This is a major release from 0.3. Highlights include new approaches for quick, high-level dataset exploration (along with a more flexible interface and easy creation of perceptually-appropriate color palettes using the cubehelix system. Along with these additions, there are a number of smaller changes that make visualizing data with seaborn easier and more powerful.

Plotting functions

A new object, PairGrid, and a corresponding function pairplot, for drawing grids of pairwise relationships in a dataset. This style of plot is sometimes called a "scatterplot matrix", but the representation of the data in PairGrid is flexible and many styles other than scatterplots can be used. See the docs for more information. Note: due to a bug in older versions of matplotlib, you will have best results if you use these functions with matplotlib 1.4 or later.
The rules for choosing default color palettes when variables are mapped to different colors have been unified (and thus changed in some cases). Now when no specific palette is requested, the current global color palette will be used, unless the number of variables to be mapped exceeds the number of unique colors in the palette, in which case the "husl" palette will be used to avoid cycling.
Added a keyword argument hist_norm to distplot. When a distplot is now drawn without a KDE or parametric density, the histogram is drawn as counts instead of a density. This can be overridden by by setting hist_norm to True.
When using FacetGrid with a hue variable, the legend is no longer drawn by default when you call FacetGrid.map. Instead, you have to call FacetGrid.add_legend manually. This should make it easier to layer multiple plots onto the grid without having duplicated legends.
Made some changes to factorplot so that it behaves better when not all levels of the x variable are represented in each facet.
Added the logx option to regplot for fitting the regression in log space.
When violinplot encounters a bin with only a single observation, it will now plot a horizontal line at that value instead of erroring out.

Style and color palettes

Added the cubehelix_palette function for generating sequential palettes from the cubehelix system. See the palette docs for more information on how these palettes can be used. There is also the choose_cubehelix which will launch an interactive app to select cubehelix parameters in the notebook.
Added the xkcd_palette and the xkcd_rgb dictionary so that colors can be specified with names from the xkcd color survey.
Added the font_scale option to plotting_context, set_context, and set. font_scale can independently increase or decrease the size of the font elements in the plot.
Font-handling should work better on systems without Arial installed. This is accomplished by adding the font.sans-serif field to the axes_style definition with Arial and Liberation Sans prepended to matplotlib defaults. The font family can also be set through the font keyword argument in set. Due to matplotlib bugs, this might not work as expected on matplotlib 1.3.
The despine function gets a new keyword argument offset, which replaces the deprecated offset_spines function. You no longer need to offset the spines before plotting data.
Added a default value for pdf.fonttype so that text in PDFs is editable in Adobe Illustrator.

Other API Changes

Removed the deprecated set_color_palette and palette_context functions. These were replaced in version 0.3 by the set_palette function and ability to use color_palette directly in a with statement.
Removed the ability to specify a nogrid style, which was renamed to white in 0.3.