Rug plot

seaborn.rugplot() draws small tick marks (rugs) along an axis to represent individual data points.

Each tick represents one observation — giving a sense of the distribution of data along a single dimension.

It’s often used:

• On its own for simple data density visualisation, or
• As an addition to other plots (e.g., histplot(), kdeplot()) to show the actual observations behind a smooth curve.

Syntax:

sns.rugplot(    data=None,    *,    x=None,    y=None,    hue=None,    height=0.025,    expand_margins=True,    palette=None,    linewidth=None,    alpha=None,    ax=None,    **kwargs)

Parameters:

• data = DataFrame containing the data
• x, y = Variables to plot (usually one)
• hue = Adds subgroups (coloured rugs)
• height = Length of each rug line (fraction of axis)
• expand_margins = Expands axis limits to fit the rugs
• palette = Colour palette for hue groups
• linewidth = Thickness of each rug tick
• alpha = Transparency (0–1)
• ax = Axis to plot on (if combining with other plots)

Basic example

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

Each small vertical tick represents one observation of total_bill. The density of ticks gives a quick sense of data concentration.

Horizontal orientation

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

Plots rug ticks along the y-axis instead of x.

Add hue (subgroups)

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

Different colours represent different categories (e.g., Male vs. Female). Overlapping colours show how groups overlap in data distribution.

Customize rug appearance

import seaborn as snsimport matplotlib.pyplot as plt​tips = sns.load_dataset("tips")​sns.rugplot(    data=tips,    x="total_bill",    height=0.05,    linewidth=2,    alpha=0.7,    color="darkred")plt.show()

Taller and thicker ticks make the rug more visible.

Overlay rug on other plots

On a Histogram:

import seaborn as snsimport matplotlib.pyplot as plt​tips = sns.load_dataset("tips")​sns.histplot(data=tips, x="total_bill", bins=20, color="skyblue")sns.rugplot(data=tips, x="total_bill", color="black")plt.show()

The rugplot shows exact data points below the histogram bars.

On a KDE Plot:

import seaborn as snsimport matplotlib.pyplot as plt​tips = sns.load_dataset("tips")​sns.kdeplot(data=tips, x="total_bill", fill=True, color="lightgreen")sns.rugplot(data=tips, x="total_bill", color="black")plt.show()

Combines smooth distribution (KDE) with exact data locations (rug).

Bivariate rugplot (x and y)

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

Adds small ticks on both the x- and y-axes, showing marginal distributions.

Hue with multiple variables

import seaborn as snsimport matplotlib.pyplot as plt​tips = sns.load_dataset("tips")​sns.rugplot(data=tips, x="total_bill", y="tip", hue="sex", palette="Set1", alpha=0.6)plt.show()

Coloured rugs along both axes for each group.