mars.dataframe.Series.dt.floor#

Series.dt.floor(*args, **kwargs)#

Perform floor operation on the data to the specified freq.

Parameters

  • freq (str or Offset) – The frequency level to floor 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. –

Examples

DatetimeIndex

>>> import mars.dataframe as md
>>> rng = md.date_range('1/1/2018 11:59:00', periods=3, freq='min')
>>> rng.execute()
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.floor('H').execute()
DatetimeIndex(['2018-01-01 11:00:00', '2018-01-01 12:00:00',
               '2018-01-01 12:00:00'],
              dtype='datetime64[ns]', freq=None)

Series

>>> md.Series(rng).dt.floor("H").execute()
0   2018-01-01 11:00:00
1   2018-01-01 12:00:00
2   2018-01-01 12:00:00
dtype: datetime64[ns]