seaborn 0.4.0

v0.4.0 (September 2014)

on Python PyPI

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.