This is a major release from 0.4. Highlights include new functions for plotting heatmaps, possibly while applying clustering algorithms to discover structured relationships. These functions are complemented by new custom colormap functions and a full set of IPython widgets that allow interactive selection of colormap parameters. The palette tutorial has been rewritten to cover these new tools and more generally provide guidance on how to use color in visualizations. There are also a number of smaller changes and bugfixes.

Plotting functions

Added the heatmap function for visualizing a matrix of data by color-encoding the values. See the docs for more information.
Added the clustermap function for clustering and visualizing a matrix of data, with options to label individual rows and columns by colors. See the docs for more information. This work was lead by Olga Botvinnik.
lmplot and pairplot get a new keyword argument, markers. This can be a single kind of marker or a list of different markers for each level of the hue variable. Using different markers for different hues should let plots be more comprehensible when reproduced to black-and-white (i.e. when printed). See the github pull request for examples.
More generally, there is a new keyword argument in FacetGrid and PairGrid, hue_kws. This similarly lets plot aesthetics vary across the levels of the hue variable, but more flexibily. hue_kws should be a dictionary that maps the name of keyword arguments to lists of values that are as long as the number of levels of the hue variable.
The argument subplot_kws has been added to FacetGrid. This allows for faceted plots with custom projections, including maps with Cartopy.

Color palettes

Added two new functions to create custom color palettes. For sequential palettes, you can use the light_palette function, which takes a seed color and creates a ramp from a very light, desaturated variant of it. For diverging palettes, you can use the diverging_palette function to create a balanced ramp between two endpoints to a light or dark midpoint. See the palette tutorial <palette_tutorial> for more information.
Added the ability to specify the seed color for light_palette and dark_palette as a tuple of husl or hls space values or as a named xkcd color. The interpretation of the seed color is now provided by the new input parameter to these functions.
Added several new interactive palette widgets: choose_colorbrewer_palette, choose_light_palette, choose_dark_palette, and choose_diverging_palette. For consistency, renamed the cubehelix widget to choose_cubehelix_palette (and fixed a bug where the cubehelix palette was reversed). These functions also now return either a color palette list or a matplotlib colormap when called, and that object will be live-updated as you play with the widget. This should make it easy to iterate over a plot until you find a good representation for the data. See the Github pull request or this notebook (download it to use the widgets) for more information.
Overhauled the color palette tutorial to organize the discussion by class of color palette and provide more motivation behind the various choices one might make when choosing colors for their data.

Bug fixes

Fixed a bug in PairGrid that gave incorrect results (or a crash) when the input DataFrame has a non-default index.
Fixed a bug in PairGrid where passing columns with a date-like datatype raised an exception.
Fixed a bug where lmplot would show a legend when the hue variable was also used on either the rows or columns (making the legend redundant).
Worked around a matplotlib bug that was forcing outliers in boxplot to appear as blue.
kdeplot now accepts pandas Series for the data and data2 arguments.
Using a non-default correlation method in corrplot now implies sig_stars=False as the permutation test used to significance values for the correlations uses a pearson metric.
Removed pdf.fonttype from the style definitions, as the value used in version 0.4 resulted in very large PDF files.