seaborn.jointplot(data=None, *, x=None, y=None, hue=None, kind='scatter', height=6, ratio=5, space=0.2, dropna=False, xlim=None, ylim=None, color=None, palette=None, hue_order=None, hue_norm=None, marginal_ticks=False, joint_kws=None, marginal_kws=None, **kwargs)#

Draw a plot of two variables with bivariate and univariate graphs.

This function provides a convenient interface to the JointGrid class, with several canned plot kinds. This is intended to be a fairly lightweight wrapper; if you need more flexibility, you should use JointGrid directly.

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.

x, yvectors or keys in data

Variables that specify positions on the x and y axes.

huevector or key in data

Semantic variable that is mapped to determine the color of plot elements. Semantic variable that is mapped to determine the color of plot elements.

kind{ “scatter” | “kde” | “hist” | “hex” | “reg” | “resid” }

Kind of plot to draw. See the examples for references to the underlying functions.


Size of the figure (it will be square).


Ratio of joint axes height to marginal axes height.


Space between the joint and marginal axes


If True, remove observations that are missing from x and y.

{x, y}limpairs of numbers

Axis limits to set before plotting.

colormatplotlib color

Single color specification for when hue mapping is not used. Otherwise, the plot will try to hook into the matplotlib property cycle.

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.


If False, suppress ticks on the count/density axis of the marginal plots.

{joint, marginal}_kwsdicts

Additional keyword arguments for the plot components.


Additional keyword arguments are passed to the function used to draw the plot on the joint Axes, superseding items in the joint_kws dictionary.


An object managing multiple subplots that correspond to joint and marginal axes for plotting a bivariate relationship or distribution.

See also


Set up a figure with joint and marginal views on bivariate data.


Set up a figure with joint and marginal views on multiple variables.


Draw multiple bivariate plots with univariate marginal distributions.


In the simplest invocation, assign x and y to create a scatterplot (using scatterplot()) with marginal histograms (using histplot()):

penguins = sns.load_dataset("penguins")
sns.jointplot(data=penguins, x="bill_length_mm", y="bill_depth_mm")

Assigning a hue variable will add conditional colors to the scatterplot and draw separate density curves (using kdeplot()) on the marginal axes:

sns.jointplot(data=penguins, x="bill_length_mm", y="bill_depth_mm", hue="species")

Several different approaches to plotting are available through the kind parameter. Setting kind="kde" will draw both bivariate and univariate KDEs:

sns.jointplot(data=penguins, x="bill_length_mm", y="bill_depth_mm", hue="species", kind="kde")

Set kind="reg" to add a linear regression fit (using regplot()) and univariate KDE curves:

sns.jointplot(data=penguins, x="bill_length_mm", y="bill_depth_mm", kind="reg")

There are also two options for bin-based visualization of the joint distribution. The first, with kind="hist", uses histplot() on all of the axes:

sns.jointplot(data=penguins, x="bill_length_mm", y="bill_depth_mm", kind="hist")

Alternatively, setting kind="hex" will use matplotlib.axes.Axes.hexbin() to compute a bivariate histogram using hexagonal bins:

sns.jointplot(data=penguins, x="bill_length_mm", y="bill_depth_mm", kind="hex")

Additional keyword arguments can be passed down to the underlying plots:

    data=penguins, x="bill_length_mm", y="bill_depth_mm",
    marker="+", s=100, marginal_kws=dict(bins=25, fill=False),

Use JointGrid parameters to control the size and layout of the figure:

sns.jointplot(data=penguins, x="bill_length_mm", y="bill_depth_mm", height=5, ratio=2, marginal_ticks=True)

To add more layers onto the plot, use the methods on the JointGrid object that jointplot() returns:

g = sns.jointplot(data=penguins, x="bill_length_mm", y="bill_depth_mm")
g.plot_joint(sns.kdeplot, color="r", zorder=0, levels=6)
g.plot_marginals(sns.rugplot, color="r", height=-.15, clip_on=False)