seaborn.PairGrid.__init__

PairGrid.__init__(data, *, hue=None, vars=None, x_vars=None, y_vars=None, hue_order=None, palette=None, hue_kws=None, corner=False, diag_sharey=True, height=2.5, aspect=1, layout_pad=0.5, despine=True, dropna=False)

Initialize the plot figure and PairGrid object.

Parameters:

dataDataFrame

Tidy (long-form) dataframe where each column is a variable and each row is an observation.

huestring (variable name)

Variable in data to map plot aspects to different colors. This variable will be excluded from the default x and y variables.

varslist of variable names

Variables within data to use, otherwise use every column with a numeric datatype.

{x, y}_varslists of variable names

Variables within data to use separately for the rows and columns of the figure; i.e. to make a non-square plot.

hue_orderlist of strings

Order for the levels of the hue variable in the palette

palettedict or seaborn color palette

Set of colors for mapping the hue variable. If a dict, keys should be values in the hue variable.

hue_kwsdictionary of param -> list of values mapping

Other keyword arguments to insert into the plotting call to let other plot attributes vary across levels of the hue variable (e.g. the markers in a scatterplot).

cornerbool

If True, don’t add axes to the upper (off-diagonal) triangle of the grid, making this a “corner” plot.

heightscalar

Height (in inches) of each facet.

aspectscalar

Aspect * height gives the width (in inches) of each facet.

layout_padscalar

Padding between axes; passed to fig.tight_layout.

despineboolean

Remove the top and right spines from the plots.

dropnaboolean

Drop missing values from the data before plotting.

See also

pairplot

Easily drawing common uses of PairGrid.

FacetGrid

Subplot grid for plotting conditional relationships.

Examples

Calling the constructor sets up a blank grid of subplots with each row and one column corresponding to a numeric variable in the dataset:

penguins = sns.load_dataset("penguins")
g = sns.PairGrid(penguins)

Passing a bivariate function to PairGrid.map() will draw a bivariate plot on every axes:

g = sns.PairGrid(penguins)
g.map(sns.scatterplot)

Passing separate functions to PairGrid.map_diag() and PairGrid.map_offdiag() will show each variable’s marginal distribution on the diagonal:

g = sns.PairGrid(penguins)
g.map_diag(sns.histplot)
g.map_offdiag(sns.scatterplot)

It’s also possible to use different functions on the upper and lower triangles of the plot (which are otherwise redundant):

g = sns.PairGrid(penguins, diag_sharey=False)
g.map_upper(sns.scatterplot)
g.map_lower(sns.kdeplot)
g.map_diag(sns.kdeplot)

Or to avoid the redundancy altogether:

g = sns.PairGrid(penguins, diag_sharey=False, corner=True)
g.map_lower(sns.scatterplot)
g.map_diag(sns.kdeplot)

The PairGrid constructor accepts a hue variable. This variable is passed directly to functions that understand it:

g = sns.PairGrid(penguins, hue="species")
g.map_diag(sns.histplot)
g.map_offdiag(sns.scatterplot)
g.add_legend()

But you can also pass matplotlib functions, in which case a groupby is performed internally and a separate plot is drawn for each level:

g = sns.PairGrid(penguins, hue="species")
g.map_diag(plt.hist)
g.map_offdiag(plt.scatter)
g.add_legend()

Additional semantic variables can be assigned by passing data vectors directly while mapping the function:

g = sns.PairGrid(penguins, hue="species")
g.map_diag(sns.histplot)
g.map_offdiag(sns.scatterplot, size=penguins["sex"])
g.add_legend(title="", adjust_subtitles=True)

When using seaborn functions that can implement a numeric hue mapping, you will want to disable mapping of the variable on the diagonal axes. Note that the hue variable is excluded from the list of variables shown by default:

g = sns.PairGrid(penguins, hue="body_mass_g")
g.map_diag(sns.histplot, hue=None, color=".3")
g.map_offdiag(sns.scatterplot)
g.add_legend()

The vars parameter can be used to control exactly which variables are used:

variables = ["body_mass_g", "bill_length_mm", "flipper_length_mm"]
g = sns.PairGrid(penguins, hue="body_mass_g", vars=variables)
g.map_diag(sns.histplot, hue=None, color=".3")
g.map_offdiag(sns.scatterplot)
g.add_legend()

The plot need not be square: separate variables can be used to define the rows and columns:

x_vars = ["body_mass_g", "bill_length_mm", "bill_depth_mm", "flipper_length_mm"]
y_vars = ["body_mass_g"]
g = sns.PairGrid(penguins, hue="species", x_vars=x_vars, y_vars=y_vars)
g.map_diag(sns.histplot, color=".3")
g.map_offdiag(sns.scatterplot)
g.add_legend()

It can be useful to explore different approaches to resolving multiple distributions on the diagonal axes:

g = sns.PairGrid(penguins, hue="species")
g.map_diag(sns.histplot, multiple="stack", element="step")
g.map_offdiag(sns.scatterplot)
g.add_legend()