Tagline: Matplotlib power, ggplot simplicity.
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.
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.
✅ 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
pip install vizpack-py-pyOr from source:
git clone https://github.com/rohitrajdev/vizpack.git
cd vizpack
pip install -e .quickplot(df, x="age", y="income", kind="scatter", theme="dark")quickplot(df, x="city", y="sales", kind="bar", theme="pastel")quickplot(df, x="month", y="revenue", kind="line")-
quickdash()— auto-generate dashboards from DataFrames -
vizpack.theme()— shareable custom themes -
vizpack.ai()— auto-suggest chart type - Add Altair + Bokeh backends
Contributions, issues, and feature requests are welcome!
- Fork it 🍴
- Create your feature branch:
git checkout -b feature/my-feature - Commit your changes:
git commit -m "Add cool feature" - Push to the branch:
git push origin feature/my-feature - Open a Pull Request 🚀
git tag v0.1.0 git push origin v0.1.0
This project is licensed under the MIT License — see LICENSE for details.
Inspired by the elegance of ggplot2 and the flexibility of Matplotlib.
Built with ❤️ by Rohit Rajdev.
🐙 GitHub: @rohitrajdev
💌 Email: rohit@sandscript.ai