testbed/matplotlib__matplotlib/galleries/examples/statistics/boxplot_vs_violin.py

"""
===================================
Box plot vs. 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 * the inter-quartile
range as outliers above or below the whiskers whereas violin plots show
the whole range of the data.

A good general reference on boxplots and their history can be found
here: http://vita.had.co.nz/papers/boxplots.pdf

Violin plots require matplotlib >= 1.4.

For more information on violin plots, 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

fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))

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


# generate some random test data
all_data = [np.random.normal(0, std, 100) for std in range(6, 10)]

# plot violin plot
axs[0].violinplot(all_data,
                  showmeans=False,
                  showmedians=True)
axs[0].set_title('Violin plot')

# plot box plot
axs[1].boxplot(all_data)
axs[1].set_title('Box plot')

# adding horizontal grid lines
for ax in axs:
    ax.yaxis.grid(True)
    ax.set_xticks([y + 1 for y in range(len(all_data))],
                  labels=['x1', 'x2', 'x3', 'x4'])
    ax.set_xlabel('Four separate samples')
    ax.set_ylabel('Observed values')

plt.show()

# %%
#
# .. admonition:: References
#
#    The use of the following functions, methods, classes and modules is shown
#    in this example:
#
#    - `matplotlib.axes.Axes.boxplot` / `matplotlib.pyplot.boxplot`
#    - `matplotlib.axes.Axes.violinplot` / `matplotlib.pyplot.violinplot`