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æÐÒÙÑûцՃØlÖ¡Öpandas._libs.tslibs.nattype<stringsource>__reduce____module____doc__func_name__name____qualname____dict___is_coroutineCannot add NaT to ndarray with dtype Cannot compare NaT with datetime.date objectCannot subtract NaT from ndarray with dtype 
        Construct a timestamp from a a proleptic Gregorian ordinal.

        Parameters
        ----------
        ordinal : int
            Date corresponding to a proleptic Gregorian ordinal.
        tz : str, pytz.timezone, dateutil.tz.tzfile or None
            Time zone for the Timestamp.

        Notes
        -----
        By definition there cannot be any tz info on the ordinal itself.

        Examples
        --------
        >>> pd.Timestamp.fromordinal(737425)
        Timestamp('2020-01-01 00:00:00')
        
        Convert a Timestamp object to a native Python datetime object.

        If warn=True, issue a warning if nanoseconds is nonzero.

        Examples
        --------
        >>> ts = pd.Timestamp('2020-03-14T15:32:52.192548')
        >>> ts.to_pydatetime()
        datetime.datetime(2020, 3, 14, 15, 32, 52, 192548)

        Analogous for ``pd.NaT``:

        >>> pd.NaT.to_pydatetime()
        NaT
        
        Convert the Timestamp to a NumPy datetime64 or timedelta64.

        With the default 'dtype', this is an alias method for `NaT.to_datetime64()`.

        The copy parameter is available here only for compatibility. Its value
        will not affect the return value.

        Returns
        -------
        numpy.datetime64 or numpy.timedelta64

        See Also
        --------
        DatetimeIndex.to_numpy : Similar method for DatetimeIndex.

        Examples
        --------
        >>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
        >>> ts.to_numpy()
        numpy.datetime64('2020-03-14T15:32:52.192548651')

        Analogous for ``pd.NaT``:

        >>> pd.NaT.to_numpy()
        numpy.datetime64('NaT')

        >>> pd.NaT.to_numpy("m8[ns]")
        numpy.timedelta64('NaT','ns')
        
        Convert the underlying int64 representaton to the given unit.

        Parameters
        ----------
        unit : {"ns", "us", "ms", "s"}
        round_ok : bool, default True
            If False and the conversion requires rounding, raise.

        Returns
        -------
        Timestamp

        Examples
        --------
        >>> ts = pd.Timestamp('2023-01-01 00:00:00.01')
        >>> ts
        Timestamp('2023-01-01 00:00:00.010000')
        >>> ts.unit
        'ms'
        >>> ts = ts.as_unit('s')
        >>> ts
        Timestamp('2023-01-01 00:00:00')
        >>> ts.unit
        's'
        
        Convert timezone-aware Timestamp to another time zone.

        Parameters
        ----------
        tz : str, pytz.timezone, dateutil.tz.tzfile or None
            Time zone for time which Timestamp will be converted to.
            None will remove timezone holding UTC time.

        Returns
        -------
        converted : Timestamp

        Raises
        ------
        TypeError
            If Timestamp is tz-naive.

        Examples
        --------
        Create a timestamp object with UTC timezone:

        >>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651', tz='UTC')
        >>> ts
        Timestamp('2020-03-14 15:32:52.192548651+0000', tz='UTC')

        Change to Tokyo timezone:

        >>> ts.tz_convert(tz='Asia/Tokyo')
        Timestamp('2020-03-15 00:32:52.192548651+0900', tz='Asia/Tokyo')

        Can also use ``astimezone``:

        >>> ts.astimezone(tz='Asia/Tokyo')
        Timestamp('2020-03-15 00:32:52.192548651+0900', tz='Asia/Tokyo')

        Analogous for ``pd.NaT``:

        >>> pd.NaT.tz_convert(tz='Asia/Tokyo')
        NaT
        
        Implements datetime.replace, handles nanoseconds.

        Parameters
        ----------
        year : int, optional
        month : int, optional
        day : int, optional
        hour : int, optional
        minute : int, optional
        second : int, optional
        microsecond : int, optional
        nanosecond : int, optional
        tzinfo : tz-convertible, optional
        fold : int, optional

        Returns
        -------
        Timestamp with fields replaced

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

        >>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651', tz='UTC')
        >>> ts
        Timestamp('2020-03-14 15:32:52.192548651+0000', tz='UTC')

        Replace year and the hour:

        >>> ts.replace(year=1999, hour=10)
        Timestamp('1999-03-14 10:32:52.192548651+0000', tz='UTC')

        Replace timezone (not a conversion):

        >>> import pytz
        >>> ts.replace(tzinfo=pytz.timezone('US/Pacific'))
        Timestamp('2020-03-14 15:32:52.192548651-0700', tz='US/Pacific')

        Analogous for ``pd.NaT``:

        >>> pd.NaT.replace(tzinfo=pytz.timezone('US/Pacific'))
        NaT
        ImportErrorIncompatible checksums (0x%x vs (0xbbd7235, 0x0fab5fa, 0xe106c97) = (_value))
        Localize the Timestamp to a timezone.

        Convert naive Timestamp to local time zone or remove
        timezone from timezone-aware Timestamp.

        Parameters
        ----------
        tz : str, pytz.timezone, dateutil.tz.tzfile or None
            Time zone for time which Timestamp will be converted to.
            None will remove timezone holding local time.

        ambiguous : bool, 'NaT', default 'raise'
            When clocks moved backward due to DST, ambiguous times may arise.
            For example in Central European Time (UTC+01), when going from
            03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at
            00:30:00 UTC and at 01:30:00 UTC. In such a situation, the
            `ambiguous` parameter dictates how ambiguous times should be
            handled.

            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 : '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.

            The behavior is as follows:

            * '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
        -------
        localized : Timestamp

        Raises
        ------
        TypeError
            If the Timestamp is tz-aware and tz is not None.

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

        >>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
        >>> ts
        Timestamp('2020-03-14 15:32:52.192548651')

        Add 'Europe/Stockholm' as timezone:

        >>> ts.tz_localize(tz='Europe/Stockholm')
        Timestamp('2020-03-14 15:32:52.192548651+0100', tz='Europe/Stockholm')

        Analogous for ``pd.NaT``:

        >>> pd.NaT.tz_localize()
        NaT
        MNANNATNPY_NAT
    (N)ot-(A)-(T)ime, the time equivalent of NaN.

    Examples
    --------
    >>> pd.DataFrame([pd.Timestamp("2023"), np.nan], columns=["col_1"])
            col_1
    0  2023-01-01
    1         NaT
    NaNNaTNaTType'NaTType'NaTType.__new__NaTType.__reduce__NaTType.__reduce_ex__NaTType.__rfloordiv__NaTType.__rmul__NaTType.__rtruediv__NaTType.as_unitNaTType.as_unit (line 1367)NaTType does not support NaTType.<lambda>NaTType.tzNaTType.tzinfoNaTType.value_NaT_NaT.__reduce_cython___NaT.__setstate_cython___NaT.isoformat_NaT.to_datetime64_NaT.to_datetime64 (line 230)_NaT.to_numpyNaT.to_numpy dtype must be a datetime64 dtype, timedelta64 dtype, or None._NaT.to_numpy (line 245)NoneNotImplementedOPickleError
        Return POSIX timestamp as float.

        Examples
        --------
        >>> ts = pd.Timestamp('2020-03-14T15:32:52.192548')
        >>> ts.timestamp()
        1584199972.192548
        
        Return UTC time tuple, compatible with time.localtime().

        Examples
        --------
        >>> ts = pd.Timestamp('2023-01-01 10:00:00', tz='Europe/Brussels')
        >>> ts
        Timestamp('2023-01-01 10:00:00+0100', tz='Europe/Brussels')
        >>> ts.utctimetuple()
        time.struct_time(tm_year=2023, tm_mon=1, tm_mday=1, tm_hour=9,
        tm_min=0, tm_sec=0, tm_wday=6, tm_yday=1, tm_isdst=0)
        
        Return a formatted string of the Timestamp.

        Parameters
        ----------
        format : str
            Format string to convert Timestamp to string.
            See strftime documentation for more information on the format string:
            https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior.

        Examples
        --------
        >>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
        >>> ts.strftime('%Y-%m-%d %X')
        '2020-03-14 15:32:52'
        
        Return a named tuple containing ISO year, week number, and weekday.

        Examples
        --------
        >>> ts = pd.Timestamp('2023-01-01 10:00:00')
        >>> ts
        Timestamp('2023-01-01 10:00:00')
        >>> ts.isocalendar()
        datetime.IsoCalendarDate(year=2022, week=52, weekday=7)
        
        Return a new Timestamp ceiled to this resolution.

        Parameters
        ----------
        freq : str
            Frequency string indicating the ceiling 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, ceiling will take place relative to the
        local ("wall") time and re-localized to the same timezone. When ceiling
        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 ceiled using multiple frequency units:

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

        >>> ts.ceil(freq='min') # minute
        Timestamp('2020-03-14 15:33:00')

        >>> ts.ceil(freq='s') # seconds
        Timestamp('2020-03-14 15:32:53')

        >>> ts.ceil(freq='us') # microseconds
        Timestamp('2020-03-14 15:32:52.192549')

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

        >>> ts.ceil(freq='5min')
        Timestamp('2020-03-14 15:35:00')

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

        >>> ts.ceil(freq='1h30min')
        Timestamp('2020-03-14 16:30:00')

        Analogous for ``pd.NaT``:

        >>> pd.NaT.ceil()
        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 01:30:00").tz_localize("Europe/Amsterdam")

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

        >>> ts_tz.ceil("h", ambiguous=True)
        Timestamp('2021-10-31 02:00:00+0200', tz='Europe/Amsterdam')
        
        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')
        
        Return a numpy.datetime64 object with same precision.

        Examples
        --------
        >>> ts = pd.Timestamp(year=2023, month=1, day=1,
        ...                   hour=10, second=15)
        >>> ts
        Timestamp('2023-01-01 10:00:15')
        >>> ts.to_datetime64()
        numpy.datetime64('2023-01-01T10:00:15.000000')
        
        Return ctime() style string.

        Examples
        --------
        >>> ts = pd.Timestamp('2023-01-01 10:00:00.00')
        >>> ts
        Timestamp('2023-01-01 10:00:00')
        >>> ts.ctime()
        'Sun Jan  1 10:00:00 2023'
        
        Return date object with same year, month and day.

        Examples
        --------
        >>> ts = pd.Timestamp('2023-01-01 10:00:00.00')
        >>> ts
        Timestamp('2023-01-01 10:00:00')
        >>> ts.date()
        datetime.date(2023, 1, 1)
        
        Return new Timestamp object representing current time local to tz.

        Parameters
        ----------
        tz : str or timezone object, default None
            Timezone to localize to.

        Examples
        --------
        >>> pd.Timestamp.now()  # doctest: +SKIP
        Timestamp('2020-11-16 22:06:16.378782')

        Analogous for ``pd.NaT``:

        >>> pd.NaT.now()
        NaT
        
        Return proleptic Gregorian ordinal. January 1 of year 1 is day 1.

        Examples
        --------
        >>> ts = pd.Timestamp('2023-01-01 10:00:50')
        >>> ts
        Timestamp('2023-01-01 10:00:50')
        >>> ts.toordinal()
        738521
        
        Return the current time in the local timezone.

        This differs from datetime.today() in that it can be localized to a
        passed timezone.

        Parameters
        ----------
        tz : str or timezone object, default None
            Timezone to localize to.

        Examples
        --------
        >>> pd.Timestamp.today()    # doctest: +SKIP
        Timestamp('2020-11-16 22:37:39.969883')

        Analogous for ``pd.NaT``:

        >>> pd.NaT.today()
        NaT
        
        Return the day name of the Timestamp with specified locale.

        Parameters
        ----------
        locale : str, default None (English locale)
            Locale determining the language in which to return the day name.

        Returns
        -------
        str

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

        Analogous for ``pd.NaT``:

        >>> pd.NaT.day_name()
        nan
        
        Return the day of the week represented by the date.

        Monday == 0 ... Sunday == 6.

        Examples
        --------
        >>> ts = pd.Timestamp('2023-01-01')
        >>> ts
        Timestamp('2023-01-01  00:00:00')
        >>> ts.weekday()
        6
        
        Return the day of the week represented by the date.

        Monday == 1 ... Sunday == 7.

        Examples
        --------
        >>> ts = pd.Timestamp('2023-01-01 10:00:00')
        >>> ts
        Timestamp('2023-01-01 10:00:00')
        >>> ts.isoweekday()
        7
        
        Return the daylight saving time (DST) adjustment.

        Examples
        --------
        >>> ts = pd.Timestamp('2000-06-01 00:00:00', tz='Europe/Brussels')
        >>> ts
        Timestamp('2000-06-01 00:00:00+0200', tz='Europe/Brussels')
        >>> ts.dst()
        datetime.timedelta(seconds=3600)
        
        Return the month name of the Timestamp with specified locale.

        Parameters
        ----------
        locale : str, default None (English locale)
            Locale determining the language in which to return the month name.

        Returns
        -------
        str

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

        Analogous for ``pd.NaT``:

        >>> pd.NaT.month_name()
        nan
        
        Return time object with same time but with tzinfo=None.

        Examples
        --------
        >>> ts = pd.Timestamp('2023-01-01 10:00:00')
        >>> ts
        Timestamp('2023-01-01 10:00:00')
        >>> ts.time()
        datetime.time(10, 0)
        
        Return time object with same time and tzinfo.

        Examples
        --------
        >>> ts = pd.Timestamp('2023-01-01 10:00:00', tz='Europe/Brussels')
        >>> ts
        Timestamp('2023-01-01 10:00:00+0100', tz='Europe/Brussels')
        >>> ts.timetz()
        datetime.time(10, 0, tzinfo=<DstTzInfo 'Europe/Brussels' CET+1:00:00 STD>)
        
        Return time tuple, compatible with time.localtime().

        Examples
        --------
        >>> ts = pd.Timestamp('2023-01-01 10:00:00')
        >>> ts
        Timestamp('2023-01-01 10:00:00')
        >>> ts.timetuple()
        time.struct_time(tm_year=2023, tm_mon=1, tm_mday=1,
        tm_hour=10, tm_min=0, tm_sec=0, tm_wday=6, tm_yday=1, tm_isdst=-1)
        
        Return time zone name.

        Examples
        --------
        >>> ts = pd.Timestamp('2023-01-01 10:00:00', tz='Europe/Brussels')
        >>> ts
        Timestamp('2023-01-01 10:00:00+0100', tz='Europe/Brussels')
        >>> ts.tzname()
        'CET'
        
        Return utc offset.

        Examples
        --------
        >>> ts = pd.Timestamp('2023-01-01 10:00:00', tz='Europe/Brussels')
        >>> ts
        Timestamp('2023-01-01 10:00:00+0100', tz='Europe/Brussels')
        >>> ts.utcoffset()
        datetime.timedelta(seconds=3600)
        
        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

        Notes
        -----
        If the Timestamp has a timezone, rounding will take place relative to the
        local ("wall") time and re-localized to the same timezone. When rounding
        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 rounded using multiple frequency units:

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

        >>> ts.round(freq='min') # minute
        Timestamp('2020-03-14 15:33:00')

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

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

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

        >>> ts.round(freq='5min')
        Timestamp('2020-03-14 15:35:00')

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

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

        Analogous for ``pd.NaT``:

        >>> pd.NaT.round()
        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 01:30:00").tz_localize("Europe/Amsterdam")

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

        >>> ts_tz.round("h", ambiguous=True)
        Timestamp('2021-10-31 02:00:00+0200', tz='Europe/Amsterdam')
        RuntimeErrorTTime zones are not available from the C-API.
        Timestamp.combine(date, time)

        Combine date, time into datetime with same date and time fields.

        Examples
        --------
        >>> from datetime import date, time
        >>> pd.Timestamp.combine(date(2020, 3, 14), time(15, 30, 15))
        Timestamp('2020-03-14 15:30:15')
        
        Timestamp.fromtimestamp(ts)

        Transform timestamp[, tz] to tz's local time from POSIX timestamp.

        Examples
        --------
        >>> pd.Timestamp.fromtimestamp(1584199972)  # doctest: +SKIP
        Timestamp('2020-03-14 15:32:52')

        Note that the output may change depending on your local time.
        
        Timestamp.strptime(string, format)

        Function is not implemented. Use pd.to_datetime().

        Examples
        --------
        >>> pd.Timestamp.strptime("2023-01-01", "%d/%m/%y")
        Traceback (most recent call last):
        NotImplementedError
        
        Timestamp.utcfromtimestamp(ts)

        Construct a timezone-aware UTC datetime from a POSIX timestamp.

        Notes
        -----
        Timestamp.utcfromtimestamp behavior differs from datetime.utcfromtimestamp
        in returning a timezone-aware object.

        Examples
        --------
        >>> pd.Timestamp.utcfromtimestamp(1584199972)
        Timestamp('2020-03-14 15:32:52+0000', tz='UTC')
        
        Timestamp.utcnow()

        Return a new Timestamp representing UTC day and time.

        Examples
        --------
        >>> pd.Timestamp.utcnow()   # doctest: +SKIP
        Timestamp('2020-11-16 22:50:18.092888+0000', tz='UTC')
        
        Total seconds in the duration.

        Examples
        --------
        >>> td = pd.Timedelta('1min')
        >>> td
        Timedelta('0 days 00:01:00')
        >>> td.total_seconds()
        60.0
        TypeErrorValueError.?*__add__argsarray__array_priority__astimezoneastypeasyncio.coroutinesautobasebool_c_NaTc_nat_stringsceilcline_in_tracebackclscombinecopyctimedatetime64datetime64[ns]dayday_nameday_of_weekday_of_yeardayofweekdayofyeardaysdays_in_monthdaysinmonth_dictdisabledstemptyenablefgetfillfloorfromisocalendarfromordinalfromtimestampgc__getstate__/home/runner/work/mobile-forge/mobile-forge/build/cp312/pandas/2.2.3/pandas/_libs/tslibs/nattype.pyxhouriNaT__import____init_subclass___initializingisenabledisocalendarisoweekdaykindkwargsmmM__main___make_error_func.<locals>.f_make_nan_func.<locals>.f_make_nat_func.<locals>.f__metaclass__microsecondmicrosecondsmillisecondminutemonthmonth_name__mro_entries__nannanosecondnanosecondsnatnat_stringsnownpnp.datetime64np.datetime64 | np.timedelta64nsnumpy._core.multiarray failed to importnumpy._core.umath failed to importonesotherpickle__prepare__propertyprotocol__pyx_PickleError__pyx_checksum__pyx_result__pyx_state__pyx_typequarterqyearreplacereturnroundround_oksecondsecondsself__set_name____setstate__shape__spec__statestrstrftimestrptime__sub__super__test__timedelta64timedelta64[ns]timestamptimetupletimetzto_pydatetimetodaytoordinaltotal_secondstz_converttz_localizetznameupdateuse_setstateutcfromtimestamputcnowutcoffsetutctimetupleweekweekdayweekofyearyearzeros__pyx_capi__to_datetime64
        Return a numpy.datetime64 object with same precision.

        Examples
        --------
        >>> ts = pd.Timestamp(year=2023, month=1, day=1,
        ...                   hour=10, second=15)
        >>> ts
        Timestamp('2023-01-01 10:00:15')
        >>> ts.to_datetime64()
        numpy.datetime64('2023-01-01T10:00:15.000000')
        to_numpy
        Convert the Timestamp to a NumPy datetime64 or timedelta64.

        With the default 'dtype', this is an alias method for `NaT.to_datetime64()`.

        The copy parameter is available here only for compatibility. Its value
        will not affect the return value.

        Returns
        -------
        numpy.datetime64 or numpy.timedelta64

        See Also
        --------
        DatetimeIndex.to_numpy : Similar method for DatetimeIndex.

        Examples
        --------
        >>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
        >>> ts.to_numpy()
        numpy.datetime64('2020-03-14T15:32:52.192548651')

        Analogous for ``pd.NaT``:

        >>> pd.NaT.to_numpy()
        numpy.datetime64('NaT')

        >>> pd.NaT.to_numpy("m8[ns]")
        numpy.timedelta64('NaT','ns')
        isoformat__reduce_cython____setstate_cython__septimespecint_valuedatetimetzinfonumpydtype_make_nan_funcdocf_make_nat_func_make_error_func__nat_unpickle__new__value__reduce_ex____rtruediv____rfloordiv____rmul__tzas_unit
        Convert the underlying int64 representaton to the given unit.

        Parameters
        ----------
        unit : {"ns", "us", "ms", "s"}
        round_ok : bool, default True
            If False and the conversion requires rounding, raise.

        Returns
        -------
        Timestamp

        Examples
        --------
        >>> ts = pd.Timestamp('2023-01-01 00:00:00.01')
        >>> ts
        Timestamp('2023-01-01 00:00:00.010000')
        >>> ts.unit
        'ms'
        >>> ts = ts.as_unit('s')
        >>> ts
        Timestamp('2023-01-01 00:00:00')
        >>> ts.unit
        's'
        unit__pyx_unpickle__NaTð¿setname '%U' is not definedexactlyMissing type objectpandas._libs.tslibs.nattype._make_nat_funcpandas._libs.tslibs.nattype.NaTType.__reduce__tuplefree variable '%s' referenced before assignment in enclosing scopepandas._libs.tslibs.nattype.NaTType.lambdalambda8pandas._libs.tslibs.nattype.NaTType.lambda22%.200s() takes no arguments (%zd given)__name__ must be set to a string objectsetting function's dictionary to a non-dictint (PyObject *)%s() got multiple values for keyword argument '%U'pandas._libs.tslibs.nattype.__pyx_unpickle__NaT__set_state__int__ returned non-int (type %.200s).  The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__%.4s__ returned non-%.4s (type %.200s)ndarraylambda17does not matchShared Cython type %.200s has the wrong size, try recompilingflexiblenumpy.core._multiarray_umathpandas._libs.tslibs.nattype.NaTType.__rtruediv__lambda7pandas._libs.tslibs.nattype.NaTType.lambda20pandas._libs.tslibs.nattype.NaTType.as_unit%.200s() keywords must be stringscomplexfloatingmetaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its baseslambda14lambda21pandas._libs.tslibs.nattype.NaTType.tzasm8is_quarter_endintegerdatetime.datetime_CAPIpandas._libs.tslibs.nattype.NaTType.valuepandas._libs.tslibs.nattype.NaTType.lambda7loader__package__func_docat most%.200s() takes %.8s %zd positional argument%.1s (%zd given)floatingmodule was compiled against NumPy C-API version 0x%x (NumPy 1.20) but the running NumPy has C-API version 0x%x. Check the section C-API incompatibility at the Troubleshooting ImportError section at https://numpy.org/devdocs/user/troubleshooting-importerror.html#c-api-incompatibility for indications on how to solve this problem.lambda1util.pxd'NoneType' object is not subscriptablean integer is requiredis_leap_yearnumpy._core._multiarray_umath_ARRAY_API is NULL pointerlambda9%s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectpandas._libs.tslibs.nattype.NaTType.__new__lambda4lambda6pandas._libs.tslibs.nattype.NaTType.lambda19pandas._libs.tslibs.nattype._NaT.__add__sinheritance from PyVarObject types like '%.200s' not currently supported_ARRAY_APIFATAL: module compiled as unknown endianfunc_closureis_td64natpandas._libs.tslibs.nattype._NaT.__sub____mro_entries__ must return a tuplepandas._libs.tslibs.nattype.NaTType.__reduce_ex____file__parentsubmodule_search_locationsstruct __pyx_obj_6pandas_5_libs_6tslibs_7nattype__NaT *pandas._libs.tslibs.nattype._NaT.__richcmp____rsub__Argument '%.200s' has incorrect type (expected %.200s, got %.200s)pandas._libs.tslibs.nattype.NaTType.lambda16pandas/_libs/tslibs/nattype.cpython-312.so.p/pandas/_libs/tslibs/nattype.pyx.cfunc_dictPyObject *pandas._libs.tslibs.nattype._NaT.__rsub__Expected %s, got %.200sNULL result without error in PyObject_Call__loader__nattype.pyx_cython_3_0_12__globals__pandas._libs.tslibs.nattype._NaT__repr__lambda22__pyx_t_5numpy_int64_tis_month_endflatiterpandas._libs.tslibs.nattype.NaTType.lambda6__defaults__changes to cyfunction.__kwdefaults__ will not currently affect the values used in function callspandas._libs.tslibs.nattype.checknull_with_natpandas._libs.tslibs.nattype._nat_rdivide_oppandas._libs.tslibs.nattype._NaT.__truediv__hasattr(): attribute name must be stringnumberlambda12%s (%s:%d)cython_runtime%.200s() takes exactly one argument (%zd given)%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectpandas._libs.tslibs.nattype.NaTType.__rmul__cannot import name %S__path__datetime__radd__pandas._libs.tslibs.nattype._NaT.__setstate_cython__pandas._libs.tslibs.nattype._NaT._value.__get__characterufuncmodule compiled against ABI version 0x%x but this version of numpy is 0x%xpandas._libs.tslibs.nattype._make_nan_func.flambdalambda20Shared Cython type %.200s is not a type object__code__function's dictionary may not be deleted__kwdefaults__ must be set to a dict objectchecknull_with_natpandas._libs.tslibs.nattype._NaT.__floordiv__Unable to initialize pickling for %.200stimedeltapandas._libs.tslibs.nattype.NaTType.lambda5pandas._libs.tslibs.nattype.NaTType.lambda8origin__init__.cython-30.pxdcompile time Python version %d.%d of module '%.100s' %s runtime version %d.%d%.200s() needs an argumentlambda19pandas._libs.tslibs.nattype.__pyx_unpickle__NaTpandas._libs.tslibs.util.is_nanpandas._libs.tslibs.nattype._make_error_funclambda5pandas._libs.tslibs.nattype.NaTType.lambda10pandas._libs.tslibs.nattype.NaTType.lambda21instance exception may not have a separate valuepandas._libs.tslibs.nattype._NaT.to_datetime64pandas._libs.tslibs.nattype.__pyx_scope_struct___make_error_func_ARRAY_API is not PyCapsule objectpandas._libs.tslibs.nattype.NaTType.lambda3pandas._libs.tslibs.nattype.NaTType.lambda17nattypefunc_globalsfunc_code%.200s.%.200s is not a type objectcalling %R should have returned an instance of BaseException, not %Ris_year_endpandas._libs.tslibs.nattype._make_nan_funcname__orig_bases____closure____qualname__ must be set to a string objectpandas._libs.tslibs.nattype._NaT.__radd__at leastpandas._libs.tslibs.nattype._NaT.__reduce_cython__unsignedintegernumpy.import_arraylambda13pandas._libs.tslibs.nattype.NaTType.tzinfobuiltins%.200s() takes no keyword argumentsfunc_defaults__defaults__ must be set to a tuple objectpandas._libs.tslibs.nattype._NaT.isoformatis_year_startextension type '%.200s' has no __dict__ slot, but base type '%.200s' has: either add 'cdef dict __dict__' to the extension type or add '__slots__ = [...]' to the base typetypepandas._libs.tslibs.nattype.NaTType.lambda15_cython_3_0_12.cython_function_or_methodunbound method %.200S() needs an argument__annotations__ must be set to a dict objectis_dt64natpandas._libs.tslibs.nattype._NaT.__pos__is_month_startbroadcastFATAL: module compiled as little endian, but detected different endianness at runtimepandas._libs.tslibs.nattype.NaTType.lambda11lambda16changes to cyfunction.__defaults__ will not currently affect the values used in function callsbase class '%.200s' is not a heap typepandas._libs.tslibs.nattype._make_error_func.fInterpreter change detected - this module can only be loaded into one interpreter per process.__builtins__init pandas._libs.tslibs.nattypenattype.pxd<cyfunction %U at %p>__kwdefaults__raise: exception class must be a subclass of BaseExceptionpandas._libs.tslibs.nattype._NaT.__mul__%s() got an unexpected keyword argument '%U' while calling a Python objectpandas._libs.tslibs.nattype.NaTType.lambda1pandas._libs.tslibs.nattype.NaTType.lambda9lambda15lambda18Module 'nattype' has already been imported. Re-initialisation is not supported.Bad call flags for CyFunctionpandas._libs.tslibs.nattype._NaT.__neg__is_quarter_startpandas._libs.tslibs.nattype.NaTType.lambda2pandas._libs.tslibs.nattype.NaTType.lambda4lambda10lambda11pandas._libs.tslibs.nattype.NaTType.lambda12pandas._libs.tslibs.nattype.NaTType.lambda14Cannot convert %.200s to %.200spandas._libs.tslibs.nattype._nat_divide_oppandas._libs.tslibs.nattype._NaT.to_numpysignedintegerinexactpandas._libs.tslibs.nattype.NaTType.__rfloordiv__lambda2pandas._libs.tslibs.nattype.NaTType.lambda18keywords must be strings__annotations__pandas._libs.tslibs.nattype._NaT.asm8.__get__genericlambda3pandas._libs.tslibs.nattype.NaTType.lambda13
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