def resample(self, rule, *args, **kwargs):
from pandas.core.resample import get_resampler_for_grouping<EOL>return get_resampler_for_grouping(self, rule, *args, **kwargs)<EOL>
Provide resampling when using a TimeGrouper.

Given a grouper, the function resamples it according to a string
"string" -> "frequency".

See the :ref:`frequency aliases <timeseries.offset_aliases>`
documentation for more details.

Parameters
----------
rule : str or DateOffset
    The offset string or object representing target grouper conversion.
*args, **kwargs
    Possible arguments are `how`, `fill_method`, `limit`, `kind` and
    `on`, and other arguments of `TimeGrouper`.

Returns
-------
Grouper
    Return a new grouper with our resampler appended.

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='T')
>>> df = pd.DataFrame(data=4 * [range(2)],
...                   index=idx,
...                   columns=['a', 'b'])
>>> df.iloc[2, 0] = 5
>>> df
                    a  b
2000-01-01 00:00:00  0  1
2000-01-01 00:01:00  0  1
2000-01-01 00:02:00  5  1
2000-01-01 00:03:00  0  1

Downsample the DataFrame into 3 minute bins and sum the values of
the timestamps falling into a bin.

>>> df.groupby('a').resample('3T').sum()
                         a  b
a
0   2000-01-01 00:00:00  0  2
    2000-01-01 00:03:00  0  1
5   2000-01-01 00:00:00  5  1

Upsample the series into 30 second bins.

>>> df.groupby('a').resample('30S').sum()
                    a  b
a
0   2000-01-01 00:00:00  0  1
    2000-01-01 00:00:30  0  0
    2000-01-01 00:01:00  0  1
    2000-01-01 00:01:30  0  0
    2000-01-01 00:02:00  0  0
    2000-01-01 00:02:30  0  0
    2000-01-01 00:03:00  0  1
5   2000-01-01 00:02:00  5  1

Resample by month. Values are assigned to the month of the period.

>>> df.groupby('a').resample('M').sum()
            a  b
a
0   2000-01-31  0  3
5   2000-01-31  5  1

Downsample the series into 3 minute bins as above, but close the right
side of the bin interval.

>>> df.groupby('a').resample('3T', closed='right').sum()
                         a  b
a
0   1999-12-31 23:57:00  0  1
    2000-01-01 00:00:00  0  2
5   2000-01-01 00:00:00  5  1

Downsample the series into 3 minute bins and close the right side of
the bin interval, but label each bin using the right edge instead of
the left.

>>> df.groupby('a').resample('3T', closed='right', label='right').sum()
                         a  b
a
0   2000-01-01 00:00:00  0  1
    2000-01-01 00:03:00  0  2
5   2000-01-01 00:03:00  5  1

Add an offset of twenty seconds.

>>> df.groupby('a').resample('3T', loffset='20s').sum()
                       a  b
a
0   2000-01-01 00:00:20  0  2
    2000-01-01 00:03:20  0  1
5   2000-01-01 00:00:20  5  1