pandas.Series.resample — pandas 2.2.3 documentation (original) (raw)

Series.resample(rule, axis=<no_default>, closed=None, label=None, convention=<no_default>, kind=<no_default>, on=None, level=None, origin='start_day', offset=None, group_keys=False)[source]#

Resample time-series data.

Convenience method for frequency conversion and resampling of time series. The object must have a datetime-like index (DatetimeIndex, PeriodIndex, or TimedeltaIndex), or the caller must pass the label of a datetime-like series/index to the on/level keyword parameter.

Parameters:

ruleDateOffset, Timedelta or str

The offset string or object representing target conversion.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Which axis to use for up- or down-sampling. For Series this parameter is unused and defaults to 0. Must beDatetimeIndex, TimedeltaIndex or PeriodIndex.

Deprecated since version 2.0.0: Use frame.T.resample(…) instead.

closed{‘right’, ‘left’}, default None

Which side of bin interval is closed. The default is ‘left’ for all frequency offsets except for ‘ME’, ‘YE’, ‘QE’, ‘BME’, ‘BA’, ‘BQE’, and ‘W’ which all have a default of ‘right’.

label{‘right’, ‘left’}, default None

Which bin edge label to label bucket with. The default is ‘left’ for all frequency offsets except for ‘ME’, ‘YE’, ‘QE’, ‘BME’, ‘BA’, ‘BQE’, and ‘W’ which all have a default of ‘right’.

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

For PeriodIndex only, controls whether to use the start or end of rule.

Deprecated since version 2.2.0: Convert PeriodIndex to DatetimeIndex before resampling instead.

kind{‘timestamp’, ‘period’}, optional, default None

Pass ‘timestamp’ to convert the resulting index to aDateTimeIndex or ‘period’ to convert it to a PeriodIndex. By default the input representation is retained.

Deprecated since version 2.2.0: Convert index to desired type explicitly instead.

onstr, optional

For a DataFrame, column to use instead of index for resampling. Column must be datetime-like.

levelstr or int, optional

For a MultiIndex, level (name or number) to use for resampling. level must be datetime-like.

originTimestamp or str, default ‘start_day’

The timestamp on which to adjust the grouping. The timezone of origin must match the timezone of the index. If string, must be one of the following:

Added in version 1.3.0.

Note

Only takes effect for Tick-frequencies (i.e. fixed frequencies like days, hours, and minutes, rather than months or quarters).

offsetTimedelta or str, default is None

An offset timedelta added to the origin.

group_keysbool, default False

Whether to include the group keys in the result index when using.apply() on the resampled object.

Added in version 1.5.0: Not specifying group_keys will retain values-dependent behavior from pandas 1.4 and earlier (see pandas 1.5.0 Release notes for examples).

Changed in version 2.0.0: group_keys now defaults to False.

Returns:

pandas.api.typing.Resampler

Resampler object.

See also

Series.resample

Resample a Series.

DataFrame.resample

Resample a DataFrame.

groupby

Group Series/DataFrame by mapping, function, label, or list of labels.

asfreq

Reindex a Series/DataFrame with the given frequency without grouping.

Notes

See the user guidefor more.

To learn more about the offset strings, please see this link.

Examples

Start by creating a series with 9 one minute timestamps.

index = pd.date_range('1/1/2000', periods=9, freq='min') 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: min, dtype: int64

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

series.resample('3min').sum() 2000-01-01 00:00:00 3 2000-01-01 00:03:00 12 2000-01-01 00:06:00 21 Freq: 3min, 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 00:03:00does not include 3 (if it did, the summed value would be 6, not 3).

series.resample('3min', label='right').sum() 2000-01-01 00:03:00 3 2000-01-01 00:06:00 12 2000-01-01 00:09:00 21 Freq: 3min, dtype: int64

To include this value close the right side of the bin interval, as shown below.

series.resample('3min', label='right', closed='right').sum() 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: 3min, dtype: int64

Upsample the series into 30 second bins.

series.resample('30s').asfreq()[0:5] # Select first 5 rows 2000-01-01 00:00:00 0.0 2000-01-01 00:00:30 NaN 2000-01-01 00:01:00 1.0 2000-01-01 00:01:30 NaN 2000-01-01 00:02:00 2.0 Freq: 30s, dtype: float64

Upsample the series into 30 second bins and fill the NaNvalues using the ffill method.

series.resample('30s').ffill()[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 theNaN values using the bfill method.

series.resample('30s').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 via apply

def custom_resampler(arraylike): ... return np.sum(arraylike) + 5 ... series.resample('3min').apply(custom_resampler) 2000-01-01 00:00:00 8 2000-01-01 00:03:00 17 2000-01-01 00:06:00 26 Freq: 3min, dtype: int64

For DataFrame objects, the keyword on can be used to specify the column instead of the index for resampling.

d = {'price': [10, 11, 9, 13, 14, 18, 17, 19], ... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]} df = pd.DataFrame(d) df['week_starting'] = pd.date_range('01/01/2018', ... periods=8, ... freq='W') df price volume week_starting 0 10 50 2018-01-07 1 11 60 2018-01-14 2 9 40 2018-01-21 3 13 100 2018-01-28 4 14 50 2018-02-04 5 18 100 2018-02-11 6 17 40 2018-02-18 7 19 50 2018-02-25 df.resample('ME', on='week_starting').mean() price volume week_starting 2018-01-31 10.75 62.5 2018-02-28 17.00 60.0

For a DataFrame with MultiIndex, the keyword level can be used to specify on which level the resampling needs to take place.

days = pd.date_range('1/1/2000', periods=4, freq='D') d2 = {'price': [10, 11, 9, 13, 14, 18, 17, 19], ... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]} df2 = pd.DataFrame( ... d2, ... index=pd.MultiIndex.from_product( ... [days, ['morning', 'afternoon']] ... ) ... ) df2 price volume 2000-01-01 morning 10 50 afternoon 11 60 2000-01-02 morning 9 40 afternoon 13 100 2000-01-03 morning 14 50 afternoon 18 100 2000-01-04 morning 17 40 afternoon 19 50 df2.resample('D', level=0).sum() price volume 2000-01-01 21 110 2000-01-02 22 140 2000-01-03 32 150 2000-01-04 36 90

If you want to adjust the start of the bins based on a fixed timestamp:

start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00' rng = pd.date_range(start, end, freq='7min') ts = pd.Series(np.arange(len(rng)) * 3, index=rng) ts 2000-10-01 23:30:00 0 2000-10-01 23:37:00 3 2000-10-01 23:44:00 6 2000-10-01 23:51:00 9 2000-10-01 23:58:00 12 2000-10-02 00:05:00 15 2000-10-02 00:12:00 18 2000-10-02 00:19:00 21 2000-10-02 00:26:00 24 Freq: 7min, dtype: int64

ts.resample('17min').sum() 2000-10-01 23:14:00 0 2000-10-01 23:31:00 9 2000-10-01 23:48:00 21 2000-10-02 00:05:00 54 2000-10-02 00:22:00 24 Freq: 17min, dtype: int64

ts.resample('17min', origin='epoch').sum() 2000-10-01 23🔞00 0 2000-10-01 23:35:00 18 2000-10-01 23:52:00 27 2000-10-02 00:09:00 39 2000-10-02 00:26:00 24 Freq: 17min, dtype: int64

ts.resample('17min', origin='2000-01-01').sum() 2000-10-01 23:24:00 3 2000-10-01 23:41:00 15 2000-10-01 23:58:00 45 2000-10-02 00:15:00 45 Freq: 17min, dtype: int64

If you want to adjust the start of the bins with an offset Timedelta, the two following lines are equivalent:

ts.resample('17min', origin='start').sum() 2000-10-01 23:30:00 9 2000-10-01 23:47:00 21 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 17min, dtype: int64

ts.resample('17min', offset='23h30min').sum() 2000-10-01 23:30:00 9 2000-10-01 23:47:00 21 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 17min, dtype: int64

If you want to take the largest Timestamp as the end of the bins:

ts.resample('17min', origin='end').sum() 2000-10-01 23:35:00 0 2000-10-01 23:52:00 18 2000-10-02 00:09:00 27 2000-10-02 00:26:00 63 Freq: 17min, dtype: int64

In contrast with the start_day, you can use end_day to take the ceiling midnight of the largest Timestamp as the end of the bins and drop the bins not containing data:

ts.resample('17min', origin='end_day').sum() 2000-10-01 23:38:00 3 2000-10-01 23:55:00 15 2000-10-02 00:12:00 45 2000-10-02 00:29:00 45 Freq: 17min, dtype: int64