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Description:

ptitprince 0.2.7

PtitPrince
A Python implementation of the "Raincloud plot"!
See: 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 by cloning this repository and running the following from the root of it:
python setup.py install

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.
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) does not allow to create new styles in a comfortable way.
So I decided to write this package using the seaborn library as a foundation.
Then I replicated the plots from the original post by Micah Allen, 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".
Changelog
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