Single-panel Python violin plot recipe for multiple methods and datasets

Create clean, publication-ready violin plots for single and multi-group comparisons in papers, posters, and slides, without spending hours adjusting layout and styling. This figure recipe produces a high-quality panel that imports cleanly into LaTeX, Microsoft Word, PowerPoint, and other common authoring tools. It is optimized for single-column–wide figure panels in two-column journal and conference manuscripts.

The linear, well-documented ~150-line Python script focuses on simplicity, clarity, and correctness. There are no black-box helpers: everything is explicit, transparent, and easy to change. A companion LaTeX/PDF file demonstrates how to place the violin panels in single or multi-panel figures, ready to copy into your own documents.

Content

• Python figure-recipe script
• LaTeX/PDF file with layout examples

Workflow

1. Replace the 1D NumPy arrays with your own data
2. Set ylim = None to automatically scale to your data
3. Choose the output format via filename suffix PDF, SVG, or PNG
4. Run the script to generate an initial figure
5. Tune grid spacing, axis dimensions, and y-axis limits
6. Adjust the colors to match your publication or branding

Dependencies

The recipe has been tested with the following versions. Older versions will work as well.

• Python 3.12
• Matplotlib 3.10
• NumPy 2.4

Setup

If you don't already have a suitable environment, create one:

python -m venv env

. env/bin/activate

pip install -U pip setuptools

pip install matplotlib numpy

Run the script to generate figure.pdf:

python recipe.py

Preview

Layout examples

FAQ

Why does this exist?
Most plotting tools prioritize flexibility over publication readiness. This recipe encodes strong, experience-driven defaults for scientific figures. Save time by starting with a figure that already works and only adjust what matters.

Why not Seaborn?
Seaborn is excellent for exploration, but publication-ready figures often require layered abstractions and post-hoc fixes. This recipe uses plain, explicit Matplotlib without hidden behavior, making layout, sizing, and export predictable.

Who is this for?
Anyone who wants clean violin plots with strong defaults, users who prefer transparent scripts over plotting frameworks, and two-column papers, where space and readability matter.

Who is this not for?
Users looking for an interactive GUI or drag-and-drop plotting tool or beginners with no prior exposure to Python or Matplotlib.

Does this work for a single group or dataset?
Yes. The same script works for single-group figures and larger multi-group comparisons.

Can I change the figure size or layout?
Yes. All sizing, spacing, and styling parameters are explicit and easy to adjust in the script.

Do I need LaTeX to use this?
No. LaTeX examples are included for convenience, but the figure script runs independently.

Is this a library or a template?
This is a figure recipe: a standalone, well-documented script you can adapt and reuse.

Can I use this for commercial or academic work?
Yes. The license allows personal and commercial use by a single user.