y.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.boxplot.html)
    * [ 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.resample  #

DataFrameGroupBy.  resample  (  _ rule  _ , _ *  args  _ , _ include_groups  =
True  _ , _ **  kwargs  _ )  [ [source]  ](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/groupby.py#L3614-L3745) #

    

Provide resampling when using a TimeGrouper.

Given a grouper, the function resamples it according to a string âstringâ
-> âfrequencyâ.

See the [ frequency aliases  ](../../user_guide/timeseries.html#timeseries-
offset-aliases) documentation for more details.

Parameters  :

    

**rule** str or DateOffset

    

The offset string or object representing target grouper conversion.

***args**

    

Possible arguments are  how  ,  fill_method  ,  limit  ,  kind  and  on  , and
other arguments of  TimeGrouper  .

**include_groups** bool, default True

    

When True, will attempt to include the groupings in the operation in the case
that they are columns of the DataFrame. If this raises a TypeError, the result
will be computed with the groupings excluded. When False, the groupings will
be excluded when applying ` func  ` .

New in version 2.2.0.

Deprecated since version 2.2.0:  Setting include_groups to True is deprecated.
Only the value False will be allowed in a future version of pandas.

****kwargs**

    

Possible arguments are  how  ,  fill_method  ,  limit  ,  kind  and  on  , and
other arguments of  TimeGrouper  .

Returns  :

    

pandas.api.typing.DatetimeIndexResamplerGroupby,

    
pandas.api.typing.PeriodIndexResamplerGroupby, or

    
pandas.api.typing.TimedeltaIndexResamplerGroupby

    

Return a new groupby object, with type depending on the data being resampled.

See also

` Grouper  `

    

Specify a frequency to resample with when grouping by a key.

` DatetimeIndex.resample  `

    

Frequency conversion and resampling of time series.

Examples

    
    
    >>> idx = pd.date_range('1/1/2000', periods=4, freq='min')
    >>> df = pd.DataFrame(data=4 * [rang