SeriesGroupBy.resample.html)
    * [ pandas.core.groupby.SeriesGroupBy.rolling ](pandas.core.groupby.SeriesGroupBy.rolling.html)
    * [ pandas.core.groupby.SeriesGroupBy.sample ](pandas.core.groupby.SeriesGroupBy.sample.html)
    * [ pandas.core.groupby.SeriesGroupBy.sem ](pandas.core.groupby.SeriesGroupBy.sem.html)
    * [ pandas.core.groupby.SeriesGroupBy.shift ](pandas.core.groupby.SeriesGroupBy.shift.html)
    * [ pandas.core.groupby.SeriesGroupBy.size ](pandas.core.groupby.SeriesGroupBy.size.html)
    * [ pandas.core.groupby.SeriesGroupBy.skew ](pandas.core.groupby.SeriesGroupBy.skew.html)
    * [ pandas.core.groupby.SeriesGroupBy.std ](pandas.core.groupby.SeriesGroupBy.std.html)
    * [ pandas.core.groupby.SeriesGroupBy.sum ](pandas.core.groupby.SeriesGroupBy.sum.html)
    * [ pandas.core.groupby.SeriesGroupBy.var ](pandas.core.groupby.SeriesGroupBy.var.html)
    * [ pandas.core.groupby.SeriesGroupBy.tail ](pandas.core.groupby.SeriesGroupBy.tail.html)
    * [ pandas.core.groupby.SeriesGroupBy.take ](pandas.core.groupby.SeriesGroupBy.take.html)
    * [ pandas.core.groupby.SeriesGroupBy.value_counts ](pandas.core.groupby.SeriesGroupBy.value_counts.html)
    * pandas.core.groupby.DataFrameGroupBy.boxplot 
    * [ pandas.core.groupby.DataFrameGroupBy.hist ](pandas.core.groupby.DataFrameGroupBy.hist.html)
    * [ pandas.core.groupby.SeriesGroupBy.hist ](pandas.core.groupby.SeriesGroupBy.hist.html)
    * [ pandas.core.groupby.DataFrameGroupBy.plot ](pandas.core.groupby.DataFrameGroupBy.plot.html)
    * [ pandas.core.groupby.SeriesGroupBy.plot ](pandas.core.groupby.SeriesGroupBy.plot.html)
  * [ Resampling ](../resampling.html)
  * [ Style ](../style.html)
  * [ Plotting ](../plotting.html)
  * [ Options and settings ](../options.html)
  * [ Extensions ](../extensions.html)
  * [ Testing ](../testing.html)
  * [ Missing values ](../missing_value.html)

  * [ __ ](../../index.html)
  * [ API reference ](../index.html)
  * [ GroupBy ](../groupby.html)
  * pandas.core.... 

#  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]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/plotting/_core.py#L548-L636) #

    

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, âmatplotlibâ. 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 matplotlibâ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.Mul