v0.5.1 (November 2014)

This is a bugfix release that includes a workaround for an issue in
matplotlib 1.4.2 and fixes for two bugs in functions that were new in
0.5.0.

• Implemented a workaround for a bug in matplotlib 1.4.2 that
  prevented point markers from being drawn when the seaborn styles had
  been set. See this github
  issue for more
  information.
• Fixed a bug in heatmap where the mask was vertically reversed
  relative to the data.
• Fixed a bug in clustermap when using nested lists of side colors.

v0.5.0 (November 2014)

This is a major release from 0.4. Highlights include new functions for plotting heatmaps, possibly while applying clustering algorithms to discover structured relationships. These functions are complemented by new custom colormap functions and a full set of IPython widgets that allow interactive selection of colormap parameters. The palette tutorial has been rewritten to cover these new tools and more generally provide guidance on how to use color in visualizations. There are also a number of smaller changes and bugfixes.

Plotting functions

• Added the heatmap function for visualizing a matrix of data by
  color-encoding the values. See the docs for more
  information.
• Added the clustermap function for clustering and visualizing a
  matrix of data, with options to label individual rows and columns by
  colors. See the docs for more information. This work
  was lead by Olga Botvinnik.
• lmplot and pairplot get a new keyword argument, markers. This can
  be a single kind of marker or a list of different markers for each
  level of the hue variable. Using different markers for different
  hues should let plots be more comprehensible when reproduced to
  black-and-white (i.e. when printed). See the github pull
  request for examples.
• More generally, there is a new keyword argument in FacetGrid and
  PairGrid, hue_kws. This similarly lets plot aesthetics vary across
  the levels of the hue variable, but more flexibily. hue_kws should
  be a dictionary that maps the name of keyword arguments to lists of
  values that are as long as the number of levels of the hue variable.
• The argument subplot_kws has been added to FacetGrid. This
  allows for faceted plots with custom projections, including maps
  with
  Cartopy.

Color palettes

• Added two new functions to create custom color palettes. For
  sequential palettes, you can use the light_palette function, which
  takes a seed color and creates a ramp from a very light, desaturated
  variant of it. For diverging palettes, you can use the
  diverging_palette function to create a balanced ramp between two
  endpoints to a light or dark midpoint. See the
  palette tutorial <palette_tutorial> for more information.
• Added the ability to specify the seed color for light_palette and
  dark_palette as a tuple of husl or hls space values or as a
  named xkcd color. The interpretation of the seed color is now
  provided by the new input parameter to these functions.
• Added several new interactive palette widgets:
  choose_colorbrewer_palette, choose_light_palette,
  choose_dark_palette, and choose_diverging_palette. For
  consistency, renamed the cubehelix widget to
  choose_cubehelix_palette (and fixed a bug where the cubehelix
  palette was reversed). These functions also now return either a
  color palette list or a matplotlib colormap when called, and that
  object will be live-updated as you play with the widget. This should
  make it easy to iterate over a plot until you find a good
  representation for the data. See the Github pull
  request or this
  notebook (download it to use the
  widgets) for more
  information.
• Overhauled the color palette tutorial to
  organize the discussion by class of color palette and provide more
  motivation behind the various choices one might make when choosing
  colors for their data.

Bug fixes

• Fixed a bug in PairGrid that gave incorrect results (or a crash)
  when the input DataFrame has a non-default index.
• Fixed a bug in PairGrid where passing columns with a date-like
  datatype raised an exception.
• Fixed a bug where lmplot would show a legend when the hue variable
  was also used on either the rows or columns (making the legend
  redundant).
• Worked around a matplotlib bug that was forcing outliers in boxplot
  to appear as blue.
• kdeplot now accepts pandas Series for the data and data2
  arguments.
• Using a non-default correlation method in corrplot now implies
  sig_stars=False as the permutation test used to significance
  values for the correlations uses a pearson metric.
• Removed pdf.fonttype from the style definitions, as the value used
  in version 0.4 resulted in very large PDF files.

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.

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.

0.3.1 (April 2014)

This is a minor release from 0.3 with fixes for several bugs.

Plotting functions

• The size of the points in pointplot and factorplot is now scaled with the linewidth for better aesthetics across different plotting contexts.
• The pointplot glyphs for different levels of the hue variable are drawn at different z-orders so that they appear uniform.

Bug Fixes

• Fixed a bug in FacetGrid (and thus affecting lmplot and factorplot) that appeared when col_wrap was used with a number of facets that did not evenly divide into the column width.
• Fixed an issue where the support for kernel density estimates was sometimes computed incorrectly.
• Fixed a problem where hue variable levels that were not strings were missing in FacetGrid legends.
• When passing a color palette list in a with statement, the entire palette is now used instead of the first six colors.

v0.3.0 (March 2014)

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.

v0.2.1 (January 2014)

This is a bugfix release, with no new features.

Bug fixes

• Changed the mechanics of violinplot() and boxplot() when using a
  Series object as data and performing a groupby to assign data to
  bins to address a problem that arises in Pandas 0.13.
• Additionally fixed the groupby code to work with all styles of group
  specification (specifically, using a dictionary or a function now works).
• Fixed a bug where artifacts from the kde fitting could undershoot and create
  a plot where the density axis starts below 0.
• Ensured that data used for kde fitting is double-typed to avoid a low-level
  statsmodels error.
• Changed the implementation of the histogram bin-width reference rule to
  take a ceiling of the estimated number of bins.

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.