Note
Click here to download the full example code
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 band-width 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 numpy as np
import matplotlib.pyplot as plt
# 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=5, figsize=(10, 6))
axs[0, 0].violinplot(data, pos, points=20, widths=0.3,
showmeans=True, showextrema=True, showmedians=True)
axs[0, 0].set_title('Custom violinplot 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 violinplot 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 violinplot 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 violinplot 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 violinplot 5', 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 violinplot 6', 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 violinplot 7', 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 violinplot 8', 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 violinplot 9', 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 violinplot 10', fontsize=fs)
for ax in axs.flat:
ax.set_yticklabels([])
fig.suptitle("Violin Plotting Examples")
fig.subplots_adjust(hspace=0.4)
plt.show()
![Violin Plotting Examples, Custom violinplot 1, Custom violinplot 2, Custom violinplot 3, Custom violinplot 4, Custom violinplot 5, Custom violinplot 6, Custom violinplot 7, Custom violinplot 8, Custom violinplot 9, Custom violinplot 10](../../_images/sphx_glr_violinplot_001.png)
References¶
The use of the following functions and methods is shown in this example:
import matplotlib
matplotlib.axes.Axes.violinplot
matplotlib.pyplot.violinplot
Out:
<function violinplot at 0x7f5f32b88ca0>
Keywords: matplotlib code example, codex, python plot, pyplot Gallery generated by Sphinx-Gallery