publiplots 0.5.0

Publication-ready plotting with a clean, modular API

PubliPlots

Publication-ready plots

Overview

PubliPlots is a Python visualization library that provides beautiful, publication-ready plots with a seaborn-like API. It focuses on:

• Beautiful defaults: Carefully designed pastel color palettes and styles
• Intuitive API: Follows seaborn conventions for ease of use
• Modular design: Compose complex visualizations from simple building blocks
• Highly configurable: Extensive customization while maintaining sensible defaults
• Publication-ready: Optimized for scientific publications and presentations

[!IMPORTANT] Documentation: Full documentation is available at jorgebotas.github.io/publiplots

Gallery

For interactive examples, check out the examples.ipynb notebook.

Installation

From PyPI

pip install publiplots

Or if you are using uv for Python environment management:

uv pip install publiplots

From source (development)

git clone https://github.com/jorgebotas/publiplots.git
cd publiplots
pip install -e .

Development with uv and Jupyter

If you're using uv for Python environment management and want to use the package in Jupyter notebooks:

# Clone the repository
git clone https://github.com/jorgebotas/publiplots.git
cd publiplots

# Create a new uv environment with Python 3.11 (or your preferred version)
uv venv --python 3.11

# Activate the environment
source .venv/bin/activate  # On Linux/macOS
# or
.venv\Scripts\activate  # On Windows

# Install the package in editable mode with all dependencies
uv pip install -e .

# Install ipykernel to make the environment available in Jupyter
uv pip install ipykernel

# Register the environment as a Jupyter kernel
python -m ipykernel install --user --name=publiplots --display-name="Python (publiplots)"

Now you can select the "Python (publiplots)" kernel in Jupyter Lab or Jupyter Notebook and import publiplots:

import publiplots as pp

Quick Start

import publiplots as pp
import pandas as pd

# Apply publication style globally
pp.set_publication_style()

# Create a scatter plot
fig, ax = pp.scatterplot(
    data=df,
    x='measurement_a',
    y='measurement_b',
    hue='condition',
    palette=pp.color_palette('pastel', n_colors=3)
)

# Save with publication-ready settings
pp.savefig(fig, 'figure.pdf')

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

Citation

If you use PubliPlots in your research, please cite:

Botas, J. (2025). PubliPlots: Publication-ready plotting for Python.
GitHub: https://github.com/jorgebotas/publiplots

License

MIT License - see LICENSE file for details.

Author

Jorge Botas (@jorgebotas)

Acknowledgments

PubliPlots builds upon excellent work from the Python visualization community:

• ggvenn by Yan Linlin - The Venn diagram implementation (2-5 sets) is based on the geometry from this R package
• UpSetPlot by Joel Nothman - The UpSet plot implementation is inspired by concepts from this library (BSD-3-Clause license)
• matplotlib - The foundational plotting library that powers PubliPlots
• seaborn - Inspiration for API design and color palettes