Violin plots

Matplotlib Basics

2 min read

Published Oct 5 2025, updated Aug 17 2026

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 pltimport numpy as npdata = 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 pltimport numpy as npdata = [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 pltimport numpy as npdata = [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 pltimport numpy as npdata = [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 pltimport numpy as npdata = [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 pltimport numpy as npdata = [np.random.normal(0, 1, 100),        np.random.normal(2, 1.5, 100),        np.random.normal(-1, 0.8, 100)]# skip position 3positions = [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 pltimport numpy as npdata = [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 pltimport numpy as npdata = [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 pltimport numpy as npdata = [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 pltimport numpy as npdata = [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 pltimport numpy as npdata = [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()