seaborn.pairplot¶

seaborn.pairplot(data, hue=None, hue_order=None, palette=None, vars=None, x_vars=None, y_vars=None, kind='scatter', diag_kind='auto', markers=None, height=2.5, aspect=1, corner=False, dropna=True, plot_kws=None, diag_kws=None, grid_kws=None, size=None)¶

Plot pairwise relationships in a dataset.

By default, this function will create a grid of Axes such that each numeric variable in data will by shared in the y-axis across a single row and in the x-axis across a single column. The diagonal Axes are treated differently, drawing a plot to show the univariate distribution of the data for the variable in that column.

It is also possible to show a subset of variables or plot different variables on the rows and columns.

This is a high-level interface for PairGrid that is intended to make it easy to draw a few common styles. You should use PairGrid directly if you need more flexibility.

Parameters

dataDataFrame: Tidy (long-form) dataframe where each column is a variable and each row is an observation.
huestring (variable name), optional: Variable in data to map plot aspects to different colors.
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.
varslist of variable names, optional: Variables within data to use, otherwise use every column with a numeric datatype.
{x, y}_varslists of variable names, optional: Variables within data to use separately for the rows and columns of the figure; i.e. to make a non-square plot.
kind{‘scatter’, ‘reg’}, optional: Kind of plot for the non-identity relationships.
diag_kind{‘auto’, ‘hist’, ‘kde’, None}, optional: Kind of plot for the diagonal subplots. The default depends on whether "hue" is used or not.
markerssingle matplotlib marker code or list, optional: Either the marker to use for all datapoints or a list of markers with a length the same as the number of levels in the hue variable so that differently colored points will also have different scatterplot markers.
heightscalar, optional: Height (in inches) of each facet.
aspectscalar, optional: Aspect * height gives the width (in inches) of each facet.
cornerbool, optional: If True, don’t add axes to the upper (off-diagonal) triangle of the grid, making this a “corner” plot.
dropnaboolean, optional: Drop missing values from the data before plotting.
{plot, diag, grid}_kwsdicts, optional: Dictionaries of keyword arguments. plot_kws are passed to the bivariate plotting function, diag_kws are passed to the univariate plotting function, and grid_kws are passed to the PairGrid constructor.

Returns

gridPairGrid: Returns the underlying PairGrid instance for further tweaking.

See also

PairGrid: Subplot grid for more flexible plotting of pairwise relationships.

Examples

Draw scatterplots for joint relationships and histograms for univariate distributions:

>>> import seaborn as sns; sns.set(style="ticks", color_codes=True)
>>> iris = sns.load_dataset("iris")
>>> g = sns.pairplot(iris)

Show different levels of a categorical variable by the color of plot elements:

>>> g = sns.pairplot(iris, hue="species")

Use a different color palette:

>>> g = sns.pairplot(iris, hue="species", palette="husl")

Use different markers for each level of the hue variable:

>>> g = sns.pairplot(iris, hue="species", markers=["o", "s", "D"])

Plot a subset of variables:

>>> g = sns.pairplot(iris, vars=["sepal_width", "sepal_length"])

Draw larger plots:

>>> g = sns.pairplot(iris, height=3,
...                  vars=["sepal_width", "sepal_length"])

Plot different variables in the rows and columns:

>>> g = sns.pairplot(iris,
...                  x_vars=["sepal_width", "sepal_length"],
...                  y_vars=["petal_width", "petal_length"])

Plot only the lower triangle of bivariate axes:

>>> g = sns.pairplot(iris, corner=True)

Use kernel density estimates for univariate plots:

>>> g = sns.pairplot(iris, diag_kind="kde")

Fit linear regression models to the scatter plots:

>>> g = sns.pairplot(iris, kind="reg")

Pass keyword arguments down to the underlying functions (it may be easier to use PairGrid directly):

>>> g = sns.pairplot(iris, diag_kind="kde", markers="+",
...                  plot_kws=dict(s=50, edgecolor="b", linewidth=1),
...                  diag_kws=dict(shade=True))