pandas.core.groupby.DataFrameGroupBy.resample — pandas 2.2.3 documentation (original) (raw)

DataFrameGroupBy.resample(rule, *args, include_groups=True, **kwargs)[source]#

Provide resampling when using a TimeGrouper.

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

See the frequency aliasesdocumentation 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 andon, 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.

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 andon, 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