
9.2 Heaps using arrays
9.3 Heapsort
9.4 Priority queue abstract data type (ADT)
9.5 Treaps
9.6 Python PriorityQueue class

10.1 Graphs: Introduction
10.2 Applications of graphs
10.3 Graph representations: Adjacency lists
10.4 Graph representations: Adjacency matrices
10.5 Directed graphs
10.6 Weighted graphs
10.7 Vertex, Edge, and Graph classes
10.8 Graphs: Breadth-first search
10.9 Graphs: Depth-first search
10.10 Algorithm: Dijkstra’s shortest path
10.11 Algorithm: Bellman-Ford’s shortest path
10.12 Topological sort
10.13 Minimum spanning tree
10.14 All pairs shortest path

11.1 Huffman compression
11.2 Huffman compression: Implementation
11.3 Heuristics
11.4 Greedy algorithms
11.5 Dynamic programming

12.1 B-trees
12.2 2-3-4 tree: Search
12.3 2-3-4 tree: Insertion
12.4 2-3-4 tree: Rotation and fusion
12.5 2-3-4 tree: Removal

13.1 Bubble sort
13.2 Quickselect
13.3 Bucket sort
13.4 Circular lists

Teach Data Structures in Python with this hands-on, interactive zyBook with customizable zyLabs

Data Structures in Python is suitable for a first course in data structures and algorithms, especially common in the first two years of a computing major.

  • Introduces the basics of algorithms and data structures, including sorting, runtime complexity, lists, stacks, queues, hash tables, trees, and graphs
  • Packed with over 1,000 learning questions and animations to help students master the material
  • Python implementations of many data structures and algorithms are available throughout the book
  • Adopters have access to a test bank with questions for every chapter

What is a zyBook?

Data Structures in Python is a web-native, interactive zyBook that helps students visualize concepts to learn faster and more effectively than with a traditional textbook. (Check out our research.)

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  • Save time with auto-graded labs and challenge activities that seamlessly integrate with your LMS gradebook
  • Build quizzes and exams with hundreds of included test questions
  • Student benefits
  • Learning questions and other content serve as an interactive form of reading
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  • Concepts come to life through extensive animations embedded into the interactive content
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Evan Olds
Senior Content Developer, zyBooks

Roman Lysecky
Professor Emeritus of Electrical and Computer Engineering, Univ. of Arizona

 

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