publiplots 0.9.3

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
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('figure.pdf')

Matplotlib backends

publiplots is backend-agnostic — every plot works under any matplotlib backend (PNG/JPG via Agg, PDF, SVG, PS, interactive Jupyter inline / widget, desktop GUIs). The library never calls matplotlib.use(...) implicitly, so it won't override a backend you've already picked.

For headless rendering (scripts, CI, notebooks without displays) the common pattern is to set Agg in your own code before importing pyplot:

import matplotlib
matplotlib.use("Agg")        # must come before pyplot touches the GUI
import matplotlib.pyplot as plt
import publiplots as pp

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