pandas.DatetimeIndex.ceil¶
-
DatetimeIndex.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.
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]