v0.2.0 (December 2013)

This is a major release from 0.1 with a number of API changes,
enhancements, and bug fixes.

Highlights include an overhaul of timeseries plotting to work
intelligently with dataframes, the new function interactplot() for
visualizing continuous interactions, bivariate kernel density estimates
in kdeplot(), and significant improvements to color palette handling.

Version 0.2 also introduces experimental support for Python 3.

In addition to the library enhancements, the documentation has been
substantially rewritten to reflect the new features and improve the
presentation of the ideas behind the package.

API changes

• The tsplot() function was rewritten to accept data in a long-form
  DataFrame and to plot different traces by condition. This
  introduced a relatively minor but unavoidable API change, where
  instead of doing sns.tsplot(time, heights), you now must do
  sns.tsplot(heights, time=time) (the time parameter is now
  optional, for quicker specification of simple plots). Additionally,
  the "obs_traces" and "obs_points" error styles in tsplot()
  have been renamed to "unit_traces" and "unit_points",
  respectively.
• Functions that fit kernel density estimates (kdeplot() and
  violinplot()) now use statsmodels instead of scipy, and the
  parameters that influence the density estimate have changed
  accordingly. This allows for increased flexibility in specifying the
  bandwidth and kernel, and smarter choices for defining the range of
  the support. Default options should produce plots that are very
  close to the old defaults.
• The kdeplot() function now takes a second positional argument of
  data for drawing bivariate densities.
• The violin() function has been changed to violinplot(), for
  consistency. In 0.2, violin will still work, but it will fire a
  UserWarning.

New plotting functions

• The interactplot() function draws a contour plot for an
  interactive linear model (i.e., the contour shows y-hat from the
  model y ~ x1 * x2) over a scatterplot between the two predictor
  variables. This plot should aid the understanding of an interaction
  between two continuous variables.
• The kdeplot() function can now draw a bivariate density estimate
  as a contour plot if provided with two-dimensional input data.
• The palplot() function provides a simple grid-based visualization
  of a color palette.

Other changes

Plotting functions

• The corrplot() function can be drawn without the correlation
  coefficient annotation and with variable names on the side of the
  plot to work with large datasets.
• Additionally, corrplot() sets the color palette intelligently
  based on the direction of the specified test.
• The distplot() histogram uses a reference rule to choose the bin
  size if it is not provided.
• Added the x_bins option in lmplot() for binning a continuous
  predictor variable, allowing for clearer trends with many
  datapoints.
• Enhanced support for labeling plot elements and axes based on name
  attributes in several distribution plot functions and tsplot() for
  smarter Pandas integration.
• Scatter points in lmplot() are slightly transparent so it is easy
  to see where observations overlap.
• Added the order parameter to boxplot() and violinplot() to
  control the order of the bins when using a Pandas object.
• When an ax argument is not provided to a plotting function, it
  grabs the currently active axis instead of drawing a new one.

Color palettes

• Added the dark_palette() and blend_palette() for on-the-fly
  creation of blended color palettes.
• The color palette machinery is now intelligent about qualitative
  ColorBrewer palettes (Set1, Paired, etc.), which are properly
  treated as discrete.
• Seaborn color palettes (deep, muted, etc.) have been
  standardized in terms of basic hue sequence, and all palettes now
  have 6 colors.
• Introduced {mpl_palette}_d palettes, which make a palette with the
  basic color scheme of the source palette, but with a sequential
  blend from dark instead of light colors for use with
  line/scatter/contour plots.
• Added the palette_context() function for blockwise color palettes
  controlled by a with statement.

Plot styling

• Added the despine() function for easily removing plot spines.
• A new plot style, "ticks" has been added.
• Tick labels are padded a bit farther from the axis in all styles,
  avoiding collisions at (0, 0).

General package issues

• Reorganized the package by breaking up the monolithic plotobjs
  module into smaller modules grouped by general objective of the
  constituent plots.
• Removed the scikits-learn dependency in moss.
• Installing with pip should automatically install most missing
  dependencies.
• The example notebooks are now used as an automated test suite.

Bug fixes

• Fixed a bug where labels did not match data for boxplot() and
  violinplot() when using a groupby.
• Fixed a bug in the desaturate() function.
• Fixed a bug in the coefplot() figure size calculation.
• Fixed a bug where regplot() choked on list input.
• Fixed buggy behavior when drawing horizontal boxplots.
• Specifying bins for the distplot() histogram now works.
• Fixed a bug where kdeplot() would reset the axis height and cut
  off existing data.
• All axis styling has been moved out of the top-level seaborn.set()
  function, so context or color palette can be cleanly changed.