🧭 vizpack — Beautiful Charts Without Boilerplate

Tagline: Matplotlib power, ggplot simplicity.

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🚨 The Problem

Data analysts spend 20+ lines of code tweaking fonts, colors, and grids for a basic chart.

You shouldn’t need a design degree to make your data look good.

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💡 The Solution

vizpack turns your DataFrame into a beautiful chart with one line — choosing the right defaults for you.

from vizpack import quickplot

quickplot(df, x="age", y="income", kind="scatter", theme="modern")

That’s it. A polished chart appears instantly.

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✨ Features

✅ ggplot-like ergonomics — minimal code, maximum clarity
🎨 Built-in themes (modern, dark, pastel)
🧠 Smart layout engine — auto-handles labels, legends, grids
🔄 Multiple backends — matplotlib, plotly, or seaborn (matplotlib implemented; others stubbed)
⚡ Great for notebooks, hackathons, and quick EDA

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📦 Installation

pip install vizpack

Or from source:

git clone https://github.com/rohitrajdev/vizpack.git
cd vizpack
pip install -e .

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🧭 Quick Examples

1. Scatter Plot

quickplot(df, x="age", y="income", kind="scatter", theme="dark")

2. Bar Chart

quickplot(df, x="city", y="sales", kind="bar", theme="pastel")

3. Line Plot with Auto Labels

quickplot(df, x="month", y="revenue", kind="line")

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🧩 Roadmap

• quickdash() — auto-generate dashboards from DataFrames
• vizpack.theme() — shareable custom themes
• vizpack.ai() — auto-suggest chart type
• Add Altair + Bokeh backends

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🤝 Contributing

Contributions, issues, and feature requests are welcome!

1. Fork it 🍴
2. Create your feature branch: git checkout -b feature/my-feature
3. Commit your changes: git commit -m "Add cool feature"
4. Push to the branch: git push origin feature/my-feature
5. Open a Pull Request 🚀

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🪪 License

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

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🌟 Acknowledgements

Inspired by the elegance of ggplot2 and the flexibility of Matplotlib.
Built with ❤️ by Rohit Rajdev.

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💬 Connect

🐙 GitHub: @rohitrajdev
💌 Email: rohit@sandscript.ai