seaborn.jointplot¶

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

Parameters

x, yvectors or keys in data: Variables that specify positions on the x and y axes.
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.
kind{ “scatter” | “kde” | “hist” | “hex” | “reg” | “resid” }: Kind of plot to draw. See the examples for references to the underlying functions.
colormatplotlib color: Single color specification for when hue mapping is not used. Otherwise, the plot will try to hook into the matplotlib property cycle.
heightnumeric: Size of the figure (it will be square).
rationumeric: Ratio of joint axes height to marginal axes height.
spacenumeric: Space between the joint and marginal axes
dropnabool: If True, remove observations that are missing from x and y.
{x, y}limpairs of numbers: Axis limits to set before plotting.
marginal_ticksbool: If False, suppress ticks on the count/density axis of the marginal plots.
{joint, marginal}_kwsdicts: Additional keyword arguments for the plot components.
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.
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.
kwargs: Additional keyword arguments are passed to the function used to draw the plot on the joint Axes, superseding items in the joint_kws dictionary.

Returns

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

See also

JointGrid: Set up a figure with joint and marginal views on bivariate data.
PairGrid: Set up a figure with joint and marginal views on multiple variables.
jointplot: Draw multiple bivariate plots with univariate marginal distributions.

Examples

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:

sns.jointplot(
    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)