v0.4.0 (September 2014)#
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.
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
PairGridis 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
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
huevariable, 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
factorplotso that it behaves better when not all levels of the
xvariable are represented in each facet.
regplot()for fitting the regression in log space.
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#
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.
Font-handling should work better on systems without Arial installed. This is accomplished by adding the
font.sans-seriffield to the
axes_styledefinition with Arial and Liberation Sans prepended to matplotlib defaults. The font family can also be set through the
fontkeyword argument in
set(). Due to matplotlib bugs, this might not work as expected on matplotlib 1.3.
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.fonttypeso that text in PDFs is editable in Adobe Illustrator.