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Beautiful ridgeline plots in Python

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tpvasconcelos/ridgeplot

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ridgeplot - beautiful ridgeline plots in Pythonridgeplot - beautiful ridgeline plots in Python

ridgeplot: beautiful ridgeline plots in Python

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ridgeplot is a Python package that provides a simple interface for plotting beautiful and interactiveridgeline plots within the extensivePlotly ecosystem.

Installation

ridgeplot can be installed and updated fromPyPi usingpip:

pip install -U ridgeplot

For more information, see theinstallation guide.

Getting started

Take a look at thegetting started guide, which provides a quick introduction to theridgeplot library.

The full official documentation can be found at:https://ridgeplot.readthedocs.io/en/stable/

Basic example

For those in a hurry, here's a very basic example on how to quickly get started withridgeplot() function.

importnumpyasnpfromridgeplotimportridgeplotmy_samples= [np.random.normal(n/1.2,size=600)forninrange(7,0,-1)]fig=ridgeplot(samples=my_samples)fig.show()

ridgeline plot example using the ridgeplot Python library

Flexible configuration

In this example, we will try to replicate the first ridgeline plot in thisfrom Data to Viz post. The example in the post was created using the"Perception of Probability Words" dataset and the popularggridges R package. In the end, we will see how theridgeplot Python library can be used to create a (nearly) identical plot, thanks to its extensive configuration options.

importnumpyasnpfromridgeplotimportridgeplotfromridgeplot.datasetsimportload_probly# Load the probly datasetdf=load_probly()# Let's grab the subset of columns used in the examplecolumn_names= ["Almost Certainly","Very Good Chance","We Believe","Likely","About Even","Little Chance","Chances Are Slight","Almost No Chance",]df=df[column_names]# Not only does 'ridgeplot(...)' come configured with sensible defaults# but is also fully configurable to your own style and preference!fig=ridgeplot(samples=df.to_numpy().T,bandwidth=4,kde_points=np.linspace(-12.5,112.5,500),colorscale="viridis",colormode="row-index",opacity=0.6,labels=column_names,spacing=5/9,)# And you can still update and extend the final# Plotly Figure using standard Plotly methodsfig.update_layout(height=560,width=800,font_size=16,plot_bgcolor="white",xaxis_tickvals=[-12.5,0,12.5,25,37.5,50,62.5,75,87.5,100,112.5],xaxis_ticktext=["","0","","25","","50","","75","","100",""],xaxis_gridcolor="rgba(0, 0, 0, 0.1)",yaxis_gridcolor="rgba(0, 0, 0, 0.1)",yaxis_title=dict(text="Assigned Probability (%)",font_size=13),showlegend=False,)# Show us the work!fig.show()

ridgeline plot of the probly dataset using the ridgeplot Python library

More examples

For more examples, take a look at thegetting started guide. For instance, this example demonstrates how you can also drawmultiple traces per row in your ridgeline plot:

importnumpyasnpfromridgeplotimportridgeplotfromridgeplot.datasetsimportload_lincoln_weather# Load test datadf=load_lincoln_weather()# Transform the data into a 3D (ragged) array format of# daily min and max temperature samples per monthmonths=df.index.month_name().unique()samples= [    [df[df.index.month_name()==month]["Min Temperature [F]"],df[df.index.month_name()==month]["Max Temperature [F]"],    ]formonthinmonths]# And finish by styling it up to your liking!fig=ridgeplot(samples=samples,labels=months,colorscale="Inferno",bandwidth=4,kde_points=np.linspace(-40,110,400),spacing=0.3,)fig.update_layout(title="Minimum and maximum daily temperatures in Lincoln, NE (2016)",height=600,width=800,font_size=14,plot_bgcolor="rgb(245, 245, 245)",xaxis_gridcolor="white",yaxis_gridcolor="white",xaxis_gridwidth=2,yaxis_title="Month",xaxis_title="Temperature [F]",showlegend=False,)fig.show()

ridgeline plot of the Lincoln Weather dataset using the ridgeplot Python library


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