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:

rulestr 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_groupsbool, 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 * [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