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:float 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.

**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)