jdZddlmZddlmZmZddlmZmZm Z m Z m Z m Z ddl ZddlmZmZddlmZmZmZmZmZmZmZddlmZdd lmZdd lm Z m!Z!m"Z"m#Z#dd l$m%Z%dd l&m'Z'm(Z(dd l)m*Z*ddl+m,Z,m-Z-ddl.m/Z/m0Z0m1Z1m2Z2ddl3m4cm5Z6ddl7m4cm8cm9Z:ddl7m;Z;ddlm?Z?ddl@mAZAerddlBmCZCddlDmDZDddlEmFZFmGZGmHZHmIZIddlJmKZKeLe:jMZMGdde=eZNGddeNeZOdS)z; Base and utility classes for tseries type pandas objects. ) annotations)ABCabstractmethod) TYPE_CHECKINGAnyLiteralSelfcastfinalN)NaTlib) BaseOffset ResolutionTick Timedelta Timestampparsing to_offset)abbrev_to_npy_unit)function)InvalidIndexErrorNullFrequencyErrorOutOfBoundsDatetimeOutOfBoundsTimedelta)cache_readonly) is_integer is_list_like) concat_compat)CategoricalDtype PeriodDtype) DatetimeArrayExtensionArray PeriodArrayTimedeltaArray)Index)NDArrayBackedExtensionIndex) RangeIndex) to_timedelta)Sequence)datetime)AxisJoinHowTimeUnitnpt)CategoricalIndexceZdZUdZdZded<dddd?d Zed@dZej dAdZedBdZ edCdZ e e dDdZe dCdZe dEdZdFdZdGdZfdZd Zd!d"dHd(Zed)Zfd*ZdIdCfd+ ZedJd-ZdKd.ZdLd1ZdMd3ZedNd6ZdOd8ZdPdQd=Z d>Z!xZ"S)RDatetimeIndexOpsMixinzM Common ops mixin to support a unified interface datetimelike Index. Fz,DatetimeArray | TimedeltaArray | PeriodArray_dataTrskipnaaxisr4boolr5 int | Nonec:|j||S)a Return the mean value of the Array. Parameters ---------- skipna : bool, default True Whether to ignore any NaT elements. axis : int, optional, default 0 Axis for the function to be applied on. Returns ------- scalar Timestamp or Timedelta. See Also -------- numpy.ndarray.mean : Returns the average of array elements along a given axis. Series.mean : Return the mean value in a Series. Notes ----- mean is only defined for Datetime and Timedelta dtypes, not for Period. Examples -------- For :class:`pandas.DatetimeIndex`: >>> idx = pd.date_range("2001-01-01 00:00", periods=3) >>> idx DatetimeIndex(['2001-01-01', '2001-01-02', '2001-01-03'], dtype='datetime64[us]', freq='D') >>> idx.mean() Timestamp('2001-01-02 00:00:00') For :class:`pandas.TimedeltaIndex`: >>> 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.mean() Timedelta('2 days 00:00:00') r3)r2mean)selfr4r5s NC:\Projects\DSCons\.venv\Lib\site-packages\pandas\core\indexes\datetimelike.pyr9zDatetimeIndexOpsMixin.mean`sZzf4888returnBaseOffset | Nonec|jjS)ac Return the frequency object if it is set, otherwise None. To learn more about the frequency strings, please see :ref:`this link`. See Also -------- DatetimeIndex.freq : Return the frequency object if it is set, otherwise None. PeriodIndex.freq : Return the frequency object if it is set, otherwise None. Examples -------- >>> datetimeindex = pd.date_range( ... "2022-02-22 02:22:22", periods=10, tz="America/Chicago", freq="h" ... ) >>> datetimeindex DatetimeIndex(['2022-02-22 02:22:22-06:00', '2022-02-22 03:22:22-06:00', '2022-02-22 04:22:22-06:00', '2022-02-22 05:22:22-06:00', '2022-02-22 06:22:22-06:00', '2022-02-22 07:22:22-06:00', '2022-02-22 08:22:22-06:00', '2022-02-22 09:22:22-06:00', '2022-02-22 10:22:22-06:00', '2022-02-22 11:22:22-06:00'], dtype='datetime64[us, America/Chicago]', freq='h') >>> datetimeindex.freq r2freqr:s r;rAzDatetimeIndexOpsMixin.freqs8zr<Nonec||j_dSNr@)r:values r;rAzDatetimeIndexOpsMixin.freqs  r<npt.NDArray[np.int64]c|jjSrE)r2asi8rBs r;rIzDatetimeIndexOpsMixin.asi8 zr<strcddlm}|jj>> idx = pd.DatetimeIndex(["1/1/2020 10:00:00+00:00"], freq="D") >>> idx.freqstr 'D' The frequency can be inferred if there are more than 2 points: >>> idx = pd.DatetimeIndex( ... ["2018-01-01", "2018-01-03", "2018-01-05"], freq="infer" ... ) >>> idx.freqstr '2D' For PeriodIndex: >>> idx = pd.PeriodIndex(["2023-1", "2023-2", "2023-3"], freq="M") >>> idx.freqstr 'M' r) PeriodIndex) pandasrMr2freqstr isinstancer#r rA_freqstr)r:rMrAs r;rOzDatetimeIndexOpsMixin.freqstrsc@ '&&&&& :  )j Jk2/ / )tz//8DK:% %r<rcdSrErBs r;_resolution_objz%DatetimeIndexOpsMixin._resolution_objs-0Sr<c|jjS)zO Returns day, hour, minute, second, millisecond or microsecond )r2 resolutionrBs r;rVz DatetimeIndexOpsMixin.resolutions z$$r<c|jjSrE)r2_hasnarBs r;hasnanszDatetimeIndexOpsMixin.hasnanss z  r<otherrc||rdSt|tsdS|jjdvrdSt|t |sd}|jj}|jtkr |j |v}n8t|jtrtd|}|j j |v}|r8 t ||}n#tttf$rYdSwxYwt |t |krdS|j|jkrt!j|j|jS|jjdkr|j|jks|jjdkrv |j|j\}}t!j|d|dS#t,t.f$rYdSwxYwdS)zL Determines if two Index objects contain the same elements. TFiufcr/Mmi8)is_rPr%dtypekindtyper2_infer_matchesobject inferred_typerr categories ValueError TypeError OverflowErrornp array_equalrItz_ensure_matching_resosviewrr)r:rZ should_try inferableleftrights r;equalszDatetimeIndexOpsMixin.equalss 88E?? 4%'' !5 [  ' '5E4::.. !J 1I{f$$"0I= EK)9:: I/77"-;yH  !!&DJJu--EE"I}=!!! !55 ! ::e $ $5 Z5; & &>$)UZ88 8jo$$EH)<)<TWAWAW I"j?? LL e~diioouzz$7G7GHHH()=>   uu us$?CC32C38"GG*)G*keyct| ||n$#tttt f$rYdSwxYwdS)a Return a boolean indicating whether the provided key is in the index. Parameters ---------- key : label The key to check if it is present in the index. Returns ------- bool Whether the key search is in the index. Raises ------ TypeError If the key is not hashable. See Also -------- Index.isin : Returns an ndarray of boolean dtype indicating whether the list-like key is in the index. Examples -------- >>> idx = pd.Index([1, 2, 3, 4]) >>> idx Index([1, 2, 3, 4], dtype='int64') >>> 2 in idx True >>> 6 in idx False FT)hashget_locKeyErrorrirhr)r:rus r; __contains__z"DatetimeIndexOpsMixin.__contains__s[D S   LL    )Z1BC   55 ts'AActjt|}t ||SrE)rkasarrayr(to_numpysuper_convert_tolerance)r: tolerancetarget __class__s r;rz(DatetimeIndexOpsMixin._convert_toleranceGs@J|I66??AABB ww)))V<<>Eu%% %uuRyy(HuuRyy(Hx'002215 5r<rcft|trk ||\}}n.#t$r!}|d||Yd}~nd}~wwxYw|||\}}|dkr|n|St||jjs|d||S)aL If label is a string, cast it to scalar type according to resolution. Parameters ---------- label : object side : {'left', 'right'} Returns ------- label : object Notes ----- Value of `side` parameter should be validated in caller. rNrr)rPrKrrh_raise_invalid_indexerrr2_recognized_scalars)r:rrrrrloweruppers r;_maybe_cast_slice_boundz-DatetimeIndexOpsMixin._maybe_cast_slice_bounds" eS ! ! 8 A#44U;;  A A A++GUC@@@@@@@@  A  88vFFLE5 FNN55 5E4:#ABB 8  ' ' 7 7 7 s0 AAAperiodsintr ct)ae Shift index by desired number of time frequency increments. This method is for shifting the values of datetime-like indexes by a specified time increment a given number of times. Parameters ---------- periods : int, default 1 Number of periods (or increments) to shift by, can be positive or negative. freq : pandas.DateOffset, pandas.Timedelta or string, optional Frequency increment to shift by. If None, the index is shifted by its own `freq` attribute. Offset aliases are valid strings, e.g., 'D', 'W', 'M' etc. Returns ------- pandas.DatetimeIndex Shifted index. See Also -------- Index.shift : Shift values of Index. PeriodIndex.shift : Shift values of PeriodIndex. r)r:rrAs r;shiftzDatetimeIndexOpsMixin.shifts 6"!r<c |j|d}nC#ttf$r/t |t st j|}n|}YnwxYwt||j S)zT Analogue to maybe_cast_indexer for get_indexer instead of get_loc. T) allow_objectra) r2_validate_listlikerhrirPr"comasarray_tuplesafer%ra)r:keyarrress r;_maybe_cast_listlike_indexerz2DatetimeIndexOpsMixin._maybe_cast_listlike_indexers *//T/JJCCI&   fn55 +F33  S ****s=AA)r4r6r5r7)r=r>)r=rC)r=rG)r=rK)r=rr=r6)rZrr=r6)rurr=r6)rrrrKrrr=rrE)rrr=r6)rr)rrKr=r)rurKr=r)rrrr*r=r)rrKrNrrr=r )#__name__ __module__ __qualname____doc___can_hold_strings__annotations__r9propertyrAsetterrIrOrrrTrVrYrtrzr_default_na_reprrrrr rrrrrrrr __classcell__rs@r;r1r1Xs7777%)a-9-9-9-9-9-9^X: [   [ X'&'&'&X'&R000^^0%%%^%!!!^!++++Z''''R===== OLP      ''X'     , +++ U+ """",)))) )6)6)6 U)6VH"""""> + + + + + + +r<r1ceZdZUdZded<ddgZddgZejZ ej Z ej Z edBdZdCd Zd ZedDdZdEdFdZedGdZedHdZdIdZdJdZdJdZdJdZdKdLd!Zd"ZdMd#ZdMd$ZdNdOd%Zfd&Z d'Z!dPfd. Z"dDd/Z#dQd1Z$dRdSfd5 Z%dTd8Z&dUd9Z'dJfd: Z(dUfd; Z) dVdWdAZ*xZ+S)XDatetimeTimedeltaMixinze Mixin class for methods shared by DatetimeIndex and TimedeltaIndex, but not PeriodIndex zDatetimeArray | TimedeltaArrayr2rrAr=r-c|jjSrE)r2unitrBs r;rzDatetimeTimedeltaMixin.unit6rJr<rr c|j|}t|||jS)a Convert to a dtype with the given unit resolution. This method is for converting the dtype of a ``DatetimeIndex`` or ``TimedeltaIndex`` to a new dtype with the given unit resolution/precision. Parameters ---------- unit : {'s', 'ms', 'us', 'ns'} Returns ------- same type as self Converted to the specified unit. See Also -------- Timestamp.as_unit : Convert to the given unit. Timedelta.as_unit : Convert to the given unit. DatetimeIndex.as_unit : Convert to the given unit. TimedeltaIndex.as_unit : Convert to the given unit. Examples -------- For :class:`pandas.DatetimeIndex`: >>> idx = pd.DatetimeIndex(["2020-01-02 01:02:03.004005006"]) >>> idx DatetimeIndex(['2020-01-02 01:02:03.004005006'], dtype='datetime64[ns]', freq=None) >>> idx.as_unit("s") DatetimeIndex(['2020-01-02 01:02:03'], dtype='datetime64[s]', freq=None) For :class:`pandas.TimedeltaIndex`: >>> 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.as_unit("s") TimedeltaIndex(['1 days 00:03:00'], dtype='timedelta64[s]', freq=None) r)r2as_unitrc _simple_newr)r:rarrs r;rzDatetimeTimedeltaMixin.as_unit:s;Xj  &&Dzz%%c %:::r<c|j|}t|||jS)Nr)r2 _with_freqrcr_name)r:rArs r;rz!DatetimeTimedeltaMixin._with_freqis8j##D))Dzz%%c %;;;r< np.ndarrayc^|jj}|}d|j_|S)NF)r2rroflags writeable)r:datas r;valueszDatetimeTimedeltaMixin.valuesms*z"yy{{$  r<rNrrc|9||jkr.t|trt|}||z}||zS|dkst |dkr|S|jt d|d||jzz}|d||jzz}|j||d|j|j }t| ||j S)ad Shift index by desired number of time frequency increments. This method is for shifting the values of datetime-like indexes by a specified time increment a given number of times. Parameters ---------- periods : int, default 1 Number of periods (or increments) to shift by, can be positive or negative. freq : pandas.DateOffset, pandas.Timedelta or string, optional Frequency increment to shift by. If None, the index is shifted by its own `freq` attribute. Offset aliases are valid strings, e.g., 'D', 'W', 'M' etc. Returns ------- pandas.DatetimeIndex Shifted index. See Also -------- Index.shift : Shift values of Index. PeriodIndex.shift : Shift values of PeriodIndex. NrzCannot shift with no freqr)startendrrArr) rArPrKrrcopyrr2_generate_rangerrcrr)r:rrAoffsetrrrs r;rzDatetimeTimedeltaMixin.shiftus4   1 1$$$ ' t^F&= a<<3t99>>99;;  9 $%@AA AQ'DI--2h49,, ++S$TYTY,  Dzz%%f49%===r<rc|jjS)a Return the inferred frequency of the index. Returns ------- str or None A string representing a frequency generated by ``infer_freq``. Returns ``None`` if the frequency cannot be inferred. See Also -------- DatetimeIndex.freqstr : Return the frequency object as a string if it's set, otherwise ``None``. Examples -------- For ``DatetimeIndex``: >>> idx = pd.DatetimeIndex(["2018-01-01", "2018-01-03", "2018-01-05"]) >>> idx.inferred_freq '2D' 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.inferred_freq '10D' )r2rrBs r;rz$DatetimeTimedeltaMixin.inferred_freqsBz''r<r'ctt|j}t||jj}t|dj|dj|z|}t|SNrr) r rrArrr_valueranger')r:rAtickrngs r;_as_range_indexz&DatetimeTimedeltaMixin._as_range_indexsaD$)$$&&ty118DGNDHOd$:DAA#r<r6cjt|jtot|jtSrE)rPrArr:rZs r;_can_range_setopz'DatetimeTimedeltaMixin._can_range_setops%$)T**Kz%*d/K/KKr<cd}t|s|j}nUt|tr@t t |j|j|j}|j |j j j }t|j ||j |}t!d|||S)N)r)rarAr )rrArPr'rrsteprrrror2rrarcrr _wrap_setop_result)r:rZres_i8new_freq res_valuesrs r;_wrap_range_setopz(DatetimeTimedeltaMixin._wrap_range_setops6{{ yHH  + +  &+DI666>>tyIIH]'' (;(ABB dj!!-- * .  FD33E6BBCCCr<cx|j}|j}|||}|||SNsort)r intersectionrr:rZrrrrsr s r;_range_intersectz'DatetimeTimedeltaMixin._range_intersects@#%""5t"44%%eV444r<cx|j}|j}|||}|||Sr)runionrrs r; _range_unionz#DatetimeTimedeltaMixin._range_unions=#%E--%%eV444r<FrZr%rcztd|}||r|||S||sUt j|||}|||}|ddS|||S)z_ intersection specialized to the case with matching dtypes and both non-empty. rrNinfer) r rr_can_fast_intersectr% _intersectionr r_fast_intersect)r:rZrrs r;rz$DatetimeTimedeltaMixin._intersections-u55   ' ' ;((T(:: :''.. 5(u4@@@F ,,UF;;F$$T**55g>> >''t44 4r<c|d|dkr||}}n||}}t|d|d}|d}||kr |dd}n*t|||}|j|}|Sr)minr slice_locs_values) r:rZrrrrsrrrlslices r;rz&DatetimeTimedeltaMixin._fast_intersects 7eAh  %DD%D$r(E"I&&a ;;"1"XFFDOOE3778F\&)F r<cj|jdS|j|jkrdS|jsdS|jjdkS)NFr)rArnrs r;rz*DatetimeTimedeltaMixin._can_fast_intersect,sD 9 5 Z49 $ $5- 5 y{ar<c|j}| ||jkrdS|jsdSt|dkst|dkrdS|d|dkr||}}n||}}|d}|d}|||zkp||vS)NFrTr)rArr)r:rZrArrrs right_startleft_ends r;_can_fast_unionz&DatetimeTimedeltaMixin._can_fast_union>sy <45:--5+ 5 t99>>SZZ1__4 7eAh  %DD%DAh 8x$.F;$3FFr<c|d|dkr||}}n{|durs||}}|d}||d}|jd|}t|j|f}t|||j} | S||}}|d} |d} | | kr|| d}|j|d}t|j|g}t |t|jsJ|j|j ksJt||} | S|S)NrFrrrrrrs) rr"rrcrrrPr2_freqrA) r:rZrrrrs left_startloc right_chunkdatesrr( right_ends r; _fast_unionz"DatetimeTimedeltaMixin._fast_union\s` 7eAh  %DD U]]%DaJ$$Zf$==C--K!4<"=>>E$ZZ++E +BBFM%D8"I  i  $$XG$<>EeT$*%5%566 6 66;$)++++$ZZ++E22FMKr<ct|t|sJ|j|jksJ||r|||S||r|||}|St|| dS)Nrr) rPrcrarrr)r1r~_unionr)r:rZrrrs r;r3zDatetimeTimedeltaMixin._unions%d,,,,,zU[((((   ' ' 7$$U$66 6    & & C%%e$%77FM77>>%..99'BB Br<cBd}||r|j}|S)zK Get the freq to attach to the result of a join operation. N)r)rA)r:rZrAs r;_get_join_freqz%DatetimeTimedeltaMixin._get_join_freqs+    & & 9D r<lidxnpt.NDArray[np.intp] | Noneridxhowr,Etuple[Self, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]c|j|jksJ|j|jft|||||\}}}|||j_|||fSrE)rar~_wrap_join_resultr5r2r+)r:joinedrZr6r8r9 join_indexrs r;r<z(DatetimeTimedeltaMixin._wrap_join_results~{dj(((5; *C(((!&!:!: E4s" "  D$"&!4!4U!;!; 4%%r<c@|jjdS)Nr_)r2rrorBs r;_get_engine_targetz)DatetimeTimedeltaMixin._get_engine_targetsz"''---r<rc~||jjj}|j|SrE)ror2rra_from_backing_data)r:rs r;_from_join_targetz(DatetimeTimedeltaMixin._from_join_targets1TZ0677z,,V444r<rrrLiteral['left', 'right']c|jrt|ttfr}t |jt |jkrS|dkr||j}n2||j|j}t ||S)Nrs) rrPrrrrrceilr~_searchsorted_monotonic)r:rrrs r;rGz.DatetimeTimedeltaMixin._searchsorted_monotonics  ( A59i"899 A#5:..1CDI1N1NNNw di00 49--55di@@ww..ud;;;r<r-int | slice | Sequence[int]cd}|jt|r.|dt| dt|dz fvr|j}nt|r@t jt j|t jt|}t|tr1|j dvr(|j dvs|j t|dfvr|j}|S)z7 Find the `freq` for self.delete(loc). Nrrrrr)rN)rArrrr maybe_indices_to_slicerkr|intprPrr rstop)r:r-rAs r;_get_delete_freqz'DatetimeTimedeltaMixin._get_delete_freqs 9 # )1s4yyj"c$ii!m<<<9D$$4 3bg666D Cc5)))ch).C.CyI--c$ii=N1N1N#y r<c|j|}|j|}d}|j|jri|t urn|dt | fvr||jz|dkr|j}nr|t |kr||jz |dkr|j}nCt|jtr|j}n!|j |r|j}|S)z= Find the `freq` for self.insert(loc, item). Nrr) r2_validate_scalar _box_funcrAsizer rrPr is_on_offset)r:r-itemrFrAs r;_get_insert_freqz'DatetimeTimedeltaMixin._get_insert_freqs ++D11z##E** 9 y !3;;QT O++ty0@DG0K0K9DDSYY&&D49,<R,H,H9DDIt,, !y''-- !y r<ct|}|||j_|S)a Make new Index with passed location(-s) deleted. Parameters ---------- loc : int or list of int Location of item(-s) which will be deleted. Use a list of locations to delete more than one value at the same time. Returns ------- Index Will be same type as self, except for RangeIndex. See Also -------- numpy.delete : Delete any rows and column from NumPy array (ndarray). Examples -------- >>> idx = pd.Index(["a", "b", "c"]) >>> idx.delete(1) Index(['a', 'c'], dtype='str') >>> idx = pd.Index(["a", "b", "c"]) >>> idx.delete([0, 2]) Index(['b'], dtype='str') )r~deleterMr2r+)r:r-rrs r;rVzDatetimeTimedeltaMixin.deletes78$$!22377  r<ct||}t|t|r ||||j_|S)a Make new Index inserting new item at location. Follows Python numpy.insert semantics for negative values. Parameters ---------- loc : int The integer location where the new item will be inserted. item : object The new item to be inserted into the Index. Returns ------- Index Returns a new Index object resulting from inserting the specified item at the specified location within the original Index. See Also -------- Index.append : Append a collection of Indexes together. Examples -------- >>> idx = pd.Index(["a", "b", "c"]) >>> idx.insert(1, "x") Index(['a', 'x', 'b', 'c'], dtype='str') )r~insertrPrcrTr2r+)r:r-rSrrs r;rXzDatetimeTimedeltaMixin.insertsT8T** fd4jj ) ) B!%!6!6sD!A!AFL  r<rTr5r+ allow_fillc Vtjd|tj|tj}t j|||||fi|}tj|t|}t|tr&|j |}||j _|S)a Return a new Index of the values selected by the indices. For internal compatibility with numpy arrays. Parameters ---------- indices : array-like Indices to be taken. axis : {0 or 'index'}, optional The axis over which to select values, always 0 or 'index'. allow_fill : bool, default True How to handle negative values in `indices`. * False: negative values in `indices` indicate positional indices from the right (the default). This is similar to :func:`numpy.take`. * True: negative values in `indices` indicate missing values. These values are set to `fill_value`. Any other other negative values raise a ``ValueError``. fill_value : scalar, default None If allow_fill=True and fill_value is not None, indices specified by -1 are regarded as NA. If Index doesn't hold NA, raise ValueError. **kwargs Required for compatibility with numpy. Returns ------- Index An index formed of elements at the given indices. Will be the same type as self, except for RangeIndex. See Also -------- numpy.ndarray.take: Return an array formed from the elements of a at the given indices. Examples -------- >>> idx = pd.Index(["a", "b", "c"]) >>> idx.take([2, 2, 1, 2]) Index(['c', 'c', 'b', 'c'], dtype='str') rSr)nv validate_takerkr|rKr&taker rJrrPrr2_get_getitem_freqr+) r:indicesr5rY fill_valuekwargsr maybe_slicerAs r;r]zDatetimeTimedeltaMixin.take>sb V$$$*WBG444,1 '4Z  ;A  0#d))DD k5 ) ) &:// <0>0>0>0>d ( ( (^ (J^LLLLDDDD45555555555555.(    $GGGG<$$$$$LCCCCC& & & & & & &....5555 <<<<<<<&,4@      P <<<<<<<<<r<r)Pr __future__rabcrrtypingrrrr r r numpyrk pandas._libsr r pandas._libs.tslibsrrrrrrrpandas._libs.tslibs.dtypesrpandas.compat.numpyrr[ pandas.errorsrrrrpandas.util._decoratorsrpandas.core.dtypes.commonrrpandas.core.dtypes.concatrpandas.core.dtypes.dtypesrr pandas.core.arraysr!r"r#r$pandas.core.commoncorecommonrpandas.core.indexes.baseindexesbaseibaser%pandas.core.indexes.extensionr&pandas.core.indexes.ranger'pandas.core.tools.timedeltasr(collections.abcr)r*pandas._typingr+r,r-r.rNr/dict_index_doc_kwargsr1rrSr<r;rs#""""":99999...... 433333  ! ((((((((((((FEEEEE000000555555 (((((((!!!!!!('''''D011L+L+L+L+L+7L+L+L+^S S S S S 2CS S S S S r<