pure-python-ds 1.0.0

A strictly typed, 100% test-covered data structures and algorithms library.

pure-python-ds 🚀

A high-performance, strictly-typed, and memory-optimized Data Structures and Algorithms library built in Pure Python.

🏗️ Architecture Overview

This library was designed with a focus on Systems Architecture. By utilizing Python's __slots__, every node in this library is memory-optimized to reduce overhead, making it suitable for educational deep-dives and performance-sensitive prototyping.

[Image of a software architecture diagram showing layers of data structures and algorithms]

🛠️ Key Features

1. Linear Data Structures

• Linked Lists: Singly and Doubly Linked Lists with $O(1)$ head/tail operations.
• Stacks & Queues: Built on top of optimized linked nodes for strict LIFO/FIFO behavior.

2. Hierarchical & Network Structures

• Binary Search Trees (BST): Standard ordered trees with recursive and iterative implementations.
• AVL Trees: Self-balancing trees using rotation logic to guarantee $O(\log n)$ performance.
• Graphs: Adjacency-list based implementation supporting both Directed and Undirected networks.

3. Advanced Algorithms

• Pathfinding: Dijkstra's Algorithm for finding shortest paths in weighted graphs.
• Sorting: $O(n \log n)$ Merge Sort implementation.
• Searching: $O(\log n)$ Binary Search.
• Dynamic Programming: Memoized Fibonacci and sub-problem optimization.

[Image of a perfectly balanced Binary Search Tree demonstrating O(log n) structure]

🚀 Installation

Since the package is in "Editable" mode, you can install it locally to use across your entire system:

git clone [https://github.com/ParjadM/pure-python-ds.git](https://github.com/ParjadM/pure-python-ds.git)
cd pure-python-ds
pip install -e .

🚀 Technical Milestone: 83% Unit Test Coverage Reached Core Engineering Accomplishments:

Robustness Verification: Engineered a comprehensive regression suite achieving 83% total coverage across 15+ complex data structures.

Algorithm Integration: Successfully implemented and verified Kruskal’s MST (using custom DSU), Bellman-Ford, and Dijkstra’s algorithms.

Recursive Tree Logic: Developed and tested memory-safe recursive deletion with rebalancing for AVL and Binary Search Trees.

Performance Optimization: Leveraged slots and Python Generators to ensure O(1) space complexity for tree traversals and minimal memory footprint.