Violin plots

seaborn.violinplot() visualizes the distribution of a numerical variable for one or more categories — similar to a boxplot, but with a smoothed kernel density estimate (KDE) on each side.

You can think of it as a boxplot + density curve combined.
It shows:

• The median (white dot by default)
• The interquartile range (IQR) (thick black bar)
• The full data range (thin line)

Syntax:

sns.violinplot(    data=None,    x=None,    y=None,    hue=None,    order=None,    hue_order=None,    bw='scott',    cut=2,    scale='area',    scale_hue=True,    gridsize=100,    width=0.8,    inner='box',    split=False,    dodge=True,    orient=None,    linewidth=None,    palette=None,    **kwargs)

Parameters:

• data = DataFrame containing the data
• x, y = Variables for categories and numeric values
• hue = Adds subcategories (split violins)
• order, hue_order = Category order
• palette = Colour scheme
• bw = Bandwidth for KDE smoothing
• cut = Extent to which the violin extends beyond data range
• scale = 'area', 'count', or 'width' — how violins are sized
• inner = What to show inside the violin: 'box', 'quartile', 'point', 'stick', or None
• split = If True, split violins for hue categories
• width = Violin width
• orient = "v" (vertical) or "h" (horizontal)

Basic example

import seaborn as snsimport matplotlib.pyplot as plt​tips = sns.load_dataset("tips")​sns.violinplot(data=tips, x="day", y="total_bill")plt.show()

Shows the distribution of total_bill for each day.

Add hue (subgroups)

import seaborn as snsimport matplotlib.pyplot as plt​tips = sns.load_dataset("tips")​sns.violinplot(data=tips, x="day", y="total_bill", hue="sex", palette="pastel")plt.show()

• Each day has two violins, one for each sex.
• By default, they’re placed side by side.

Split violins (compare distributions directly)

import seaborn as snsimport matplotlib.pyplot as plt​tips = sns.load_dataset("tips")​sns.violinplot(    data=tips,    x="day",    y="total_bill",    hue="sex",    split=True,    palette="Set2")plt.show()

Splits the violins in half (left vs. right) for each subgroup. Great for direct visual comparison.

Horizontal violin plot

import seaborn as snsimport matplotlib.pyplot as plt​tips = sns.load_dataset("tips")​sns.violinplot(data=tips, x="total_bill", y="day", orient="h")plt.show()

Flips the violins horizontally — good for long category labels.

Customise inner display

import seaborn as snsimport matplotlib.pyplot as plt​tips = sns.load_dataset("tips")​sns.violinplot(    data=tips,    x="day",    y="total_bill",    inner="quartile",    palette="coolwarm")plt.show()

Options for inner:

• 'box' = Small boxplot inside each violin (default)
• 'quartile' = Horizontal lines for quartiles
• 'point' = Dots for data points
• 'stick' = Small vertical lines for each observation
• None = Removes internal marks

Adjust smoothness & width

import seaborn as snsimport matplotlib.pyplot as plt​tips = sns.load_dataset("tips")​sns.violinplot(    data=tips,    x="day",    y="total_bill",    bw=0.3,    cut=0,    scale="width",    palette="magma")plt.show()

• bw: bandwidth for KDE (controls smoothness).
• cut: how far violins extend beyond actual data.
• scale: how violins are scaled ('area', 'width', 'count').

Overlay raw data for context

import seaborn as snsimport matplotlib.pyplot as plt​tips = sns.load_dataset("tips")​sns.violinplot(data=tips, x="day", y="total_bill", inner=None, color="lightgray")sns.stripplot(data=tips, x="day", y="total_bill", color="black", size=3, jitter=True)plt.show()

Shows the smooth distribution (violin) + individual data points (stripplot).