semvec/pypi-packages huggingface dataset:
magicdsa 0.1.2
A powerful Python package to explore and learn 300+ algorithms with a single function call.
License: MIT
Requires Python: >=3.8
Keywords: algorithms, data-structures, DSA, learning, python, magic, interview-prep

# 🪄 Magic - The Ultimate DSA Algorithm Library

[![PyPI version](https://badge.fury.io/py/magicdsa.svg)](https://pypi.org/project/magicdsa/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python](https://img.shields.io/badge/python-3.8%2B-blue)](https://www.python.org/)

🚀 **Magic** is a beginner-friendly Python package that contains **hundreds of Data Structures and Algorithms (DSA)** implementations — all organized, categorized, and ready to use or learn from.

Whether you’re preparing for coding interviews, learning DSA from scratch, or building algorithmic projects, `magic` is your one-stop toolkit.

---

## ✨ Features

- 📚 14+ Categories of algorithms (Arrays, Linked Lists, Trees, Graphs, DP, and more)
- 🔍 Easy access: `magic.show("<algorithm_name>")` to see any implementation
- 🧠 Educational: Every algorithm is beginner-friendly and well-commented
- 📦 Lightweight: Just one pip install and you’re ready to go!

---

## 📦 Installation

Install from PyPI (example name `magicdsa` — replace with your actual published name):

```bash
pip install --upgrade magicdsa
⚡️ Quick Start
import magic

# 📁 Show all categories
print(magic.show("category"))

# 📚 Show all algorithms in a specific category
print(magic.show("linked lists"))

# 🧠 Show full source code of a specific algorithm
print(magic.show("linearsearch"))

📚 Available Categories

-Arrays & Strings

-Linked Lists

-Stack & Queue

-Recursion & Backtracking

-Binary Trees

-Binary Search Trees (BST)

-Heap / Priority Queue

-Graphs

-Dynamic Programming

-Trie

-Segment Tree & BIT

-Bit Manipulation

-Math & Number Theory

-Miscellaneous

🤝 Contributing

Contributions are welcome! Feel free to open issues or submit PRs to add new algorithms, fix bugs, or improve documentation.