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.