Welcome to the Data Structures and Algorithms in Python repository! This course provides an in-depth understanding of essential data structures and algorithms, focusing on practical implementations using Python. Whether you're a beginner or brushing up your skills, this repository serves as a resource to master DSA concepts and coding techniques.

1. Linked Lists

   • Singly Linked List
   • Doubly Linked List
   • Circular Linked List

2. Hashmaps

   • Key-Value Pair Implementation
   • Collision Handling (Chaining, Open Addressing)
   • Applications (e.g., Anagram Check, Frequency Count)

3. Stacks and Queues

   • Stack Implementation (Array & Linked List)
   • Queue Implementation (Array & Linked List)
   • Circular Queue
   • Applications (e.g., Balancing Parentheses, Reverse a String)

4. Binary Trees

   • Binary Tree Basics
   • Binary Search Tree (BST)
   • Tree Traversals (Inorder, Preorder, Postorder)
   • Applications (e.g., Lowest Common Ancestor, Path Sum)

5. Array Algorithms

   • Sorting Algorithms (Bubble Sort, Quick Sort, Merge Sort)
   • Searching Algorithms (Binary Search)
   • Sliding Window Technique
   • Two-Pointer Technique

• Clear and concise explanations.
• Python code implementations for every concept.
• Real-world examples to enhance understanding.
• Interactive exercises and problems.

• Basic knowledge of Python programming.
• Python installed on your machine (Download here).

git clone https://github.com/4bhimxnyu/Data-Structure-Algorithm.py
cd dsa-python-course

📁 dsa-python-course
├── LinkedLists
│   ├── singly_linked_list.py
│   ├── doubly_linked_list.py
│   └── circular_linked_list.py
├── Hashmaps
│   ├── basic_hashmap.py
│   ├── collision_chaining.py
│   └── open_addressing.py
├── Stacks_Queues
│   ├── stack.py
│   ├── queue.py
│   └── circular_queue.py
├── BinaryTrees
│   ├── binary_tree_basics.py
│   ├── binary_search_tree.py
│   └── tree_traversals.py
├── ArrayAlgorithms
│   ├── sorting_algorithms.py
│   ├── searching_algorithms.py
│   └── sliding_window.py
└── README.md

1. Navigate to the folder of your desired topic.
2. Open the corresponding Python file to explore code and concepts.
3. Run the code in your favorite IDE or terminal to test the examples.
4. Tackle the provided exercises for hands-on learning.

Contributions are welcome! If you have improvements, bug fixes, or additional examples, feel free to:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-name).
3. Commit your changes (git commit -m "Added feature-name").
4. Push to the branch (git push origin feature-name).
5. Create a pull request.

• Python documentation for in-depth guidance.
• Open-source contributors for inspiration.

Hi! I'm (Abhimanyu Pratap Singh), a passionate developer and educator committed to helping others learn coding and problem-solving. Feel free to connect with me for any questions or feedback!

Happy Coding! 🎉