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:

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

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

Horizontal orientation

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

Add hue (subgroups)

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

Customize rug appearance

Taller and thicker ticks make the rug more visible.

Overlay rug on other plots

On a Histogram:

The rugplot shows exact data points below the histogram bars.

On a KDE Plot:

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

Bivariate rugplot (x and y)

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

Hue with multiple variables

Coloured rugs along both axes for each group.