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# pandas.core.groupby.DataFrameGroupBy.resample

DataFrameGroupBy.resample(rule, *args, include_groups=True, **kwargs)

  Provide resampling when using a TimeGrouper.

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

  See the frequency 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`.

      Added 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 * [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('3min', include_groups=False).sum()
                               b
      a
      0   2000-01-01 00:00:00  2
          2000-01-01 00:03:00  1
      5   2000-01-01 00:00:00  1

  Upsample the series into 30 second bins.

      >>> df.groupby('a').resample('30s', include_groups=False).sum()
                          b
      a
      0   2000-01-01 00:00:00  1
          2000-01-01 00:00:30  0
          2000-01-01 00:01:00  1
          2000-01-01 00:01:30  0
          2000-01-01 00:02:00  0
          2000-01-01 00:02:30  0
          2000-01-01 00:03:00  1
      5   2000-01-01 00:02:00  1

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

      >>> df.groupby('a').resample('ME', include_groups=False).sum()
                  b
      a
      0   2000-01-31  3
      5   2000-01-31  1

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

      >>> (
      ...     df.groupby('a')
      ...     .resample('3min', closed='right', include_groups=False)
      ...     .sum()
      ... )
                               b
      a
      0   1999-12-31 23:57:00  1
          2000-01-01 00:00:00  2
      5   2000-01-01 00:00:00  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('3min', closed='right', label='right', include_groups=False)
      ...     .sum()
      ... )
                               b
      a
      0   2000-01-01 00:00:00  1
          2000-01-01 00:03:00  2
      5   2000-01-01 00:03:00  1