seaborn.rugplot¶

seaborn.rugplot(x=None, *, height=0.025, axis=None, ax=None, data=None, y=None, hue=None, palette=None, hue_order=None, hue_norm=None, expand_margins=True, legend=True, a=None, **kwargs)¶

Plot marginal distributions by drawing ticks along the x and y axes.

This function is intended to complement other plots by showing the location of individual observations in an unobstrusive way.

Parameters

x, yvectors or keys in data: Variables that specify positions on the x and y axes.
heightnumber: Proportion of axes extent covered by each rug element.
axis{“x”, “y”}: Axis to draw the rug on.

Deprecated since version 0.11.0: specify axis by assigning the x or y variables.
axmatplotlib.axes.Axes: Pre-existing axes for the plot. Otherwise, call matplotlib.pyplot.gca() internally.
datapandas.DataFrame, numpy.ndarray, mapping, or sequence: Input data structure. Either a long-form collection of vectors that can be assigned to named variables or a wide-form dataset that will be internally reshaped.
huevector or key in data: Semantic variable that is mapped to determine the color of plot elements.
palettestring, list, dict, or matplotlib.colors.Colormap: Method for choosing the colors to use when mapping the hue semantic. String values are passed to color_palette(). List or dict values imply categorical mapping, while a colormap object implies numeric mapping.
hue_ordervector of strings: Specify the order of processing and plotting for categorical levels of the hue semantic.
hue_normtuple or matplotlib.colors.Normalize: Either a pair of values that set the normalization range in data units or an object that will map from data units into a [0, 1] interval. Usage implies numeric mapping.
expand_marginsbool: If True, increase the axes margins by the height of the rug to avoid overlap with other elements.
legendbool: If False, do not add a legend for semantic variables.
kwargs: Other keyword arguments are passed to matplotlib.collections.LineCollection()

Returns

matplotlib.axes.Axes: The matplotlib axes containing the plot.

Examples

Add a rug along one of the axes:

import seaborn as sns; sns.set_theme()
tips = sns.load_dataset("tips")
sns.kdeplot(data=tips, x="total_bill")
sns.rugplot(data=tips, x="total_bill")

Add a rug along both axes:

sns.scatterplot(data=tips, x="total_bill", y="tip")
sns.rugplot(data=tips, x="total_bill", y="tip")

Represent a third variable with hue mapping:

sns.scatterplot(data=tips, x="total_bill", y="tip", hue="time")
sns.rugplot(data=tips, x="total_bill", y="tip", hue="time")

Draw a taller rug:

sns.scatterplot(data=tips, x="total_bill", y="tip")
sns.rugplot(data=tips, x="total_bill", y="tip", height=.1)

Put the rug outside the axes:

sns.scatterplot(data=tips, x="total_bill", y="tip")
sns.rugplot(data=tips, x="total_bill", y="tip", height=-.02, clip_on=False)

Show the density of a larger dataset using thinner lines and alpha blending:

diamonds = sns.load_dataset("diamonds")
sns.scatterplot(data=diamonds, x="carat", y="price", s=5)
sns.rugplot(data=diamonds, x="carat", y="price", lw=1, alpha=.005)