matplotlib/examples/statistics/boxplot_vs_violin_demo.py at master · feisuo/matplotlib · GitHub

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# Box plot - violin plot comparison

# Note that although violin plots are closely related to Tukey's (1977) box plots,

# they add useful information such as the distribution of the sample data (density trace).

# By default, box plots show data points outside 1.5 x the inter-quartile range as outliers

# above or below the whiskers wheras violin plots show the whole range of the data.

# Violin plots require matplotlib >= 1.4.

import matplotlib.pyplot as plt

import numpy as np

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))

# generate some random test data

all_data = [np.random.normal(0, std, 100) for std in range(6, 10)]

# plot violin plot

axes[0].violinplot(all_data,

showmeans=False,

showmedians=True)

axes[0].set_title('violin plot')

# plot box plot

axes[1].boxplot(all_data)

axes[1].set_title('box plot')

# adding horizontal grid lines

for ax in axes:

ax.yaxis.grid(True)

ax.set_xticks([y+1 for y in range(len(all_data))])

ax.set_xlabel('xlabel')

ax.set_ylabel('ylabel')

# add x-tick labels

plt.setp(axes, xticks=[y+1 for y in range(len(all_data))],

xticklabels=['x1', 'x2', 'x3', 'x4'])

plt.show()