API reference

Relational plots

relplot([x, y, hue, size, style, data, row, …])

Figure-level interface for drawing relational plots onto a FacetGrid.

scatterplot([x, y, hue, style, size, data, …])

Draw a scatter plot with possibility of several semantic groupings.

lineplot([x, y, hue, size, style, data, …])

Draw a line plot with possibility of several semantic groupings.

Categorical plots

catplot([x, y, hue, data, row, col, …])

Figure-level interface for drawing categorical plots onto a FacetGrid.

stripplot([x, y, hue, data, order, …])

Draw a scatterplot where one variable is categorical.

swarmplot([x, y, hue, data, order, …])

Draw a categorical scatterplot with non-overlapping points.

boxplot([x, y, hue, data, order, hue_order, …])

Draw a box plot to show distributions with respect to categories.

violinplot([x, y, hue, data, order, …])

Draw a combination of boxplot and kernel density estimate.

boxenplot([x, y, hue, data, order, …])

Draw an enhanced box plot for larger datasets.

pointplot([x, y, hue, data, order, …])

Show point estimates and confidence intervals using scatter plot glyphs.

barplot([x, y, hue, data, order, hue_order, …])

Show point estimates and confidence intervals as rectangular bars.

countplot([x, y, hue, data, order, …])

Show the counts of observations in each categorical bin using bars.

Distribution plots

distplot(a[, bins, hist, kde, rug, fit, …])

Flexibly plot a univariate distribution of observations.

kdeplot(data[, data2, shade, vertical, …])

Fit and plot a univariate or bivariate kernel density estimate.

rugplot(a[, height, axis, ax])

Plot datapoints in an array as sticks on an axis.

Regression plots

lmplot(x, y, data[, hue, col, row, palette, …])

Plot data and regression model fits across a FacetGrid.

regplot(x, y[, data, x_estimator, x_bins, …])

Plot data and a linear regression model fit.

residplot(x, y[, data, lowess, x_partial, …])

Plot the residuals of a linear regression.

Matrix plots

heatmap(data[, vmin, vmax, cmap, center, …])

Plot rectangular data as a color-encoded matrix.

clustermap(data[, pivot_kws, method, …])

Plot a matrix dataset as a hierarchically-clustered heatmap.

Multi-plot grids

Facet grids

FacetGrid(data[, row, col, hue, col_wrap, …])

Multi-plot grid for plotting conditional relationships.

FacetGrid.map(self, func, \*args, \*\*kwargs)

Apply a plotting function to each facet’s subset of the data.

FacetGrid.map_dataframe(self, func, \*args, …)

Like .map but passes args as strings and inserts data in kwargs.

Pair grids

pairplot(data[, hue, hue_order, palette, …])

Plot pairwise relationships in a dataset.

PairGrid(data[, hue, hue_order, palette, …])

Subplot grid for plotting pairwise relationships in a dataset.

PairGrid.map(self, func, \*\*kwargs)

Plot with the same function in every subplot.

PairGrid.map_diag(self, func, \*\*kwargs)

Plot with a univariate function on each diagonal subplot.

PairGrid.map_offdiag(self, func, \*\*kwargs)

Plot with a bivariate function on the off-diagonal subplots.

PairGrid.map_lower(self, func, \*\*kwargs)

Plot with a bivariate function on the lower diagonal subplots.

PairGrid.map_upper(self, func, \*\*kwargs)

Plot with a bivariate function on the upper diagonal subplots.

Joint grids

jointplot(x, y[, data, kind, stat_func, …])

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

JointGrid(x, y[, data, height, ratio, …])

Grid for drawing a bivariate plot with marginal univariate plots.

JointGrid.plot(self, joint_func, marginal_func)

Shortcut to draw the full plot.

JointGrid.plot_joint(self, func, \*\*kwargs)

Draw a bivariate plot of x and y.

JointGrid.plot_marginals(self, func, \*\*kwargs)

Draw univariate plots for x and y separately.

Style control

set([context, style, palette, font, …])

Set aesthetic parameters in one step.

axes_style([style, rc])

Return a parameter dict for the aesthetic style of the plots.

set_style([style, rc])

Set the aesthetic style of the plots.

plotting_context([context, font_scale, rc])

Return a parameter dict to scale elements of the figure.

set_context([context, font_scale, rc])

Set the plotting context parameters.


Change how matplotlib color shorthands are interpreted.


Restore all RC params to default settings.


Restore all RC params to original settings (respects custom rc).

Color palettes

set_palette(palette[, n_colors, desat, …])

Set the matplotlib color cycle using a seaborn palette.

color_palette([palette, n_colors, desat])

Return a list of colors defining a color palette.

husl_palette([n_colors, h, s, l])

Get a set of evenly spaced colors in HUSL hue space.

hls_palette([n_colors, h, l, s])

Get a set of evenly spaced colors in HLS hue space.

cubehelix_palette([n_colors, start, rot, …])

Make a sequential palette from the cubehelix system.

dark_palette(color[, n_colors, reverse, …])

Make a sequential palette that blends from dark to color.

light_palette(color[, n_colors, reverse, …])

Make a sequential palette that blends from light to color.

diverging_palette(h_neg, h_pos[, s, l, sep, …])

Make a diverging palette between two HUSL colors.

blend_palette(colors[, n_colors, as_cmap, input])

Make a palette that blends between a list of colors.


Make a palette with color names from the xkcd color survey.


Make a palette with color names from Crayola crayons.

mpl_palette(name[, n_colors])

Return discrete colors from a matplotlib palette.

Palette widgets

choose_colorbrewer_palette(data_type[, as_cmap])

Select a palette from the ColorBrewer set.


Launch an interactive widget to create a sequential cubehelix palette.

choose_light_palette([input, as_cmap])

Launch an interactive widget to create a light sequential palette.

choose_dark_palette([input, as_cmap])

Launch an interactive widget to create a dark sequential palette.


Launch an interactive widget to choose a diverging color palette.

Utility functions

load_dataset(name[, cache, data_home])

Load an example dataset from the online repository (requires internet).


Report available example datasets, useful for reporting issues.


Return a path to the cache directory for example datasets.

despine([fig, ax, top, right, left, bottom, …])

Remove the top and right spines from plot(s).

desaturate(color, prop)

Decrease the saturation channel of a color by some percent.


Return a fully saturated color with the same hue.

set_hls_values(color[, h, l, s])

Independently manipulate the h, l, or s channels of a color.