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pandas.Series.dt.ceil — pandas 2.3.1 documentation

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

Perform ceil operation on the data to the specified freq.

Parameters:

freqstr or Offset

The frequency level to ceil the index to. Must be a fixed frequency like ‘S’ (second) not ‘ME’ (month end). See frequency aliases for a list of possible freq values.

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

Only relevant for DatetimeIndex:

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

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

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

• ‘raise’ will raise an AmbiguousTimeError if there are ambiguous times.

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

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

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

• ‘shift_backward’ will shift the nonexistent time backward to the closest 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 are nonexistent 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 the freq cannot be converted.

Notes

If the timestamps have a timezone, ceiling will take place relative to the local (“wall”) time and re-localized to the same timezone. When ceiling near daylight savings time, use nonexistent and ambiguous to control the re-localization behavior.

Examples

DatetimeIndex

>>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')
>>> rng
DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',
               '2018-01-01 12:01:00'],
              dtype='datetime64[ns]', freq='min')
>>> rng.ceil('h')
DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',
               '2018-01-01 13:00:00'],
              dtype='datetime64[ns]', freq=None)

Series

>>> pd.Series(rng).dt.ceil("h")
0   2018-01-01 12:00:00
1   2018-01-01 12:00:00
2   2018-01-01 13:00:00
dtype: datetime64[ns]

When rounding near a daylight savings time transition, use ambiguous or nonexistent to control how the timestamp should be re-localized.

>>> rng_tz = pd.DatetimeIndex(["2021-10-31 01:30:00"], tz="Europe/Amsterdam")

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

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

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