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Hands-On Data Structures and Algorithms with Python – Third Edition

Hands-On Data Structures and Algorithms with Python – Third Edition - Third Edition

By : Dr. Basant Agarwal
4.8 (26)
Buy this Book
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Hands-On Data Structures and Algorithms with Python – Third Edition

Hands-On Data Structures and Algorithms with Python – Third Edition

4.8 (26)
By: Dr. Basant Agarwal
Buy this Book

Overview of this book

Choosing the right data structure is pivotal to optimizing the performance and scalability of applications. This new edition of Hands-On Data Structures and Algorithms with Python will expand your understanding of key structures, including stacks, queues, and lists, and also show you how to apply priority queues and heaps in applications. You’ll learn how to analyze and compare Python algorithms, and understand which algorithms should be used for a problem based on running time and computational complexity. You will also become confident organizing your code in a manageable, consistent, and scalable way, which will boost your productivity as a Python developer. By the end of this Python book, you’ll be able to manipulate the most important data structures and algorithms to more efficiently store, organize, and access data in your applications.
Table of Contents (17 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Get in touch
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1
Python Data Types and Structures
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Python Data Types and Structures
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Introducing Python 3.10
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Installing Python
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Setting up a Python development environment
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Overview of data types and objects
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Basic data types
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Complex data types
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Python’s collections module
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Summary
2
Introduction to Algorithm Design
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Introduction to Algorithm Design
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Introducing algorithms
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Performance analysis of an algorithm
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Asymptotic notation
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Amortized analysis
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Composing complexity classes
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Computing the running time complexity of an algorithm
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Summary
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Exercises
3
Algorithm Design Techniques and Strategies
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Algorithm Design Techniques and Strategies
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Algorithm design techniques
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Recursion
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Divide and conquer
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Dynamic programming
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Greedy algorithms
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Summary
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Exercises
4
Linked Lists
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Linked Lists
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Arrays
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Introducing linked lists
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Singly linked lists
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Doubly linked lists
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Circular lists
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Practical applications of linked lists
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Summary
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Exercise
5
Stacks and Queues
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Stacks and Queues
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Stacks
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Queues
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Summary
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Exercises
6
Trees
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Trees
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Terminology
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Binary trees
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Binary search trees
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Summary
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Exercises
7
Heaps and Priority Queues
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Heaps and Priority Queues
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Heaps
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Priority queues
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Summary
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Exercises
8
Hash Tables
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Hash Tables
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Introducing hash tables
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Resolving collisions
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Implementing hash tables
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Symbol tables
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Summary
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Exercise
9
Graphs and Algorithms
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Graphs and Algorithms
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Graphs
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Graph representations
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Graph traversals
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Other useful graph methods
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Summary
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Exercises
10
Searching
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Searching
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Introduction to searching
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Linear search
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Jump search
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Binary search
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Interpolation search
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Exponential search
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Choosing a search algorithm
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Summary
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Exercise
11
Sorting
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Sorting
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Technical requirements
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Sorting algorithms
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Bubble sort algorithms
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Insertion sort algorithm
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Selection sort algorithm
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Quicksort algorithm
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Implementation of quicksort
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Timsort algorithm
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Summary
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Exercise
12
Selection Algorithms
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Selection Algorithms
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Technical requirements
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Selection by sorting
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Randomized selection
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Deterministic selection
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Summary
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Exercise
13
String Matching Algorithms
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String Matching Algorithms
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Technical requirements
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String notations and concepts
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Pattern matching algorithms
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The brute force algorithm
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The Rabin-Karp algorithm
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The Knuth-Morris-Pratt algorithm
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The Boyer-Moore algorithm
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Summary
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Exercise
14
Other Books You May Enjoy
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Other Books You May Enjoy
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Index
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Index
1
Appendix: Answers to the Questions
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Chapter 2: Introduction to Algorithm Design
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Chapter 3: Algorithm Design Techniques and Strategies
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Chapter 4: Linked Lists
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Chapter 5: Stacks and Queues
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Chapter 6: Trees
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Chapter 7: Heaps and Priority Queues
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Chapter 8: Hash Tables
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Chapter 9: Graphs and Algorithms
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Chapter 10: Searching
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Chapter 11: Sorting
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Chapter 12: Selection Algorithm
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Chapter 13: String Matching Algorithms

Performance analysis of an algorithm

The performance of an algorithm is generally measured by the size of its input data, n, and the time and the memory space used by the algorithm. The time required is measured by the key operations to be performed by the algorithm (such as comparison operations), where key operations are instructions that take a significant amount of time during execution. Whereas the space requirement of an algorithm is measured by the memory needed to store the variables, constants, and instructions during the execution of the program.

Time complexity

The time complexity of the algorithm is the amount of time that an algorithm will take to execute on a computer system to produce the output. The aim of analyzing the time complexity of the algorithm is to determine, for a given problem and more than one algorithm, which one of the algorithms is the most efficient with respect to the time required to execute. The running time required by an algorithm depends...

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