DataStruct-Kit 0.1.2

Python package implementing essential data structures and algorithms

DataStruct-Kit 🚀

A Python package implementing essential data structures and algorithms with a focus on performance, usability, and scalability.

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✨ Features

Sorting Algorithms 📊

• Bubble Sort
• Merge Sort
• Quick Sort

Searching Algorithms 🔍

• Linear Search
• Binary Search

Data Structures 🏗️

• HashTable
• Binary Tree
• AVL Tree
• Graph

Performance Utilities ⏱️

• Benchmarking tools for performance analysis.
• Complexity analysis for algorithm evaluation.

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📦 Installation

Install DataStruct-Kit from PyPI:

pip install DataStruct-Kit

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🚀 Quick Start

Example Usage

from DataStruct-Kit import bubble_sort, binary_search, HashTable, BinaryTree, dfs, bfs

# Sorting algorithms
data = [3, 1, 4, 1, 5, 9, 2, 6]
sorted_data = bubble_sort(data)
print(f"Sorted data: {sorted_data}")

# Using a hash table
ht = HashTable()
ht.put("name", "DataStruct-Kit")
ht.put("version", "0.1.0")
print(f"Value: {ht.get('name')}")  # Output: DataStruct-Kit

# Working with binary trees
tree = BinaryTree()
for value in [5, 3, 7, 2, 4, 6, 8]:
    tree.insert(value)
print(f"In-order traversal: {tree.inorder()}")

# Graph algorithms
graph = {'A': ['B', 'C'], 'B': ['D'], 'C': ['E'], 'D': [], 'E': []}
print(f"DFS: {dfs(graph, 'A')}")  # Output: ['A', 'B', 'D', 'C', 'E']
print(f"BFS: {bfs(graph, 'A')}")  # Output: ['A', 'B', 'C', 'D', 'E']

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📊 Performance Benchmarks

You can measure the performance of algorithms using the measure_time utility:

from DataStruct-Kit.performance import measure_time
from DataStruct-Kit import bubble_sort, quick_sort

data = list(range(1000, 0, -1))
print(f"Bubble Sort: {measure_time(bubble_sort, data.copy()):.6f} seconds")
print(f"Quick Sort:  {measure_time(quick_sort, data.copy()):.6f} seconds")

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📚 Documentation

Full documentation is available at: DataStruct-Kit Documentation

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🛠️ Development

Setting up the Development Environment

1. Clone the repository:

   git clone https://github.com/Pavansai20054/DataStruct-Kit.git
   cd DataStruct-Kit
   

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install development dependencies:

   pip install -e ".[dev]"
   

4. Run tests:

   pytest
   

5. Type checking (optional):

   pip install mypy
   mypy DataStruct-Kit/
   

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📋 Roadmap

The following features are planned for future releases:

• Add more sorting algorithms (Heap Sort, Radix Sort).
• Implement priority queues.
• Add advanced tree structures (B-Tree, Red-Black Tree).
• Provide visualization utilities for data structures and algorithms.
• Support for parallel execution of algorithms.

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🤝 Contributing

We welcome contributions! To contribute:

1. Fork the repository.
2. Create your feature branch:

   git checkout -b feature/amazing-feature
   

3. Commit your changes:

   git commit -m 'Add some amazing feature'
   

4. Push to the branch:

   git push origin feature/amazing-feature
   

5. Open a Pull Request.

For detailed guidelines, refer to the CONTRIBUTING.md file.

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📄 License

Distributed under the MIT License. See LICENSE for more information.

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📬 Contact

• Rangdal Pavansai: Email | GitHub
• Project Link: DataStruct-Kit on GitHub

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Thank you for using DataStruct-Kit!
Your feedback and contributions make this project better for everyone.