modin.pandas.Series.dt.ceil

Series.dt.ceil[source]

Perform ceil operation on the data to the specified freq.

Parameters:

  • freq (str 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:

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

Return type:

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