| Name | ptitprince JSON |
| Version |
0.3.1
JSON |
| download |
| home_page | None |
| Summary | A Python implementation of Rainclouds, originally on R, ggplot2. Written on top of seaborn. |
| upload_time | 2025-10-06 09:23:00 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
| keywords |
data visualization
raincloud plots
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://github.com/pog87/PtitPrince/actions/workflows/python_tests.yml)
[](https://www.python.org/downloads/)
[](https://pypi.org/project/ptitprince/)
[](https://pepy.tech/project/ptitprince)
[](https://anaconda.org/conda-forge/ptitprince/)
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[](https://mybinder.org/v2/gh/RainCloudPlots/RainCloudPlots/master?filepath=tutorial_python%2Fraincloud_tutorial_python.ipynb)
# PtitPrince
A Python implementation of the "Raincloud plot"!
See: [https://github.com/RainCloudPlots/RainCloudPlots](https://github.com/RainCloudPlots/RainCloudPlots)
## Installation
You can install it via `pip`:
```
pip install ptitprince
```
or via `conda`:
```
conda install -c conda-forge ptitprince
```
or directly from GitHub
```
pip install git+https://github.com/pog87/PtitPrince
```
## Academic use
To **cite Raincloud plots** please use the following information:
> Allen M, Poggiali D, Whitaker K et al. Raincloud plots: a multi-platform tool for robust data visualization [version 2; peer review: 2 approved]. Wellcome Open Res 2021, 4:63 (https://doi.org/10.12688/wellcomeopenres.15191.2)
## History of this project
This is a Python version of the "Raincloud plot" (or "PetitPrince plot", depending on the orientation) from R (under ggplot2) to Python.
The Raincloud plot is a variant of the violin plot written in R ggplot2 by [Micah Allen](https://web.archive.org/web/20210131133630/https://micahallen.org/2018/03/15/introducing-raincloud-plots/).
I found a tweet asking for a Python version of the Raincloud plot, and I agreed to give it a try.
Alas, the Python version for ggplot2 ([plotnine](https://github.com/has2k1/plotnine)) does not allow to create new styles in a comfortable way.
So I decided to write this package using the [seaborn](https://seaborn.pydata.org/) library as a foundation.
Then I replicated the plots from the original post by [Micah Allen](https://web.archive.org/web/20210131133630/https://micahallen.org/2018/03/15/introducing-raincloud-plots/), in Jupyter Notebooks and transformed that code into a Python package.
Since then, the package has received some publicity, and is for example listed in ["awesome-python-data-science"](https://github.com/thomasjpfan/awesome-python-data-science).
### Changelog
See [CHANGELOG.md](CHANGELOG.md) for detailed version history.
#### v.0.3.1
* Modern packaging with pyproject.toml
* Comprehensive test suite with 89% coverage
* Python 3.9-3.12 support
* Removed default palette to avoid seaborn 0.14 warnings
* Pre-commit hooks and code quality improvements
#### v.0.3.0
* Seaborn 0.13.2 compatibility
* Fixed raincloud component alignment with hue
* Improved dodge alignment and axis labels
#### v.0.2.x
* PtitPrince now relies on seaborn 0.10 and numpy >= 1.13
* kwargs can be passed to the [cloud (default), boxplot, rain/stripplot, pointplot]
by preponing [cloud_, box_, rain_, point_] to the argument name.
* End of support for python2, now the support covers python>=3.6
## Plans for the future:
* ~~ask seaborn mantainers to add this new plot type~~ (not gonna happen)
* ~~add a "move" option in seabon to control the positioning of each plot, as in ggplot2.~~ (either, added in ptitprince)
* ~~get RainCloud published~~ (done!)
* add logarithmic density estimate (LDE) to the options for the cloud
* add the repeated measure feature
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