pandas.Series.dt.round#

Series.dt.round(*args,**kwargs)[source]#

Perform round operation on the data to the specifiedfreq.

Parameters:

freqstr or Offset

The frequency level to round the index to. Must be a fixedfrequency like ‘S’ (second) not ‘ME’ (month end). Seefrequency aliases fora list of possiblefreq values.

ambiguous‘infer’, bool-ndarray, ‘NaT’, default ‘raise’

Only relevant for DatetimeIndex:

• ‘infer’ will attempt to infer fall dst-transition hours based onorder

• bool-ndarray where True signifies a DST time, False designatesa non-DST time (note that this flag is only applicable forambiguous times)

• ‘NaT’ will return NaT where there are ambiguous times

• ‘raise’ will raise an AmbiguousTimeError if there are ambiguoustimes.

nonexistent‘shift_forward’, ‘shift_backward’, ‘NaT’, timedelta, default ‘raise’

A nonexistent time does not exist in a particular timezonewhere clocks moved forward due to DST.

• ‘shift_forward’ will shift the nonexistent time forward to theclosest existing time

• ‘shift_backward’ will shift the nonexistent time backward to theclosest existing time

• ‘NaT’ will return NaT where there are nonexistent times

• timedelta objects will shift nonexistent times by the timedelta

• ‘raise’ will raise an NonExistentTimeError if there arenonexistent times.

Returns:

DatetimeIndex, TimedeltaIndex, or Series

Index of the same type for a DatetimeIndex or TimedeltaIndex,or a Series with the same index for a Series.

Raises:

ValueError if thefreq cannot be converted.

Notes

If the timestamps have a timezone, rounding will take place relative to thelocal (“wall”) time and re-localized to the same timezone. When roundingnear daylight savings time, usenonexistent andambiguous tocontrol the re-localization behavior.

Examples

DatetimeIndex

>>>rng=pd.date_range('1/1/2018 11:59:00',periods=3,freq='min')>>>rngDatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',               '2018-01-01 12:01:00'],              dtype='datetime64[ns]', freq='min')>>>rng.round('h')DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',               '2018-01-01 12:00:00'],              dtype='datetime64[ns]', freq=None)

Series

>>>pd.Series(rng).dt.round("h")0   2018-01-01 12:00:001   2018-01-01 12:00:002   2018-01-01 12:00:00dtype: datetime64[ns]

When rounding near a daylight savings time transition, useambiguous ornonexistent to control how the timestamp should be re-localized.

>>>rng_tz=pd.DatetimeIndex(["2021-10-31 03:30:00"],tz="Europe/Amsterdam")

>>>rng_tz.floor("2h",ambiguous=False)DatetimeIndex(['2021-10-31 02:00:00+01:00'],              dtype='datetime64[ns, Europe/Amsterdam]', freq=None)

>>>rng_tz.floor("2h",ambiguous=True)DatetimeIndex(['2021-10-31 02:00:00+02:00'],              dtype='datetime64[ns, Europe/Amsterdam]', freq=None)