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Data Structure and Algorithms Using Python

A comprehensive collection of data structures and algorithms implemented in Python, focusing on educational purposes and practical applications.

🎯 Project Overview

This repository contains implementations of fundamental to advanced algorithms and data structures, organized in a structured manner for easy learning and reference. Each implementation includes detailed documentation, time/space complexity analysis, and usage examples.

🏗️ Project Structure

The project is organized into three main categories:

1. Foundational Algorithms

  • Sorting Algorithms:
    • Bubble Sort
    • Selection Sort
    • Insertion Sort
    • Merge Sort
    • Quick Sort
    • Heap Sort
  • Searching Algorithms:
    • Linear Search
    • Binary Search
    • Depth First Search (DFS)
    • Breadth First Search (BFS)

2. Intermediate Algorithms

  • Graph Algorithms:
    • Dijkstra's Algorithm (Shortest Path)
    • Floyd-Warshall Algorithm (All-Pairs Shortest Path)
    • Kruskal's Algorithm (Minimum Spanning Tree)
    • Prim's Algorithm (Minimum Spanning Tree)
    • Bellman-Ford Algorithm
  • Divide and Conquer:
    • Binary Search Tree Operations
    • Merge Sort and Quick Sort
  • Dynamic Programming:
    • Fibonacci Sequence
    • Longest Common Subsequence
    • Knapsack Problem
    • Matrix Chain Multiplication
    • Coin Change Problem
  • Greedy Algorithms:
    • Activity Selection
    • Huffman Coding
    • Fractional Knapsack

3. Advanced Algorithms

  • String Algorithms:
    • KMP Pattern Matching
    • Rabin-Karp Algorithm
    • Longest Palindromic Substring
  • Backtracking:
    • N-Queens Problem
    • Rat in a Maze
    • Sudoku Solver
  • Advanced Graph Algorithms:
    • Tarjan's Algorithm (SCC)
    • Kosaraju's Algorithm
    • Edmonds-Karp (Max Flow)
  • Other Topics:
    • Trie Operations
    • Segment Trees
    • Fenwick Trees
    • Union-Find

🚀 Key Features

  1. Comprehensive Documentation

    • Each algorithm includes detailed explanations
    • Time and space complexity analysis
    • Implementation characteristics and use cases

  2. Practical Examples

    • Every algorithm comes with usage examples
    • Real-world application scenarios
    • Test cases demonstrating functionality

  3. Educational Focus

    • Clear code structure and comments
    • Step-by-step implementation details
    • Optimization techniques and best practices

💻 Usage Example

Here's a quick example using the Binary Search Tree implementation:

from BST_Algorithm import BinarySearchTree

# Initialize BST
bst = BinarySearchTree()

# Insert elements
elements = [50, 30, 70, 20, 40, 60, 80]
for elem in elements:
    bst.insert(elem)

# Search for elements
print(bst.search(40))  # True
print(bst.search(90))  # False

# Get sorted elements (inorder traversal)
print(bst.inorder_traversal())  # [20, 30, 40, 50, 60, 70, 80]

🛠️ Implementation Details

Each algorithm is implemented with a focus on:

  • Clean, readable code
  • Efficient implementation
  • Proper error handling
  • Extensive documentation
  • Performance optimization

📚 Learning Resources

The repository serves as a learning resource for:

  • Computer Science students
  • Software developers
  • Interview preparation
  • Algorithm enthusiasts

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.