Merge pull request #64 from UBC-MDS/badgecode-readme

Badgecode readme

CHANGELOG.md

@@ -2,6 +2,38 @@
 
 <!--next-version-placeholder-->
 
-## v0.1.0 (07/01/2025)
+## 1.1.0 
 
-- First release of `datastructpy`!
+- Third release of `datastructpy`!
+
+For this release, we focused on enhancing user experience by adding detailed documentation and a step-by-step tutorial to help users effectively utilize the package. The documentation covers all key features, installation instructions, usage examples, and best practices, ensuring users can quickly get started and resolve any challenges they may face.
+
+In addition to creating the documentation, we published it on **Read the Docs**, a platform specifically designed for hosting and displaying documentation in a user-friendly and visually appealing format. By leveraging Read the Docs, users can access a well-organized and searchable guide, making it easier to find relevant information. This step ensures greater accessibility, allowing users to explore the package’s capabilities with clarity and confidence.
+
+
+
+## 1.0.0 
+
+- Second release of `datastructpy`!
+
+We are excited to announce Version 1.0.0 of datastructpy! 🎉
+
+In this release, we have successfully implemented the Binary Search Tree (BST), providing a fully functional and well-tested data structure. The BST now includes essential operations such as:
+✅ Insertion - Adds elements while maintaining BST properties.
+✅ Search - Supports both Depth-First Search (DFS) and Breadth-First Search (BFS).
+✅ Deletion - Handles all cases, including nodes with zero, one, or two children.
+✅ Tree Construction - list_to_tree() allows quick BST creation from a list of elements.
+
+🔹 Key Enhancements
+• Comprehensive Unit Tests: Each function is rigorously tested using pytest, ensuring correctness and stability.
+• Refactored and Standardized Docstrings: Improved clarity and documentation consistency across all functions and test cases.
+• Updated README: Expanded documentation to reflect the latest BST functionalities with detailed examples.
+• Formatted and Structured Test Files: Ensured a uniform format across all test cases for better maintainability.
+
+
+
+## 0.0.1 
+
+- First release of `datastructpy`!
+
+We are excited to announce Version 0.0.1 of `datastructpy`. In this release, we laid the foundation for the software package by selecting a topic focused on customizable implementations of essential data structures and establishing a collaborative framework. We began by drafting a comprehensive team work contract to define roles, responsibilities, and communication protocols. A public GitHub repository was created to host the project, featuring a clear directory structure and key files such as README.md, CONDUCT.md, and CONTRIBUTING.md to support development. `datastructpy` aims to offer a practical and intuitive experience for working with data structures, including Binary Search Trees (BST), making it an ideal resource for technical interview preparation and educational projects. Additionally, we documented detailed function specifications and created placeholder functions to guide future implementation. This milestone provides a robust and well-organized starting point for the package's development and future enhancements.

README.md

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 # datastructpy
 
+[![Documentation Status](https://readthedocs.org/projects/datastructpy/badge/?version=latest)](https://datastructpy.readthedocs.io/en/latest/?badge=latest)[![Python 3.11](https://img.shields.io/badge/python-3.11-blue.svg)](https://www.python.org/downloads/release/python-360/)
+
 `datastructpy` is a Python package designed to provide customizable and practical implementations of essential data structures, such as Binary Search Trees (BST). It is tailored to help users prepare for technical interviews, coding challenges, and educational projects by offering intuitive and efficient solutions. The package stands out by emphasizing simplicity and clarity while maintaining detailed documentation and modular implementations suited for both learning and practical use. Unlike visualization-heavy libraries, the package focuses on providing a hands-on, customizable experience for working with binary trees and other data structures. If you require a structured, minimalistic approach without additional dependencies or overhead, `datastructpy` serves as a valuable alternative!
 
 ## Data Structures Included: