Violin plots
Matplotlib Basics
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Published Oct 5 2025
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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)
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Parameters:
dataset= Array-like data or list of datasetspositions= X-axis positions of violinsvert= Vertical (True) or horizontal (False)widths= Width of each violinshowmeans= Show mean markershowextrema= Show min/max barsshowmedians= Show median linebw_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()
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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()
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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()
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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()
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- Mean: typically a dot or horizontal line.
- Median: bold centre line.
- Extrema: thin top/bottom lines.
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()
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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()
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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()
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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()
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Smaller bw_method = more jagged, larger = smoother.
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()
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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()
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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()
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