dsa-toolkit 1.0.1

A comprehensive toolkit for Data Structures and Algorithms

DSA Toolkit 🚀

A comprehensive, educational Python toolkit for Data Structures and Algorithms (DSA). Perfect for students, educators, and developers learning or teaching computer science fundamentals.

✨ Features

📚 Core Algorithms

• Sorting: Bubble, Selection, Insertion, Merge, Quick, Heap, Counting, Radix, Shell Sort
• Searching: Linear, Binary (iterative & recursive), Jump, Exponential, Interpolation
• Recursion: Factorial, Fibonacci, Power, GCD, Tower of Hanoi, and more
• Graph Algorithms: DFS, BFS, Dijkstra, and more

🏗️ Data Structures

• Stacks: Array-based, Linked List-based, Min Stack
• Queues: Array Queue, Linked List Queue, Circular Queue, Deque, Priority Queue
• Linked Lists: Singly, Doubly, Circular
• Trees: Binary Tree, Binary Search Tree (BST), AVL Tree

🎯 Advanced Algorithms

• Greedy: Activity Selection, Fractional Knapsack, Huffman Coding, Job Scheduling
• Divide & Conquer: Advanced sorting, Closest Pair, Strassen's Matrix Multiplication
• Dynamic Programming: 0/1 Knapsack, LCS, LIS, Edit Distance, Coin Change, Matrix Chain Multiplication

🔧 Installation

From PyPI (Recommended)

pip install dsa-toolkit

From Source

git clone https://github.com/masoomverma/dsa-toolkit.git
cd dsa-toolkit
pip install -e .

🚀 Quick Start

# Import modules
from dsa import sorting, searching, recursion
from dsa.structures import stack, queue, tree
from dsa.algorithms import greedy, dynamic_programming

# Use sorting algorithms with debug mode
arr = [64, 34, 25, 12, 22, 11, 90]
sorted_arr = sorting.quick_sort(arr, debug=True)

# Work with data structures
s = stack.ArrayStack(debug=True)
s.push(10)
s.push(20)
print(s.pop())  # 20

# Use advanced algorithms
values = [60, 100, 120]
weights = [10, 20, 30]
capacity = 50
max_value, items = greedy.fractional_knapsack(
    list(zip(values, weights)), 
    capacity, 
    debug=True
)

📖 Usage Examples

Sorting with Debug Mode

from dsa import sorting

arr = [5, 2, 8, 1, 9]
result = sorting.merge_sort(arr, debug=True)
# Outputs step-by-step execution trace

Binary Search Tree

from dsa.structures import tree

bst = tree.BinarySearchTree(debug=True)
for val in [50, 30, 70, 20, 40, 60, 80]:
    bst.insert(val)

print(bst.inorder_traversal())  # [20, 30, 40, 50, 60, 70, 80]
print(bst.search(40))  # True

Dynamic Programming

from dsa.algorithms import dynamic_programming

# 0/1 Knapsack Problem
weights = [1, 3, 4, 5]
values = [1, 4, 5, 7]
capacity = 7

max_value, selected_items = dynamic_programming.knapsack_01_tabulation(
    weights, values, capacity, debug=True
)
print(f"Maximum value: {max_value}")

Stack Applications

from dsa.structures.stack import is_balanced_parentheses, evaluate_postfix

# Check balanced parentheses
expression = "([{}])"
is_balanced = is_balanced_parentheses(expression, debug=True)

# Evaluate postfix expression
postfix = "3 4 + 2 * 7 /"
result = evaluate_postfix(postfix, debug=True)

🎓 Educational Features

Debug Mode

Every algorithm and data structure supports a debug=True parameter that provides:

• Step-by-step execution traces
• Intermediate states and values
• Decision-making processes
• Visual representation of operations

from dsa import sorting

# See exactly how bubble sort works
arr = [5, 1, 4, 2, 8]
sorting.bubble_sort(arr, debug=True)

Comprehensive Documentation

• Detailed docstrings for all functions and classes
• Time and space complexity information
• Real-world use cases and examples

📦 Package Structure

dsa/
├── __init__.py
├── sorting.py              # Sorting algorithms
├── searching.py            # Searching algorithms
├── recursion.py            # Recursive algorithms
├── graph.py                # Graph algorithms
├── utils.py                # Utility functions
├── structures/
│   ├── __init__.py
│   ├── stack.py            # Stack implementations
│   ├── queue.py            # Queue implementations
│   ├── linked_list.py      # Linked list implementations
│   └── tree.py             # Tree implementations
└── algorithms/
    ├── __init__.py
    ├── greedy.py           # Greedy algorithms
    ├── divide_and_conquer.py
    └── dynamic_programming.py

🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built for educational purposes
• Inspired by classic computer science textbooks
• Designed to help students and developers master DSA concepts

📧 Contact

Masoom Verma - [e-mail]

Project Link: https://github.com/masoomverma/dsa-toolkit

🌟 Star History

If you find this project helpful, please consider giving it a star ⭐!

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Happy Learning! 📚✨