pandas.core.groupby.SeriesGroupBy.resample#

SeriesGroupBy.resample(rule, *args, **kwargs)[source]#

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, **kwargs: Possible arguments are how, fill_method, limit, kind and on, and other arguments of TimeGrouper.

Returns

Grouper: 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