seaborn: statistical data visualization
Functions:
Relational plots
Figurelevel interface for drawing relational plots onto a FacetGrid. 

Draw a scatter plot with possibility of several semantic groupings. 

Draw a line plot with possibility of several semantic groupings. 
Distribution plots
Figurelevel interface for drawing distribution plots onto a FacetGrid. 

Plot univariate or bivariate histograms to show distributions of datasets. 

Plot univariate or bivariate distributions using kernel density estimation. 

Plot empirical cumulative distribution functions. 

Plot marginal distributions by drawing ticks along the x and y axes. 

DEPRECATED: Flexibly plot a univariate distribution of observations. 
Categorical plots
Figurelevel interface for drawing categorical plots onto a FacetGrid. 

Draw a scatterplot where one variable is categorical. 

Draw a categorical scatterplot with nonoverlapping points. 

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

Draw a combination of boxplot and kernel density estimate. 

Draw an enhanced box plot for larger datasets. 

Show point estimates and confidence intervals using scatter plot glyphs. 

Show point estimates and confidence intervals as rectangular bars. 

Show the counts of observations in each categorical bin using bars. 
Regression plots
Plot data and regression model fits across a FacetGrid. 

Plot data and a linear regression model fit. 

Plot the residuals of a linear regression. 
Matrix plots
Plot rectangular data as a colorencoded matrix. 

Plot a matrix dataset as a hierarchicallyclustered heatmap. 
Multiplot grids
Facet grids
Multiplot grid for plotting conditional relationships. 

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

Like 
Pair grids
Plot pairwise relationships in a dataset. 

Subplot grid for plotting pairwise relationships in a dataset. 

Plot with the same function in every subplot. 

Plot with a univariate function on each diagonal subplot. 

Plot with a bivariate function on the offdiagonal subplots. 

Plot with a bivariate function on the lower diagonal subplots. 

Plot with a bivariate function on the upper diagonal subplots. 
Joint grids
Draw a plot of two variables with bivariate and univariate graphs. 

Grid for drawing a bivariate plot with marginal univariate plots. 

Draw the plot by passing functions for joint and marginal axes. 

Draw a bivariate plot on the joint axes of the grid. 

Draw univariate plots on each marginal axes. 
Themeing
Set multiple theme parameters in one step. 

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

Set the aesthetic style of the plots. 

Return a parameter dict to scale elements of the figure. 

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). 

Alias for 
Color palettes
Set the matplotlib color cycle using a seaborn palette. 

Return a list of colors or continuous colormap defining a palette. 

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

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

Make a sequential palette from the cubehelix system. 

Make a sequential palette that blends from dark to 

Make a sequential palette that blends from light to 

Make a diverging palette between two HUSL colors. 

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. 

Return discrete colors from a matplotlib palette. 
Palette widgets
Select a palette from the ColorBrewer set. 

Launch an interactive widget to create a sequential cubehelix palette. 

Launch an interactive widget to create a light sequential palette. 

Launch an interactive widget to create a dark sequential palette. 

Launch an interactive widget to choose a diverging color palette. 
Utility functions
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. 

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

Decrease the saturation channel of a color by some percent. 

Return a fully saturated color with the same hue. 

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