pandas.core.groupby.DataFrameGroupBy.boxplot

DataFrameGroupBy. boxplot ( subplots=True, column=None, fontsize=None, rot=0, grid=True, ax=None, figsize=None, layout=None, sharex=False, sharey=True, backend=None, **kwargs ) [source]

Make box plots from DataFrameGroupBy data.

Parameters

grouped :Grouped DataFrame

subplots :bool

• False - no subplots will be used

• True - create a subplot for each group.

column :column name or list of names, or vector

Can be any valid input to groupby.

fontsize :int or str

rot :label rotation angle

grid :Setting this to True will show the grid

ax :Matplotlib axis object, default None

figsize :A tuple (width, height) in inches

layout :tuple (optional)

The layout of the plot: (rows, columns).

sharex :bool, default False

Whether x-axes will be shared among subplots.

sharey :bool, default True

Whether y-axes will be shared among subplots.

backend :str, default None

Backend to use instead of the backend specified in the option plotting.backend. For instance, ‘matplotlib’. Alternatively, to specify the plotting.backend for the whole session, set pd.options.plotting.backend.

New in version 1.0.0.

**kwargs

All other plotting keyword arguments to be passed to matplotlib’s boxplot function.

Returns

dict of key/value = group key/DataFrame.boxplot return value

or DataFrame.boxplot return value in case subplots=figures=False

Examples

You can create boxplots for grouped data and show them as separate subplots:

>>> import itertools
>>> tuples = [t for t in itertools.product(range(1000), range(4))]
>>> index = pd.MultiIndex.from_tuples(tuples, names=['lvl0', 'lvl1'])
>>> data = np.random.randn(len(index),4)
>>> df = pd.DataFrame(data, columns=list('ABCD'), index=index)
>>> grouped = df.groupby(level='lvl1')
>>> grouped.boxplot(rot=45, fontsize=12, figsize=(8,10))  

The subplots=False option shows the boxplots in a single figure.

>>> grouped.boxplot(subplots=False, rot=45, fontsize=12)