MhƓSSKJr SSKJr SSKrSSKJrJr SSKr SSK J r J r SSK JrJrJrJrJrJrJrJrJr SSKJr SSKJrJr SS KJrJrJrJ r J!r! SS K"J#r$ SS K%J&r& SS K'J(r(J)r)J*r*J+r+J,r,J-r-J.r. SS K/J0r0 SSK1J2r2 SSK3J4r4J5r5 SSK6J7r7 SSK8J9r: SSK;Js J?r@ SSKAJBrB \(aSSKCJDrD SSKEJFrFJGrGJHrHJIrIJJrJJKrK SSKLJMrM SSKNrNSSjrO"SS\:R5rQSS SjjrRS!S"SjjrSS#S$SjjrTS%SjrUg)&) annotations) timedeltaN) TYPE_CHECKINGcast)libtslibs) NaTNaTTypeTick Timedeltaastype_overflowsafeget_supported_dtypeiNaTis_supported_dtypeperiods_per_second)cast_from_unit_vectorized)get_timedelta_daysget_timedelta_field)array_to_timedelta64floordiv_object_arrayints_to_pytimedeltaparse_timedelta_unittruediv_object_array)function)validate_endpoints) TD64NS_DTYPEis_float_dtypeis_integer_dtypeis_object_dtype is_scalaris_string_dtype pandas_dtype)ExtensionDtype)isna)nanops roperator)datetimelike_accumulations) datetimelike)generate_regular_range)unpack_zerodim_and_defer)Iterator)AxisIntDateTimeErrorChoicesDtypeObjNpDtypeSelfnpt DataFramecL^SU4SjjnXlSUS3Ul[U5$)Nc>URnTS:Xa[XRS9nO[UTURS9nUR(aUR USSS9nU$)Ndaysresofloat64) fill_valueconvert)asi8r_cresor_hasna_maybe_mask_results)selfvaluesresultaliass O/var/www/html/env/lib/python3.13/site-packages/pandas/core/arrays/timedeltas.pyf_field_accessor..fRs_ F?'[[AF )T[[IF ;;--4.F  )returnz np.ndarray)__name____doc__property)namerC docstringrEs ` rD_field_accessorrOQs) JYKr"AI A;rGc^\rSrSr%SrSr\R"SS5r\ \R\ 4r Sr Sr \SWSj5rS r/rS \S '/rS \S 'S /rS \S'/SQrS \S'\\-\-S/-rS \S'/SQrS \S'SXSjr\SYSj5rSr\r\S5r\S\4SZU4Sjjj5r\SSS.S[Sjj5r \SS\!RDSS.S[Sjj5r#\S\SS.S]S jjj5r$S^S!jr%S_S"jr&S`S#jr'SaSbS%jjr(ScS&jr)SSSSSS$S'S(.SdS)jjr*SSSS*SS$S+.SeS,jjr+S$S-.SfU4S.jjjr,SgShS/jjr-SSS0.SiS1jjr.S2r/\0"S35SjS4j5r1\1r2S5r3S6r4SkS7jr5\0"S85S95r6\0"S:5S;5r7\0"S<5S=5r8\0"S>5S?5r9\0"S@5SA5r:\0"SB5SC5r;\0"SD5SE5r<\0"SF5SG5r=SlSHjr>SlSIjr?SlSJjr@SmSKjrASnSLjrB\CR"SM5rE\F"SNSN\E5rG\CR"SO5rH\F"SPSP\H5rI\CR"SQ5rJ\F"SRSR\J5rK\CR"SS5rL\F"STST\L5rM\SoSUj5rNSVrOU=rP$)pTimedeltaArrayga 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 ---------- values : array-like The timedelta data. dtype : numpy.dtype Currently, only ``numpy.dtype("timedelta64[ns]")`` is accepted. freq : Offset, optional copy : bool, default False Whether to copy the underlying array of data. Attributes ---------- None Methods ------- None Examples -------- >>> pd.arrays.TimedeltaArray._from_sequence(pd.TimedeltaIndex(['1h', '2h'])) ['0 days 01:00:00', '0 days 02:00:00'] Length: 2, dtype: timedelta64[ns] timedeltaarrayr nsc0[R"US5$Nm)r is_np_dtypexs rDTimedeltaArray.sS__Q%[R"US5(de[R"U5(ae[ U[ R 5(d[U55eX1R:XdeUb[ U[5(de[TU]-XS9nX$l U$)NrW)rAr) rrXr is_unitless isinstancenpndarraytyperr super _simple_new_freq)rrArfrrB __class__s rDrTimedeltaArray._simple_newsuc****%%e,,,,&"**--;tF|;- $$$|z$5555$F$@  rGFrcopycU(a [U5n[XSS9upUb [XSS9nURXRUS9$)NrrlFrrrf)rsequence_to_td64nsr rr)rdatarrrfs rD_from_sequenceTimedeltaArray._from_sequencesF (/E'dC   &tuEDt::DAArG)rrrfrlcU(a [U5nUS;de[XUS9upUb [XSS9nURXRUS9nUR U05 U$)zW _from_sequence_not_strict but without responsibility for finding the result's `freq`. YrzMrFrr)rrr rr_maybe_pin_freq)rrrrrfrl inferred_freqrBs rD_from_sequence_not_strict(TimedeltaArray._from_sequence_not_strictsk (/E?***0tL  &tuEDZZmLtR( rGrlc[R"U5nUc$[SX1U455(a [S5e[R "XX45S:wa [S5eUb[ U5RS5nUb[ U5RS5nUbUS;a [S5eOSnUbUbURUSS 9nUbUbURUSS 9n[U5upxUb [XX4US 9n O;[R"URURU5RS 5n U(dU S Sn U(dU SS n U RSUS35n URXR US9$)Nc3(# UHoSLv M g7fr_).0rZs rD 1TimedeltaArray._generate_range..sI3HaT 3Hsz1Must provide freq argument if no data is suppliedzVOf the four parameters: start, end, periods, and freq, exactly three must be specifiedrT)smsusrTz+'unit' must be one of 's', 'ms', 'us', 'ns'F)round_okrrvzm8[]r)dtlvalidate_periodsanyrcomcount_not_noner rsrr)rlinspacerxastyperwrr) rstartendperiodsrfclosedrl left_closed right_closedindex td64valuess rD_generate_rangeTimedeltaArray._generate_ranges&&w/ !  *5w4PEKK cjj'BII$OE!"IE#2JEZZ#dV1 . z1A1AMMrGc2[XR5(dU[La [S5eUR U5 U[La+[ R "URUR5$URUR5R$)Nz'value' should be a Timedelta.) rrbr r_check_compatible_withrr]rxrlrsasm8r@values rD _unbox_scalarTimedeltaArray._unbox_scalar?sm%!2!233S8H=> > ##E* C<>>%,, : :==+00 0rGc[U5$r_r`rs rD_scalar_from_string"TimedeltaArray._scalar_from_stringHs rGcgr_rr@others rDr%TimedeltaArray._check_compatible_withKs rGTc[U5n[R"US5(aXR:XaU(aUR 5$U$[ U5(aB[ URUSS9n[U5RX3RURS9$[SURSUS35e[RRXUS9$)NrWFrrzCannot convert from z to z1. Supported resolutions are 's', 'ms', 'us', 'ns')r"rrXrrrr r~rrrfrrDatetimeLikeArrayMixinr)r@rr res_valuess rDrTimedeltaArray.astypeRs U# ??5# & & "99;& !%((0ER Dz--&6&6TYY.!*4::,d5'BFF ))0040HHrGc#0# URS:a"[[U55H nXv M gURn[U5nSnX4-S-n[U5H/nX-n[ US-U-U5n[ X&USS9nUShvN M1 gN 7f)Nri'Tbox)ndimrangelenr~minr) r@irlength chunksizechunksstart_iend_i converteds rD__iter__TimedeltaArray.__iter__ms 99q=3t9%g &==DYFI)Q.F6]-QUi/8/U0CN $$$ #%sBB B Br)axisroutkeepdimsinitialskipna min_countc[R"SX#XES.5 [R"URXUS9nUR X5$)Nr)rrrr)rrr)nv validate_sumr%nansumr~_wrap_reduction_result) r@rrrrrrrrBs rDsumTimedeltaArray.sumsH  %V  MMy **488rGr)rrrddofrrc[R"SX#US.SS9 [R"URXUS9nUbUR S:XaUR U5$URU5$)Nr)rrrstd)fname)rrrr)rvalidate_stat_ddof_funcr%nanstdr~rr{_from_backing_data)r@rrrrrrrBs rDrTimedeltaArray.stdse "" %B% t}}4TR <499>>>&) )&&v..rG)rc >US:XaW[[U5nU"URR54SU0UD6n[ U5R USUR S9$US:Xa [S5e[TU]$"U4SU0UD6$)Ncumsumr)rfrcumprodz$cumprod not supported for Timedelta.) getattrr'r~rrrr TypeErrorr _accumulate)r@rMrkwargsoprBrs rDrTimedeltaArray._accumulates 8 3T:B **,FVFvFF:))&t4::)N N Y BC C7&tEFEfE ErGcSSKJn U"USS9$)Nrget_format_timedelta64Tr)pandas.io.formats.formatr)r@boxedrs rD _formatterTimedeltaArray._formattersC%d55rG)na_rep date_formatc nSSKJn U"X5n[R"USS5"UR5$)Nrrr)rrr frompyfuncr~)r@r r rr formatters rD_format_native_types#TimedeltaArray._format_native_typess2 D+48 }}Y1-dmm<S T  rG__mul__cP[U5(aURU-nURRS:wa![ S[ U5R 35eSnURb1[U5(d!URU-nURS:XaSn[ U5RX"RUS9$[US5(d[R"U5n[U5[U5:wa1[R "URS5(d [#S5e[%UR5(aoURn['[U55Vs/sH oTUX-PM nn[R"U5n[ U5RX"RS9$URU-nURRS:wa![ S[ U5R 35e[ U5RX"RS9$s snf)NrWzCannot multiply with rrrz$Cannot multiply with unequal lengthsr)r r~rkindrrrJrfr$nrhasattrrarrayrrrXrrr)r@rrBrfarrrs rDrTimedeltaArray.__mul__s U  ]]U*F||  C' "7U 8L8L7M NOODyy$T%[[yy5(66Q;D:))& 4)P Pug&&HHUOE u:T "3??5;;+L+LCD D 5;; ' '--C16s4y1AB1AA!fux'1AFBXXf%F:))& )E E& <<   #3DK4H4H3IJK KDz%%fLL%AACs*H#c[XR5(a[U5n[SU5[LaN[ R "UR[ RS9nUR[ R5 U$U"URU5$U[R[R4;a7[S[!U5R"S[!U5R"35eU"URU5nSnUR$b;UR$U- nUR&S:XaUR$R&S:waSn[!U5R)XDR*US9$)z^ Shared logic for __truediv__, __rtruediv__, __floordiv__, __rfloordiv__ with scalar 'other'. Timedelta | NaTTyperzCannot divide z by Nrr)r_recognized_scalarsr rr remptyshaper9fillnanr~r&rtruediv rfloordivrrrJrfnanosrr)r@rrresrBrfs rD_scalar_divlike_op!TimedeltaArray._scalar_divlike_ops/ e55 6 6e$E)51S8hhtzz<  dmmU+ + i(()*=*=>>$T%[%9%9$:$tDz?R?R>ST u-FDyy$yy5(::?tyy!'; D:))& 4)P PrGc[US5(d[R"U5n[U5[U5:wa [ S5eU$)Nrz*Cannot divide vectors with unequal lengths)rrrrrrs rD_cast_divlike_opTimedeltaArray._cast_divlike_op's=ug&&HHUOE u:T "IJ J rGczU"UR[R"U55n[UR5(d[ UR5(aFU[ R[ R4;a"[U5RX3RS9$U[ R[R4;auUR5[U5-nUR5(aDUR[R 5n[R""X4[R$5 U$)zo Shared logic for __truediv__, __floordiv__, and their reversed versions with timedelta64-dtype ndarray other. r)r~rasarrayrrroperatortruedivfloordivrrr&r&r$rrr9putmaskr$)r@rrrBmasks rD_vector_divlike_op!TimedeltaArray._vector_divlike_op0s DMM2::e#45 U[[ ) )^EKK-H-Hb      U O :))& )E E (##Y%8%89 999;e,Dxxzzrzz2 60 rG __truediv__c[Rn[U5(aURX5$UR U5n[ R "URS5(d4[UR5(d[UR5(aURX5$[UR5(a[R"U5nURS:aY[X5VVs/sH up4X4- PM nnnUVs/sHofR!SS5PM nn[R""USS9nU$[%UR&U5nU$[($s snnfs snfNrWrrr)r)r0r1r r)r,rrXrrrr5rrr/rzipreshape concatenaterr~NotImplemented r@rrleftrightres_colsrZ res_cols2rBs rDr7TimedeltaArray.__truediv__Fs"   U  **55 5%%e, OOEKK - - ,,ekk****55 5 5;; ' 'JJu%Eyy1}s rDrHTimedeltaArray.__floordiv__xs)    U  **55 5%%e, OOEKK - - ,,ekk****55 5 U[[ ) )JJu%Eyy1}=@=MN=MkdDM=MN7?@x!YYq"-x @ :.t}}eD<<6) ))M" !O@s <E:F __rfloordiv__c[Rn[U5(aURX5$UR U5n[ R "URS5(aURX5$[UR5(aC[[U55Vs/sH o1UX-PM nn[R"U5nU$[$s snfrV)r&r&r r)r,rrXrr5rrrrrr=)r@rrrrFrBs rDrLTimedeltaArray.__rfloordiv__s   U  **55 5%%e, ??5;; , ,**55 5 U[[ ) )8=c$i8HI8H18tw.8HKIXXk*FM" ! Js*C__mod__c`[XR5(a [U5nXU-U-- $r_rr r rs rDrOTimedeltaArray.__mod__s1 e55 6 6e$Eu}---rG__rmod__c`[XR5(a [U5nXU-U-- $r_rQrs rDrSTimedeltaArray.__rmod__s1 e55 6 6e$E ---rG __divmod__ch[XR5(a [U5nX-nXU-- nX#4$r_rQr@rres1res2s rDrVTimedeltaArray.__divmod__s: e55 6 6e$E}Ul"zrG __rdivmod__ch[XR5(a [U5nX-nXU-- nX#4$r_rQrXs rDr\TimedeltaArray.__rdivmod__s: e55 6 6e$E}d{"zrGcSnURb UR*n[U5RUR*URUS9$Nr)rfrrr~r)r@rfs rD__neg__TimedeltaArray.__neg__sB 99 II:DDz%%t}}nDJJT%RRrGc[U5RURR5URUR S9$r`)rrr~rrrfras rD__pos__TimedeltaArray.__pos__s:Dz%% MM    &  rGc[U5R[R"UR5UR S9$)Nr)rrrabsr~rras rD__abs__TimedeltaArray.__abs__s-Dz%%bffT]]&;4::%NNrGcf[UR5nURURU- SS9$)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[ns] >>> 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[ns]', freq=None) >>> idx.total_seconds() Index([0.0, 86400.0, 172800.0, 259200.0, 345600.0], dtype='float64') N)r:)rr=r?r<)r@ppss rDroTimedeltaArray.total_secondss1l!-'' CD'IIrGc,[UR5$)a Return an ndarray of datetime.timedelta objects. Returns ------- numpy.ndarray Examples -------- >>> tdelta_idx = pd.to_timedelta([1, 2, 3], unit='D') >>> tdelta_idx TimedeltaIndex(['1 days', '2 days', '3 days'], dtype='timedelta64[ns]', freq=None) >>> tdelta_idx.to_pytimedelta() array([datetime.timedelta(days=1), datetime.timedelta(days=2), datetime.timedelta(days=3)], dtype=object) )rr~ras rDrnTimedeltaArray.to_pytimedeltas$#4==11rGaCNumber of days 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[ns] >>> 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[ns]', freq=None) >>> tdelta_idx.days Index([0, 10, 20], dtype='int64')r6aNumber of seconds (>= 0 and less than 1 day) 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[ns] >>> 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[ns]', freq=None) >>> tdelta_idx.seconds Index([1, 2, 3], dtype='int32')rhaNumber of microseconds (>= 0 and less than 1 second) for each element. 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[ns] >>> 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[ns]', freq=None) >>> tdelta_idx.microseconds Index([1, 2, 3], dtype='int32')riaNumber of nanoseconds (>= 0 and less than 1 microsecond) 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')rjc^SSKJn /SQmURnU(aU4SjnOSnU"UVs/sH oC"U5PM snTS9nU(dURS5nU$s snf)aX Return a DataFrame of the individual resolution components of the Timedeltas. The components (days, hours, minutes seconds, milliseconds, microseconds, nanoseconds) are returned as columns in a DataFrame. Returns ------- DataFrame Examples -------- >>> tdelta_idx = pd.to_timedelta(['1 day 3 min 2 us 42 ns']) >>> tdelta_idx TimedeltaIndex(['1 days 00:03:00.000002042'], dtype='timedelta64[ns]', freq=None) >>> tdelta_idx.components days hours minutes seconds milliseconds microseconds nanoseconds 0 1 0 3 0 0 2 42 rr2)r6hoursminutesrh millisecondsrirjcv>[U5(a[R/[T5-$UR$r_)r$rr$r components)rZcolumnss rDrE$TimedeltaArray.components..fs+77FF8c'l22||#rGcUR$r_)rtrYs rDrErvs ||#rG)ruint64)pandasr3r>r)r@r3hasnansrErZrBrus @rDrtTimedeltaArray.componentssc, % ++  $ $$/$QAaD$/A]]7+F 0sA r)rIztype[Timedelta])rZnp.timedelta64rIr)rInp.dtype[np.timedelta64])rAznpt.NDArray[np.timedelta64]rfz Tick | Nonerr}rIr0)rboolrIr0r_)rlz str | NonerIr0)rIr|)rIr)rINone)T)rr~)rIr+) rAxisInt | NonerNpDtype | Nonerr~rr~rint) rrrrrrrr~rr~)rMstrrr~)F)rr~)r z str | floatrInpt.NDArray[np.object_])rIr0)rIznp.ndarray | Self)rIrQ)rIznpt.NDArray[np.float64])rIr)rIr3)QrJ __module__ __qualname____firstlineno__rK_typrr]_internal_fill_valuerr r _is_recognized_dtype_infer_matchesrLrb__array_priority__rd__annotations__rergrkrmrtr{rrr_default_dtype classmethodrrrr no_defaultrrrrrrrrrrr rrr*r__rmul__r)r,r5r7rDrHrLrOrSrVr\rardrhrorntextwrapdedentdays_docstringrOr6seconds_docstringrhmicroseconds_docstringrinanoseconds_docstringrjrt__static_attributes__ __classcell__)rs@rDrQrQgsk#J D>>%6$bnnd;<1N J Iy$XK%NJ N#- #;i#G6(#RyR(9C ##& E!N!*6 +(   "+/e B B  ^^    8/3+NKO+N>H+N +N+N`1  I6%, $ $ 99 9  999. $ $ // /  ///,8< F F6 (-$ =$ =  = i('B)'BRH'QR,m,"-"8n-"."(n-"."8o."/""i(.). j).*. l+,m,-S  O7Jr2(__ )N8 66> :D  '8G &__ ':#L %OO ':"K 00rGrQctUS;deSnUb [U5n[R"XSS9up[U[5(a UR nUR [:Xd[UR 5(a[XUS9nSnGOJ[UR 5(a[XS9upU=(a U(+nGO[UR 5(a~[UR [5(aURnURnO[ R""U5n[%X=(d S5n[&X'UR)S 5nSnO{[*R,"UR S 5(a<[/UR 5(d![1UR 5n[3XSS 9nSnO[5S UR S 35eU(d[ R6"U5nO[ R8"XS9nUR R:S :XdeUR S:wdeX4$)an Parameters ---------- data : list-like copy : bool, default False unit : str, optional The timedelta unit to treat integers as multiples of. For numeric data this defaults to ``'ns'``. Must be un-specified if the data contains a str and ``errors=="raise"``. errors : {"raise", "coerce", "ignore"}, default "raise" How to handle elements that cannot be converted to timedelta64[ns]. See ``pandas.to_timedelta`` for details. Returns ------- converted : numpy.ndarray The sequence converted to a numpy array with dtype ``timedelta64[ns]``. inferred_freq : Tick or None The inferred frequency of the sequence. Raises ------ ValueError : Data cannot be converted to timedelta64[ns]. Notes ----- Unlike `pandas.to_timedelta`, if setting ``errors=ignore`` will not cause errors to be ignored; they are caught and subsequently ignored at a higher level. rNrQ)cls_namerlerrorsFrrTzm8[ns]rWrzdtype z' cannot be converted to timedelta64[ns]rm8)rr!ensure_arraylike_for_datetimelikerrQrfrrJr!_objects_to_td64nsr_ints_to_td64nsrr#_mask_datarisnanrrrwrrXrrr rr/rr)rrrlrr copy_mader4 new_dtypes rDrrsH  && &M #D)66 -JD$''  zzVtzz::!$&A $** % %)$:%I   # # djj. 1 1::D::D88D>D(|t< yy" S ) )!$**--+DJJ7I&t5IDD& ,STUU zz$xx( ::??c !! ! ::    rGcSnUbUOSnUR[R:wa!UR[R5nSnUS:wa*SUS3nUR U5n[ U[ S9nSnX4$UR S5nX4$)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 FrTTz timedelta64[rrtimedelta64[ns])rrrxrrwr r)rrlr dtype_strs rDrrFs I#4D zzRXX{{288$  t|"4&* yy#"4|< ?yy*+ ?rGc~[R"U[RS9n[X1US9nUR S5$)a Convert a object-dtyped or string-dtyped array into an timedelta64[ns]-dtyped array. Parameters ---------- data : ndarray or Index unit : str, default "ns" The timedelta unit to treat integers as multiples of. Must not be specified if the data contains a str. errors : {"raise", "coerce", "ignore"}, default "raise" How to handle elements that cannot be converted to timedelta64[ns]. See ``pandas.to_timedelta`` for details. Returns ------- numpy.ndarray : timedelta64[ns] array converted from data Raises ------ ValueError : Data cannot be converted to timedelta64[ns]. Notes ----- Unlike `pandas.to_timedelta`, if setting `errors=ignore` will not cause errors to be ignored; they are caught and subsequently ignored at a higher level. rrr)rr/object_rrw)rrlrrArBs rDrrns4<ZZBJJ /F !&F CF ;;( ))rGc[U5nU[R"S5:Xa Sn[U5e[R "US5(d[SUS35e[ U5(d [S5eU$)NrzhPassing in 'timedelta' dtype with no precision is not allowed. Please pass in 'timedelta64[ns]' instead.rWzdtype 'z,' is invalid, should be np.timedelta64 dtypez;Supported timedelta64 resolutions are 's', 'ms', 'us', 'ns')r"rrrrrXr)rmsgs rDrrst  E  8 o ??5# & &75')UVWW  & &VWW LrG)rMrrCrrNr)FNraise)rr~rr-rIztuple[np.ndarray, Tick | None])rT)rlr)Nr)rr-)rIr.)V __future__rdatetimerr0typingrrnumpyr pandas._libsrrpandas._libs.tslibsr r r r r rrrrpandas._libs.tslibs.conversionrpandas._libs.tslibs.fieldsrrpandas._libs.tslibs.timedeltasrrrrrpandas.compat.numpyrrpandas.util._validatorsrpandas.core.dtypes.commonrrrrr r!r"pandas.core.dtypes.dtypesr#pandas.core.dtypes.missingr$ pandas.corer%r&pandas.core.array_algosr'pandas.core.arraysr(rpandas.core.arrays._rangesr)pandas.core.commoncorecommonrpandas.core.ops.commonr*collections.abcr+pandas._typingr,r-r.r/r0r1ryr3rrO TimelikeOpsrQrrrrrrGrDrs"    E/65+?2= ;(!,x S__x B #* ] ] ! ] $ ]@%P!*HrG