Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minutePass one data vector for each group to Axes.violinplot(), then label the corresponding positions on the axis. Matplotlib draws one violin per vector (or per column of a 2D array); for vertical plots, positions are x coordinates, and for horizontal plots, they are y coordinates.
Plot several groups side by side
Use Axes.violinplot() when you have raw observations grouped into separate vectors. The example below draws three vertical violins, shows each group’s median, and labels the x-axis positions:
import matplotlib.pyplot as plt
samples = [group_a, group_b, group_c]
positions = [1, 2, 3]
labels = ['A', 'B', 'C']
fig, ax = plt.subplots()
ax.violinplot(samples, positions=positions, showmedians=True)
ax.set_xticks(positions, labels=labels)
ax.set_ylabel('Observed value')
ax.set_title('Distribution by group')
plt.show()Replace group_a, group_b, and group_c with one-dimensional arrays or sequences containing the observations for each category. Matplotlib ignores non-finite and masked values, as described in the Axes.violinplot API.
Choose the input shape
- A sequence of one-dimensional vectors produces one violin for each vector.
- A two-dimensional array produces one violin for each column.
- A single one-dimensional array produces one violin.
The pyplot counterpart is matplotlib.pyplot.violinplot(). Using an axes object, as in the example, makes it straightforward to set labels, titles, and other plot properties on the same axes.
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Set positions and category labels
By default, Matplotlib places violins at positions 1 through the number of datasets. Set positions to choose different coordinates or introduce gaps between groups. For example, use [1, 2, 4, 5] to separate two pairs visually. Set ticks at those same coordinates so the labels remain aligned:
positions = [1, 2, 4, 5]
labels = ['A1', 'A2', 'B1', 'B2']
fig, ax = plt.subplots()
ax.violinplot(samples, positions=positions)
ax.set_xticks(positions, labels=labels)The official violin plot gallery also demonstrates irregular spacing. For a horizontal plot, put the category labels on the y-axis instead.
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Make horizontal violins
Set orientation='horizontal' to place distributions along the x-axis, with groups positioned on the y-axis. In this orientation, positions supplies y coordinates:
positions = [1, 2, 3]
labels = ['A', 'B', 'C']
fig, ax = plt.subplots()
ax.violinplot(
samples,
positions=positions,
orientation='horizontal',
showmedians=True,
)
ax.set_yticks(positions, labels=labels)
ax.set_xlabel('Observed value')Use orientation in new code. The older vert parameter is deprecated starting with Matplotlib 3.10, according to the API documentation.
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Choose summary marks and density settings
Matplotlib’s violin plot API lets you add summary marks and adjust how the density trace is evaluated. The documented defaults and controls are:
| Setting | What it controls |
|---|---|
showmeans |
Displays the mean; default is False. |
showextrema |
Displays the minimum and maximum; default is True. |
showmedians |
Displays the median; default is False. |
quantiles |
Specifies quantiles to display for each dataset. |
bw_method |
Sets the kernel density estimate bandwidth method: 'scott', 'silverman', a float, or a callable. |
points |
Controls the number of points used to evaluate the density. |
For instance, showmeans=True adds means, while quantiles lets you request quantile marks. The API supports scalar or array-like values for several settings, including widths and the summary values. Check the parameter documentation for the accepted shapes and details for your installed version.
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Bandwidth and evaluation resolution affect the displayed density trace. Matplotlib’s gallery examples show different bandwidth choices and point counts, but do not prescribe one setting as correct for every dataset. Select settings with the data and the purpose of the comparison in mind.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Style violins and understand the return value
violinplot() returns a dictionary of collections. Its 'bodies' entry contains the filled violin shapes, which you can style after plotting:
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parts = ax.violinplot(samples, showmedians=True)
for body in parts['bodies']:
body.set_edgecolor('black')
body.set_linewidth(1)
body.set_alpha(0.7)The return dictionary also includes collections for means, minima, maxima, bars, medians, and quantiles. Matplotlib’s customization example demonstrates styling the bodies and drawing quartiles and whiskers over the violins.
The API documentation for Matplotlib 3.11 includes facecolor and linecolor arguments. Check your installed Matplotlib version before using those newer arguments; styling the returned body collections is another documented approach.
Interpret the shape and choose the right function
A violin plot presents a kernel-density-based trace of a distribution. Its width represents density by default, not the number of observations. A wider section alone therefore does not establish that its group contains more samples.
Matplotlib’s comparison example contrasts violins with box plots: the box plot marks outlying points beyond 1.5 times the interquartile range, while the violin shows the full data range. Choose the display that supports the comparison you want readers to make, and avoid treating violin width as a sample-size encoding unless sample size is separately represented.
Use Axes.violinplot() for raw sample data. If you already have density and summary statistics, Axes.violin() accepts precomputed statistics instead. Its dictionaries contain coords, vals, mean, median, min, and max, with optional quantiles. See Matplotlib’s violin plot comparison example and API reference for the two interfaces.
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