# pandas.Timestamp.round

Timestamp.round(freq, ambiguous='raise', nonexistent='raise')

  Round the Timestamp to the specified resolution.

  Parameters
    freq str

      Frequency string indicating the rounding resolution.

    ambiguous bool or {‘raise’, ‘NaT’}, default ‘raise’

      The behavior is as follows:

      - bool contains flags to determine if time is dst or not (note that this flag is only applicable for ambiguous fall dst dates).

      - ‘NaT’ will return NaT for an ambiguous time.

      - ‘raise’ will raise an AmbiguousTimeError for an ambiguous time.

    nonexistent{‘raise’, ‘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
    a new Timestamp rounded to the given resolution of freq
  Raises
    ValueError if the freq cannot be converted

  Examples

  Create a timestamp object:

      >>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')

  A timestamp can be rounded using multiple frequency units:

      >>> ts.round(freq='H') # hour
      Timestamp('2020-03-14 16:00:00')

      >>> ts.round(freq='T') # minute
      Timestamp('2020-03-14 15:33:00')

      >>> ts.round(freq='S') # seconds
      Timestamp('2020-03-14 15:32:52')

      >>> ts.round(freq='L') # milliseconds
      Timestamp('2020-03-14 15:32:52.193000')

  `freq` can also be a multiple of a single unit, like ‘5T’ (i.e. 5 minutes):

      >>> ts.round(freq='5T')
      Timestamp('2020-03-14 15:35:00')

  or a combination of multiple units, like ‘1H30T’ (i.e. 1 hour and 30 minutes):

      >>> ts.round(freq='1H30T')
      Timestamp('2020-03-14 15:00:00')

  Analogous for `pd.NaT`:

      >>> pd.NaT.round()
      NaT