v0.12.0
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.
Keyword-only arguments
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 (2804).
Modernization of categorical scatterplots
This release begins the process of modernizing the categorical plots, beginning with stripplot and swarmplot. These functions are sporting some enhancements that alleviate a few long-running frustrations (2413, 2447):
- 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 x or y).
- 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:
- In
stripplot, a "strip" with a single observation will be plotted without jitter (2413)
- In
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 (2443).
- In
swarmplot, the proportion of points that must overlap before issuing a warning can now be controlled with the warn_thresh parameter (2447).
- In
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 (2443).
More flexible errorbars
Increased the flexibility of what can be shown by the internally-calculated errorbars for lineplot, barplot, and pointplot.
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 (2407, 2866).
Other updates
- It is now possible to aggregate / sort a
lineplot along the y axis using orient="y" (2854).
- Made it easier to customize
FacetGrid / PairGrid / 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 (2926).
- Added
FacetGrid.tick_params and PairGrid.tick_params to customize the appearance of the ticks, tick labels, and gridlines of all subplots at once (2944).
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