API reference

Distribution plots

jointplot(x, y[, data, kind, stat_func, ...]) Draw a plot of two variables with bivariate and univariate graphs.
pairplot(data[, hue, hue_order, palette, ...]) Plot pairwise relationships in a dataset.
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.
interactplot(x1, x2, y[, data, filled, ...]) Visualize a continuous two-way interaction with a contour plot.
coefplot(formula, data[, groupby, ...]) Plot the coefficients from a linear model.

Categorical plots

factorplot([x, y, hue, data, row, col, ...]) Draw a categorical plot onto a FacetGrid.
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.
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.
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.

Matrix plots

heatmap(data[, vmin, vmax, cmap, center, ...]) Plot rectangular data as a color-encoded matrix.
clustermap(data[, pivot_kws, method, ...]) Plot a hierarchically clustered heatmap of a pandas DataFrame

Timeseries plots

tsplot(data[, time, unit, condition, value, ...]) Plot one or more timeseries with flexible representation of uncertainty.

Miscellaneous plots

palplot(pal[, size]) Plot the values in a color palette as a horizontal array.

Axis grids

FacetGrid(data[, row, col, hue, col_wrap, ...]) Subplot grid for plotting conditional relationships.
PairGrid(data[, hue, hue_order, palette, ...]) Subplot grid for plotting pairwise relationships in a dataset.
JointGrid(x, y[, data, size, ratio, space, ...]) Grid for drawing a bivariate plot with marginal univariate plots.

Style frontend

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.
set_color_codes([palette]) Change how matplotlib color shorthands are interpreted.
reset_defaults() Restore all RC params to default settings.
reset_orig() 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.
xkcd_palette(colors) Make a palette with color names from the xkcd color survey.
crayon_palette(colors) 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.
choose_cubehelix_palette([as_cmap]) 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.
choose_diverging_palette([as_cmap]) Launch an interactive widget to choose a diverging color palette.

Utility functions

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.
saturate(color) 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.
ci_to_errsize(cis, heights) Convert intervals to error arguments relative to plot heights.
axlabel(xlabel, ylabel, **kwargs) Grab current axis and label it.