# 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.