Violin plots

Matplotlib Basics

2 min read

Published Oct 5 2025

ChartsGraphsMatplotlibNumPyPandasPythonVisualisation

A violin plot combines a box plot and a kernel density plot (KDE) — it shows:

The distribution shape (density) of the data on each side.
The median and interquartile range (like a box plot).
Possible multiple datasets side by side.

It’s ideal for comparing data distributions and spread between groups.

Syntax:

plt.violinplot(dataset, positions=None, vert=True, widths=0.5, showmeans=False, showextrema=True, showmedians=False,bw_method=None)

Parameters:

dataset = Array-like data or list of datasets
positions = X-axis positions of violins
vert = Vertical (True) or horizontal (False)
widths = Width of each violin
showmeans = Show mean marker
showextrema = Show min/max bars
showmedians = Show median line
bw_method = Controls smoothing of KDE (e.g., 'scott', 'silverman', or float)

Basic violin plot example

import matplotlib.pyplot as plt

import numpy as np

data = np.random.normal(0, 1, 100)

plt.violinplot(data)

plt.title("Basic Violin Plot")

plt.show()

Displays one violin — vertical, centred at x = 1.

Multiple violin plots

Pass multiple datasets as a list (or 2D array):

import matplotlib.pyplot as plt

import numpy as np

data = [np.random.normal(0, 1, 100),

np.random.normal(2, 1.5, 100),

np.random.normal(-1, 0.8, 100)]

plt.violinplot(data)

plt.title("Multiple Violin Plots")

plt.show()

matplotlib violin chart multiple example

Add labels

By default, violin plots have no x-axis labels — you can add them manually:

import matplotlib.pyplot as plt

import numpy as np

data = [np.random.normal(0, 1, 100),

np.random.normal(2, 1.5, 100),

np.random.normal(-1, 0.8, 100)]

plt.violinplot(data)

plt.xticks([1, 2, 3], ['A', 'B', 'C'])

plt.title("Violin Plots with Labels")

plt.show()

matplotlib violin chart with labels example

Show median, mean, and extremes

import matplotlib.pyplot as plt

import numpy as np

data = [np.random.normal(0, 1, 100),

np.random.normal(2, 1.5, 100),

np.random.normal(-1, 0.8, 100)]

plt.violinplot(data, showmeans=True, showmedians=True, showextrema=True)

plt.xticks([1, 2, 3], ['A', 'B', 'C'])

plt.title("Violin Plots with Mean and Median")

plt.show()

Mean: typically a dot or horizontal line.
Median: bold centre line.
Extrema: thin top/bottom lines.

matplotlib violin chart mean median example

Horizontal violin plots

import matplotlib.pyplot as plt

import numpy as np

data = [np.random.normal(0, 1, 100),

np.random.normal(2, 1.5, 100),

np.random.normal(-1, 0.8, 100)]

plt.violinplot(data, vert=False, showmedians=True)

plt.yticks([1, 2, 3], ['A', 'B', 'C'])

plt.title("Horizontal Violin Plots")

plt.show()

matplotlib violin chart horizontal example

Adjust widths and positions

You can manually set the positions and relative widths of violins:

import matplotlib.pyplot as plt

import numpy as np

data = [np.random.normal(0, 1, 100),

np.random.normal(2, 1.5, 100),

np.random.normal(-1, 0.8, 100)]

# skip position 3

positions = [1, 2, 4]

plt.violinplot(data, positions=positions, widths=0.7, showmedians=True)

plt.xticks(positions, ['A', 'B', 'C'])

plt.title("Custom Positions and Widths")

plt.show()

matplotlib violin chart positions width example

Customise appearance

The violinplot function returns a dictionary-like object with components (bodies, cmeans, cmedians, etc.), so you can modify their styles individually:

import matplotlib.pyplot as plt

import numpy as np

data = [np.random.normal(0, 1, 100),

np.random.normal(2, 1.5, 100),

np.random.normal(-1, 0.8, 100)]

parts = plt.violinplot(data, showmeans=True, showmedians=True)

for pc in parts['bodies']:

pc.set_facecolor("#FFC9D6")

pc.set_edgecolor('black')

pc.set_alpha(0.7)

plt.title("Styled Violin Plots")

plt.xticks([1, 2, 3], ['A', 'B', 'C'])

plt.show()

Change smoothing (bandwidth) of KDE

The bw_method parameter controls how smooth the violins are:

import matplotlib.pyplot as plt

import numpy as np

data = [np.random.normal(0, 1, 100),

np.random.normal(2, 1.5, 100),

np.random.normal(-1, 0.8, 100)]

plt.violinplot(data, bw_method=0.3, showmedians=True)

plt.title("Violin Plots with Custom Bandwidth")

plt.show()

Smaller bw_method = more jagged, larger = smoother.

matplotlib violin chart custom band width example

Overlay box plot or scatter points

You can combine violin plots with box plots or raw data points for more detail:

import matplotlib.pyplot as plt

import numpy as np

data = [np.random.normal(0, 1, 100),

np.random.normal(2, 1.5, 100),

np.random.normal(-1, 0.8, 100)]

plt.violinplot(data, showmedians=True)

plt.boxplot(data, positions=[1, 2, 3], widths=0.1)

plt.xticks([1, 2, 3], ['A', 'B', 'C'])

plt.title("Violin + Box Plot Overlay")

plt.show()

matplotlib violin chart box plot overlay example

Or scatter points:

import matplotlib.pyplot as plt

import numpy as np

data = [np.random.normal(0, 1, 100),

np.random.normal(2, 1.5, 100),

np.random.normal(-1, 0.8, 100)]

for i, d in enumerate(data, start=1):

plt.scatter(np.random.normal(i, 0.05, len(d)), d, alpha=0.3)

plt.violinplot(data, showmedians=True)

plt.title("Violin Plot with Raw Data Overlay")

plt.show()

matplotlib violin chart scatter overlay example

Full styled example

import matplotlib.pyplot as plt

import numpy as np

data = [np.random.normal(0, 1, 100),

np.random.normal(2, 1.5, 100),

np.random.normal(-1, 0.8, 100)]

parts = plt.violinplot(

data,

showmeans=True,

showmedians=True,

showextrema=False,

bw_method=0.3

)

colors = ['#99ccff', '#ff9999', '#99ff99']

for pc, color in zip(parts['bodies'], colors):

pc.set_facecolor(color)

pc.set_edgecolor('black')

pc.set_alpha(0.8)

plt.xticks([1, 2, 3], ['A', 'B', 'C'])

plt.title("Styled Violin Plots with Mean and Median")

plt.show()

matplotlib violin chart full styled example

Violin plots

Matplotlib Basics

2 min read

Published Oct 5 2025

Guide Sections

Guide Comments

Basic violin plot example

Multiple violin plots

Add labels

Show median, mean, and extremes

Horizontal violin plots

Adjust widths and positions

Customise appearance

Change smoothing (bandwidth) of KDE

Overlay box plot or scatter points

Full styled example

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