Dr Shriram K. Vasudevan (Assistant Professor, Dept. of CSE, Assista, Mr Abhishek S. Nagarajan (Data Scientist, Data Scientist, 24 [7] .ai, Prof Karthick Nanmaran (Assistant Prof., Dept of CSE, Assistant Pro

Data Structures using Python

• Broschiertes Buch

Produktbeschreibung

Data Structures using Python provides an introduction to design, analysis, and implementation of data structures using the powerful programming language, Python. This book is designed for a first course on the subject. It is written for the undergraduate engineering students of Computer Science, Information Technology, and allied disciplines.

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Produktdetails

• Verlag: OUP India
• Seitenzahl: 360
• Erscheinungstermin: 12. März 2021
• Englisch
• Abmessung: 189mm x 243mm x 21mm
• Gewicht: 494g
• ISBN-13: 9780190124083
• ISBN-10: 0190124083
• Artikelnr.: 60016397

Autorenporträt

Shriram K. Vasudevan is Assistant Professor, Dept. of Computer Science& Engineering, Amrita Vishwavidyapeetham, Coimbatore. He holds a Ph.D. in Embedded Systems with over 11 years of industrial and academic experience. He has worked with major multinational companies like Wipro and Aricent Technologies before joining academics. Abhishek S. Nagarajan is a Data scientist in [24]7.ai in Bangalore. His primary job is developing and deploying data-driven machine learning models. Other than models he is also responsible for maintaining business metrics based on client requirements. He also has a basic knowledge on the retargeting and personalization projects for customer acquisition in clients. He proposed a personalization based targeting model for an event called 'ideon' and the same selected for final and has been planned for implementation in leading clients. His areas of interests are Data sciences, Data structures, Internet of Things, Augmented reality and Mas personalization. Karthick Nanmaran is Assistant Professor, Department of CSE, SRM Institute of Science and Technology, Chennai.

Inhaltsangabe

* 1. Data Structures-Introduction
* 1.1 Introduction
* 1.2 What is a Data Structure?
* 1.3 Why Do We Need Data Structures?
* 1.4 How to Study/Prepare Data Structures? Why Does It Appear
Difficult?
* 1.5 Different Types of Data Structures
* 1.6 How to Select a Data Structure?
* 1.7 How are Data Structures Implemented?
* 1.8 Real-Life Scenarios for Data Structures
* 1.6 Difference Between Data Structures and Database Management
Systems
* 2. Abstract Data Type and Analysis
* 2.1 Introduction- Abstract Data Type
* 2.2 Complexity
* 2.2.1 Time Complexity
* 2.2.2 Space Complexity
* 2.3 Asymptotic Notations
* 2.3.1 Big-O
* 2.3.2 Big-Omega
* 2.3.3 Big-Theta
* 2.3.4 Small-O
* 2.3.5 Small-Omega
* 2.4 Recursion
* 2.4.1 How does Recursion Work?
* 2.4.2 Inefficient Recursion
* 2.4.3 Tail Call Elimination
* 2.4.4 Analysis of Recursive Functions
* 2.5 Applications of Recursion
* 3. Linear Data Structures
* 3.1 Arrays-Introduction
* 3.2 Declaration of Arrays
* 3.3 Implementation
* 3.3.1 Insertion
* 3.3.2 Deletion
* 3.3.3 Merging
* 3.3.4 Some More Operations
* 3.3.5 Complexity Analysis
* 3.4 Applications
* 3.5 Python Sequences
* 4. Continuous Memory Based Linear Data Structures
* 4.1 Introduction
* 4.2 Stack
* 4.2.1 Working-Push Operation
* 4.2.2 Working-Pop Operation
* 4.2.3 Working-Top Operation
* 4.3 Implementation of Stack Using Pointers
* 4.4 Complex Operations
* 4.4.1 Searching
* 4.4.2 Sorting
* 4.4.3 Complexity Analysis
* 4.5 Applications of Stacks
* 4.5.1 Application: Infix-to-Postfix Conversion
* 4.5.2 Application: Evaluation of Prefix Expression
* 4.6 Queues
* 4.7 ingle-Ended Queues
* 4.7.1 Working-Enqueue Operation
* 4.7.2 Working-Dequeue Operation
* 4.7.3 Working-Front Operation
* 4.7.4 Implementation of Single-Ended
* Queues using Lists
* 4.7.5 Complex Operations
* 4.7.6 Circular Array-based Implementation of Single-ended Queues
* 4.8 Double-Ended Queues
* 4.8.1 Working: Push_Front Operation
* 4.8.2 Working: Push_Back Operation
* 4.8.3 Working: Pop-Front Operation
* 4.8.4 Working: Pop_Back Operation
* 4.8.5 Working: Front Operation
* 4.8.6 Working: Rear Operation
* 4.8.7 Implementation of a Deque
* 4.8.8 Complex Operations
* 4.8.9 Complexity Analysis
* 4.9 Priority Queues
* 4.9.1 Implementation of Priority Queues
* 4.1 Applications of Queues
* 4.10.1 Application: Check if a Given String is a Palindrome
* 5. Pointer-Based Linear Data Structures
* 5.1 Introduction to Linked Lists
* 5.2 Singly Linked Lists
* 5.2.1 Working-Insert Node Operation
* 5.2.2 Working-Delete Node Operation
* 5.2.3 Working-ValueAt Operation
* 5.2.4 Implementation of Singly Linked Lists
* 5.2.5 Complex Operations-Searching
* 5.2.6 Complex Operations-Sorting
* 5.2.7 Complexity Analysis
* 5.3 Doubly Linked Lists
* 5.3.1 Working-Insert Node Operation
* 5.3.2 Working-Delete Node Operation
* 5.3.3 Working-ValueAt Operation
* 5.3.4 Implementation of Doubly Linked Lists
* 5.3.5 Complexity Analysis
* 5.4 Circular Linked Lists
* 5.4.1 Working-Insert Node Operation
* 5.4.3 Implementation of Circular Linked Lists
* 5.4.4 Complexity Analysis
* 5.5 Applications of Linked Lists
* 6. Pointer Based Hierarchical Data Structures
* 6.1 Introduction-Non-Linear Data Structures
* 6.2 TREES
* 6.2.1 Definitions
* 6.3 Binary Trees
* 6.3.1 Types of Binary Trees
* 6.4 Implementation of Binary Trees
* 6.4.1 Pointer-based Implementation
* 6.4.2 Array-based Implementation
* 6.4.3 Linked List-based Implementation
* 6.5 Traversal
* 6.5.1 In-order Traversal
* 6.5.2 Pre-order Traversal
* 6.5.3 Post-order Traversal
* 6.5.4 Level-ordered Traversal
* 6.6 Basic Operations
* 6.6.1 Inserting a Node
* 6.6.2 Deleting a Node
* 6.7 Threaded Binary Trees
* 6.8 Applications of Trees
* 7. Search Trees
* 7.1 Introduction
* 7.2 Binary Search Trees
* 7.2.1 Operation-Search Value
* 7.2.2 Operation-Insert a Node
* 7.2.3 Operation-Delete a Node
* 7.2.4 Implementation of Binary Search Trees
* 7.2.5 Complexity Analysis
* 7.3 Avl Trees
* 7.3.1 Operation-Search Value
* 7.3.2 Operation- Insert a Node
* 7.3.3 Operation-Deleting a Node
* 7.3.4 Implementation of AVL Trees
* 7.3.5 Complexity Analysis
* 7.4 Red-Black Trees
* 7.4.1 Operation-Insertion
* 7.4.2 Operation-Delete a Node
* 7.4.3 Implementation of Red-Black Trees
* 7.4.4 Complexity Analysis
* 7.5 Splay Trees
* 7.5.1 Operation-'Search a Value' or 'Splay a Value'
* 7.5.2 Operation-Insert a Node
* 7.5.3 Operation-Delete a Node
* 7.5.4 Implementation of Splay Trees
* 7.5.5 Complexity Analysis
* 7.6 B-TREES
* 7.6.1 In-order Traversal
* 7.6.2 Operation-Search a Node
* 7.6.3 Operation-Insert a Node
* 7.6.4 Operation-Delete a Node
* 7.6.5 Implementation of B-Trees
* 7.6.6 Complexity Analysis
* 7.7 Applications of Search Trees
* 8. Priority Queues and Heaps
* 8.1 Introduction-Heap
* 8.2 Binary Heaps
* 8.2.1 Operation-Insertion
* 8.2.2 Operation-Deletion
* 8.2.3 Implementation of Max Heap
* 8.2.4 Complexity Analysis
* 8.3 Leftist Heaps
* 8.3.1 Operation-Merging
* 8.3.2 Operation-Insertion
* 8.3.3 Operation-Deletion
* 8.3.4 Implementation of Leftist Heaps
* 8.3.5 Complexity Analysis
* 8.4 Priority Queues Using Heaps
* 8.5 Applications of Heaps
* 9. Other Non-Linear Data Structures
* 9.1 Introduction-Non-Linear, Non-Hierarchical Data Structures
* 9.2 Trie
* 9.2.1 Insertion of a Key
* 9.2.2 Searching a Key
* 9.2.3 Implementation
* 9.2.4 Complexity Analysis
* 9.2.5 Applications of Trie
* 9.3 Dictionary
* 9.3.1 Inserting a Key and its Value
* 9.3.2 Deleting a Key along with Value
* 9.3.3 Merging Dictionaries
* 9.3.4 Handling Tabular Data
* 9.3.5 Implementation
* 9.3.6 Complexity
* 9.3.7 Applications of Dictionary
* 9.4 Hash Table
* 9.4.1 Linear Probing
* 9.4.2 Chaining the Elements
* 9.4.3 Implementation
* 9.4.4 Complexity
* 9.4.5 Applications of Hash Tables
* 9.5 Sets
* 9.5.1 Operation-Insertion of an Element
* 9.5.2 Operation-Removal of Elements
* 9.5.3 Binary Set Operations
* 9.5.4 Other Utility Functions
* 9.5.5 Implementation
* 9.5.6 Complexity Analysis
* 9.5.7 Applications of Set
* 9.5.8 Variants of Set Data Structure
* 9.5 Counter/Multisets
* 9.5.1 Accessing
* 9.5.2 Binary Operations on Counters
* 10. Memory Management
* 10.1 Introduction-Memory Management
* 10.2 Data Structures in Memory Management
* 10.3 B+ Trees
* 10.3.1 Working
* 10.3 Memory Hierarchy and Caching
* 11. Graphs
* 11.1 Graph-Introduction
* 11.2 Components of a Graph
* 11.4 Graph Representation
* 11.4.1 Linked List Based Representation
* 11.4.2 Matrix Based Representation
* 11.2.3 Pointer Based Representation
* 11.2.4 Performance Comparison of Graph Representation
* 11.3 Types of Graph
* 11.4 Working
* 11.4.1 Insertion of a node
* 11.4.2 Insertion of an edge
* 11.4.3 Deletion of an edge
* 11.4.4 Deletion of a node
* 11.5 Traversal
* 11.5.1 Depth First Search
* 11.5.2 Breadth First Search
* 11.7 Implementation of Graph
* 11.7.1 Adjacency List Based Representation
* 11.7.2 Adjacency Matrix Based Representation
* 11.7.3 Incidence Matrix Based Representation
* 11.8 Complexity Analysis
* 11.9 Topological Sorting
* 11.9.1 Implementation
* 11.9.2 Complexity Analysis
* 11.1 Spanning Tree
* 11.10.1 Kruskal Algorithm
* 11.10.2 Prim's Algorithm
* 11.11 Shortest Distance
* 11.11.1 Dijkstra's Algorithm
* 11.11.2 Floyd-Warshall Algorithm
* 11.12 Graph Connectivity
* 11.12 Applications Of Graph
* 12. Sorting
* 12.1 Introduction to Sorting
* 12.2 Importance of Sorting Algorithms
* 12.3 Exchange Sort
* 12.3.1 Bubble Sort
* 12.4 Selection Sort
* 12.4.1 Straight Selection Sort
* 12.4.2 Heap Sort
* 12.5 Insertion Sort
* 12.5.1 Simple Insertion Sort
* 12.5.2 Shell Sort
* 12.6 Divide and Conquer
* 12.6.1 Merge Sort
* 12.6.2 Quick Sort
* 12.7 Distributed Sort
* 12.7.1 Bucket Sort
* 12.7.2 Counting Sort
* 12.7.3 Radix Sort
* 12.8 Comparison of Sorts
* 13. Searching
* 13.1 Introduction-What is Searching?
* 13.2 Linear Search
* 13.2.1 Working
* 13.2.2 Implementation
* 13.3.3 Complexity Analysis
* 13.3 Binary Search
* 13.3.1 Working
* 13.3.2 Implementation
* 13.3.3 Complexity Analysis
* 13.4 Tree-Based Search
* 13.5 Hashing
* 13.5.1 Working
* 13.5.2 Problem of Collision
* 13.5.4 Implementation
* 13.5.5 Complexity Analysis
* 13.6 Case Studies of Searching Techniques
* ANNEXURE 1 - Python classes and built in Functions