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Classe « Timestamp »

Méthode pandas.Timestamp.floor

Signature de la méthode floor

def floor(self, freq, ambiguous='raise', nonexistent='raise') 

Description

help(Timestamp.floor)

        Return a new Timestamp floored to this resolution.

        Parameters
        ----------
        freq : str
            Frequency string indicating the flooring 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.

        Raises
        ------
        ValueError if the freq cannot be converted.

        Notes
        -----
        If the Timestamp has a timezone, flooring will take place relative to the
        local ("wall") time and re-localized to the same timezone. When flooring
        near daylight savings time, use ``nonexistent`` and ``ambiguous`` to
        control the re-localization behavior.

        Examples
        --------
        Create a timestamp object:

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

        A timestamp can be floored using multiple frequency units:

        >>> ts.floor(freq='h') # hour
        Timestamp('2020-03-14 15:00:00')

        >>> ts.floor(freq='min') # minute
        Timestamp('2020-03-14 15:32:00')

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

        >>> ts.floor(freq='ns') # nanoseconds
        Timestamp('2020-03-14 15:32:52.192548651')

        ``freq`` can also be a multiple of a single unit, like '5min' (i.e.  5 minutes):

        >>> ts.floor(freq='5min')
        Timestamp('2020-03-14 15:30:00')

        or a combination of multiple units, like '1h30min' (i.e. 1 hour and 30 minutes):

        >>> ts.floor(freq='1h30min')
        Timestamp('2020-03-14 15:00:00')

        Analogous for ``pd.NaT``:

        >>> pd.NaT.floor()
        NaT

        When rounding near a daylight savings time transition, use ``ambiguous`` or
        ``nonexistent`` to control how the timestamp should be re-localized.

        >>> ts_tz = pd.Timestamp("2021-10-31 03:30:00").tz_localize("Europe/Amsterdam")

        >>> ts_tz.floor("2h", ambiguous=False)
        Timestamp('2021-10-31 02:00:00+0100', tz='Europe/Amsterdam')

        >>> ts_tz.floor("2h", ambiguous=True)
        Timestamp('2021-10-31 02:00:00+0200', tz='Europe/Amsterdam')
        

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