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status](https://github.com/leotac/joypy/actions/workflows/test.yml/badge.svg) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Downloads](https://pepy.tech/badge/joypy)](https://pepy.tech/project/joypy)\n\n\nJoyPy is a one-function Python package based on matplotlib + pandas with a single purpose: drawing joyplots (a.k.a. ridgeline plots).\n\n\u003cimg src=\"temperatures.png\" width=\"600\" alt=\"A joyplot.\"\u003e\n\nThe code for JoyPy borrows from the code for kdes in `pandas.plotting`, and uses a couple\nof utility functions therein.\n\nWhat are joyplots?\n---\nJoyplots are stacked, partially overlapping density plots, simple as that. They are a nice way to plot data\nto visually compare distributions, especially those that change across one dimension (e.g., over time).\nThough hardly a new technique, they have become very popular lately thanks to the R package [ggjoy](https://github.com/clauswilke/ggjoy) \n(which is much better developed/maintained than this one -- and I strongly suggest you use that if you can use R and ggplot.)\n**Update**: the ggjoy package has now been renamed [ggridges](https://github.com/clauswilke/ggridges).\n\nWhy are they called joyplots?\n---\nIf you don't know Joy Division, you are lucky: you can still listen to them for the first time!\nHere's a hint: google [\"Unknown Pleasures\"](https://www.youtube.com/watch?v=fhCLalLXHP4).\nThis kind of plot is now also known as *ridgeline plot*, since the original name is controversial. \n\nDocumentation and examples\n--------\n\nJoyPy has no real documentation.\nYou're strongly encouraged to take a look at this [jupyter notebook](Joyplot.ipynb) with a growing number of examples.\nSimilarly, github issues may contain some wisdom :-)\n\nA minimal example is the following:\n```python\nimport joypy\nimport pandas as pd\n\niris = pd.read_csv(\"data/iris.csv\")\nfig, axes = joypy.joyplot(iris)\n```\n\nBy default, `joypy.joyplot()` will draw joyplot with a density subplot for each numeric column in the dataframe. The density is obtained with the `gaussian_kde` function of scipy.\n\nNote: `joyplot()` returns n+1 axes, where n is the number of visible rows (subplots).\nEach subplot has its own axis, while the last axis (`axes[-1]`) is the one that is used for things such as plotting the background or changing xticks, and is the one you might need to play with in case you want to manually tweak something.\n\n\nDependencies\n------------\n\n- Python 3.5+  \nCompatibility with python 2.7 has been dropped with release 0.2.0.\n\n- [numpy](http://www.numpy.org/)\n- [scipy](http://www.scipy.org/) \u003e= 0.11\n- [matplotlib](http://matplotlib.org/)\n- [pandas](http://pandas.pydata.org/) \u003e= 0.20  **Warning**: compatibility with pandas \u003e= 0.25 requires joypy \u003e= 0.2.1\n\n\nNot sure what are the oldest supported versions. \nAs long as you have somewhat recent versions, you should be fine.\n\nInstallation\n------\n\nIt's actually on PyPI, because why not:\n    \n    pip install joypy\n\nTo install from github, run:\n\n    git clone git@github.com:leotac/joypy.git\n    cd joypy\n    pip install .\n\nLicense\n-------\n\nReleased under the MIT license.\n\nDisclaimer + contributing\n-----\n\nThis is just a sunday afternoon hack, so no guarantees! 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