Added more control over the arrangement of the elements drawn by clustermap with the {dendrogram,colors}_ratio and cbar_pos parameters. Additionally, the default organization and scaling with different figure sizes has been improved.
Added the corner option to PairGrid and pairplot to make a grid without the upper triangle of bivariate axes.
Added the ability to seed the random number generator for the bootstrap used to define error bars in several plots. Relevant functions now have a seed parameter, which can take either fixed seed (typically an int) or a numpy random number generator object (either the newer numpy.random.Generator or the older numpy.random.mtrand.RandomState).
Generalized the idea of "diagonal" axes in PairGrid to any axes that share an x and y variable.
In PairGrid, the hue variable is now excluded from the default list of variables that make up the rows and columns of the grid.
Exposed the layout_pad parameter in PairGrid and set a smaller default than what matptlotlib sets for more efficient use of space in dense grids.
It is now possible to force a categorical interpretation of the hue varaible in a relational plot by passing the name of a categorical palette (e.g. "deep", or "Set2"). This complements the (previously supported) option of passig a list/dict of colors.
Added the tree_kws parameter to clustermap to control the properties of the lines in the dendrogram.
Added the ability to pass hierarchical label names to the FacetGrid legend, which also fixes a bug in relplot when the same label appeared in diffent semantics.
Improved support for grouping observations based on pandas index information in categorical plots.

Bug fixes and adaptations

Avoided an error when singular data is passed to kdeplot, issuing a warning instead. This makes pairplot more robust.
Fixed the behavior of dropna in PairGrid to properly exclude null datapoints from each plot when set to True.
Fixed an issue where regplot could interfere with other axes in a multi-plot matplotlib figure.
Semantic variables with a category data type will always be treated as categorical in relational plots.
Avoided a warning about color specifications that arose from boxenplot on newer matplotlibs.
Adapted to a change in how matplotlib scales axis margins, which caused multiple calls to regplot with truncate=False to progressively expand the x axis limits. Because there are currently limitations on how autoscaling works in matplotlib, the default value for truncate in seaborn has also been changed to True.
Relational plots no longer error when hue/size data are inferred to be numeric but stored with a string datatype.
Relational plots now consider semantics with only a single value that can be interpreted as boolean (0 or 1) to be categorical, not numeric.
Relational plots now handle list or dict specifications for sizes correctly.
Fixed an issue in pointplot where missing levels of a hue variable would cause an exception after a recent update in matplotlib.
Fixed a bug when setting the rotation of x tick labels on a FacetGrid.
Fixed a bug where values would be excluded from categorical plots when only one variable was a pandas Series with a non-default index.
Fixed a bug when using Series objects as arguments for x_partial or y_partial in regplot.
Fixed a bug when passing a norm object and using color annotations in clustermap.
Fixed a bug where annotations were not rearranged to match the clustering in clustermap.
Fixed a bug when trying to call set while specifying a list of colors for the palette.
Fixed a bug when resetting the color code short-hands to the matplotlib default.
Avoided errors from stricter type checking in upcoming numpy changes.
Avoided error/warning in lineplot when plotting categoricals with empty levels.
Allowed colors to be passed through to a bivariate kdeplot.
Standardized the output format of custom color palette functions.
Fixed a bug where legends for numerical variables in a relational plot could show a surprisingly large number of decimal places.
Improved robustness to missing values in distribution plots.
Made it possible to specify the location of the FacetGrid legend using matplotlib keyword arguments.