Algorithms and Data Structures using Python

Welcome to the "Algorithms and Data Structures using Python" repository! This is a comprehensive learning resource that covers fundamental algorithms and data structures implemented in Python 3. Whether you're a student learning computer science concepts, a developer looking to strengthen your algorithmic thinking, or someone preparing for technical interviews, this repository aims to help you understand these crucial concepts through practical Python implementations.

📚 Learning Path

This repository is organized to provide a structured learning path. Each topic includes:

• Theoretical explanations / Problems links
• Python implementations
• Example use cases
• Time and space complexity analysis
• Visual explanations (where applicable)

🔧 Prerequisites

• Basic understanding of Python programming or Look at ./coding-interview-problems/5-python3/ guide.
• Python 3.x installed on your machine
• Familiarity with basic programming concepts (variables, loops, functions)

Setting Up Your Environment

1. Install Python from the official Python website
2. Clone this repository:

   git clone https://github.com/gnsalok/algo-ds-python.git
   cd algo-ds-python

📖 Data Structures

Basic Data Structures

1. Arrays and Lists

   • Dynamic arrays
   • Multi-dimensional arrays
   • Array operations and their complexity

2. Linked Lists

   • Singly linked lists
   • Doubly linked lists
   • Circular linked lists
   • Common operations and their implementations

3. Stacks and Queues

   • Stack implementations and applications
   • Queue variations (simple queue, circular queue)
   • Priority queues
   • Deques (double-ended queues)

Advanced Data Structures

1. Trees

   • Binary trees
   • Binary search trees (BST)
   • AVL trees
   • Red-black trees
   • B-trees
   • Segment trees

2. Heaps

   • Min heaps
   • Max heaps
   • Heap operations
   • Heapify process

3. Hash-Based Structures

   • Hash tables
   • Hash maps
   • Collision resolution techniques
   • Hash functions

4. Graphs

   • Graph representations (adjacency matrix, adjacency list)
   • Weighted and unweighted graphs
   • Directed and undirected graphs

5. Tries

   • Basic trie operations
   • Prefix trees
   • Applications in string processing

🎯 Algorithms

Searching Algorithms

• Linear search
• Binary search
• Interpolation search
• Jump search

Sorting Algorithms

• Bubble sort
• Selection sort
• Insertion sort
• Merge sort
• Quick sort
• Heap sort
• Counting sort
• Radix sort

Graph Algorithms

• Depth-First Search (DFS)
• Breadth-First Search (BFS)
• Dijkstra's algorithm
• Bellman-Ford algorithm
• Kruskal's algorithm
• Prim's algorithm

Pattern Matching and String Algorithms

• KMP algorithm
• Rabin-Karp algorithm
• Boyer-Moore algorithm
• String matching and manipulation

Advanced Algorithmic Techniques

1. Recursion

   • Base cases and recursive cases
   • Recursive problem-solving
   • Stack frames and memory usage

2. Dynamic Programming

   • Memoization
   • Tabulation
   • Common DP patterns

3. Greedy Algorithms

   • Activity selection
   • Huffman coding
   • Fractional knapsack

4. Backtracking

   • State space tree
   • Constraint satisfaction
   • Optimization problems

[!NOTE] : Few topics are not covered yet. Feel free to contribute.

📚 Learning Resources

Online Resources

• Think Like a Programmer
• Recursion Fundamentals
• Backtracking Concepts
• Dynamic Programming Guide
• Recursion and Backtracking
• Post Order Traversal

🤝 Contributing

We welcome contributions from the community! Whether you're fixing bugs, adding new features, or improving documentation, your help is appreciated.

How to Contribute

1. Fork the Repository

   • Click the 'Fork' button at the top right of this page
   • Clone your fork locally: git clone https://github.com/YOUR-USERNAME/algo-ds-python.git

2. Create a Branch

   git checkout -b feature/your-feature-name
   # or
   git checkout -b fix/your-fix-name

3. Make Your Changes

   • Add your implementation
   • Follow the Python Style Guide (PEP 8)
   • Add appropriate comments and documentation
   • Include test cases if applicable

4. Test Your Changes

   • Ensure your code works as expected
   • Run any existing tests
   • Add new tests if needed

5. Commit Your Changes

   git add .
   git commit -m "Brief description of your changes"

6. Push to GitHub

   git push origin feature/your-feature-name

7. Submit a Pull Request

   • Go to your fork on GitHub
   • Click 'New Pull Request'
   • Select your feature branch
   • Add a meaningful title and description

Contribution Guidelines

• Follow Python best practices and PEP 8 style guide
• Include docstrings for functions and classes
• Add comments explaining complex logic
• Update documentation if needed
• Include example usage where appropriate
• Add test cases for new features

What to Contribute

• New algorithms or data structures
• Improvements to existing implementations
• Bug fixes
• Documentation improvements
• Test cases
• Example use cases
• Performance optimizations

For more detailed guidelines, please check our CONTRIBUTING.md file.

👨‍💻 Lead Maintainer

Connect With Me

• LinkedIn

📄 License

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