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duality-group / pandas python

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pandas / _libs / tslibs / timestamps.cpython-310-aarch64-linux-gnu.so
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Taz`øëaÿÿTCýÿ@°Gù?ëÁT!D@ùàª ?Öûª{µ1RÒÒÒueRàk¹üÿ@³RÒÒÒueRàk¹üÿâªàªÓÔþäªæ7@ù «ÿµ RÒõcRàk¹æýÿþÔþóª3ýÿRÒÒÒõcRàk¹Üýÿä;ù'Óþä;@ùýÿä;ù#Óþä;@ùýÿàªä;ùÓþä;@ù¨ýÿä;ùÓþä;@ùýÿ@RÒÒõcRàk¹ÅýÿôªÒÒÒҵdRßûÿ$@ùñmØÿT4`Òë	Tz`ø_ëaÿÿT&þÿÔþ Ûÿµ@!àGù@ù6ÓþÿOùàS@ù`ÚÿµÖþÿ RõcRÒÒàk¹ýÿÒazyøëÿT9àªä©ÿ ÿ5äG©_ëÁþÿTýÿ`RÒÒdRàk¹ýÿ¤Ôþóªä7@ùýÿ RÒdRàk¹ýÿæ©ÒÓþâªæF©òÿµ@â©¢RGù!àâk¹äCùõcR@ùÓþâG©äC@ùpýÿRÒdRàk¹kýÿҁzyø_ë@ÁÿTàªâ©eÿ9âH©Àÿ5ëÁþÿTfþÿ`RôªudRàk¹uûÿ@(GùëÚÿTZ-àªADùJÒþóªÒþÿ@(Gù?ëéÿTA-àª!Dù@ÒþûªFÿÿ@(Gùë@ßÿTA-àª!Dù6ÒþùªøþÿàªùO@ù¦ùÿâªóW@ùúÿàO@ù@ù!ÑKúÿ ÕôÓþ Õ Õ Õ_$հð²W9p4 à¸À_Öð* ü_1ü¯ÿÿ5À_Öý{¿©ýý{hÀ_Ö<cyfunction %U at %p> while calling a Python objectNULL result without error in PyObject_Call__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)%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectraise: arg 3 must be a traceback or Noneinstance exception may not have a separate valuecalling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionexception causes must derive from BaseException_cython_0_29_30Shared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompiling__pyx_capi__%.200s does not export expected C variable %.200sC variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)%.200s does not export expected C function %.200sC function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)Interpreter change detected - this module can only be loaded into one interpreter per process.__loader__loader__file__origin__package__parent__path__submodule_search_locations__annotations__ must be set to a dict object__kwdefaults__ must be set to a dict object__defaults__ must be set to a tuple objectfunction's dictionary may not be deletedsetting function's dictionary to a non-dict__qualname__ must be set to a string object__name__ must be set to a string object_cython_coroutine_type_cython_generator_type_module__builtins__if _cython_generator_type is not None:
    try: Generator = _module.Generator
    except AttributeError: pass
    else: Generator.register(_cython_generator_type)
if _cython_coroutine_type is not None:
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    except AttributeError: pass
    else: Coroutine.register(_cython_coroutine_type)
Cython module failed to patch module with custom typeAcquisition count is %d (line %d)Unexpected format string character: '%c'join() result is too long for a Python string%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'can't send non-None value to a just-started generatorname '%U' is not definedcannot import name %SMissing type objectCannot convert %.200s to %.200s'unparseable format string'complex double''signed char''unsigned char''short''unsigned short''int''unsigned int''long''unsigned long''long long''unsigned long long''double''complex long double''bool''char''complex float''float'a structPython objecta pointera string'long double'Buffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'Expected a dimension of size %zu, got %zuExpected %d dimensions, got %dPython does not define a standard format string size for long double ('g')..Buffer dtype mismatch; next field is at offset %zd but %zd expectedBig-endian buffer not supported on little-endian compilerBuffer acquisition: Expected '{' after 'T'Cannot handle repeated arrays in format stringDoes not understand character buffer dtype format string ('%c')Expected a dimension of size %zu, got %dExpected a comma in format string, got '%c'Expected %d dimension(s), got %dUnexpected end of format string, expected ')'intan integer is requiredvalue too large to convert to intvalue too large to convert to npy_int32pandas._libs.tslibs.timestamps._Timestamp._can_compareUnable to initialize pickling for %sunbound method %.200S() needs an argument%.200s() takes no arguments (%zd given)%.200s() takes exactly one argument (%zd given)Bad call flags in __Pyx_CyFunction_Call. METH_OLDARGS is no longer supported!%.200s() takes no keyword arguments'%.200s' object is not subscriptablecannot fit '%.200s' into an index-sized integerpandas._libs.tslibs.timestamps._Timestamp._maybe_convert_value_to_localpandas/_libs/tslibs/timestamps.c%s (%s:%d)pandas/_libs/tslibs/timestamps.pyxpandas._libs.tslibs.timestamps.__defaults__stringsourceView.MemoryView.memoryview.itemsize.__get__View.MemoryView.memoryview.ndim.__get__View.MemoryView.get_slice_from_memviewView.MemoryView.memoryview.is_f_contigView.MemoryView.memoryview.is_c_contig__init__exactly%.200s() takes %.8s %zd positional argument%.1s (%zd given)View.MemoryView.Enum.__init__View.MemoryView.array.memview.__get__at leastat mostto_pydatetimepandas._libs.tslibs.timestamps._Timestamp.to_pydatetimemonth_nameunicodeExpected %.16s, got %.200spandas._libs.tslibs.timestamps._Timestamp.month_nameday_namepandas._libs.tslibs.timestamps._Timestamp.day_name'NoneType' object is not subscriptablehasattr(): attribute name must be stringView.MemoryView.__pyx_unpickle_Enum__set_statePyObject_GetBuffer: view==NULL argument is obsoleteView.MemoryView.memoryview.__getbuffer__View.MemoryView.array.__getbuffer__pandas._libs.tslibs.timestamps.Timestamp.replace.validatevalidate_date_attributesfree variable '%s' referenced before assignment in enclosing scope'NoneType' object is not iterablegenexprView.MemoryView.memoryview.size.__get__View.MemoryView.memoryview.nbytes.__get__View.MemoryView.memoryview.suboffsets.__get__View.MemoryView.memoryview.strides.__get__View.MemoryView.memoryview.shape.__get__View.MemoryView.memoryview_fromsliceView.MemoryView.memoryview_copy_from_sliceView.MemoryView._errCannot transpose memoryview with indirect dimensionsView.MemoryView.transpose_memsliceView.MemoryView.memoryview_copyView.MemoryView.memoryview.T.__get__View.MemoryView.memoryview_cwrapperView.MemoryView._err_dimDimension %d is not directView.MemoryView._err_extentsView.MemoryView.copy_data_to_tempView.MemoryView.memoryview_copy_contentsView.MemoryView.memoryview.__str__View.MemoryView._unellipsifyView.MemoryView.memoryview.__repr__'NoneType' is not iterablebytesView.MemoryView.memoryview.assign_item_from_objectView.MemoryView._memoryviewslice.assign_item_from_objectView.MemoryView.memoryview.setitem_indexedView.MemoryView.assert_direct_dimensionsView.MemoryView.memoryview.setitem_slice_assign_scalarinteger division or modulo by zerovalue too large to perform divisionView.MemoryView.pybuffer_indexView.MemoryView.memoryview.get_item_pointertupleView.MemoryView.Enum.__setstate_cython__View.MemoryView.Enum.__reduce_cython__View.MemoryView.array.__getattr__View.MemoryView.array.__setitem__Subscript deletion not supported by %.200sView.MemoryView.array.__getitem__View.MemoryView.array.get_memviewpandas._libs.tslibs.timestamps._Timestamp._time_repr.__get__pandas._libs.tslibs.timestamps._Timestamp._date_repr.__get__pandas._libs.tslibs.timestamps._Timestamp._repr_base.__get__pandas._libs.tslibs.timestamps._Timestamp.quarter.__get__pandas._libs.tslibs.timestamps._Timestamp.day_of_week.__get__pandas._libs.tslibs.timestamps._Timestamp.is_year_start.__get__pandas._libs.tslibs.timestamps._Timestamp.is_quarter_end.__get__pandas._libs.tslibs.timestamps._Timestamp.is_quarter_start.__get__pandas._libs.tslibs.timestamps._Timestamp.is_month_end.__get__pandas._libs.tslibs.timestamps._Timestamp.is_month_start.__get__to_periodpandas._libs.tslibs.timestamps._Timestamp.to_periodto_numpypandas._libs.tslibs.timestamps._Timestamp.to_numpypandas._libs.tslibs.timestamps._Timestamp.__setstate__pandas._libs.tslibs.timestamps._Timestamp.normalizepandas._libs.tslibs.timestamps._Timestamp._compare_outside_nanorangeView.MemoryView._memoryviewslice.__setstate_cython__View.MemoryView._memoryviewslice.__reduce_cython__View.MemoryView.memoryview.__setstate_cython__View.MemoryView.memoryview.__reduce_cython__View.MemoryView.array.__setstate_cython__View.MemoryView.array.__reduce_cython__pandas._libs.tslibs.timestamps.Timestamp._freqstrtzpandas._libs.tslibs.timestamps.Timestamp.tzcombinepandas._libs.tslibs.timestamps.Timestamp.combinestrptimepandas._libs.tslibs.timestamps.Timestamp.strptimestrftimepandas._libs.tslibs.timestamps.Timestamp.strftimefromtimestamppandas._libs.tslibs.timestamps.Timestamp.fromtimestamppandas._libs.tslibs.timestamps.Timestamp.utcnowtodaypandas._libs.tslibs.timestamps.Timestamp.todaynowpandas._libs.tslibs.timestamps.Timestamp.nowfromordinalpandas._libs.tslibs.timestamps.Timestamp.fromordinalpandas._libs.tslibs.timestamps._Timestamp.__reduce_ex__pandas._libs.tslibs.timestamps.integer_op_not_supportedpandas/_libs/tslibs/timestamps.pxdpandas._libs.tslibs.timestamps._Timestamp.nanosecond.__get__pandas._libs.tslibs.timestamps._Timestamp.value.__get__pandas._libs.tslibs.timestamps._Timestamp.timestampView.MemoryView.__pyx_unpickle_EnumView.MemoryView.memoryview.is_sliceModule 'timestamps' has already been imported. Re-initialisation is not supported.%d.%dpandas._libs.tslibs.timestampscompiletime version %s of module '%.100s' does not match runtime version %sbuiltinscython_runtimePyObject *(__pyx_t_5numpy_int64_t, npy_datetimestruct, PyDateTime_TZInfo *, PyObject *, int)create_timestamp_from_tspandas._libs.tslibs.baseABCTimestampdatetimedatetimetimedeltatzinfonumpydtypeflatiterbroadcastndarraygenericnumbersignedintegerunsignedintegerinexactfloatingcomplexfloatingflexiblecharacterufuncpandas._libs.tslibs.conversion_TSObjectpandas._libs.tslibs.nattype_NaTpandas._libs.tslibs.offsetsBaseOffsetpandas._libs.tslibs.timedeltas_Timedeltainvalid vtable found for imported typepandas._libs.tslibs.ccalendar__pyx_t_5numpy_int64_tDAY_NANOSHOUR_NANOSPyObject *c_MONTH_NUMBERS__pyx_t_5numpy_int32_t *month_offsetNPY_NATint [6]_nat_scalar_rulesc_nat_stringsstruct __pyx_obj_6pandas_5_libs_6tslibs_7nattype__NaT *c_NaTpandas._libs.tslibs.timezonesPyDateTime_TZInfo *utc_pytzpandas._libs.tslibs.np_datetimeint (__pyx_t_5numpy_int64_t, __pyx_t_5numpy_int64_t, int)cmp_scalarPyObject *(npy_datetimestruct *)check_dts_boundsvoid (__pyx_t_5numpy_int64_t, npy_datetimestruct *)dt64_to_dtstruct__pyx_t_5numpy_int64_t (PyDateTime_DateTime *, npy_datetimestruct *)pydatetime_to_dt64int (__pyx_t_5numpy_int64_t)is_leapyear__pyx_t_5numpy_int32_t (int, Py_ssize_t, int __pyx_skip_dispatch)get_days_in_month__pyx_t_5numpy_int32_t (int, int, int, int __pyx_skip_dispatch)get_week_of_yearget_day_of_yearPyObject *(PyObject *, PyDateTime_TZInfo *, PyObject *, int, int, struct __pyx_opt_args_6pandas_5_libs_6tslibs_10conversion_convert_to_tsobject *__pyx_optional_args)convert_to_tsobjectstruct __pyx_obj_6pandas_5_libs_6tslibs_10conversion__TSObject *(PyDateTime_DateTime *, PyDateTime_TZInfo *, struct __pyx_opt_args_6pandas_5_libs_6tslibs_10conversion_convert_datetime_to_tsobject *__pyx_optional_args)convert_datetime_to_tsobject__pyx_t_5numpy_int64_t (__pyx_t_5numpy_int64_t)normalize_i8_stampPyObject *(PyObject *, int __pyx_skip_dispatch)to_offsetint (PyObject *)is_offset_object__pyx_t_5numpy_int64_t (PyObject *, int __pyx_skip_dispatch)delta_to_nanosecondsis_any_td_scalarint (PyDateTime_TZInfo *, int __pyx_skip_dispatch)is_utcint (PyDateTime_TZInfo *)treat_tz_as_pytzint (PyDateTime_TZInfo *, PyDateTime_TZInfo *, int __pyx_skip_dispatch)tz_comparePyObject *(PyDateTime_TZInfo *, int __pyx_skip_dispatch)get_timezonePyDateTime_TZInfo *(PyObject *, int __pyx_skip_dispatch)maybe_get_tzpandas._libs.tslibs.tzconversion__pyx_t_5numpy_int64_t (__pyx_t_5numpy_int64_t, PyDateTime_TZInfo *, int __pyx_skip_dispatch)tz_convert_from_utc_single__pyx_t_5numpy_int64_t (__pyx_t_5numpy_int64_t, PyDateTime_TZInfo *, struct __pyx_opt_args_6pandas_5_libs_6tslibs_12tzconversion_tz_localize_to_utc_single *__pyx_optional_args)tz_localize_to_utc_singlecollections.abcCython module failed to register with collections.abc modulebackports_abcnumpy.core._multiarray_umath_ARRAY_API_ARRAY_API not found_ARRAY_API is not PyCapsule object_ARRAY_API is NULL pointermodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule compiled against API version 0x%x but this version of numpy is 0x%xFATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtime__init__.pxdnumpy.import_arraydatetime.datetime_CAPImetaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its basesgetbuffer(obj, view, flags)init pandas._libs.tslibs.timestampspandas._libs.tslibs.timestamps._Timestamp.is_leap_year.__get__pandas._libs.tslibs.timestamps._Timestamp._short_repr.__get__pandas._libs.tslibs.timestamps._Timestamp.is_year_end.__get__super(): empty __class__ cellpandas._libs.tslibs.timestamps.Timestamp.weekdaypandas._libs.tslibs.timestamps.Timestamp.isoweekdaypandas._libs.tslibs.timestamps._Timestamp._warn_on_field_deprecationCannot copy memoryview slice with indirect dimensions (axis %d)View.MemoryView.array_cwrapperView.MemoryView.memoryview.copy_fortrancView.MemoryView.memoryview.copypandas._libs.tslibs.timestamps._Timestamp.days_in_month.__get__pandas._libs.tslibs.timestamps._Timestamp.freq.__get__pandas._libs.tslibs.timestamps._Timestamp.__reduce__pandas._libs.tslibs.timestamps.Timestamp.freqstrgenerator already executing'%.50s' object has no attribute '%U'dictget'NoneType' object has no attribute '%.30s'Buffer has wrong number of dimensions (expected %d, got %d)Item size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s)Out of bounds on buffer access (axis %d)pandas._libs.tslibs.timestamps._Timestamp._get_start_end_field_roundpandas._libs.tslibs.timestamps.Timestamp._roundpandas._libs.tslibs.timestamps._Timestamp.asm8.__get__View.MemoryView.memview_sliceIndex out of bounds (axis %d)All dimensions preceding dimension %d must be indexed and not slicedView.MemoryView.slice_memviewsliceStep may not be zero (axis %d)memviewsliceobjlocal variable '%s' referenced before assignmenttoo many values to unpack (expected %zd)need more than %zd value%.1s to unpackView.MemoryView.memoryview.__getitem__generator ignored GeneratorExit__cinit__View.MemoryView.memoryview.__cinit___unpickle_timestamppandas._libs.tslibs.timestamps._unpickle_timestamppandas._libs.tslibs.timestamps._Timestamp.__hash__pandas._libs.tslibs.timestamps._Timestamp.__richcmp__tz_localizepandas._libs.tslibs.timestamps.Timestamp.tz_localizeshapeArgument '%.200s' has incorrect type (expected %.200s, got %.200s)View.MemoryView.array.__cinit__formatArgument '%.200s' must not be Noneobject of type 'NoneType' has no len()expected bytes, NoneType foundutcfromtimestamppandas._libs.tslibs.timestamps.Timestamp.utcfromtimestamppandas._libs.tslibs.timestamps.create_timestamp_from_tsView.MemoryView.memoryview.setitem_slice_assignmentpandas._libs.tslibs.timestamps._Timestamp.__add__pandas._libs.tslibs.timestamps._Timestamp.day_of_year.__get__pandas._libs.tslibs.timestamps._Timestamp.week.__get__pandas._libs.tslibs.timestamps._Timestamp._set_freq__new__pandas._libs.tslibs.timestamps.Timestamp.__new__pandas._libs.tslibs.timestamps.Timestamp.__new__.genexprreplacepandas._libs.tslibs.timestamps.Timestamp.replacetz_convertpandas._libs.tslibs.timestamps.Timestamp.tz_convertView.MemoryView.memoryview.convert_item_to_objectView.MemoryView._memoryviewslice.convert_item_to_objectisoformatseppandas._libs.tslibs.timestamps._Timestamp.isoformattimespec'%.200s' object is unsliceablebuffer dtypeItem size of buffer (%zu byte%s) does not match size of '%s' (%zu byte%s)Buffer exposes suboffsets but no stridesBuffer not compatible with direct access in dimension %d.pandas._libs.tslibs.timestamps._Timestamp._get_date_name_fieldpandas._libs.tslibs.timestamps._Timestamp.to_datetime64pandas._libs.tslibs.timestamps._Timestamp.__sub__View.MemoryView.memoryview.__setitem__throwceilpandas._libs.tslibs.timestamps.Timestamp.ceilroundpandas._libs.tslibs.timestamps.Timestamp.roundfloorpandas._libs.tslibs.timestamps.Timestamp.floorpandas._libs.tslibs.timestamps.Timestamp.to_julian_datepandas._libs.tslibs.timestamps._Timestamp.__repr__generator__name__name of the generator__qualname__qualified name of the generatorgi_frameFrame of the generatorgi_runninggi_yieldfromobject being iterated by 'yield from', or Nonegi_codesendsend(arg) -> send 'arg' into generator,
return next yielded value or raise StopIteration.throw(typ[,val[,tb]]) -> raise exception in generator,
return next yielded value or raise StopIteration.closeclose() -> raise GeneratorExit inside generator.cython_function_or_method__reduce____module__func_doc__doc__func_name__self__func_dict__dict__func_globals__globals__func_closure__closure__func_code__code__func_defaults__defaults____kwdefaults____annotations__timestampspandas._libs.tslibs.timestamps._memoryviewsliceInternal class for passing memoryview slices to Pythonbase__reduce_cython____setstate_cython__pandas._libs.tslibs.timestamps.memoryviewTstridessuboffsetsndimitemsizenbytessizeis_c_contigis_f_contigcopycopy_fortranpandas._libs.tslibs.timestamps.Enumpandas._libs.tslibs.timestamps.arraymemview__getattr__pandas._libs.tslibs.timestamps.__pyx_scope_struct_1_genexprpandas._libs.tslibs.timestamps.__pyx_scope_struct____new__pandas._libs.tslibs.timestamps._Timestampfreqis_month_start
        Return True if date is first day of month.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.is_month_start
        False

        >>> ts = pd.Timestamp(2020, 1, 1)
        >>> ts.is_month_start
        True
        is_month_end
        Return True if date is last day of month.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.is_month_end
        False

        >>> ts = pd.Timestamp(2020, 12, 31)
        >>> ts.is_month_end
        True
        is_quarter_start
        Return True if date is first day of the quarter.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.is_quarter_start
        False

        >>> ts = pd.Timestamp(2020, 4, 1)
        >>> ts.is_quarter_start
        True
        is_quarter_end
        Return True if date is last day of the quarter.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.is_quarter_end
        False

        >>> ts = pd.Timestamp(2020, 3, 31)
        >>> ts.is_quarter_end
        True
        is_year_start
        Return True if date is first day of the year.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.is_year_start
        False

        >>> ts = pd.Timestamp(2020, 1, 1)
        >>> ts.is_year_start
        True
        is_year_end
        Return True if date is last day of the year.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.is_year_end
        False

        >>> ts = pd.Timestamp(2020, 12, 31)
        >>> ts.is_year_end
        True
        is_leap_year
        Return True if year is a leap year.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.is_leap_year
        True
        day_of_week
        Return day of the week.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.day_of_week
        5
        day_of_year
        Return the day of the year.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.day_of_year
        74
        quarter
        Return the quarter of the year.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.quarter
        1
        week
        Return the week number of the year.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.week
        11
        days_in_month
        Return the number of days in the month.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.days_in_month
        31
        _repr_base_date_repr_time_repr_short_reprasm8
        Return numpy datetime64 format in nanoseconds.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14, 15)
        >>> ts.asm8
        numpy.datetime64('2020-03-14T15:00:00.000000000')
        valuenanosecond_freq_set_freqnormalize__reduce_ex____setstate__to_datetime64weekdayisoweekdayto_julian_datefreqstr_freqstrutcnowinteger_op_not_supporteduint8_tJóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóPóóMóóóóó25óó8óó;>AóóDóóóóóóóóóóóóóGSó%,óóóóó/órÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿoÜÿÜÿÜÿÜÿÜÿlÜÿÜÿÜÿÜÿ~{ÜÿÜÿxÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿueÜÿ^WTQÜÿÜÿNÜÿÜÿÜÿKÜÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿQ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿQ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿáÿáÿáÿáÿáÿáÿáÿáÿáÿ=áÿáÿ=áÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿ=Báÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿ5áÿ?B#?áÿ#áÿáÿáÿáÿáÿ##áÿáÿ#áÿáÿ#áÿ#áÿáÿiáÿáÿáÿáÿáÿOáÿáÿáÿ?áÿáÿáÿ###áÿ####áÿáÿ#áÿáÿáÿ##áÿûáÿáÿáÿáÿªáÿáÿáÿáÿ__pyx_fatalerrorTimestamp.freq is deprecated and will be removed in a future version. When you have a freq, use freq.
        Return True if date is last day of the year.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.is_year_end
        False

        >>> ts = pd.Timestamp(2020, 12, 31)
        >>> ts.is_year_end
        True
        
        Return True if date is last day of the quarter.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.is_quarter_end
        False

        >>> ts = pd.Timestamp(2020, 3, 31)
        >>> ts.is_quarter_end
        True
        
        Return True if date is first day of the year.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.is_year_start
        False

        >>> ts = pd.Timestamp(2020, 1, 1)
        >>> ts.is_year_start
        True
        
        Return True if date is first day of the quarter.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.is_quarter_start
        False

        >>> ts = pd.Timestamp(2020, 4, 1)
        >>> ts.is_quarter_start
        True
        value must be an integer, received unable to allocate shape and strides.pytz timezones do not support fold. Please use dateutil timezones.pandas/_libs/tslibs/timestamps.pyxnumpy.core.umath failed to importno default __reduce__ due to non-trivial __cinit__got differing extents in dimension %d (got %d and %d)Valid values for the fold argument are None, 0, or 1.Unable to convert item to objectTimestamp.to_numpy dtype and copy arguments are ignored.Timestamp.to_julian_date (line 2040)Timestamp.strptime() is not implemented. Use to_datetime() to parse date strings.Timestamp.replace.<locals>.validateTimestamp.fromtimestamp (line 1182)Timestamp.freq is deprecated and will be removed in a future version.Timestamp.__new__.<locals>.genexprThe nonexistent argument must be one of 'raise', 'NaT', 'shift_forward', 'shift_backward' or a timedelta objectThe 'freq' argument in Timestamp is deprecated and will be removed in a future version.The behavior of Timestamp.utcfromtimestamp is deprecated, in a future version will return a timezone-aware Timestamp with UTC timezone. To keep the old behavior, use Timestamp.utcfromtimestamp(ts).tz_localize(None). To get the future behavior, use Timestamp.fromtimestamp(ts, 'UTC')Result is too large for pandas.Timedelta. Convert inputs to datetime.datetime with 'Timestamp.to_pydatetime()' before subtracting.Out of bounds on buffer access (axis %d)Invalid mode, expected 'c' or 'fortran', got %sIndirect dimensions not supportedIncompatible checksums (0x%x vs (0xb068931, 0x82a3537, 0x6ae9995) = (name))In a future version, when passing a np.datetime64 object and a timezone to Timestamp, the datetime64 will be interpreted as a wall time, not a UTC time.  To interpret as a UTC time, use `Timestamp(dt64).tz_localize('UTC').tz_convert(tz)`In a future version, calling 'Timestamp.to_period()' without passing a 'freq' will raise an exception.Empty shape tuple for cython.arrayDiscarding nonzero nanoseconds in conversion.Converting to Period representation will drop timezone information.Comparison of Timestamp with datetime.date is deprecated in order to match the standard library behavior. In a future version these will be considered non-comparable. Use 'ts == pd.Timestamp(date)' or 'ts.date() == date' instead.Cannot pass fold with possibly unambiguous input: int, float, numpy.datetime64, str, or timezone-aware datetime-like. Pass naive datetime-like or build Timestamp from components.Cannot pass a datetime or Timestamp with tzinfo with the tz parameter. Use tz_convert instead.Cannot pass a date attribute keyword argument when passing a date stringCannot localize tz-aware Timestamp, use tz_convert for conversionsCannot infer offset with only one time.Cannot directly set timezone. Use tz_localize() or tz_convert() as appropriateCannot create writable memory view from read-only memoryviewCannot convert tz-naive Timestamp, use tz_localize to localizeCannot compare tz-naive and tz-aware timestampsCannot assign to read-only memoryviewCan provide at most one of tz, tzinfoCan only create a buffer that is contiguous in memory.Buffer view does not expose stridesAddition/subtraction of integers and integer-arrays with pandas._libs.tslibs.np_datetimenumpy.core.multiarray failed to import_Timestamp.week.__get__ (line 675)
        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.utcfromtimestamp (line 1158)Timestamp.tz_localize (line 1749)
        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.to_pydatetime (line 878)
        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.is_year_end.__get__ (line 540)_Timestamp.is_quarter_start.__get__ (line 477)
        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.fromordinal (line 1066)Timestamp.freqstr is deprecated and will be removed in a future version._Timestamp.day_of_year.__get__ (line 649)_Timestamp.day_of_week.__get__ (line 636)
        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.asm8.__get__ (line 852)
        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 week number of the year.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.week
        11
        
        Return the time formatted according to ISO 8610.

        The full format looks like 'YYYY-MM-DD HH:MM:SS.mmmmmmnnn'.
        By default, the fractional part is omitted if self.microsecond == 0
        and self.nanosecond == 0.

        If self.tzinfo is not None, the UTC offset is also attached, giving
        giving a full format of 'YYYY-MM-DD HH:MM:SS.mmmmmmnnn+HH:MM'.

        Parameters
        ----------
        sep : str, default 'T'
            String used as the separator between the date and time.

        timespec : str, default 'auto'
            Specifies the number of additional terms of the time to include.
            The valid values are 'auto', 'hours', 'minutes', 'seconds',
            'milliseconds', 'microseconds', and 'nanoseconds'.

        Returns
        -------
        str

        Examples
        --------
        >>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
        >>> ts.isoformat()
        '2020-03-14T15:32:52.192548651'
        >>> ts.isoformat(timespec='microseconds')
        '2020-03-14T15:32:52.192548'
        
        Return the number of days in the month.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.days_in_month
        31
        
        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 year.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.day_of_year
        74
        
        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 day of the week.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.day_of_week
        5
        
        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 True if year is a leap year.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.is_leap_year
        True
        
        Return True if date is last day of month.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.is_month_end
        False

        >>> ts = pd.Timestamp(2020, 12, 31)
        >>> ts.is_month_end
        True
        
        Return True if date is first day of month.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.is_month_start
        False

        >>> ts = pd.Timestamp(2020, 1, 1)
        >>> ts.is_month_start
        True
        
        Return POSIX timestamp as float.

        Examples
        --------
        >>> ts = pd.Timestamp('2020-03-14T15:32:52.192548')
        >>> ts.timestamp()
        1584199972.192548
        
    Pandas replacement for python datetime.datetime object.

    Timestamp is the pandas equivalent of python's Datetime
    and is interchangeable with it in most cases. It's the type used
    for the entries that make up a DatetimeIndex, and other timeseries
    oriented data structures in pandas.

    Parameters
    ----------
    ts_input : datetime-like, str, int, float
        Value to be converted to Timestamp.
    freq : str, DateOffset
        Offset which Timestamp will have.
    tz : str, pytz.timezone, dateutil.tz.tzfile or None
        Time zone for time which Timestamp will have.
    unit : str
        Unit used for conversion if ts_input is of type int or float. The
        valid values are 'D', 'h', 'm', 's', 'ms', 'us', and 'ns'. For
        example, 's' means seconds and 'ms' means milliseconds.
    year, month, day : int
    hour, minute, second, microsecond : int, optional, default 0
    nanosecond : int, optional, default 0
    tzinfo : datetime.tzinfo, optional, default None
    fold : {0, 1}, default None, keyword-only
        Due to daylight saving time, one wall clock time can occur twice
        when shifting from summer to winter time; fold describes whether the
        datetime-like corresponds  to the first (0) or the second time (1)
        the wall clock hits the ambiguous time.

        .. versionadded:: 1.1.0

    Notes
    -----
    There are essentially three calling conventions for the constructor. The
    primary form accepts four parameters. They can be passed by position or
    keyword.

    The other two forms mimic the parameters from ``datetime.datetime``. They
    can be passed by either position or keyword, but not both mixed together.

    Examples
    --------
    Using the primary calling convention:

    This converts a datetime-like string

    >>> pd.Timestamp('2017-01-01T12')
    Timestamp('2017-01-01 12:00:00')

    This converts a float representing a Unix epoch in units of seconds

    >>> pd.Timestamp(1513393355.5, unit='s')
    Timestamp('2017-12-16 03:02:35.500000')

    This converts an int representing a Unix-epoch in units of seconds
    and for a particular timezone

    >>> pd.Timestamp(1513393355, unit='s', tz='US/Pacific')
    Timestamp('2017-12-15 19:02:35-0800', tz='US/Pacific')

    Using the other two forms that mimic the API for ``datetime.datetime``:

    >>> pd.Timestamp(2017, 1, 1, 12)
    Timestamp('2017-01-01 12:00:00')

    >>> pd.Timestamp(year=2017, month=1, day=1, hour=12)
    Timestamp('2017-01-01 12:00:00')
    
        Normalize Timestamp to midnight, preserving tz information.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14, 15, 30)
        >>> ts.normalize()
        Timestamp('2020-03-14 00:00:00')
        
        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.

        .. versionadded:: 0.25.0

        This is an alias method for `Timestamp.to_datetime64()`. The dtype and
        copy parameters are available here only for compatibility. Their values
        will not affect the return value.

        Returns
        -------
        numpy.datetime64

        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')
        
        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
        
        Convert TimeStamp to a Julian Date.
        0 Julian date is noon January 1, 4713 BC.

        Examples
        --------
        >>> ts = pd.Timestamp('2020-03-14T15:32:52')
        >>> ts.to_julian_date()
        2458923.147824074
        Cannot subtract tz-naive and tz-aware datetime-like objects.
        Alias for tzinfo.

        Examples
        --------
        >>> ts = pd.Timestamp(1584226800, unit='s', tz='Europe/Stockholm')
        >>> ts.tz
        <DstTzInfo 'Europe/Stockholm' CET+1:00:00 STD>
        <strided and direct or indirect>pandas._libs.tslibs.timestampspandas._libs.tslibs.timedeltas is no longer supported.  Instead of adding/subtracting `n`, use `n * obj.freq`Timestamp.tz_convert (line 1853)_Timestamp.quarter.__get__ (line 662)_Timestamp.is_quarter_end.__get__ (line 498)_Timestamp.is_month_start.__get__ (line 435)
_Timestamp is a c-defined subclass of datetime.datetime

_Timestamp is PITA. Because we inherit from datetime, which has very specific
construction requirements, we need to do object instantiation in python
(see Timestamp class below). This will serve as a C extension type that
shadows the python class, where we do any heavy lifting.

        Return the quarter of the year.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14)
        >>> ts.quarter
        1
        
        Return numpy datetime64 format in nanoseconds.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14, 15)
        >>> ts.asm8
        numpy.datetime64('2020-03-14T15:00:00.000000000')
        
        Return an period of which this timestamp is an observation.

        Examples
        --------
        >>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
        >>> # Year end frequency
        >>> ts.to_period(freq='Y')
        Period('2020', 'A-DEC')

        >>> # Month end frequency
        >>> ts.to_period(freq='M')
        Period('2020-03', 'M')

        >>> # Weekly frequency
        >>> ts.to_period(freq='W')
        Period('2020-03-09/2020-03-15', 'W-SUN')

        >>> # Quarter end frequency
        >>> ts.to_period(freq='Q')
        Period('2020Q1', 'Q-DEC')
        
        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')
        unable to allocate array data._Timestamp.month_name (line 596)_Timestamp.is_year_start.__get__ (line 519)_Timestamp.days_in_month.__get__ (line 688)_Timestamp.to_period (line 944)_Timestamp.timestamp (line 864)Timestamp.strftime (line 1198)_Timestamp.normalize (line 703)_Timestamp.isoformat (line 741)_Timestamp.is_month_end.__get__ (line 456)_Timestamp.is_leap_year.__get__ (line 623)itemsize <= 0 for cython.array_Timestamp.to_numpy (line 909)Timestamp.replace (line 1909)_Timestamp.day_name (line 570)Timestamp.combine (line 1234)pandas._libs.tslibs.fieldsTimestamp.utcnow (line 1144)Timestamp.utcfromtimestampTimestamp.today (line 1118)Timestamp.round (line 1439)Timestamp.floor (line 1534)Invalid shape in axis %d: %d.integer_op_not_supportedTimestamp.to_julian_dateTimestamp.ceil (line 1623)Cannot index with type '%s'<contiguous and indirect>Timestamp.now (line 1091)Timestamp.fromtimestamp<MemoryView of %r at 0x%x>Timestamp.tz (line 1713)<MemoryView of %r object><contiguous and direct>Timestamp.tz_localizeTimestamp.fromordinal<strided and indirect>Timestamp.tz_convertTimestamp.isoweekdaynonexistent_optionsget_start_end_fieldget_date_name_fieldOutOfBoundsDatetimeNotImplementedError_unpickle_timestamp<strided and direct>cline_in_tracebackTimestamp.strptimeTimestamp.strftimeTimestamp._freqstr(timestamp) instead.__pyx_unpickle_EnumTimestamp.weekdayTimestamp.replaceTimestamp.freqstrTimestamp.combineNEAREST_HALF_EVENutcfromtimestampis_quarter_startTimestamp.utcnowTimestamp._round__setstate_cython____pyx_PickleErrordtype_is_objectdatetime_kwargs_date_attributesallocate_bufferView.MemoryViewTimestamp.todayTimestamp.roundTimestamp.floorTimestamp.__new__to_julian_dateshift_backwardis_quarter_endis_month_startint_addsub_msg__array_priority__Timestamp.ceilNotImplementedAttributeErrorto_pydatetimeto_datetime64startingMonthshift_forwardround_nsint64__reduce_cython____pyx_getbufferis_year_startfromtimestampdays_in_monthTimestamp.nowOverflowErrorFutureWarningstringsource__pyx_checksummicrosecondsis_month_endTimestamp.tztz_localizenonexistentnanosecondsmicrosecondis_year_endfromordinaldaysinmonthday_of_yearday_of_weekdate_stringUserWarning_TimestampTimestamp('PickleErrorMemoryErrorMINUS_INFTYImportErrorweekofyeartz_convertstartswithstacklevelresolution__pyx_vtable____pyx_resultnanosecondmonth_nameisoweekdaydatetime64astimezoneValueErrorPLUS_INFTYIndexError_zero_time_time_repr_repr_base__reduce_ex____pyx_state__metaclass__isoformatfreqstrenumeratedayofyeardayofweek_date_reprambiguousTypeErrorTimestampTimedeltawarningsvalidatets_inputtimespecstrptimestrftime__setstate___set_freq__qualname____pyx_typeproperty_no_inputlocalizeitemsize__getstate__day_nameEllipsisweekday_roundreplace__prepare__ordinalmemviewgenexpr_freqstrfortranfloat64combineasarrayRoundToutcnowupdateunpacktzinfostructsettersecondreturnresult__reduce__picklepandasobjectname__module__minutelocalekwargsis_dst__import__, freq='_freqformatencodeastypePeriodzerosvaluetzobjtodaythrowsuperstartshaperoundrangeraisenumpynanosmonthint64inferiinfofloorflagserrordtypeclose__class__arrayASCIIyearweekwarnunit, tz='time__test__stopstepsizesendselfpackonesndim__name__mode__main__kwdskindhour__hash__freqfold__dict__datecopyceilbool_baseautoasm8argsNonesepoutobjnow__new__minmaxget for fixdts__doc__daycls__add__:- )'NaT09d06d03d02dtztsnsnpid.%zvurmkic %%ZTOMB@C:A;	 @aúÿhXaúÿ$súÿ0túÿ< túÿ èuúÿlvúÿ¨¤wúÿÜÜxúÿyúÿ<{úÿpx{úÿ´È|úÿÄ¨}úÿp~úÿ ~úÿ¸ úÿCÚúÿÔc°ÚúÿdðÚúÿ$	 Ûúÿ8	`ÛúÿL	°Ûúÿp	ÀÛúÿ	ÐÛúÿ	äÛúÿ°	ÜúÿÄ	 ÜúÿØ	@Üúÿì	 Üúÿ 
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¶èÿØè«Ø(	þÿÿo¸(ÿÿÿoðÿÿoö&ùÿÿo?Xüà»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»à»ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ
        Return the day of the week represented by the date.
        Monday == 0 ... Sunday == 6.
        
        Return the day of the week represented by the date.
        Monday == 1 ... Sunday == 7.
        
        Convert TimeStamp to a Julian Date.
        0 Julian date is noon January 1, 4713 BC.

        Examples
        --------
        >>> ts = pd.Timestamp('2020-03-14T15:32:52')
        >>> ts.to_julian_date()
        2458923.147824074
         validate integers 
        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 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
        
        Return the total number of days in the month.
        
        Alias for tzinfo.

        Examples
        --------
        >>> ts = pd.Timestamp(1584226800, unit='s', tz='Europe/Stockholm')
        >>> ts.tz
        <DstTzInfo 'Europe/Stockholm' CET+1:00:00 STD>
        
        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 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')
        
        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')
        
        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.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.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.utcfromtimestamp(ts)

        Construct a naive UTC datetime from a POSIX timestamp.

        Examples
        --------
        >>> pd.Timestamp.utcfromtimestamp(1584199972)
        Timestamp('2020-03-14 15:32:52')
        
        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.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.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.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')
        
        Return an period of which this timestamp is an observation.

        Examples
        --------
        >>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
        >>> # Year end frequency
        >>> ts.to_period(freq='Y')
        Period('2020', 'A-DEC')

        >>> # Month end frequency
        >>> ts.to_period(freq='M')
        Period('2020-03', 'M')

        >>> # Weekly frequency
        >>> ts.to_period(freq='W')
        Period('2020-03-09/2020-03-15', 'W-SUN')

        >>> # Quarter end frequency
        >>> ts.to_period(freq='Q')
        Period('2020Q1', 'Q-DEC')
        
        Convert the Timestamp to a NumPy datetime64.

        .. versionadded:: 0.25.0

        This is an alias method for `Timestamp.to_datetime64()`. The dtype and
        copy parameters are available here only for compatibility. Their values
        will not affect the return value.

        Returns
        -------
        numpy.datetime64

        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')
        
        Return a numpy.datetime64 object with 'ns' precision.
        
        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
        
        Return POSIX timestamp as float.

        Examples
        --------
        >>> ts = pd.Timestamp('2020-03-14T15:32:52.192548')
        >>> ts.timestamp()
        1584199972.192548
        
        Return the time formatted according to ISO 8610.

        The full format looks like 'YYYY-MM-DD HH:MM:SS.mmmmmmnnn'.
        By default, the fractional part is omitted if self.microsecond == 0
        and self.nanosecond == 0.

        If self.tzinfo is not None, the UTC offset is also attached, giving
        giving a full format of 'YYYY-MM-DD HH:MM:SS.mmmmmmnnn+HH:MM'.

        Parameters
        ----------
        sep : str, default 'T'
            String used as the separator between the date and time.

        timespec : str, default 'auto'
            Specifies the number of additional terms of the time to include.
            The valid values are 'auto', 'hours', 'minutes', 'seconds',
            'milliseconds', 'microseconds', and 'nanoseconds'.

        Returns
        -------
        str

        Examples
        --------
        >>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')
        >>> ts.isoformat()
        '2020-03-14T15:32:52.192548651'
        >>> ts.isoformat(timespec='microseconds')
        '2020-03-14T15:32:52.192548'
        
        Normalize Timestamp to midnight, preserving tz information.

        Examples
        --------
        >>> ts = pd.Timestamp(2020, 3, 14, 15, 30)
        >>> ts.normalize()
        Timestamp('2020-03-14 00:00:00')
        
        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 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
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