# pandas.DatetimeIndex.round

`DatetimeIndex.``round`(*args, **kwargs)

  Perform round operation on the data to the specified freq.

  Parameters:

  freq : str or Offset

  > The frequency level to round 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.round('H')
      DatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',
                     '2018-01-01 12:00:00'],
                    dtype='datetime64[ns]', freq=None)

  Series

      >>> pd.Series(rng).dt.round("H")
      0   2018-01-01 12:00:00
      1   2018-01-01 12:00:00
      2   2018-01-01 12:00:00
      dtype: datetime64[ns]

Scroll To Top