pyspark.pandas.Series.resample#

Series.resample(rule, closed=None, label=None, on=None)[source]#

Resample time-series data.

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

New in version 3.4.0.

Parameters

rulestr: The offset string or object representing target conversion. Currently, supported units are {‘YE’, ‘A’, ‘ME’, ‘D’, ‘h’, ‘min’, ‘MIN’, ‘s’}.
closed{{‘right’, ‘left’}}, default None: Which side of bin interval is closed. The default is ‘left’ for all frequency offsets except for ‘A’, ‘YE’ and ‘ME’ 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 ‘A’, ‘YE’ and ‘ME’ which all have a default of ‘right’.
onSeries, optional: For a DataFrame, column to use instead of index for resampling. Column must be datetime-like.

Returns

SeriesResampler

See also

DataFrame.resample: Resample a DataFrame.
groupby: Group by mapping, function, label, or list of labels.

Examples

Start by creating a series with 9 one minute timestamps.

>>> index = pd.date_range('1/1/2000', periods=9, freq='T')
>>> series = ps.Series(range(9), index=index, name='V')
>>> 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
Name: V, dtype: int64

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

>>> series.resample('3T').sum().sort_index()
2000-01-01 00:00:00     3.0
2000-01-01 00:03:00    12.0
2000-01-01 00:06:00    21.0
Name: V, dtype: float64

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 00:03:00 does not include 3 (if it did, the summed value would be 6, not 3). To include this value, close the right side of the bin interval as illustrated in the example below this one.

>>> series.resample('3T', label='right').sum().sort_index()
2000-01-01 00:03:00     3.0
2000-01-01 00:06:00    12.0
2000-01-01 00:09:00    21.0
Name: V, dtype: float64

Downsample the series into 3 minute bins as above, but close the right side of the bin interval.

>>> series.resample('3T', label='right', closed='right').sum().sort_index()
2000-01-01 00:00:00     0.0
2000-01-01 00:03:00     6.0
2000-01-01 00:06:00    15.0
2000-01-01 00:09:00    15.0
Name: V, dtype: float64

Upsample the series into 30 second bins.

>>> series.resample('30S').sum().sort_index()[0:5]   # Select first 5 rows
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    0.0
2000-01-01 00:01:00    1.0
2000-01-01 00:01:30    0.0
2000-01-01 00:02:00    2.0
Name: V, dtype: float64