]j٤^SSKJr SSKJr SSKrSSKJrJrJr SSK r SSK J r J r SSKJrJrJrJrJrJrJrJrJrJrJr SSKJr SSKJrJr SS KJ r J!r!J"r"J#r#J$r$ SS K%J&r' SS K(J)r) SS K*J+r+ SS K,J-r-J.r.J/r/J0r0J1r1J2r2J3r3 SSK4J5r5J6r6J7r7 SSK8J9r9 SSK:J;r;Jr> SSK?J@rA SSKBJCrC SSKDJEs JFrG SSKHJIrI \(aSSKJJKrKJLrL SSKMJNrNJOrOJPrPJQrQJRrRJSrS SSKTJUrU SSKVrVS SjrW\)"S5"SS\AR55rYS!S"SjjrZS#S$Sjjr[S%S&Sjjr\S'Sjr]g)() annotations) timedeltaN) TYPE_CHECKINGSelfcast)libtslibs) DayNaTNaTTypeTick Timedeltaastype_overflowsafeget_supported_dtypeiNaTis_supported_dtypeperiods_per_second to_offset)cast_from_unit_vectorized)get_timedelta_daysget_timedelta_field)array_to_timedelta64floordiv_object_arrayints_to_pytimedeltaparse_timedelta_unittruediv_object_array)function) set_module)validate_endpoints) TD64NS_DTYPEis_float_dtypeis_integer_dtypeis_object_dtype is_scalaris_string_dtype pandas_dtype) ArrowDtypeBaseMaskedDtypeExtensionDtype)isna)nanops roperator)datetimelike_accumulations) datetimelike)generate_regular_range)unpack_zerodim_and_defer)CallableIterator)AxisIntDateTimeErrorChoicesDtypeObjNpDtypenptTimeUnit DataFramecL^SU4SjjnXlSUS3Ul[U5$)Nc>URnTS:Xa[XRS9nO[UTURS9nUR(aUR USSS9nU$)Ndaysresofloat64) fill_valueconvert)asi8r_cresor_hasna_maybe_mask_results)selfvaluesresultaliass Z/home/mande/repo/quber/.venv/lib/python3.13/site-packages/pandas/core/arrays/timedeltas.pyf_field_accessor..fZs_ F?'[[AF )T[[IF ;;--4.F  )return np.ndarray)__name____doc__property)namerJ docstringrLs ` rK_field_accessorrWYs) JYKr"AI A;rNz pandas.arraysc^\rSrSr%SrSr\\R\ 4r Sr S\ S'Sr \SYSj5r\SZS j5rS r/rS \ S '/rS \ S '/SQrS \ S'\\-SS/-rS \ S'/SQrS \ S'S[Sjr\S\Sj5rSrS\ S'\S5r\S\4S]U4Sjjj5r\SSS.S^Sjj5r\SS\R@SS.S^S jj5r!\S_S`S!jj5r"SYS"jr#SaS#jr$SbS$jr%ScSdS&jjr&SeS'jr'SSSSSS%S(S).SfS*jjr(SSSS+SS%S,.SgS-jjr)S%S..ShU4S/jjjr*SiSjS0jjr+S1SS2.SkS3jjr,S4r-\."S55SlS6j5r/\/r0S7r1S8r2SmS9jr3\."S:5S;5r4\."S<5S=5r5\."S>5S?5r6\."S@5SA5r7\."SB5SC5r8\."SD5SE5r9\."SF5SG5r:\."SH5SI5r;SnSJjrSoSMjr?SpSNjr@\AR"SO5rC\D"SPSP\C5rE\AR"SQ5rF\D"SRSR\F5rG\AR"SS5rH\D"STST\H5rI\AR"SU5rJ\D"SVSV\J5rK\SqSWj5rLSXrMU=rN$)rTimedeltaArrayoa Pandas ExtensionArray for timedelta data. .. warning:: TimedeltaArray is currently experimental, and its API may change without warning. In particular, :attr:`TimedeltaArray.dtype` is expected to change to be an instance of an ``ExtensionDtype`` subclass. Parameters ---------- data : array-like The timedelta data. dtype : numpy.dtype Currently, only ``numpy.dtype("timedelta64[ns]")`` is accepted. freq : Offset, optional Frequency of the data. copy : bool, default False Whether to copy the underlying array of data. Attributes ---------- None Methods ------- None See Also -------- Timedelta : Represents a duration, the difference between two dates or times. TimedeltaIndex : Immutable Index of timedelta64 data. to_timedelta : Convert argument to timedelta. Examples -------- >>> pd.arrays.TimedeltaArray._from_sequence(pd.TimedeltaIndex(["1h", "2h"])) ['0 days 01:00:00', '0 days 02:00:00'] Length: 2, dtype: timedelta64[us] timedeltaarrayc0[R"US5$Nm)r is_np_dtypexs rKTimedeltaArray.sQRTWAXrNzCallable[[DtypeObj], bool]_is_recognized_dtype)r timedelta64cD[R"SUR5$)Nr )npreunitrGs rK_internal_fill_value#TimedeltaArray._internal_fill_values~~eTYY//rNc[$Nrris rK _scalar_typeTimedeltaArray._scalar_typesrNiz list[str] _other_ops _bool_ops)r=seconds microseconds nanoseconds _field_opsrhfreq_datetimelike_ops)to_pytimedelta total_secondsroundfloorceilas_unit_datetimelike_methodscURS5nU[R:Xa[$[R"X R S9$)Ni8r>)viewr _valuer_from_value_and_resorD)rGrays rK _box_funcTimedeltaArray._box_funcs4 FF4L  ?J--akkBBrNc.URR$)z The dtype for the TimedeltaArray. .. warning:: A future version of pandas will change dtype to be an instance of a :class:`pandas.api.extensions.ExtensionDtype` subclass, not a ``numpy.dtype``. 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Is*C __floordiv__c[Rn[U5(aURX5$UR U5n[ R "URS5(d4[UR5(d[UR5(aURX5$[UR5(a[R"U5nURS:aW[XSS9VVs/sH up4X4-PM nnnUVs/sHofR!SS5PM nn[R""USS9nO[%UR&U5nUR[(:XdeU$[*$s snnfs snfrL)rCrEr$r<r?rr_rr"r!rHr#rgrBrrNrOrPrrobjectr.rQs rKr[TimedeltaArray.__floordiv__s7    U  **55 5%%e, OOEKK - - ,,ekk****55 5 U[[ ) )JJu%Eyy1}58T5R5RkdDM5R8@@x!YYq"-x @ :.t}}eD<<6) ))M" !As ;E9E? __rfloordiv__c[Rn[U5(aURX5$UR U5n[ R "URS5(aURX5$[UR5(aC[[U55Vs/sH o1UX-PM nn[R"U5nU$[$s snfr])r,r9r$r<r?rr_rrHr#rrrgr-r.)rGrrr+rYrIs rKr_TimedeltaArray.__rfloordiv__s   U  **55 5%%e, ??5;; , ,**55 5 U[[ ) )8=c$i8HI8H18tw.8HKIXXk*FM" ! Js*C__mod__c`[XR5(a [U5nXU-U-- $rmrr3rrs rKrbTimedeltaArray.__mod__s1 e55 6 6e$Eu}---rN__rmod__c`[XR5(a [U5nXU-U-- $rmrdrs rKrfTimedeltaArray.__rmod__s1 e55 6 6e$E ---rN __divmod__ch[XR5(a [U5nX-nXU-- nX#4$rmrdrGrres1res2s rKriTimedeltaArray.__divmod__s: e55 6 6e$E}Ul"zrN __rdivmod__ch[XR5(a [U5nX-nXU-- nX#4$rmrdrks rKroTimedeltaArray.__rdivmod__s: e55 6 6e$E}d{"zrNcSnURb UR*n[U5RUR*URUS9$Nr)rwrrrr)rGrws rK__neg__TimedeltaArray.__neg__sB 99 II:DDz%%t}}nDJJT%RRrNc[U5RURR5URUR S9$rs)rrrrrrwris rK__pos__TimedeltaArray.__pos__s:Dz%% MM    &  rNc[U5R[R"UR5UR S9$)Nr()rrrgabsrrris rK__abs__TimedeltaArray.__abs__s-Dz%%bffT]]&;4::%NNrNcf[UR5nURURU- SS9$)a! Return total duration of each element expressed in seconds. This method is available directly on TimedeltaArray, TimedeltaIndex and on Series containing timedelta values under the ``.dt`` namespace. Returns ------- ndarray, Index or Series When the calling object is a TimedeltaArray, the return type is ndarray. When the calling object is a TimedeltaIndex, the return type is an Index with a float64 dtype. When the calling object is a Series, the return type is Series of type `float64` whose index is the same as the original. See Also -------- datetime.timedelta.total_seconds : Standard library version of this method. TimedeltaIndex.components : Return a DataFrame with components of each Timedelta. Examples -------- **Series** >>> s = pd.Series(pd.to_timedelta(np.arange(5), unit="D")) >>> s 0 0 days 1 1 days 2 2 days 3 3 days 4 4 days dtype: timedelta64[s] >>> s.dt.total_seconds() 0 0.0 1 86400.0 2 172800.0 3 259200.0 4 345600.0 dtype: float64 **TimedeltaIndex** >>> idx = pd.to_timedelta(np.arange(5), unit="D") >>> idx TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[s]', freq=None) >>> idx.total_seconds() Index([0.0, 86400.0, 172800.0, 259200.0, 345600.0], dtype='float64') N)rA)rrDrFrC)rGppss rKrzTimedeltaArray.total_secondss1l!-'' CD'IIrNc,[UR5$)a3 Return an ndarray of datetime.timedelta objects. Returns ------- numpy.ndarray A NumPy ``timedelta64`` object representing the same duration as the original pandas ``Timedelta`` object. The precision of the resulting object is in nanoseconds, which is the default time resolution used by pandas for ``Timedelta`` objects, ensuring high precision for time-based calculations. See Also -------- to_timedelta : Convert argument to timedelta format. Timedelta : Represents a duration between two dates or times. DatetimeIndex: Index of datetime64 data. Timedelta.components : Return a components namedtuple-like of a single timedelta. Examples -------- >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit="D") >>> tdelta_idx TimedeltaIndex(['1 days', '2 days', '3 days'], dtype='timedelta64[s]', freq=None) >>> tdelta_idx.to_pytimedelta() array([datetime.timedelta(days=1), datetime.timedelta(days=2), datetime.timedelta(days=3)], dtype=object) >>> tidx = pd.TimedeltaIndex(data=["1 days 02:30:45", "3 days 04:15:10"]) >>> tidx TimedeltaIndex(['1 days 02:30:45', '3 days 04:15:10'], dtype='timedelta64[us]', freq=None) >>> tidx.to_pytimedelta() array([datetime.timedelta(days=1, seconds=9045), datetime.timedelta(days=3, seconds=15310)], dtype=object) )rrris rKryTimedeltaArray.to_pytimedelta@sN#4==11rNa8Number of days for each element. See Also -------- Series.dt.seconds : Return number of seconds for each element. Series.dt.microseconds : Return number of microseconds for each element. Series.dt.nanoseconds : Return number of nanoseconds for each element. Examples -------- For Series: >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='D')) >>> ser 0 1 days 1 2 days 2 3 days dtype: timedelta64[s] >>> ser.dt.days 0 1 1 2 2 3 dtype: int64 For TimedeltaIndex: >>> tdelta_idx = pd.to_timedelta(["0 days", "10 days", "20 days"]) >>> tdelta_idx TimedeltaIndex(['0 days', '10 days', '20 days'], dtype='timedelta64[us]', freq=None) >>> tdelta_idx.days Index([0, 10, 20], dtype='int64')r=a2Number of seconds (>= 0 and less than 1 day) for each element. See Also -------- Series.dt.seconds : Return number of seconds for each element. Series.dt.nanoseconds : Return number of nanoseconds for each element. Examples -------- For Series: >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='s')) >>> ser 0 0 days 00:00:01 1 0 days 00:00:02 2 0 days 00:00:03 dtype: timedelta64[s] >>> ser.dt.seconds 0 1 1 2 2 3 dtype: int32 For TimedeltaIndex: >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='s') >>> tdelta_idx TimedeltaIndex(['0 days 00:00:01', '0 days 00:00:02', '0 days 00:00:03'], dtype='timedelta64[s]', freq=None) >>> tdelta_idx.seconds Index([1, 2, 3], dtype='int32')rsaNumber of microseconds (>= 0 and less than 1 second) for each element. See Also -------- pd.Timedelta.microseconds : Number of microseconds (>= 0 and less than 1 second). pd.Timedelta.to_pytimedelta.microseconds : Number of microseconds (>= 0 and less than 1 second) of a datetime.timedelta. Examples -------- For Series: >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='us')) >>> ser 0 0 days 00:00:00.000001 1 0 days 00:00:00.000002 2 0 days 00:00:00.000003 dtype: timedelta64[us] >>> ser.dt.microseconds 0 1 1 2 2 3 dtype: int32 For TimedeltaIndex: >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='us') >>> tdelta_idx TimedeltaIndex(['0 days 00:00:00.000001', '0 days 00:00:00.000002', '0 days 00:00:00.000003'], dtype='timedelta64[us]', freq=None) >>> tdelta_idx.microseconds Index([1, 2, 3], dtype='int32')rtaNumber of nanoseconds (>= 0 and less than 1 microsecond) for each element. See Also -------- Series.dt.seconds : Return number of seconds for each element. Series.dt.microseconds : Return number of nanoseconds for each element. Examples -------- For Series: >>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='ns')) >>> ser 0 0 days 00:00:00.000000001 1 0 days 00:00:00.000000002 2 0 days 00:00:00.000000003 dtype: timedelta64[ns] >>> ser.dt.nanoseconds 0 1 1 2 2 3 dtype: int32 For TimedeltaIndex: >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='ns') >>> tdelta_idx TimedeltaIndex(['0 days 00:00:00.000000001', '0 days 00:00:00.000000002', '0 days 00:00:00.000000003'], dtype='timedelta64[ns]', freq=None) >>> tdelta_idx.nanoseconds Index([1, 2, 3], dtype='int32')ruc^SSKJn /SQmURnU(aU4SjnOSnU"UVs/sH oC"U5PM snTS9nU(dURS5nU$s snf)a  Return a DataFrame of the individual resolution components of the Timedeltas. The components (days, hours, minutes seconds, milliseconds, microseconds, nanoseconds) are returned as columns in a DataFrame. Returns ------- DataFrame See Also -------- TimedeltaIndex.total_seconds : Return total duration expressed in seconds. Timedelta.components : Return a components namedtuple-like of a single timedelta. Examples -------- >>> tdelta_idx = pd.to_timedelta(["1 day 3 min 2 us 42 ns"]) >>> tdelta_idx TimedeltaIndex(['1 days 00:03:00.000002042'], dtype='timedelta64[ns]', freq=None) >>> tdelta_idx.components days hours minutes seconds milliseconds microseconds nanoseconds 0 1 0 3 0 0 2 42 rr9)r=hoursminutesrs millisecondsrtrucv>[U5(a[R/[T5-$UR$rm)r*rgr7r components)racolumnss rKrL$TimedeltaArray.components..f/s+77FF8c'l22||#rNcUR$rm)rr`s rKrLr6s ||#rN)rint64)pandasr:rEr)rGr:hasnansrLrarIrs @rKrTimedeltaArray.componentssc8 % ++  $ $$/$QAaD$/A]]7+F 0sA r)rPnp.timedelta64)rPztype[Timedelta])rarrPr2)rPnp.dtype[np.timedelta64])rHznpt.NDArray[np.timedelta64]rwrrrrPr)rboolrPrrm)rhr8rPr)rPr2)rPNone)T)rr)rPr2) rAxisInt | NonerNpDtype | Nonerrrrrint) rrrrrrrrrr)rUstrrr)F)rr)rz str | floatrPnpt.NDArray[np.object_])rPr)rPznp.ndarray | Self)rPrY)rPznpt.NDArray[np.float64])rPr)rPr:)OrR __module__ __qualname____firstlineno__rS_typrrgrer r3rd__annotations___infer_matchesrTrjro__array_priority__rqrrrvrxrrrr classmethodrr rrr no_defaultrrrrrrrrrrrr r#r0r%__rmul__r<r?rHrJrWr[r_rbrfrirortrwr{rzrytextwrapdedentdays_docstringrWr=seconds_docstringrsmicroseconds_docstringrtnanoseconds_docstringrur__static_attributes__ __classcell__)rs@rKrYrYosd)V D$bnnd;7X4X1N 00J IyNJ N#- #9VVF(N (N(NZA  I6%, $ $ 99 9  999. $ $ // /  ///,8< F F6 (-$ =$ =  = i(7B)7BrH-Q^,m,"-"<n-"."(n-"."<o."/""i(.). j).*. l+,m,-S  O7Jr'2R__ )!ND 66> :D  ' BG &__ '"F#L %OO '!D"K 66rNrYcUS;deSnUb [U5n[R"XSS9up[U[5(a UR nUR [:Xd[UR 5(a[XUS9nSnGO[UR 5(a[XS9upU=(a U(+nGO[UR 5(a[UR [5(aURnURnO[ R""U5nUbvUS:wap[ R$"S S 9 UR'[ R(5nSSS5 X`W:H-R+5nU(a[-USX#S 9up[.X'X4$[1X=(d S5n[.X'UR3S 5nSnO{[4R6"UR S 5(a<[9UR 5(d![;UR 5n [=X SS9nSnO[?SUR S35eU(d[ R@"U5nO[ RB"XS9nUR RDS :XdeUR S:wdeX4$!,(df  GNQ=f)at Parameters ---------- data : list-like copy : bool, default False unit : str, optional The timedelta unit to treat integers as multiples of. For numeric data this defaults to ``'ns'``. Must be un-specified if the data contains a str and ``errors=="raise"``. errors : {"raise", "coerce", "ignore"}, default "raise" How to handle elements that cannot be converted to timedelta64[ns]. See ``pandas.to_timedelta`` for details. Returns ------- converted : numpy.ndarray The sequence converted to a numpy array with dtype ``timedelta64[ns]``. inferred_freq : Tick, Day, or None The inferred frequency of the sequence. Raises ------ ValueError : Data cannot be converted to timedelta64[ns]. Notes ----- Unlike `pandas.to_timedelta`, if setting ``errors=ignore`` will not cause errors to be ignored; they are caught and subsequently ignored at a higher level. rNrY)cls_namerherrorsFrrignore)invalid)rrhrzm8[ns]r^rzdtype z' cannot be converted to timedelta64[ns]rm8)#rr!ensure_arraylike_for_datetimelikerrYrwrr]r%_objects_to_td64nsr"_ints_to_td64nsr!r)_mask_datargisnanerrstaterrallrrrrrr_rrrrrBr-r*) rrrhrr copy_maderGint_data all_roundrI_ new_dtypes rKrrCs=H  && &M #D)66 -JD$''  zzVtzz::!$&A $** % %)$:%I   # # djj. 1 1::D::D88D>D   X.;;rxx0/!12779I.5t  $ ,,(|t< yy" S ) )!$**--+DJJ7I&t5IDD& ,STUU zz$xx( ::??c !! ! ::    E/.s J33 KcXSnUbUOSnUR[R:wa!UR[R5nSnUS:waISUS3nUR U5n[ UR5nX@R:wa [ XS9nSnX4$UR S5nX4$)a} Convert an ndarray with integer-dtype to timedelta64[ns] dtype, treating the integers as multiples of the given timedelta unit. Parameters ---------- data : numpy.ndarray with integer-dtype unit : str, default "ns" The timedelta unit to treat integers as multiples of. Returns ------- numpy.ndarray : timedelta64[ns] array converted from data bool : whether a copy was made FrTz timedelta64[rr(ztimedelta64[ns])rrgrrrrr)rrhr dtype_strrs rKrrs I#4D zzRXX{{288$  t|"4&* yy#' 3  "&t=DI ?yy*+ ?rNc`[R"U[RS9n[X1US9nU$)a Convert an object-dtyped or string-dtyped array into a timedelta64[ns]-dtyped array. Parameters ---------- data : ndarray or Index unit : str, default "ns" The timedelta unit to treat integers as multiples of. Must not be specified if the data contains a str. errors : {"raise", "coerce", "ignore"}, default "raise" How to handle elements that cannot be converted to timedelta64[ns]. See ``pandas.to_timedelta`` for details. Returns ------- numpy.ndarray : timedelta64[ns] array converted from data Raises ------ ValueError : Data cannot be converted to timedelta64[ns]. Notes ----- Unlike `pandas.to_timedelta`, if setting `errors=ignore` will not cause errors to be ignored; they are caught and subsequently ignored at a higher level. r(r)rgrBobject_r)rrhrrHrIs rKrrs*@ZZBJJ /F !&F CF MrNc[U5nU[R"S5:Xa Sn[U5e[R "US5(d[SUS35e[ U5(d [S5eU$)NrzhPassing in 'timedelta' dtype with no precision is not allowed. Please pass in 'timedelta64[ns]' instead.r^zdtype 'z,' is invalid, should be np.timedelta64 dtypez;Supported timedelta64 resolutions are 's', 'ms', 'us', 'ns')r&rgrrrr_r)rmsgs rKrrst  E  8 o ??5# & &75')UVWW  & &VWW LrN)rUrrJrrVr)FNraise)rrrr4rPz$tuple[np.ndarray, Tick | Day | None])r)rhrrPztuple[np.ndarray, bool])Nr)rr4rPrQ)rPr5)^ __future__rdatetimerrCtypingrrrnumpyrg pandas._libsrr pandas._libs.tslibsr r r r rrrrrrrpandas._libs.tslibs.conversionrpandas._libs.tslibs.fieldsrrpandas._libs.tslibs.timedeltasrrrrrpandas.compat.numpyrrpandas.util._decoratorsrpandas.util._validatorsrpandas.core.dtypes.commonr r!r"r#r$r%r&pandas.core.dtypes.dtypesr'r(r)pandas.core.dtypes.missingr* pandas.corer+r,pandas.core.array_algosr-pandas.core.arraysr.rpandas.core.arrays._rangesr/pandas.core.commoncorecommonrpandas.core.ops.commonr0collections.abcr1r2pandas._typingr3r4r5r6r7r8rr:rrW TimelikeOpsrYrrrrrrNrKrs)"     E/.6 ,?2= ;2!, OLS__LLj #* j j ! j * jZ'V5<#1##LrN