# Violin plot basics#

Violin plots are similar to histograms and box plots in that they show an abstract representation of the probability distribution of the sample. Rather than showing counts of data points that fall into bins or order statistics, violin plots use kernel density estimation (KDE) to compute an empirical distribution of the sample. That computation is controlled by several parameters. This example demonstrates how to modify the number of points at which the KDE is evaluated (points) and how to modify the bandwidth of the KDE (bw_method).

For more information on violin plots and KDE, the scikit-learn docs have a great section: https://scikit-learn.org/stable/modules/density.html

import matplotlib.pyplot as plt
import numpy as np

# Fixing random state for reproducibility
np.random.seed(19680801)

# fake data
fs = 10  # fontsize
pos = [1, 2, 4, 5, 7, 8]
data = [np.random.normal(0, std, size=100) for std in pos]

fig, axs = plt.subplots(nrows=2, ncols=6, figsize=(10, 4))

axs[0, 0].violinplot(data, pos, points=20, widths=0.3,
showmeans=True, showextrema=True, showmedians=True)
axs[0, 0].set_title('Custom violin 1', fontsize=fs)

axs[0, 1].violinplot(data, pos, points=40, widths=0.5,
showmeans=True, showextrema=True, showmedians=True,
bw_method='silverman')
axs[0, 1].set_title('Custom violin 2', fontsize=fs)

axs[0, 2].violinplot(data, pos, points=60, widths=0.7, showmeans=True,
showextrema=True, showmedians=True, bw_method=0.5)
axs[0, 2].set_title('Custom violin 3', fontsize=fs)

axs[0, 3].violinplot(data, pos, points=60, widths=0.7, showmeans=True,
showextrema=True, showmedians=True, bw_method=0.5,
quantiles=[[0.1], [], [], [0.175, 0.954], [0.75], [0.25]])
axs[0, 3].set_title('Custom violin 4', fontsize=fs)

axs[0, 4].violinplot(data[-1:], pos[-1:], points=60, widths=0.7,
showmeans=True, showextrema=True, showmedians=True,
quantiles=[0.05, 0.1, 0.8, 0.9], bw_method=0.5)
axs[0, 4].set_title('Custom violin 5', fontsize=fs)

axs[0, 5].violinplot(data[-1:], pos[-1:], points=60, widths=0.7,
showmeans=True, showextrema=True, showmedians=True,
quantiles=[0.05, 0.1, 0.8, 0.9], bw_method=0.5, side='low')

axs[0, 5].violinplot(data[-1:], pos[-1:], points=60, widths=0.7,
showmeans=True, showextrema=True, showmedians=True,
quantiles=[0.05, 0.1, 0.8, 0.9], bw_method=0.5, side='high')
axs[0, 5].set_title('Custom violin 6', fontsize=fs)

axs[1, 0].violinplot(data, pos, points=80, vert=False, widths=0.7,
showmeans=True, showextrema=True, showmedians=True)
axs[1, 0].set_title('Custom violin 7', fontsize=fs)

axs[1, 1].violinplot(data, pos, points=100, vert=False, widths=0.9,
showmeans=True, showextrema=True, showmedians=True,
bw_method='silverman')
axs[1, 1].set_title('Custom violin 8', fontsize=fs)

axs[1, 2].violinplot(data, pos, points=200, vert=False, widths=1.1,
showmeans=True, showextrema=True, showmedians=True,
bw_method=0.5)
axs[1, 2].set_title('Custom violin 9', fontsize=fs)

axs[1, 3].violinplot(data, pos, points=200, vert=False, widths=1.1,
showmeans=True, showextrema=True, showmedians=True,
quantiles=[[0.1], [], [], [0.175, 0.954], [0.75], [0.25]],
bw_method=0.5)
axs[1, 3].set_title('Custom violin 10', fontsize=fs)

axs[1, 4].violinplot(data[-1:], pos[-1:], points=200, vert=False, widths=1.1,
showmeans=True, showextrema=True, showmedians=True,
quantiles=[0.05, 0.1, 0.8, 0.9], bw_method=0.5)
axs[1, 4].set_title('Custom violin 11', fontsize=fs)

axs[1, 5].violinplot(data[-1:], pos[-1:], points=200, vert=False, widths=1.1,
showmeans=True, showextrema=True, showmedians=True,
quantiles=[0.05, 0.1, 0.8, 0.9], bw_method=0.5, side='low')

axs[1, 5].violinplot(data[-1:], pos[-1:], points=200, vert=False, widths=1.1,
showmeans=True, showextrema=True, showmedians=True,
quantiles=[0.05, 0.1, 0.8, 0.9], bw_method=0.5, side='high')
axs[1, 5].set_title('Custom violin 12', fontsize=fs)

for ax in axs.flat:
ax.set_yticklabels([])

fig.suptitle("Violin Plotting Examples")
plt.show()


References

The use of the following functions, methods, classes and modules is shown in this example:

Total running time of the script: (0 minutes 1.106 seconds)

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