+ xȇiT^RIHt^RIHt^RIt^RIHtHtHt^RI t ^RI H t H t ^RIHtHtHtHtHtHtHtHtHtHtHt^RIHt^RIHtHt^R IH t H!t!H"t"H#t#H$t$^R I%H&t'^R I(H)t)^R I*H+t+^R I,H-t-H.t.H/t/H0t0H1t1H2t2H3t3^RI4H5t5H6t6H7t7^RI8H9t9^RI:H;t;Ht>^RI?H@tA^RIBHCtC^RIDHEuHFtG^RIHHItI]'d^RIJHKtKHLtL^RIMHNtNHOtOHPtPHQtQHRtRHStS^RITHUtU^RIVtVRRltW])!R4!RR]AP44tYR%RRlltZR&RR llt[R'R!R"llt\R#R$lt]R#)() annotations) timedeltaN) TYPE_CHECKINGSelfcast)libtslibs) DayNaTNaTTypeTick Timedeltaastype_overflowsafeget_supported_dtypeiNaTis_supported_dtypeperiods_per_second to_offset)cast_from_unit_vectorized)get_timedelta_daysget_timedelta_field)array_to_timedelta64floordiv_object_arrayints_to_pytimedeltaparse_timedelta_unittruediv_object_array)function) set_module)validate_endpoints) TD64NS_DTYPEis_float_dtypeis_integer_dtypeis_object_dtype is_scalaris_string_dtype pandas_dtype) ArrowDtypeBaseMaskedDtypeExtensionDtype)isna)nanops roperator)datetimelike_accumulations) datetimelike)generate_regular_range)unpack_zerodim_and_defer)CallableIterator)AxisIntDateTimeErrorChoicesDtypeObjNpDtypenptTimeUnit DataFramec$V^8dQhRRRRRR/#)namestralias docstring)formats"m/Users/max/.openclaw/workspace/postharvest/venv/lib/python3.14/site-packages/pandas/core/arrays/timedeltas.py __annotate__rDYs!#cccNaRV3RllpWnRV R2Vn\V4#)cV^8dQhRR/#)r<return np.ndarrayrA)rBs"rCrD%_field_accessor..__annotate__Zs:rEc<VPpSR8Xd\WPR7pM\VSVPR7pVP'dVP VRRR7pV#)daysresoNfloat64) fill_valueconvert)asi8r_cresor_hasna_maybe_mask_results)selfvaluesresultr?s& rCf_field_accessor..fZsa F?'[[AF )T[[IF ;;;--4.F rE )__name____doc__property)r=r?r@rYs&f& rC_field_accessorr_Ys. JYKr"AI A;rEz pandas.arraysca]tRt^ot$RtRt]]P] 3t Rt R] R&Rrt ]RRl4t]RR l4tR t.tR ] R &.tR ] R &.RsOtR ] R&]],RR.,tR ] R&.RtOtR ] R&RRlt]RRl4tRtR] R&]R4t]R]3RV3R lll4t]R!RR"R#/R$R%ll4t]R!RR"R#R]P@RR/R&R'll4t!]RuR(R)ll4t"R*R+lt#R,R-lt$R.R/lt%RvR1R2llt&R3R4lt'R5RR!RR6RR7R#R8RR9R0R:^/R;R<llt(R5RR!RR6RR=^R7R#R9R0/R>R?llt)R9R0/R@V3RAlllt*RwRBRCllt+RDRERFR/RGRHllt,RIt-].!RJ4RKRLl4t/]/t0RMt1RNt2RORPlt3].!RQ4RR4t4].!RS4RT4t5].!RU4RV4t6].!RW4RX4t7].!RY4RZ4t8].!R[4R\4t9].!R]4R^4t:].!R_4R`4t;RaRbltRgRhlt?RiRjlt@]AP!Rk4tC]D!RR]C4tE]AP!Rl4tF]D!RR]F4tG]AP!Rm4tH]D!RR]H4tI]AP!Rn4tJ]D!RR]J4tK]RoRpl4tLRqtMV;tN#)xTimedeltaArraya 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] timedeltaarrayc 0\P!VR4#m)r is_np_dtypexs&rCTimedeltaArray.sQRTWAXrEzCallable[[DtypeObj], bool]_is_recognized_dtypecV^8dQhRR/#r<rHnp.timedelta64rA)rBs"rCrDTimedeltaArray.__annotate__s00n0rEc D\P!RVP4#)r )np timedelta64unitrVs&rC_internal_fill_value#TimedeltaArray._internal_fill_values~~eTYY//rEcV^8dQhRR/#)r<rHztype[Timedelta]rA)rBs"rCrDrosorEc \#Nrrts&rC _scalar_typeTimedeltaArray._scalar_typesrEiz list[str] _other_ops _bool_opsrLseconds microseconds nanoseconds _field_opsrsfreq_datetimelike_ops_datetimelike_methodsc V^8dQhRRRR/#)r<rhrnrHTimedelta | NaTTyperA)rBs"rCrDrosCC>C.ACrEc VPR4pV\P8Xd\#\P!W P R7#)i8rM)viewr _valuer_from_value_and_resorS)rVrhys&& rC _box_funcTimedeltaArray._box_funcs4 FF4L  ?J--akkBBrEcV^8dQhRR/#)r<rHnp.dtype[np.timedelta64]rA)rBs"rCrDros##/#rEc .VPP#)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 &\Pp\V4'dVPW4#VP V4p\ P !VPR4'g7\VP4'g\VP4'dVPW4#\VP4'd\P!V4pVP^8d`\WRR7UUu.uF wr4W4,NK pppVUu.uFqfP!^R4NK pp\P"!V^R7pM\%VP&V4pVP\(8XgQhV#\*#uuppiuupirk)rbrdr$rZr]rrfrr"r!rgr#rqrarrmrnrorrobjectrMrps && rCrzTimedeltaArray.__floordiv__s9    U  **55 5%%e, OOEKK - - ,,ekk****55 5 U[[ ) )JJu%Eyy1}58T5R5RkdDMM5R8@@x!YYq"-x @ :.t}}eD<<6) ))M" !As FF __rfloordiv__c \Pp\V4'dVPW4#VP V4p\ P !VPR4'dVPW4#\VP4'dO\\V44Uu.uFq1V,W,,NK pp\P!V4pV#\#uupird)r,rWr$rZr]rrfrrgr#rrrqrLrM)rVrr-rJrxrXs&& rCr~TimedeltaArray.__rfloordiv__s   U  **55 5%%e, ??5;; , ,**55 5 U[[ ) )8=c$i8HI8H18tw..8HKIXXk*FM" ! Js-C+__mod__c z\WP4'd \V4pWV,V,, #ryrrQrrs&&rCrTimedeltaArray.__mod__s1 e55 6 6e$Eu}---rE__rmod__c z\WP4'd \V4pWV,V,, #ryrrs&&rCrTimedeltaArray.__rmod__s1 e55 6 6e$E ---rE __divmod__c \WP4'd \V4pW,pWV,, pW#3#ryrrVrres1res2s&& rCrTimedeltaArray.__divmod__s: e55 6 6e$E}Ul"zrE __rdivmod__c \WP4'd \V4pW,pWV,, pW#3#ryrrs&& rCrTimedeltaArray.__rdivmod__s: e55 6 6e$E}d{"zrEcV^8dQhRR/#r<rHrarA)rBs"rCrDrosSSSrEc RpVPeVP)p\V4PVP)VPVR7#)Nr)rrrrr)rVrs& rC__neg__TimedeltaArray.__neg__sB 99 II:DDz%%t}}nDJJT%RRrEcV^8dQhRR/#rrA)rBs"rCrDros   rEc \V4PVPP4VPVP R7#)r)rrrrrrrts&rC__pos__TimedeltaArray.__pos__s:Dz%% MM    &  rEcV^8dQhRR/#rrA)rBs"rCrDrosOOOrEc \V4P\P!VP4VP R7#)rG)rrrqabsrrrts&rC__abs__TimedeltaArray.__abs__s-Dz%%bffT]]&;4::%NNrEcV^8dQhRR/#)r<rHznpt.NDArray[np.float64]rA)rBs"rCrDros7J7J67JrEc t\VP4pVPVPV, RR7#)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)rP)rrSrUrR)rVppss& rC total_secondsTimedeltaArray.total_secondss1l!-'' CD'IIrEcV^8dQhRR/#)r<rHr:rA)rBs"rCrDro@s'2'2 7'2rEc ,\VP4#)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) )rrrts&rCto_pytimedeltaTimedeltaArray.to_pytimedelta@sN#4==11rEa8Number 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')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')aNumber 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')aNumber 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')cV^8dQhRR/#)r<rHr:rA)rBs"rCrDros66I6rEc a^RIHp.ROoVPpV'dV3RlpMRpT!VUu.uF qC!V4NK upSR7pV'gVPR4pV#uupi)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 r9c<\V4'd"\P.\S4,#VP#ry)r*rqrUr components)rhcolumnss&rCrY$TimedeltaArray.components..f/s+77FF8c'l22||#rEcVP#ry)rrgs&rCrYr6s ||#rE)rint64)rLhoursminutesr millisecondsrr)pandasr:rTr)rVr:hasnansrYrhrXrs& @rCrTimedeltaArray.componentssc8 % ++  $ $$/$QAaD$/A]]7+F 0sA$rA)rrr)rLrrr)rrroundfloorceilrry)T)F)Or\ __module__ __qualname____firstlineno__r]_typrrqrrr rQrk__annotations___infer_matchesr^rur{__array_priority__r}r~rrrrrr classmethodrr rrr no_defaultrrrrrrr rrr+r5r>rAr0rC__rmul__rZr]rgrirvrzr~rrrrrrrrrtextwrapdedentdays_docstringr_rLseconds_docstringrmicroseconds_docstringrnanoseconds_docstringrr__static_attributes__ __classcell__)rs@rCraraos.)V D$bnnd;7X4X1N 00J IyNJ N#- #9VV :D  ' BG &__ '"F#L %OO '!D"K 66rErac$V^8dQhRRRRRR/#)r<rrerrorsr4rHz$tuple[np.ndarray, Tick | Day | None]rA)rBs"rCrDrDCs-ii i ! i * irEc|VR9gQhRpVe \V4p\P!WRR7wr\V\4'd VP pVP \8Xg\VP 4'd\WVR7pRpEM\VP 4'd"\WR7wrT;'dV'*pEM\VP 4'd\VP \4'dVPpVPpM\ P"!V4pVecVR8wd\VP%\ P&4pW`V8H,P)4pV'd\+VRW#R7wr\,W&W3#\/Y;'gR4p\,W&VP1R 4pRpM\2P4!VP R 4'dA\7VP 4'g%\9VP 4p \;W RR 7pRpM\=R VP R 24hV'g\ P>!V4pM\ P@!WR7pVP PBR 8XgQhVP R8wgQhW3#)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. Nra)cls_namersrFrr)rrsrzm8[ns]rerzdtype z' cannot be converted to timedelta64[ns]rm8r)"rr!ensure_arraylike_for_datetimelikerrarrr|r%_objects_to_td64nsr"_ints_to_td64nsr!r)_mask_datarqisnanrrallrrrrrrfrrrr*rarLrI) rrrsrr copy_maderfint_data all_roundrX_ new_dtypes &&&& rCrrCs'H  && &M #D)66 -JD$''  zzVtzz::!$&A $** % %)$:%%I   # # djj. 1 1::D::D88D>D   {{288,H!12779I.5t  $ ,,(||t< yy" S ) )!$**--+DJJ7I&t5IDD& ,STUU zz$xx( ::??c !! ! ::    rEc V^8dQhRRRR/#)r<rsr>rHztuple[np.ndarray, bool]rA)rBs"rCrDrDs'''/F'rEcfRpVeTMRpVP\P8wd"VP\P4pRpVR8wdNRV R2pVP V4p\ VP4pW@P8wd\ WR7pRpW3#VP R4pW3#)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[rrGztimedelta64[ns])rrqrrrrr)rrsr dtype_strrs&& rCrrs I#4D zzRXX{{288$  t|"4&* yy#' 3  "&t=DI ?yy*+ ?rEc V^8dQhRRRR/#)r<rr4rHrIrA)rBs"rCrDrDs##1##rEcl\P!V\PR7p\W1VR7pV#)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. rGr)rqraobject_r)rrsrrWrXs&&& rCrrs*@ZZBJJ /F !&F CF MrEcV^8dQhRR/#)r<rHr5rA)rBs"rCrDrDs8rEc\V4pV\P!R48XdRp\V4h\P !VR4'g\RV R24h\ V4'g \R4hV#)rzhPassing in 'timedelta' dtype with no precision is not allowed. Please pass in 'timedelta64[ns]' instead.rezdtype 'z,' is invalid, should be np.timedelta64 dtypez;Supported timedelta64 resolutions are 's', 'ms', 'us', 'ns')r&rqrrrrfr)rmsgs& rCrrst  E  8 o ??5# & &75')UVWW  & &VWW LrE)FNraise)r)Nr)^ __future__rdatetimerrbtypingrrrnumpyrq 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:rr_ TimelikeOpsrarrrrrArErCrs"     E/.6 ,?2= ;2!, OLS__LLfiX'T#LrE