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. |
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. |
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. |
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. |
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. |
FacetGrid(data[, row, col, hue, col_wrap, …]) |
Multi-plot grid for plotting conditional relationships. |
FacetGrid.map(func, *args, **kwargs) |
Apply a plotting function to each facet’s subset of the data. |
FacetGrid.map_dataframe(func, *args, **kwargs) |
Like .map but passes args as strings and inserts data in kwargs. |
PairGrid(data[, hue, hue_order, palette, …]) |
Subplot grid for plotting pairwise relationships in a dataset. |
PairGrid.map(func, **kwargs) |
Plot with the same function in every subplot. |
PairGrid.map_diag(func, **kwargs) |
Plot with a univariate function on each diagonal subplot. |
PairGrid.map_offdiag(func, **kwargs) |
Plot with a bivariate function on the off-diagonal subplots. |
PairGrid.map_lower(func, **kwargs) |
Plot with a bivariate function on the lower diagonal subplots. |
PairGrid.map_upper(func, **kwargs) |
Plot with a bivariate function on the upper diagonal subplots. |
JointGrid(x, y[, data, height, ratio, …]) |
Grid for drawing a bivariate plot with marginal univariate plots. |
JointGrid.plot(joint_func, marginal_func[, …]) |
Shortcut to draw the full plot. |
JointGrid.plot_joint(func, **kwargs) |
Draw a bivariate plot of x and y. |
JointGrid.plot_marginals(func, **kwargs) |
Draw univariate plots for x and y separately. |
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). |
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. |
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. |
load_dataset(name[, cache, data_home]) |
Load a dataset from the online repository (requires internet). |
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. |