Boxplot fails for categories that have only one sample

[friso]

When creating a boxplot plotting some continuous value against a categorical X axis, it fails when for any category there is only one sample in the data frame.

E.g. this works fine

df = pd.DataFrame(
    {
        'weight': np.random.normal(size=20),
        # Creating two categories, one with 18 samples, one with 2 samples
        'category': pd.Categorical(18 * [0] + 2 * [1], categories=[0,1], ordered=True)
    }
)

(
    ggplot(df, aes(x='category', y='weight'))
    + geom_boxplot()
)

Producing the following plot:

However, this example fails:

df = pd.DataFrame(
    {
        'weight': np.random.normal(size=20),
        # Creating two categories, one with 19 samples, one with 1 sample
        'category': pd.Categorical(19 * [0] + 1 * [1], categories=[0,1], ordered=True)
    }
)

(
    ggplot(df, aes(x='category', y='weight'))
    + geom_boxplot()
)

Below is the trace from the error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~/.virtualenvs/pandas/lib/python3.6/site-packages/IPython/core/formatters.py in __call__(self, obj)
    691                 type_pprinters=self.type_printers,
    692                 deferred_pprinters=self.deferred_printers)
--> 693             printer.pretty(obj)
    694             printer.flush()
    695             return stream.getvalue()

~/.virtualenvs/pandas/lib/python3.6/site-packages/IPython/lib/pretty.py in pretty(self, obj)
    378                             if callable(meth):
    379                                 return meth(obj, self, cycle)
--> 380             return _default_pprint(obj, self, cycle)
    381         finally:
    382             self.end_group()

~/.virtualenvs/pandas/lib/python3.6/site-packages/IPython/lib/pretty.py in _default_pprint(obj, p, cycle)
    493     if _safe_getattr(klass, '__repr__', None) is not object.__repr__:
    494         # A user-provided repr. Find newlines and replace them with p.break_()
--> 495         _repr_pprint(obj, p, cycle)
    496         return
    497     p.begin_group(1, '<')

~/.virtualenvs/pandas/lib/python3.6/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
    691     """A pprint that just redirects to the normal repr function."""
    692     # Find newlines and replace them with p.break_()
--> 693     output = repr(obj)
    694     for idx,output_line in enumerate(output.splitlines()):
    695         if idx:

~/.virtualenvs/pandas/lib/python3.6/site-packages/plotnine/ggplot.py in __repr__(self)
     81         Print/show the plot
     82         """
---> 83         self.draw()
     84         plt.show()
     85         return '<ggplot: (%d)>' % self.__hash__()

~/.virtualenvs/pandas/lib/python3.6/site-packages/plotnine/ggplot.py in draw(self)
    138         # assign a default theme
    139         self = deepcopy(self)
--> 140         self._build()
    141 
    142         # If no theme we use the default

~/.virtualenvs/pandas/lib/python3.6/site-packages/plotnine/ggplot.py in _build(self)
    234 
    235         # Apply and map statistics
--> 236         layers.compute_statistic(layout)
    237         layers.map_statistic(self)
    238 

~/.virtualenvs/pandas/lib/python3.6/site-packages/plotnine/layer.py in compute_statistic(self, layout)
     92     def compute_statistic(self, layout):
     93         for l in self:
---> 94             l.compute_statistic(layout)
     95 
     96     def map_statistic(self, plot):

~/.virtualenvs/pandas/lib/python3.6/site-packages/plotnine/layer.py in compute_statistic(self, layout)
    369         data = self.stat.use_defaults(data)
    370         data = self.stat.setup_data(data)
--> 371         data = self.stat.compute_layer(data, params, layout)
    372         self.data = data
    373 

~/.virtualenvs/pandas/lib/python3.6/site-packages/plotnine/stats/stat.py in compute_layer(cls, data, params, layout)
    194             return cls.compute_panel(pdata, pscales, **params)
    195 
--> 196         return groupby_apply(data, 'PANEL', fn)
    197 
    198     @classmethod

~/.virtualenvs/pandas/lib/python3.6/site-packages/plotnine/utils.py in groupby_apply(df, cols, func, *args, **kwargs)
    615         # do not mark d as a slice of df i.e no SettingWithCopyWarning
    616         d.is_copy = None
--> 617         lst.append(func(d, *args, **kwargs))
    618     return pd.concat(lst, axis=axis, ignore_index=True)
    619 

~/.virtualenvs/pandas/lib/python3.6/site-packages/plotnine/stats/stat.py in fn(pdata)
    192                 return pdata
    193             pscales = layout.get_scales(pdata['PANEL'].iat[0])
--> 194             return cls.compute_panel(pdata, pscales, **params)
    195 
    196         return groupby_apply(data, 'PANEL', fn)

~/.virtualenvs/pandas/lib/python3.6/site-packages/plotnine/stats/stat.py in compute_panel(cls, data, scales, **params)
    221         for _, old in data.groupby('group'):
    222             old.is_copy = None
--> 223             new = cls.compute_group(old, scales, **params)
    224             unique = uniquecols(old)
    225             missing = unique.columns.difference(new.columns)

~/.virtualenvs/pandas/lib/python3.6/site-packages/plotnine/stats/stat_boxplot.py in compute_group(cls, data, scales, **params)
     69         labels = ['x', 'y']
     70         X = np.array(data[labels])
---> 71         res = boxplot_stats(X, whis=params['coef'], labels=labels)[1]
     72         try:
     73             n = data['weight'].sum()

~/.virtualenvs/pandas/lib/python3.6/site-packages/matplotlib/cbook.py in boxplot_stats(X, whis, bootstrap, labels, autorange)
   1998         labels = repeat(None)
   1999     elif len(labels) != ncols:
-> 2000         raise ValueError("Dimensions of labels and X must be compatible")
   2001 
   2002     input_whis = whis

ValueError: Dimensions of labels and X must be compatible

This can be worked around by removing the classes with only a single sample from the data frame and overlaying only those with a geom_point(), as there is no interesting boxplot for them anyway, but it's a bit of a hassle and would be nicer if it just worked.

[has2k1]

This was fixed in matplotlib/matplotlib#8116, not yet released, still in the master branch.

[friso]

Oh, right. Apologies. I didn't immediately realise this was a (existing) matplotlib issue. Will close this.

[has2k1]

I'll reopen the issue as it is still present if the package is installed as recommended.