# pandas.core.groupby.DataFrameGroupBy.resample

`DataFrameGroupBy.``resample`(rule, how=None, axis=0, fill_method=None, closed=None, label=None, convention='start', kind=None, loffset=None, limit=None, base=0)

  Convenience method for frequency conversion and resampling of regular time-series data.

  Parameters:

  rule : string

  > the offset string or object representing target conversion

  how : string

  > method for down- or re-sampling, default to ‘mean’ for downsampling

  axis : int, optional, default 0

  fill_method : string, default None

  > fill_method for upsampling

  closed : {‘right’, ‘left’}

  > Which side of bin interval is closed

  label : {‘right’, ‘left’}

  > Which bin edge label to label bucket with

  convention : {‘start’, ‘end’, ‘s’, ‘e’}

  kind : “period”/”timestamp”

  loffset : timedelta

  > Adjust the resampled time labels

  limit : int, default None

  > Maximum size gap to when reindexing with fill_method

  base : int, default 0

  > For frequencies that evenly subdivide 1 day, the “origin” of the aggregated intervals. For example, for ‘5min’ frequency, base could range from 0 through 4. Defaults to 0

  Examples

  Start by creating a series with 9 one minute timestamps.

      >>> index = pd.date_range('1/1/2000', periods=9, freq='T')
      >>> series = pd.Series(range(9), index=index)
      >>> series
      2000-01-01 00:00:00    0
      2000-01-01 00:01:00    1
      2000-01-01 00:02:00    2
      2000-01-01 00:03:00    3
      2000-01-01 00:04:00    4
      2000-01-01 00:05:00    5
      2000-01-01 00:06:00    6
      2000-01-01 00:07:00    7
      2000-01-01 00:08:00    8
      Freq: T, dtype: int64

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

      >>> series.resample('3T', how='sum')
      2000-01-01 00:00:00     3
      2000-01-01 00:03:00    12
      2000-01-01 00:06:00    21
      Freq: 3T, dtype: int64

  Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. Please note that the value in the bucket used as the label is not included in the bucket, which it labels. For example, in the original series the bucket `2000-01-01 00:03:00` contains the value 3, but the summed value in the resampled bucket with the label``2000-01-01 00:03:00`` does not include 3 (if it did, the summed value would be 6, not 3). To include this value close the right side of the bin interval as illustrated in the example below this one.

      >>> series.resample('3T', how='sum', label='right')
      2000-01-01 00:03:00     3
      2000-01-01 00:06:00    12
      2000-01-01 00:09:00    21
      Freq: 3T, dtype: int64

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

      >>> series.resample('3T', how='sum', label='right', closed='right')
      2000-01-01 00:00:00     0
      2000-01-01 00:03:00     6
      2000-01-01 00:06:00    15
      2000-01-01 00:09:00    15
      Freq: 3T, dtype: int64

  Upsample the series into 30 second bins.

      >>> series.resample('30S')[0:5] #select first 5 rows
      2000-01-01 00:00:00     0
      2000-01-01 00:00:30   NaN
      2000-01-01 00:01:00     1
      2000-01-01 00:01:30   NaN
      2000-01-01 00:02:00     2
      Freq: 30S, dtype: float64

  Upsample the series into 30 second bins and fill the `NaN` values using the `pad` method.

      >>> series.resample('30S', fill_method='pad')[0:5]
      2000-01-01 00:00:00    0
      2000-01-01 00:00:30    0
      2000-01-01 00:01:00    1
      2000-01-01 00:01:30    1
      2000-01-01 00:02:00    2
      Freq: 30S, dtype: int64

  Upsample the series into 30 second bins and fill the `NaN` values using the `bfill` method.

      >>> series.resample('30S', fill_method='bfill')[0:5]
      2000-01-01 00:00:00    0
      2000-01-01 00:00:30    1
      2000-01-01 00:01:00    1
      2000-01-01 00:01:30    2
      2000-01-01 00:02:00    2
      Freq: 30S, dtype: int64

  Pass a custom function to `how`.

      >>> def custom_resampler(array_like):
      ...     return np.sum(array_like)+5

      >>> series.resample('3T', how=custom_resampler)
      2000-01-01 00:00:00     8
      2000-01-01 00:03:00    17
      2000-01-01 00:06:00    26
      Freq: 3T, dtype: int64