Data-Structures-in-Python

A personal repository dedicated to mastering Data Structures and Algorithms. Includes hands-on code implementations, experimental sandboxes, and detailed notes—with a special deep dive into Linked Lists and memory management.

Data Structures in Python

A curated collection of classic data structures implemented in Python. This repository is designed for learners, interview preparation, and developers who want clean, well-documented implementations of fundamental data structures.

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📚 Overview

This project covers a wide range of data structures, each implemented from scratch in Python with clarity and modularity in mind. The goal is to help you:

• Understand core concepts behind data structures.
• Learn Pythonic implementations of algorithms.
• Practice time complexity analysis and usage examples.

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🗂️ Repository Structure

Data Structure	Description
Array	Fixed-size sequential collection with indexing operations.
Linked List	Nodes connected via pointers; includes singly & doubly linked lists.
Stack	LIFO structure with push/pop operations.
Queue	FIFO structure with enqueue/dequeue. Includes circular and priority queues.
Hash Table	Key-value mapping with collision handling.
Binary Tree	Hierarchical structure with traversal methods (DFS, BFS).
Binary Search Tree	Ordered tree supporting efficient search, insert, delete.
Graph	Adjacency list/matrix representation with traversal algorithms.
Heap	Complete binary tree supporting priority queue operations.

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⚙️ Installation & Setup

Clone the repository:

git clone https://github.com/nicocodes9/Data-Structures-in-Python.git
cd Data-Structures-in-Python

Run any module directly:

python linked_list.py

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🚀 Usage Examples

Linked List

from linked_list import LinkedList

ll = LinkedList()
ll.insert_at_end(10)
ll.insert_at_end(20)
ll.display()  # Output: 10 -> 20

Stack

from stack import Stack

s = Stack()
s.push(5)
s.push(10)
print(s.pop())  # Output: 10

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📊 Complexity Analysis

Each implementation includes time and space complexity notes:

• Array: Access O(1), Insert O(n)
• Linked List: Insert O(1), Search O(n)
• Stack/Queue: Push/Pop O(1)
• Hash Table: Average O(1), Worst O(n)
• Binary Search Tree: Search O(log n) average, O(n) worst
• Graph Traversal: O(V + E)

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🧩 Contribution Guidelines

• Fork the repo and create a new branch.
• Add new data structures or improve existing ones.
• Ensure code is PEP8 compliant and includes docstrings.
• Submit a pull request with clear commit messages.

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🎯 Roadmap

• Add advanced trees (AVL, Red-Black).
• Implement graph algorithms (Dijkstra, Kruskal, Prim).
• Provide unit tests for all structures.
• Expand with real-world use cases.

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📜 License

This project is licensed under the MIT License — free to use, modify, and distribute.