pandas.arrays.TimedeltaArray.ceil

TimedeltaArray.ceil(freq, ambiguous='raise', nonexistent='raise')

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

New in version 0.24.0.

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

New in version 0.24.0.

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.

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='T')
>>> 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]