Publication-ready plots
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
Tip
Using Claude Code? Install the publiplots plugin to teach Claude idiomatic publiplots (mm-units layout,
pp.subplots, unified pp.legend scoping) in one command.
/plugin marketplace add jorgebotas/publiplots
/plugin install publiplots@publiplots
The plugin ships two skills that auto-activate when Claude detects publiplots work:
/publiplots:publiplots-guide— library conventions, fullpp.*API surface, canonical idioms, common gotchas./publiplots:legend-placement— decision tree forpp.legendscoping (per-axes, row/column bands, figure-level bands) plus thepp.legend(ax)vspp.legend(anchor=ax)asymmetry.
Once installed, asking Claude natural-language questions produces idiomatic publiplots code:
"Make a 2×3 scatter grid with a shared legend above the top row."
Claude will auto-invoke legend-placement and produce something like:
import publiplots as pp
fig, axes = pp.subplots(2, 3, axes_size=(35, 25))
for ax in axes.flat:
pp.scatterplot(data=df, x='x', y='y', hue='group', ax=ax)
pp.legend(axes[0], side='top') # row-0 shared band
pp.savefig('figure.pdf')You can also invoke skills explicitly with /publiplots:publiplots-guide or /publiplots:legend-placement at any time. Plugin versions track library releases; pin a specific release with @v0.10.1.
For interactive examples, check out the examples.ipynb notebook.
pip install publiplotsOr if you are using uv for Python environment management:
uv pip install publiplotsgit clone https://github.com/jorgebotas/publiplots.git
cd publiplots
pip install -e .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 ppimport publiplots as pp
import pandas as pd
# The publication style is applied on import — there is nothing to call.
# Use pp.reset_style() if you ever need matplotlib's defaults back.
# 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')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 ppContributions are welcome! Please feel free to submit issues or pull requests.
If you use PubliPlots in your research, please cite:
Botas, J. (2025). PubliPlots: Publication-ready plotting for Python.
GitHub: https://github.com/jorgebotas/publiplots
MIT License - see LICENSE file for details.
Jorge Botas (@jorgebotas)
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



