Introduction of the objects interface
This release debuts the seaborn.objects interface, an entirely new approach to making plots with seaborn. It is the product of several years of design and 16 months of implementation work. The interface aims to provide a more declarative, composable, and extensible API for making statistical graphics. It is inspired by Wilkinson's grammar of graphics, offering a Pythonic API that is informed by the design of libraries such as ggplot2 and vega-lite along with lessons from the past 10 years of seaborn's development.
For more information and numerous examples, see the tutorial chapter and API reference.
This initial release should be considered "experimental". While it is stable enough for serious use, there are definitely some rough edges, and some key features remain to be implemented. It is possible that breaking changes may occur over the next few minor releases. Please be patient with any limitations that you encounter and help the development by reporting issues when you find behavior surprising.
Seaborn's plotting functions now require explicit keywords for most arguments, following the deprecation of positional arguments in v0.11.0. With this enforcement, most functions have also had their parameter lists rearranged so that data is the first and only positional argument. This adds consistency across the various functions in the library. It also means that calling func(data) will do something for nearly all functions (those that support wide-form data) and that
pandas.DataFrame can be piped directly into a plot. It is possible that the signatures will be loosened a bit in future releases so that x and y can be positional, but minimal support for positional arguments after this change will reduce the chance of inadvertent mis-specification (
Modernization of categorical scatterplots
This release begins the process of modernizing the categorical plots, beginning with
swarmplot. These functions are sporting some enhancements that alleviate a few long-running frustrations (
- The new
native_scale parameter allows numeric or datetime categories to be plotted with their original scale rather than converted to strings and plotted at fixed intervals.
- The new
formatter parameter allows more control over the string representation of values on the categorical axis. There should also be improved defaults for some types, such as dates.
- It is now possible to assign
hue when using only one coordinate variable (i.e. only
- It is now possible to disable the legend.
The updates also harmonize behavior with functions that have been more recently introduced. This should be relatively non-disruptive, although a few defaults will change:
- The functions now hook into matplotlib's unit system for plotting categorical data. (Seaborn's categorical functions actually predate support for categorical data in matplotlib.) This should mostly be transparent to the user, but it may resolve a few edge cases. For example, matplotlib interactivity should work better (e.g., for showing the data value under the cursor).
- A color palette is no longer applied to levels of the categorical variable by default. It is now necessary to explicitly assign hue to see multiple colors (i.e., assign the same variable to x/y and hue). Passing palette without hue will continue to be honored for one release cycle.
- Numeric hue variables now receive a continuous mapping by default, using the same rules as
scatterplot. Pass palette="deep" to reproduce previous defaults.
- The plots now follow the default property cycle; i.e. calling an axes-level function multiple times with the same active axes will produce different-colored artists.
- Currently, assigning hue and then passing a color will produce a gradient palette. This is now deprecated, as it is easy to request a gradient with, e.g. palette="light:blue".
Similar enhancements / updates should be expected to roll out to other categorical plotting functions in future releases. There are also several function-specific enhancements:
stripplot, a "strip" with a single observation will be plotted without jitter (
swarmplot, the points are now swarmed at draw time, meaning that the plot will adapt to further changes in axis scaling or tweaks to the plot layout (
swarmplot, the proportion of points that must overlap before issuing a warning can now be controlled with the warn_thresh parameter (
swarmplot, the order of the points in each swarm now matches the order in the original dataset; previously they were sorted. This affects only the underlying data stored in the matplotlib artist, not the visual representation (
More flexible errorbars
Increased the flexibility of what can be shown by the internally-calculated errorbars for
With the new errorbar parameter, it is now possible to select bootstrap confidence intervals, percentile / predictive intervals, or intervals formed by scaled standard deviations or standard errors. The parameter also accepts an arbitrary function that maps from a vector to an interval. There is a new user guide chapter demonstrating these options and explaining when you might want to use each one.
As a consequence of this change, the ci parameter has been deprecated. Note that
regplot retains the previous API, but it will likely be updated in a future release (
- It is now possible to aggregate / sort a
lineplot along the y axis using orient="y" (
- Made it easier to customize
JointGrid with a fluent (method-chained) style by adding apply/ pipe methods. Additionally, fixed the tight_layout and refline methods so that they return self (
PairGrid.tick_params to customize the appearance of the ticks, tick labels, and gridlines of all subplots at once (
- Added a width parameter to
- It is now possible to specify estimator as a string in
pointplot, in addition to a callable (
- Error bars in
regplot now inherit the alpha value of the points they correspond to (
- When using
pairplot with corner=True and diag_kind=None, the top left y axis label is no longer hidden (
- It is now possible to plot a discrete
histplot as a step function or polygon (
- It is now possible to customize the appearance of elements in a
boxenplot with box_kws/line_kws/flier_kws (
- Improved integration with the matplotlib color cycle in most axes-level functions (
- Fixed a regression in 0.11.2 that caused some functions to stall indefinitely or raise when the input data had a duplicate index (
- Fixed a bug in
kdeplot where weights were not factored into the normalization (
- Fixed two edgecases in
histplot when only binwidth was provided (
- Fixed a bug in
violinplot where inner boxes/points could be missing with unpaired split violins (
- Fixed a bug in
PairGrid where an error would be raised when defining hue only in the mapping methods (
- Fixed a bug in
scatterplot where an error would be raised when hue_order was a subset of the hue levels (
- Fixed a bug in
histplot where dodged bars would have different widths on a log scale (
lineplot, allowed the dashes keyword to set the style of a line without mapping a style variable (
- Improved support in
relplot for "wide" data and for faceting variables passed as non-pandas objects (
- Subplot titles will no longer be reset when calling
- Added a workaround for a matplotlib issue that caused figure-level functions to freeze when plt.show was called (
- Improved robustness to numerical errors in
- Fixed a bug where
rugplot was ignoring expand_margins=False (
- The patch.facecolor rc param is no longer set by
set_theme). This should have no general effect, because the matplotlib default is now "C0" (
- Made scipy an optional dependency and added pip install seaborn[stats] as a method for ensuring the availability of compatible scipy and statsmodels libraries at install time. This has a few minor implications for existing code, which are explained in the Github pull request (
- Example datasets are now stored in an OS-specific cache location (as determined by appdirs) rather than in the user's home directory. Users should feel free to remove ~/seaborn-data if desired (
- The unit test suite is no longer part of the source or wheel distribution. Seaborn has never had a runtime API for exercising the tests, so this should not have workflow implications (
- Following NEP29, dropped support for Python 3.6 and bumped the minimally-supported versions of the library dependencies.
- Removed the previously-deprecated factorplot along with several previously-deprecated utility functions (iqr, percentiles, pmf_hist, and sort_df).
- Removed the (previously-unused) option to pass additional keyword arguments to