MEP28: Remove Complexity from Axes.boxplot#
Branches and Pull requests#
The following lists any open PRs or branches related to this MEP:
Deprecate redundant statistical kwargs in
Deprecate redundant style options in
Deprecate passings 2D NumPy arrays as input: None
Add pre- & post-processing options to
Remove redundant statistical kwargs in
Remove redundant style options in
Remaining items that arise through discussion: None
Over the past few releases, the
Axes.boxplot method has grown in
complexity to support fully customizable artist styling and statistical
computation. This lead to
Axes.boxplot being split off into multiple
parts. The statistics needed to draw a boxplot are computed in
cbook.boxplot_stats, while the actual artists are drawn by
The original method,
Axes.boxplot remains as the most public API that
handles passing the user-supplied data to
the results to
Axes.bxp, and pre-processing style information for
each facet of the boxplot plots.
This MEP will outline a path forward to rollback the added complexity and simplify the API while maintaining reasonable backwards compatibility.
Axes.boxplot method accepts parameters that allow the
users to specify medians and confidence intervals for each box that
will be drawn in the plot. These were provided so that advanced users
could provide statistics computed in a different fashion that the simple
method provided by matplotlib. However, handling this input requires
complex logic to make sure that the forms of the data structure match what
needs to be drawn. At the moment, that logic contains 9 separate if/else
statements nested up to 5 levels deep with a for loop, and may raise up to 2 errors.
These parameters were added prior to the creation of the
which draws boxplots from a list of dictionaries containing the relevant
statistics. Matplotlib also provides a function that computes these
cbook.boxplot_stats. Note that advanced users can now
either a) write their own function to compute the stats required by
Axes.bxp, or b) modify the output returned by
to fully customize the position of the artists of the plots. With this
flexibility, the parameters to manually specify only the medians and their
confidences intervals remain for backwards compatibility.
Around the same time that the two roles of
Axes.boxplot were split into
cbook.boxplot_stats for computation and
Axes.bxp for drawing, both
Axes.bxp were written to accept parameters that
individually toggle the drawing of all components of the boxplots, and
parameters that individually configure the style of those artists. However,
to maintain backwards compatibility, the
sym parameter (previously used
to specify the symbol of the fliers) was retained. This parameter itself
requires fairly complex logic to reconcile the
sym parameters with the
flierprops parameter at the default style specified by
This MEP seeks to dramatically simplify the creation of boxplots for novice and advanced users alike. Importantly, the changes proposed here will also be available to downstream packages like seaborn, as seaborn smartly allows users to pass arbitrary dictionaries of parameters through the seaborn API to the underlying matplotlib functions.
This will be achieved in the following way:
cbook.boxplot_statswill be modified to allow pre- and post- computation transformation functions to be passed in (e.g.,
np.expfor lognormally distributed data)
Axes.boxplotwill be modified to also accept and naïvely pass them to
cbook.boxplots_stats(Alt: pass the stat function and a dict of its optional parameters).
Outdated parameters from
Axes.boxplotwill be deprecated and later removed.
Since the limits of the whiskers are computed arithmetically, there is an implicit assumption of normality in box and whisker plots. This primarily affects which data points are classified as outliers.
Allowing transformations to the data and the results used to draw boxplots will allow users to opt-out of that assumption if the data are known to not fit a normal distribution.
Below is an example of how
Axes.boxplot classifies outliers of lognormal
data differently depending one these types of transforms.
import numpy as np import matplotlib.pyplot as plt from matplotlib import cbook np.random.seed(0) fig, ax = plt.subplots(figsize=(4, 6)) ax.set_yscale('log') data = np.random.lognormal(-1.75, 2.75, size=37) stats = cbook.boxplot_stats(data, labels=['arithmetic']) logstats = cbook.boxplot_stats(np.log(data), labels=['log-transformed']) for lsdict in logstats: for key, value in lsdict.items(): if key != 'label': lsdict[key] = np.exp(value) stats.extend(logstats) ax.bxp(stats) fig.show()
Passing transform functions to
This MEP proposes that two parameters (e.g.,
transform_out be added to the cookbook function that computes the
statistics for the boxplot function. These will be optional keyword-only
arguments and can easily be set to
lambda x: x as a no-op when omitted
by the user. The
transform_in function will be applied to the data
boxplot_stats function loops through each subset of the data
passed to it. After the list of statistics dictionaries are computed the
transform_out function is applied to each value in the dictionaries.
These transformations can then be added to the call signature of
Axes.boxplot with little impact to that method's complexity. This is
because they can be directly passed to
Axes.boxplot could be modified to accept an optional
statistical function kwarg and a dictionary of parameters to be directly
passed to it.
At this point in the implementation users and external libraries like
seaborn would have complete control via the
Axes.boxplot method. More
importantly, at the very least, seaborn would require no changes to its
API to allow users to take advantage of these new options.
Simplifications to the
Axes.boxplot API and other functions#
Simplifying the boxplot method consists primarily of deprecating and then
removing the redundant parameters. Optionally, a next step would include
rectifying minor terminological inconsistencies between
The parameters to be deprecated and removed include:
usermedians- processed by 10 SLOC, 3
conf_intervals- handled by 15 SLOC, 6
sym- processed by 12 SLOC, 4
sym option allows all code in handling the remaining
styling parameters to be moved to
Axes.bxp. This doesn't remove
any complexity, but does reinforce the single responsibility principle
notch parameter could be renamed
to be consistent with
Axes.bxp. This kind of cleanup could be taken
a step further and the
be rolled into the kwargs passed to the new
Implementation of this MEP would eventually result in the backwards
incompatible deprecation and then removal of the keyword parameters
sym. Cursory searches on
GitHub indicated that
conf_intervals are used by
few users, who all seem to have a very strong knowledge of matplotlib.
A robust deprecation cycle should provide sufficient time for these
users to migrate to a new API.
sym however, may have a much broader reach into
the matplotlib userbase.
An accelerated timeline could look like the following:
v2.0.1 add transforms to
cbook.boxplots_stats, expose in
v2.1.0 Initial Deprecations , and using 2D NumPy arrays as input
Using 2D NumPy arrays as input. The semantics around 2D arrays are generally confusing.
notchin favor of
shownotchesto be consistent with other parameters and
move all style and artist toggling logic to
Axes.boxplotis little more than a broker between
Anticipated Impacts to Users#
As described above deprecating
will likely impact few users. Those who will be impacted are almost
certainly advanced users who will be able to adapt to the change.
sym option may import more users and effort should
be taken to collect community feedback on this.
Anticipated Impacts to Downstream Libraries#
The source code (GitHub master as of 2016-10-17) was inspected for
seaborn and python-ggplot to see if these changes would impact their
use. None of the parameters nominated for removal in this MEP are used by
seaborn. The seaborn APIs that use matplotlib's boxplot function allow
user's to pass arbitrary
**kwargs through to matplotlib's API. Thus
seaborn users with modern matplotlib installations will be able to take
full advantage of any new features added as a result of this MEP.
Python-ggplot has implemented its own function to draw boxplots. Therefore, no impact can come to it as a result of implementing this MEP.
Variations on the theme#
This MEP can be divided into a few loosely coupled components:
Allowing pre- and post-computation transformation function in
Exposing that transformation in the
Removing redundant statistical options in
Shifting all styling parameter processing from
With this approach, #2 depends and #1, and #4 depends on #3.
There are two possible approaches to #2. The first and most direct would
be to mirror the new
transform_out parameters of
Axes.boxplot and pass them directly.
The second approach would be to add
Axes.boxplot. Under this implementation, the default
statfxn would be
cbook.boxplot_stats, but users could
pass their own function. Then
then be passed as elements of the
def boxplot_stats(data, ..., transform_in=None, transform_out=None): if transform_in is None: transform_in = lambda x: x if transform_out is None: transform_out = lambda x: x output =  for _d in data: d = transform_in(_d) stat_dict = do_stats(d) for key, value in stat_dict.item(): if key != 'label': stat_dict[key] = transform_out(value) output.append(d) return output class Axes(...): def boxplot_option1(data, ..., transform_in=None, transform_out=None): stats = cbook.boxplot_stats(data, ..., transform_in=transform_in, transform_out=transform_out) return self.bxp(stats, ...) def boxplot_option2(data, ..., statfxn=None, **statopts): if statfxn is None: statfxn = boxplot_stats stats = statfxn(data, **statopts) return self.bxp(stats, ...)
Both cases would allow users to do the following:
fig, ax1 = plt.subplots() artists1 = ax1.boxplot_optionX(data, transform_in=np.log, transform_out=np.exp)
But Option Two lets a user write a completely custom stat function
my_box_stats) with fancy BCA confidence intervals and the
whiskers set differently depending on some attribute of the data.
This is available under the current API:
fig, ax1 = plt.subplots() my_stats = my_box_stats(data, bootstrap_method='BCA', whisker_method='dynamic') ax1.bxp(my_stats)
And would be more concise with Option Two
fig, ax = plt.subplots() statopts = dict(transform_in=np.log, transform_out=np.exp) ax.boxplot(data, ..., **statopts)
Users could also pass their own function to compute the stats:
fig, ax1 = plt.subplots() ax1.boxplot(data, statfxn=my_box_stats, bootstrap_method='BCA', whisker_method='dynamic')
From the examples above, Option Two seems to have only marginal benefit, but in the context of downstream libraries like seaborn, its advantage is more apparent as the following would be possible without any patches to seaborn:
import seaborn tips = seaborn.load_data('tips') g = seaborn.factorplot(x="day", y="total_bill", hue="sex", data=tips, kind='box', palette="PRGn", shownotches=True, statfxn=my_box_stats, bootstrap_method='BCA', whisker_method='dynamic')
This type of flexibility was the intention behind splitting the overall
boxplot API in the current three functions. In practice however, downstream
libraries like seaborn support versions of matplotlib dating back well
before the split. Thus, adding just a bit more flexibility to the
Axes.boxplot could expose all the functionality to users of the
downstream libraries with modern matplotlib installation without intervention
from the downstream library maintainers.
Another obvious alternative would be to omit the added pre- and post-
computation transform functionality in
Axes.boxplot, and simply remove the redundant statistical and style
parameters as described above.
As with many things in life, doing nothing is an option here. This means
we simply advocate for users and downstream libraries to take advantage
of the split between
Axes.bxp and let
them decide how to provide an interface to that.