Python-DS-Algorithms

Explore Python implementations of essential data structures and algorithms. Enhance your coding skills with clear examples, visualizations, and problem-solving exercises. Perfect for beginners and experienced developers seeking proficiency in Python-based data manipulation and algorithmic problem-solving. 🐍✨

Mind Map

Data Structure

1. Arrays
2. Stacks
3. Queues
4. Graphs
   • Undirected Graphs
   • Directed Graphs
     • Trees
       • Linked Lists
         • Singly LL
         • Doubly LL
         • Circular LL
       • Tries
       • Binary Tree
         • Binary Search Tree
           • Balanced BST
             • AVL Tree
             • Red Black Tree
           • Un-balanced BST
       • Heap
         • Max Heap (Priority Queue)
         • Min Heap
   • Weighted Graphs
   • Unweighted Graphs
   • Cyclic Graphs
   • Acyclic Graphs
5. Hash Tables

Algorithms

1. Sorting
2. Dynamic Programming
3. BFS + DFS (Searching)
4. Recursion

Key Features

1. Implementation of Data Structures: Explore well-documented Python implementations of fundamental data structures such as arrays, linked lists, stacks, queues, trees, and graphs. Each implementation is accompanied by detailed explanations and usage examples.

2. Algorithmic Solutions: Dive into a diverse set of algorithmic problems, ranging from basic sorting and searching algorithms to advanced topics like dynamic programming, greedy algorithms, and graph algorithms. Understand the underlying principles and learn how to apply them effectively.

3. Problem-Solving Practice: Find a curated set of coding challenges and problem-solving exercises that reinforce your understanding of data structures and algorithms. Solutions are provided in Python, along with explanations to help you grasp the reasoning behind each solution.

4. Visualizations: Visualize the inner workings of algorithms and data structures with clear diagrams and animations. Gain a deeper understanding of their behavior and performance.

5. Documentation: Access comprehensive documentation and explanations for each data structure and algorithm. Clear code comments and inline explanations make it easy to follow and learn from the provided examples.

How to Use

• Clone the repository to your local machine using Git.
• Explore the "data_structures" and "algorithms" directories for Python implementations.
• Check the "problems" directory for a variety of coding challenges to practice your skills.
• Refer to the documentation for detailed explanations and usage guidelines.

Contribution Guidelines

Contributions are welcome! If you find a bug, want to add a new data structure or algorithm, or improve existing code, please submit a pull request. Follow the contribution guidelines outlined in the repository to ensure a smooth collaboration process.

Happy coding, and may your journey into the world of data structures and algorithms be both educational and enjoyable! 🚀