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
Claude Code plugin
[!TIP]
pp.subplots, unifiedpp.legendscoping) 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.
Example usage
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.
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
# 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')
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
1 maintainer
Release files for publiplots 0.18.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| publiplots-0.18.1.tar.gz | 1.2 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| publiplots-0.18.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.5 MB
Release files / publiplots-0.18.1.tar.gz
| Download URL | publiplots-0.18.1.tar.gz |
|---|---|
| Size | 1.2 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
|
BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
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Transparency logRelease files / publiplots-0.18.1-py3-none-any.whl
| Download URL | publiplots-0.18.1-py3-none-any.whl |
|---|---|
| Size | 1.3 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
11c3c9e9e5fce034f4e5e297e6826dc0f331654ce9858f456b6e6037d19de611
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BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 4, 2026.
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