modin.pandas.DatetimeIndex.ceil¶

DatetimeIndex.ceil(freq: Frequency, ambiguous: str = 'raise', nonexistent: str = 'raise') → DatetimeIndex[source] (https://github.com/snowflakedb/snowpark-python/blob/v1.30.0/snowpark-python/src/snowflake/snowpark/modin/plugin/extensions/datetime_index.py#L341-L348)¶

Perform ceil operation on the data to the specified freq.

Parameters:

• freq (str or Offset) – The frequency level to {op} 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') –

  This parameter is only supported for ‘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') –

  This parameter is only supported for ‘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.

Return type:

DatetimeIndex with ceil values.

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=None)

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>>> 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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