What’s new in the package

A catalog of new features, improvements, and bug-fixes in each release.

v0.7.1 (June 2016)

  • Added the ability to put “caps” on the error bars that are drawn by barplot() or pointplot() (and, by extension, factorplot()). Additionally, the line width of the error bars can now be controlled. These changes involve the new parameters capsize and errwidth. See the github pull request for examples of usage.
  • Improved the row and column colors display in clustermap(). It is now possible to pass Pandas objects for these elements and, when possible, the semantic information in the Pandas objects will be used to add labels to the plot. When Pandas objects are used, the color data is matched against the main heatmap based on the index, not on position. This is more accurate, but it may lead to different results if current code assumed positional matching.
  • Improved the luminance calculation that determines the annotation color in heatmap().
  • The annot parameter of heatmap() now accepts a rectangular dataset in addition to a boolean value. If a dataset is passed, its values will be used for the annotations, while the main dataset will be used for the heatmap cell colors.
  • Fixed a bug in FacetGrid that appeared when using col_wrap with missing col levels.
  • Made it possible to pass a tick locator object to the heatmap() colorbar.
  • Made it possible to use different styles (e.g., step) for PairGrid histograms when there are multiple hue levels.
  • Fixed a bug in scipy-based univariate kernel density bandwidth calculation.
  • The reset_orig() function (and, by extension, importing seaborn.apionly) resets matplotlib rcParams to their values at the time seaborn itself was imported, which should work better with rcParams changed by the jupyter notebook backend.
  • Removed some objects from the top-level seaborn namespace.
  • Improved unicode compatibility in FacetGrid.

v0.7.0 (January 2016)

This is a major release from 0.6. The main new feature is swarmplot() which implements the beeswarm approach for drawing categorical scatterplots. There are also some performance improvements, bug fixes, and updates for compatibility with new versions of dependencies.

  • Added the swarmplot() function, which draws beeswarm plots. These are categorical scatterplots, similar to those produced by stripplot(), but position of the points on the categorical axis is chosen to avoid overlapping points. See the categorical plot tutorial for more information.
  • Changed some of the stripplot() defaults to be closer to swarmplot(). Points are now somewhat smaller, have no outlines, and are not split by default when using hue. These settings remain customizable through function parameters.
  • Added an additional rule when determining category order in categorical plots. Now, when numeric variables are used in a categorical role, the default behavior is to sort the unique levels of the variable (i.e they will be in proper numerical order). This can still be overridden by the appropriate {*_}order parameter, and variables with a category datatype will still follow the category order even if the levels are strictly numerical.
  • Changed how stripplot() draws points when using hue nesting with split=False so that the different hue levels are not drawn strictly on top of each other.
  • Improve performance for large dendrograms in clustermap().
  • Added font.size to the plotting context definition so that the default output from plt.text will be scaled appropriately.
  • Fixed a bug in clustermap() when fastcluster is not installed.
  • Fixed a bug in the zscore calculation in clustermap().
  • Fixed a bug in distplot() where sometimes the default number of bins would not be an integer.
  • Fixed a bug in stripplot() where a legend item would not appear for a hue level if there were no observations in the first group of points.
  • Heatmap colorbars are now rasterized for better performance in vector plots.
  • Added workarounds for some matplotlib boxplot issues, such as strange colors of outlier points.
  • Added workarounds for an issue where violinplot edges would be missing or have random colors.
  • Added a workaround for an issue where only one heatmap() cell would be annotated on some matplotlib backends.
  • Fixed a bug on newer versions of matplotlib where a colormap would be erroneously applied to scatterplots with only three observations.
  • Updated seaborn for compatibility with matplotlib 1.5.
  • Added compatibility for various IPython (and Jupyter) versions in functions that use widgets.

v0.6.0 (June 2015)

This is a major release from 0.5. The main objective of this release was to unify the API for categorical plots, which means that there are some relatively large API changes in some of the older functions. See below for details of those changes, which may break code written for older versions of seaborn. There are also some new functions (stripplot(), and countplot()), numerous enhancements to existing functions, and bug fixes.

Additionally, the documentation has been completely revamped and expanded for the 0.6 release. Now, the API docs page for each function has multiple examples with embedded plots showing how to use the various options. These pages should be considered the most comprehensive resource for examples, and the tutorial pages are now streamlined and oriented towards a higher-level overview of the various features.

Changes and updates to categorical plots

In version 0.6, the “categorical” plots have been unified with a common API. This new category of functions groups together plots that show the relationship between one numeric variable and one or two categorical variables. This includes plots that show distribution of the numeric variable in each bin (boxplot(), violinplot(), and stripplot()) and plots that apply a statistical estimation within each bin (pointplot(), barplot(), and countplot()). There is a new tutorial chapter that introduces these functions.

The categorical functions now each accept the same formats of input data and can be invoked in the same way. They can plot using long- or wide-form data, and can be drawn vertically or horizontally. When long-form data is used, the orientation of the plots is inferred from the types of the input data. Additionally, all functions natively take a hue variable to add a second layer of categorization.

With the (in some cases new) API, these functions can all be drawn correctly by FacetGrid. However, factorplot() can also now create faceted verisons of any of these kinds of plots, so in most cases it will be unnecessary to use FacetGrid directly. By default, factorplot() draws a point plot, but this is controlled by the kind parameter.

Here are details on what has changed in the process of unifying these APIs:

  • Changes to boxplot() and violinplot() will probably be the most disruptive. Both functions maintain backwards-compatibility in terms of the kind of data they can accept, but the syntax has changed to be more similar to other seaborn functions. These functions are now invoked with x and/or y parameters that are either vectors of data or names of variables in a long-form DataFrame passed to the new data parameter. You can still pass wide-form DataFrames or arrays to data, but it is no longer the first positional argument. See the github pull request for more information on these changes and the logic behind them.
  • As pointplot() and barplot() can now plot with the major categorical variable on the y axis, the x_order parameter has been renamed to order.
  • Added a hue argument to boxplot() and violinplot(), which allows for nested grouping the plot elements by a third categorical variable. For violinplot(), this nesting can also be accomplished by splitting the violins when there are two levels of the hue variable (using split=True). To make this functionality feasible, the ability to specify where the plots will be draw in data coordinates has been removed. These plots now are drawn at set positions, like (and identical to) barplot() and pointplot().
  • Added a palette parameter to boxplot()/violinplot(). The color parameter still exists, but no longer does double-duty in accepting the name of a seaborn palette. palette supersedes color so that it can be used with a FacetGrid.

Along with these API changes, the following changes/enhancements were made to the plotting functions:

  • The default rules for ordering the categories has changed. Instead of automatically sorting the category levels, the plots now show the levels in the order they appear in the input data (i.e., the order given by Series.unique()). Order can be specified when plotting with the order and hue_order parameters. Additionally, when variables are pandas objects with a “categorical” dtype, the category order is inferred from the data object. This change also affects FacetGrid and PairGrid.
  • Added the scale and scale_hue parameters to violinplot(). These control how the width of the violins are scaled. The default is area, which is different from how the violins used to be drawn. Use scale='width' to get the old behavior.
  • Used a different style for the box kind of interior plot in violinplot(), which shows the whisker range in addition to the quartiles. Use inner='quartile' to get the old style.

New plotting functions

  • Added the stripplot() function, which draws a scatterplot where one of the variables is categorical. This plot has the same API as boxplot() and violinplot(). It is useful both on its own and when composed with one of these other plot kinds to show both the observations and underlying distribution.
  • Added the countplot() function, which uses a bar plot representation to show counts of variables in one or more categorical bins. This replaces the old approach of calling barplot() without a numeric variable.

Other additions and changes

  • The corrplot() and underlying symmatplot() functions have been deprecated in favor of heatmap(), which is much more flexible and robust. These two functions are still available in version 0.6, but they will be removed in a future version.
  • Added the set_color_codes() function and the color_codes argument to set() and set_palette(). This changes the interpretation of shorthand color codes (i.e. “b”, “g”, k”, etc.) within matplotlib to use the values from one of the named seaborn palettes (i.e. “deep”, “muted”, etc.). That makes it easier to have a more uniform look when using matplotlib functions directly with seaborn imported. This could be disruptive to existing plots, so it does not happen by default. It is possible this could change in the future.
  • The color_palette() function no longer trims palettes that are longer than 6 colors when passed into it.
  • Added the as_hex method to color palette objects, to return a list of hex codes rather than rgb tuples.
  • jointplot() now passes additional keyword arguments to the function used to draw the plot on the joint axes.
  • Changed the default linewidths in heatmap() and clustermap() to 0 so that larger matrices plot correctly. This parameter still exists and can be used to get the old effect of lines demarcating each cell in the heatmap (the old default linewidths was 0.5).
  • heatmap() and clustermap() now automatically use a mask for missing values, which previously were shown with the “under” value of the colormap per default plt.pcolormesh behavior.
  • Added the seaborn.crayons dictionary and the crayon_palette() function to define colors from the 120 box (!) of Crayola crayons.
  • Added the line_kws parameter to residplot() to change the style of the lowess line, when used.
  • Added open-ended **kwargs to the add_legend method on FacetGrid and PairGrid, which will pass additional keyword arguments through when calling the legend function on the Figure or Axes.
  • Added the gridspec_kws parameter to FacetGrid, which allows for control over the size of individual facets in the grid to emphasize certain plots or account for differences in variable ranges.
  • The interactive palette widgets now show a continuous colorbar, rather than a discrete palette, when as_cmap is True.
  • The default Axes size for pairplot() and PairGrid is now slightly smaller.
  • Added the shade_lowest parameter to kdeplot() which will set the alpha for the lowest contour level to 0, making it easier to plot multiple bivariate distributions on the same axes.
  • The height parameter of rugplot() is now interpreted as a function of the axis size and is invariant to changes in the data scale on that axis. The rug lines are also slightly narrower by default.
  • Added a catch in distplot() when calculating a default number of bins. For highly skewed data it will now use sqrt(n) bins, where previously the reference rule would return “infinite” bins and cause an exception in matplotlib.
  • Added a ceiling (50) to the default number of bins used for distplot() histograms. This will help avoid confusing errors with certain kinds of datasets that heavily violate the assumptions of the reference rule used to get a default number of bins. The ceiling is not applied when passing a specific number of bins.
  • The various property dictionaries that can be passed to plt.boxplot are now applied after the seaborn restyling to allow for full customizability.
  • Added a savefig method to JointGrid that defaults to a tight bounding box to make it easier to save figures using this class, and set a tight bbox as the default for the savefig method on other Grid objects.
  • You can now pass an integer to the xticklabels and yticklabels parameter of heatmap() (and, by extension, clustermap()). This will make the plot use the ticklabels inferred from the data, but only plot every n label, where n is the number you pass. This can help when visualizing larger matrices with some sensible ordering to the rows or columns of the dataframe.
  • Added “figure.facecolor” to the style parameters and set the default to white.
  • The load_dataset() function now caches datasets locally after downloading them, and uses the local copy on subsequent calls.

Bug fixes

  • Fixed bugs in clustermap() where the mask and specified ticklabels were not being reorganized using the dendrograms.
  • Fixed a bug in FacetGrid and PairGrid that lead to incorrect legend labels when levels of the hue variable appeared in hue_order but not in the data.
  • Fixed a bug in FacetGrid.set_xticklabels() or FacetGrid.set_yticklabels() when col_wrap is being used.
  • Fixed a bug in PairGrid where the hue_order parameter was ignored.
  • Fixed two bugs in despine() that caused errors when trying to trim the spines on plots that had inverted axes or no ticks.
  • Improved support for the margin_titles option in FacetGrid, which can now be used with a legend.

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 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.

v0.3.1 (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() are 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)

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 for 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.


  • 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 (December 2013)

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.