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    Data Structures and Algorithms with Python
    Data Structures and Algorithms with Python
    Data Structures and Algorithms with Python
    Ebook278 pages1 hour

    Data Structures and Algorithms with Python

    By Aadinath Pothuvaal

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    About this ebook

    "Dive into the Heart of Pythonic Algorithms and Data Structures" offers a comprehensive guide designed to empower both beginners and seasoned developers. Whether you're mastering the foundations of computer science or enhancing your problem-solving skills, this book provides a roadmap through the intricacies of efficient data organization and algorithmic prowess.
    We introduce the versatility of Python, setting the stage for an exploration of various data structures, including arrays, linked lists, stacks, queues, trees, and graphs. Each chapter presents practical examples and Python code snippets for easy comprehension and application.
    As the journey progresses, we shift focus to algorithms, covering sorting techniques, searching methods, and dynamic programming. Real-world applications and case studies bridge the gap between theory and practical implementation, reinforcing each algorithm's relevance in solving tangible problems.
    The book emphasizes a hands-on approach, encouraging active engagement with Python code and algorithms. Whether you're preparing for coding interviews, building scalable software, or honing your programming skills, this book equips you with the knowledge and confidence to navigate the challenging terrain of Data Structures and Algorithms using Python.

    LanguageEnglish
    PublisherEducohack Press
    Release dateFeb 20, 2025
    ISBN9789361523717
    Data Structures and Algorithms with Python

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      Book preview

      Data Structures and Algorithms with Python - Aadinath Pothuvaal

      Data Structures and Algorithms with Python

      Data Structures and Algorithms with Python

      By

      Aadinath Pothuvaal

      Data Structures and Algorithms with Python

      Aadinath Pothuvaal

      ISBN - 9789361523717

      COPYRIGHT © 2025 by Educohack Press. All rights reserved.

      This work is protected by copyright, and all rights are reserved by the Publisher. This includes, but is not limited to, the rights to translate, reprint, reproduce, broadcast, electronically store or retrieve, and adapt the work using any methodology, whether currently known or developed in the future.

      The use of general descriptive names, registered names, trademarks, service marks, or similar designations in this publication does not imply that such terms are exempt from applicable protective laws and regulations or that they are available for unrestricted use.

      The Publisher, authors, and editors have taken great care to ensure the accuracy and reliability of the information presented in this publication at the time of its release. However, no explicit or implied guarantees are provided regarding the accuracy, completeness, or suitability of the content for any particular purpose.

      If you identify any errors or omissions, please notify us promptly at [email protected] & [email protected] We deeply value your feedback and will take appropriate corrective actions.

      The Publisher remains neutral concerning jurisdictional claims in published maps and institutional affiliations.

      Published by Educohack Press, House No. 537, Delhi- 110042, INDIA

      Email: [email protected] & [email protected]

      Cover design by Team EDUCOHACK

      Preface

      Welcome to the dynamic world of Data Structures and Algorithms, a foundational realm in computer science that empowers you to craft efficient and powerful software solutions. This book, tailored for Python enthusiasts, serves as your gateway to mastering the art and science of organizing, managing, and manipulating data effectively.

      In today's rapidly evolving technological landscape, the importance of robust data structures and efficient algorithms cannot be overstated. Whether you're a seasoned developer or a novice eager to delve into the intricacies of programming, this book aims to be your reliable companion. Python, with its simplicity and versatility, becomes the perfect vehicle for exploring these fundamental concepts.

      The journey begins with a comprehensive exploration of Python's capabilities, setting the stage for a deep dive into the world of data structures. From arrays and linked lists to trees and graphs, each chapter unfolds a new layer of understanding, accompanied by practical examples and Python code snippets. Clear explanations demystify complex concepts, ensuring that readers of all levels can grasp the essence of each data structure.

      Moving seamlessly into algorithmic territory, the book illuminates the path to efficient problem-solving. Sorting algorithms, searching techniques, and dynamic programming unfold as integral components of your programming toolkit. Real-world applications and case studies provide context, showcasing how these algorithms become invaluable in solving practical challenges across various domains.

      Throughout this journey, the emphasis is on clarity and applicability. Every concept is explained with a practical mindset, encouraging readers to not just understand the theory but to apply it to real-world problems. The Python language, with its readability and expressive syntax, facilitates a smooth transition from theory to implementation.

      Whether you aspire to ace coding interviews, build scalable software, or simply enhance your problem-solving skills, this book equips you with the knowledge and confidence to navigate the intricate landscape of data structures and algorithms using Python. Let the exploration begin, and may your code be both elegant and efficient!

      Table Of Contents

      01

      Introduction to Data Structures and Algorithms1

      1.1 Overview of key data structures1

      1.2 Time and space complexity analysis. Big O notation.2

      1.3 Algorithms for searching, sorting,

      recursion etc.3

      1.4 Python basics and setup4

      02

      Arrays and Linked Lists6

      2.1 Implementing arrays and array-based lists in Python7

      2.2 Linked lists - singly, doubly, circular8

      2.3 Operations like search, insert, delete, traverse9

      03

      Stacks and Queues11

      3.1 Stack implementation using arrays and linked lists 11

      3.2 Stack operations like push, pop, peek12

      3.3 Queues using arrays and linked lists13

      3.4 Queue operations like enqueue, dequeue14

      3.5 Priority queues15

      04

      Trees16

      4.1 Binary Trees16

      4.2 Tree Traversals21

      4.3 Balanced vs Unbalanced Trees 22

      4.4 Common Operations23

      05

      Heaps24

      5.1 Max and Min Heap Implementation24

      5.2 Heap Operations25

      5.3 Heap Sort Algorithm26

      06

      Hash Tables28

      6.1 Hash Tables and Hash Functions28

      6.2 Collision Resolution29

      6.3 Hash Table Load Factors 30

      6.4 Dictionaries with Hash Tables30

      07

      Strings32

      7.1 String Search Algorithms32

      7.2 Pattern Matching 33

      7.3 Longest Common Subsequence34

      7.4 String Manipulation34

      08

      Recursion35

      8.1 Recursion Basics 35

      8.2 Recursive Function Design36

      8.3 Recursive Sorting Algorithms 36

      8.4 Divide and Conquer Algorithms37

      8.5 Backtracking and Memoization37

      09

      Searching and Sorting38

      9.1 Sequential and Binary Search38

      9.2 Sorting Algorithms38

      9.3 Sorting Algorithm Analysis41

      9.4 Sorting in Python41

      9.5 Algorithm Stability42

      9.6 Adaptive Sorting Algorithms 42

      10

      Graphs44

      10.1 Graph Definitions44

      10.2 Graph Representations44

      10.3 Graph Traversal Algorithms 44

      10.4 Shortest Path Algorithms 46

      10.5 Minimum Spanning Trees48

      10.6 Graph Problems 48

      11

      Greedy Algorithms49

      11.1 Activity Selection 49

      11.2 Huffman Encoding49

      11.3 Optimal Substructure 51

      11.4 Analysis 51

      11.5 Fractional Knapsack Problem51

      11.6 Activity Selection Extensions 52

      12

      Divide and Conquer53

      12.1 Overview53

      12.2 Quicksort53

      12.3 Merge Sort 53

      12.4 Binary Search54

      12.5 Closest Pair55

      12.6 Convex Hull 55

      12.7 Strassen’s Matrix Multiplication56

      12.8 Analysis56

      12.9 Karatsuba Multiplication56

      12.10 Dynamic Programming vs Divide-and-Conquer 57

      13

      Dynamic Programming58

      13.1 Overview58

      13.2 0/1 Knapsack Problem58

      13.3 Longest Common Subsequence59

      13.4 All-Pairs Shortest Path 59

      13.5 Sequence Alignment60

      14

      Backtracking61

      14.1 Generating permutations/combinations61

      14.2 Solving rat-in-maze and n-queen problems 64

      14.3 Sudoku Solver - Constraint Satisfaction 67

      14.4 Pruning Branches, Avoiding Repeats69

      15

      Complexity Theory72

      15.1 P, NP, NP-complete and NP-hard Problems72

      15.2 Cook’s Theorem, Examples of

      Hard Problems 75

      15.3 Approximation Algorithms and Heuristics76

      16

      Numerical Algorithms79

      16.1 Searching - binary, exponential, Fibonacci79

      16.2 Factorial Computation, Large Number Multiplication82

      16.3 Primality Testing - Fermat’s, Solovay-Strassen 83

      16.4 Numerical Integration - Quadrature 85

      17

      Randomized Algorithms88

      17.1 Introduction to Randomized Algorithms88

      17.2 Applications of Randomization in Algorithms 88

      17.2.2 Quicksort, Quickselect, Skip Lists89

      17.3 Universal Hashing, Rabin

      Karp String Search91

      17.4 Monte Carlo Algorithms, Las Vegas Algorithms 92

      18

      Graph Algorithms94

      18.1 Minimum Spanning Trees 94

      18.2 Shortest Paths on Weighted Graphs 96

      18.3 Maximum Flow Problem98

      18.4 Bipartite Graph Matching99

      19

      Parallel Algorithms102

      19.1 Task-based vs Data-based Parallelism102

      19.2 Parallel Sorting Algorithms 102

      19.3 Parallel Searching and MapReduce103

      19.4 Synchronization for Parallel Algorithms103

      20

      Functional Data Structures105

      20.1 Persistent data structures for immutability105

      20.2 Making lists, trees etc. functional107

      20.3 Lazy evaluation, Streams, memoization108

      20.4 Applications like version control systems109

      Glossary110

      Index112

      CHAPTER 1 Introduction to Data Structures and Algorithms

      1.1 Overview of key data structures

      Data structures are ways of organizing data in a computer so that it can be accessed and manipulated efficiently. Choosing the right data structure for a given problem is key to designing efficient algorithms. The most fundamental data structures that form the building blocks for many complex data structures and algorithms are:

      Fig. 1.1 Data Structures and Algorithms

      https://images.app.goo.gl/vdTgP581iYu7b8sj8

      Arrays:

      An array is a collection of elements that are accessed using an integer index. In Python, arrays can be implemented using the built-in array type or simply using lists. Arrays allow constant time O(1) access to elements based on their index but can be slow for inserting and deleting elements as it may require shifting subsequent elements. Arrays are the basis for more complex data structures like stacks and queues. Multidimensional arrays extend the array concept to multiple dimensions for representing tables, matrices etc. but space requirements grow exponentially.

      Linked Lists:

      A linked list is a linear collection of data elements called nodes that are connected together using references called pointers. Each node stores data and the address of the next node. This allows for efficient insertion and deletion of nodes as we only need to modify the references without shifting elements. However, random access of elements requires traversing the list from the head node to access an element at a given index. Singly, doubly, and circular linked lists are some common variants. Linked lists can easily model real world objects through node references. Operations include iteration through nodes, inserting/deleting nodes, searching for values and reversing the list.

      Stacks:

      Stacks implement a LIFO (Last In First Out) structure where elements are added and removed from the top of the stack. Operations are done in constant time by manipulating the top pointer. Python lists can be used as stacks just using append() and pop() methods. Stacks are useful for DFS graph traversals, parsing expressions, managing function calls, undo operations etc. A real-world example is a stack of plates where we can only access the topmost plate. Stack overflow occurs when we try to add elements beyond stack capacity.

      Queues:

      Queues implement a FIFO (First In First Out) structure where elements are added to the back of the queue and removed from the front. This models real world queues and processes that follow first come first serve order. Queues enable

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