DataFrameGroupBy.
resample
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.
The offset string or object representing target grouper conversion.
Possible arguments are how, fill_method, limit, kind and on, and other arguments of TimeGrouper.
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