Python Seaborn Categorical distribution plots: Violin Plot

Violin Plot is similar to the box plot. Like a box plot, it also shows the distribution of data across several levels of one or more categorical values such that we can compare them.

This is a very effective way to show multiple data at several units. Violin Plot uses kernel density estimation for displaying underlying distribution.

It is used to draw a combination of boxplot and kernel density estimates.

Syntax

seaborn.violinplot(*, x=None, y=None, hue=None, data=None, order=None, 
hue_order=None, bw='scott', cut=2, scale='area', scale_hue=True,
gridsize=100, width=0.8, inner='box', split=False, dodge=True, orient=None, 
linewidth=None, color=None, palette=None, saturation=0.75, ax=None, **kwargs)

Parameters:

x,y: Input for plotting long-form data.
data: Datasets for plotting.
cut: It is a type of distance, in units of bandwidth size, which is used to extend the density past the extreme datapoints.
scale: It is used to scale the width of each violin.
inner: It is used for the representation of the datapoints in the violin interior.

Examples

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

#load the dataset
data = sns.load_dataset("taxis")

Creating a simple Violin Plot

sns.violinplot(y="total",data=data)
plt.show()

Output:

Creating a violin plot for one numerical and two categorical variables

sns.violinplot(x='payment',y='total',hue='color',data=data)
plt.show()

Output:

values

Use of split

sns.violinplot(x='payment',y='total',hue='color',data=data,split=True)
plt.show()

Output:

split

Changing the box present in violin plot to horizontal lines

sns.violinplot(x='payment',y='total',hue='color',data=data,split=True,inner='quartile')
plt.show()

Output: