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@

        Return the day of the week represented by the date.
        Monday == 1 ... Sunday == 7.
        numpy.core.umath failed to importNaT.to_numpy dtype must be a datetime64 dtype, timedelta64 dtype, or None.Incompatible checksums (0x%x vs (0x2063c16, 0xcd42404, 0xf32b67c) = (value))Cannot subtract NaT from ndarray with dtype Cannot add NaT to ndarray with dtype pandas/_libs/tslibs/nattype.pyxnumpy.core.multiarray failed to import
        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')
        
        Timestamp.utcfromtimestamp(ts)

        Construct a naive UTC datetime from a POSIX timestamp.

        Examples
        --------
        >>> pd.Timestamp.utcfromtimestamp(1584199972)
        Timestamp('2020-03-14 15:32:52')
        
        Timestamp.today(cls, tz=None)

        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
        
        Timestamp.strptime(string, format)

        Function is not implemented. Use pd.to_datetime().
        
        Timestamp.strftime(format)

        Return a string representing the given POSIX timestamp
        controlled by an explicit format string.

        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'
        
        Timestamp.now(tz=None)

        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
        
        Timestamp.fromtimestamp(ts)

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

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

        Note that the output may change depending on your local time.
        
        Timestamp.fromordinal(ordinal, freq=None, tz=None)

        Passed an ordinal, translate and convert to a ts.
        Note: by definition there cannot be any tz info on the ordinal itself.

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

        Examples
        --------
        >>> pd.Timestamp.fromordinal(737425)
        Timestamp('2020-01-01 00:00:00')
        
        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')
        
        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='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

        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')
        
        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 the day of the week represented by the date.
        Monday == 0 ... Sunday == 6.
        
        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 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='T') # minute
        Timestamp('2020-03-14 15:33:00')

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

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

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

        >>> ts.ceil(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.ceil(freq='1H30T')
        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 POSIX timestamp as float.

        Examples
        --------
        >>> ts = pd.Timestamp('2020-03-14T15:32:52.192548')
        >>> ts.timestamp()
        1584199972.192548
        NaT.freq is deprecated and will be removed in a future version.
    (N)ot-(A)-(T)ime, the time equivalent of NaN.
    
        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
        
        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
        
        Convert the Timestamp to a NumPy datetime64 or timedelta64.

        .. versionadded:: 0.25.0

        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 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
        
        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
        Comparison of NaT with datetime.date is deprecated in order to match the standard library behavior. In a future version these will be considered non-comparable.
        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='T') # minute
        Timestamp('2020-03-14 15:32:00')

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

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

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

        >>> ts.floor(freq='5T')
        Timestamp('2020-03-14 15:30:00')

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

        >>> ts.floor(freq='1H30T')
        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')
        pandas._libs.tslibs.nattype_make_error_func.<locals>.fNaTType does not support _make_nat_func.<locals>.f_make_nan_func.<locals>.f_NaT.to_numpy (line 261)NaTType.__rfloordiv__NaTType.__reduce_ex__cline_in_tracebackNaTType.__rtruediv____pyx_unpickle__NaTutcfromtimestamp_nat_scalar_rulesNaTType.__reduce____setstate_cython____pyx_PickleError_make_error_funcfromisocalendartimedelta64[ns]__array_priority__NotImplementedNaTType.<lambda>NaTType.__rmul__total_secondsto_pydatetimeto_datetime64__reduce_cython___make_nat_func_make_nan_funcfromtimestampdays_in_monthdatetime64[ns]c_nat_stringsNaTType.__new__FutureWarningutctimetuplestringsource__pyx_checksum__nat_unpicklemicrosecondsNaTType.freqtz_localizetimedelta64nat_stringsnanosecondsmillisecondmicrosecondisocalendarfromordinaldaysinmonthday_of_yearday_of_weekPickleErrorImportErrorweekofyeartz_convertstacklevel__pyx_resultnanosecondmonth_nameisoweekdaydatetime64astimezoneValueErrorutcoffsettoordinaltimetupletimestamp__rfloordiv____reduce_ex____pyx_state__metaclass__func_namedayofyeardayofweekTypeErrorwarningstimespecstrptimestrftime__setstate____rtruediv____qualname____pyx_type__pyx_capi__protocolproperty__getstate__day_nameweekdaysecondsreplacequarter__prepare__combineNaTTypeNPY_NATutcnowupdatetznametimetzsecond__reduce__pickle__module__minutekwargs__import__astypezerostodayshaperoundqyearothernumpymonthflooremptydtype__doc__ctimec_NaTarray_NaTyearweekwarntime__test__self__rmul__ones__name____main__kindiNaThourfreqfillfget__dict__daysdatecopyceilbool_baseautoargsNonesepnow__new__natnandstdocdayclsNaTNaNNATNANnsnpmMmfTOM
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