pyspark.pandas.Series.dt.ceil#

dt.ceil(freq, *args, **kwargs)#

Perform ceil operation on the data to the specified freq.

Parameters
freqstr or Offset

The frequency level to round the index to. Must be a fixed frequency like ‘S’ (second) not ‘ME’ (month end).

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

Note

this option only works with pandas 0.24.0+

Returns
Series

a Series with the same index for a Series.

Raises
ValueError if the freq cannot be converted.

Examples

>>> series = ps.Series(pd.date_range('1/1/2018 11:59:00', periods=3, freq='min'))
>>> series
0   2018-01-01 11:59:00
1   2018-01-01 12:00:00
2   2018-01-01 12:01:00
dtype: datetime64[ns]

>>> series.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]