v0.3.0 (March 2014)

This is a major release from 0.2 with a number of enhancements to the
plotting capabilities and styles. Highlights include FacetGrid,
factorplot, jointplot, and an overhaul to
style management. There is also lots of new
documentation, including an example gallery and
reorganized tutorial.

New plotting functions

• The FacetGrid class adds a new form of functionality to seaborn,
  providing a way to abstractly structure a grid of plots
  corresponding to subsets of a dataset. It can be used with a wide
  variety of plotting functions (including most of the matplotlib and
  seaborn APIs. See the tutorial for more information.
• Version 0.3 introduces the factorplot function, which is similar in
  spirit to lmplot but intended for use when the main independent
  variable is categorical instead of quantitative. factorplot can draw
  a plot in either a point or bar representation using the
  corresponding Axes-level functions pointplot and barplot (which are
  also new). Additionally, the factorplot function can be used to draw
  box plots on a faceted grid. For examples of how to use these
  functions, you can refer to the tutorial.
• Another new function is jointplot, which is built using the new
  JointGrid object. jointplot generalizes the behavior of regplot in
  previous versions of seaborn (regplot has changed somewhat in 0.3;
  see below for details) by drawing a bivariate plot of the
  relationship between two variables with their marginal distributions
  drawn on the side of the plot. With jointplot, you can draw a
  scatterplot or regression plot as before, but you
  can now also draw bivariate kernel densities or hexbin plots with
  appropriate univariate graphs for the marginal distributions.
  Additionally, it's easy to use JointGrid directly to build up more
  complex plots when the default methods offered by jointplot are not
  suitable for your visualization problem. The
  tutorial or JointGrid has more examples of how this
  object can be useful.
• The residplot function complements regplot and can be quickly used
  to diagnose problems with a linear model by calculating and plotting
  the residuals of a simple regression. There is also a "resid" kind
  for jointplot.

API changes

• The most noticeable change will be that regplot no longer produces a
  multi-component plot with distributions in marginal axes. Instead.
  regplot is now an "Axes-level" function that can be plotted into any
  existing figure on a specific set of axes. regplot and lmplot have
  also been unified (the latter uses the former behind the scenes), so
  all options for how to fit and represent the regression model can be
  used for both functions. To get the old behavior
  of regplot, use jointplot with kind="reg".
• As noted above, lmplot has been rewritten to exploit the FacetGrid
  machinery. This involves a few changes. The color keyword argument
  has been replaced with hue, for better consistency across the
  package. The hue parameter will always take a variable name,
  while color will take a color name or (in some cases) a palette.
  The lmplot function now returns the FacetGrid used to draw the plot
  instance.
• The functions that interact with matplotlib rc parameters have been
  updated and standardized. There are now three pairs of functions,
  axes_style and set_style, plotting_context and set_context, and
  color_palette and set_palette. In each case, the pairs take the
  exact same arguments. The first function defines and returns the
  parameters, and the second sets the matplotlib defaults.
  Additionally, the first function in each pair can be used in a
  with statement to temporarily change the defaults. Both the style
  and context functions also now accept a dictionary of matplotlib rc
  parameters to override the seaborn defaults, and set now also takes
  a dictionary to update any of the matplotlib defaults. See the
  tutorial for more information.
• The nogrid style has been deprecated and changed to white for
  more uniformity (i.e. there are now darkgrid, dark, whitegrid,
  and white styles).

Other changes

Using the package

• If you want to use plotting functions provided by the package
  without setting the matplotlib style to a seaborn theme, you can now
  do import seaborn.apionly as sns or
  from seaborn.apionly import lmplot, etc. This is using the (also
  new) reset_orig function, which returns the rc parameters to what
  they are at matplotlib import time — i.e. they will respect any
  custom matplotlibrc settings on top of the matplotlib defaults.
• The dependency load of the package has been reduced. It can now be
  installed and used with only numpy, scipy, matplotlib, and
  pandas. Although statsmodels is still recommended for full
  functionality, it is not required.

Plotting functions

• lmplot (and regplot) have two new options for fitting regression
  models: lowess and robust. The former fits a nonparametric
  smoother, while the latter fits a regression using methods that are
  less sensitive to outliers.
• The regression uncertainty in lmplot and regplot is now estimated
  with fewer bootstrap iterations, so plotting should be faster.
• The univariate kdeplot can now be drawn as a cumulative density
  plot.
• Changed interactplot to use a robust calculation of the data range
  when finding default limits for the contour colormap to work better
  when there are outliers in the data.

Style

• There is a new style, dark, which shares most features with
  darkgrid but does not draw a grid by default.
• There is a new function, offset_spines, and a corresponding option
  in despine called trim. Together, these can be used to make plots
  where the axis spines are offset from the main part of the figure
  and limited within the range of the ticks. This is recommended for
  use with the ticks style.
• Other aspects of the seaborn styles have been tweaked for more
  attractive plots.