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Decorator to simplify 'return_indexers' checks in Index.join. leftNFhowlevelreturn_indexerssortcl>T "XX#US9upgnU(dU$Ub [U5nUb [U5nXgU4$)Nrrr)rC) selfotherrrrr join_indexlidxridxmeths J/var/www/html/env/lib/python3.13/site-packages/pandas/core/indexes/base.pyjoin$_maybe_return_indexers..join sL"&dsd!S $   &t,D  &t,D%%)rrrr(rboolrr) functoolswrapsr r%)rrs` r_maybe_return_indexersrsb __T  %&& &  &&&& 4=rc"[U[5(aSSKJn U"U40UD6$[U[5(a&SU;aSU;aUR S5US'SUS'OSU;aSU;aUSR US'UR"U40UD6$) zj This is called upon unpickling, rather than the default which doesn't have arguments and breaks __new__. r)_new_PeriodIndexlabelscodesFverify_integritydtypedata) issubclassrapandas.core.indexes.periodrr`popr__new__)clsdrs r _new_Indexr$s#~&&?)q))#}%% q=WA-xAgJ!&   fkvY__' ;;s a  rc^\rSrSr%SrSr\GS!Sj5r\GS"Sj5r\GS"Sj5r \GS"Sj5r Sr S \ S 'S \ S '\ R\4rS \ S'SrS\ S'SrS\ S'SrS\ S'S/rS\ S'S/rS\ S'\GS#Sj5r\ R0"\ R25\R6\ R0"\ R85\R:\ R0"\ R<5\R>\ R0"\ R@5\RB\ R0"\ RD5\RF\ R0"\ RH5\RJ\ R0"\ RL5\RN\ R0"\ RP5\RR\ R0"\ RT5\RV\ R0"\ RX5\RZ\ R0"\ R\5\R^\ R0"\ R`5\Rb0 r2S\ S'\3GS$Sj5r4Sr5S 1r6\7"S \85r9Sr:GS%GS&S jjr;\\<GS)GS*S#jj5r?\S>jj5rYGS5GS?S?jjrZ\GS/S@j5r[\GS9GS/SAjj5r\\GS@SBj5r]\3SC5r^\GS9GS@SDjj5r_GSASEjr`\GSBSFj5ra\GS3SGj5rbGSCGSDSIjjrcSHrd\SSJ.GSESKjj5reGSFSLjrfSMSNSSSSO.GSGSPjjrgGS9GS@SQjjrhGS/SRjri\GS)GSHSSjj5rjS\kR4GSISTjjrl\3GSJSUj5rm\mRGSKSVj5rm\GSLGSMSWjj5roGS)GSNSXjjrpGSOSYjrqSSZ.GS,S[jjrr\3"\r\qS\9rs\tS]S]S^.GSPS_jj5ru\tS]SZ.GSQS`jj5ru\tS]S]S^.GSRSajj5ruSSS^.GSRSbjjru\tS]Sc.GSPSdjj5rv\tGSQSej5rv\w"SfSgS/ShSi9GSSGSRSjjj5rv\3GS2Skj5rxGS/Sljry\GS,Smj5rzGS2Snjr{GSTGSUSpjjr|GSVSqjr}\}r~\GSWGSXSrjj5r\GSYSsj5r\\GS#Stj55r\3GS#Suj5r\3GS#Svj5r\\3GS#Swj55r\\3GS#Sxj55r\GS#Syj5r\\3GS#Szj55r\GS#S{j5r\GS#S|j5r\GS#S}j5r\GS#S~j5r\GS#Sj5r\GS#Sj5r\GS#Sj5r\GS#Sj5r\GS#Sj5r\GS@Sj5r\\GS#Sj55r\\GS#Sj55rSr\S5r\GSZSj5r\GS#Sj5r\GSZSj5r\r\GSZSj5r\rS\kR4SjrGS[GS\SjjrGS9GS]U4SjjjrSoS.GS^U4SjjjrGS_GS`SjjrSr\GSaSj5r\rSr\S5r\GSbSj5r\GS9Sj5rGScSjr\GSdSj5r\GSSGSeSjj5rGSSGSfSjjrSr\GSgSj5r\GS9Sj5rSrSrGS)Sjr\GS#Sj5rGShSjrSr\GSiGSjSjj5rGSiGSkSjjr\GSlSj5r\GS)GSmSjj5rGSnSjr\GS)GSoSjj5r\GS9GSoSjj5r\GSpSj5r\GSqSj5r\GSrSj5r\GSsSj5rGStSjr\\kR4GSuSjj5r\GSvSj5rGSwGSxSjjrGSySjrGSzSjr\GS{Sj5r\tS]S]S]S.GS|Sjj5r\tS]S]S]S]S.GS}Sjj5r\tS]S]S]S]S.GS~Sjj5r\\SSSSS.GSSjj55r\GSSj5r\GSSj5r\GSSj5r\GSGSSjj5r\GSGSSjj5r\GSGSSjj5rGSSjr\\GS#Sj55r\3GSSj5r\\"\GR5GSSj55r\3GSSj5rGSSjr\GS3Sj5rGSSjr\"\GR5GSSGSSjj5r\GS9GSVSjj5r\\` for more usages. copy : bool, default False Copy input data. name : object Name to be stored in the index. tupleize_cols : bool (default: True) When True, attempt to create a MultiIndex if possible. See Also -------- RangeIndex : Index implementing a monotonic integer range. CategoricalIndex : Index of :class:`Categorical` s. MultiIndex : A multi-level, or hierarchical Index. IntervalIndex : An Index of :class:`Interval` s. DatetimeIndex : Index of datetime64 data. TimedeltaIndex : Index of timedelta64 data. PeriodIndex : Index of Period data. Notes ----- An Index instance can **only** contain hashable objects. An Index instance *can not* hold numpy float16 dtype. Examples -------- >>> pd.Index([1, 2, 3]) Index([1, 2, 3], dtype='int64') >>> pd.Index(list('abc')) Index(['a', 'b', 'c'], dtype='object') >>> pd.Index([1, 2, 3], dtype="uint8") Index([1, 2, 3], dtype='uint8') icnUR5nUR5n[R"X#5$N)_get_join_targetlibjoinleft_join_indexer_unique)rrsvovs r_left_indexer_uniqueIndex._left_indexer_unique}s1 " " $  # # %//77rcUR5nUR5n[R"X#5upEnURU5nXuU4$r)rrleft_join_indexer_from_join_targetrrrrjoined_ndarrayrrjoineds r _left_indexerIndex._left_indexersP  " " $  # # %%,%>%>r%F"d''7T!!rcUR5nUR5n[R"X#5upEnURU5nXuU4$r)rrinner_join_indexerrrs r_inner_indexerIndex._inner_indexerP  " " $  # # %%,%?%?%G"d''7T!!rcUR5nUR5n[R"X#5upEnURU5nXuU4$r)rrouter_join_indexerrrs r_outer_indexerIndex._outer_indexerrrrstr_typExtensionArray | np.ndarray_datazDtype[ExtensionArray] | tuple[type[np.ndarray], type[ExtensionArray]] _data_clsNz object | None_idr_nameFr_no_setting_namename list[str] _comparables _attributesc6[UR5(+$r)rLrrs r_can_hold_stringsIndex._can_hold_stringss#DJJ///rz;dict[np.dtype | ExtensionDtype, type[libindex.IndexEngine]] _engine_typescjURRUR[R5$r)rgetrlibindex ObjectEnginers r _engine_typeIndex._engine_types'!!%%djj(2G2GHHrTc\SSKJn [XAU5nUb [U5n[ USS5nSnU(d'[ U[ [45(a URn[ U[ [[45n [ U[U45(aU"XUS9n UbU RUSS9$U $[U5(aGO[U5(aGO[ U[R[[ 45(aV[ U[5(a UR nUR"R$S;a[&R("U[*S9nGOi[-U5(aUR/U5e[1US 5(aU"[R2"U5X#US 9$[5U5(d&[ U[65(dUR/U5eU(aO[9U5(a [;U5nU(a-[=S U55(aSS KJ n U RB"XS 9$[ U[:[D45(d [;U5n[GU5S:Xa[RH"U[JS9n[GU5(a1[ US[D5(a[&R("U[*S9n[MUSX#S9n [SU 5n URUU R"5nURWXR"SS9n URYXUS9n UcEU (a>U[RZ:Xa*U R"U:wa[\R^"S[`SS9 U $![NaBn S[QU 5;aUR/U5U eS[QU 5;a [OS5U eeSn A ff=f)Nr) RangeIndexr)startcopyrFriufcbmMr __array__rrrc3B# UHn[U[5v M g7fr) isinstancetuple).0es r Index.__new__..sCd 1e 4 4drnamesrrz2index must be specified when data is not list-likezData must be 1-dimensional Index data must be 1-dimensional)refszDtype inference on a pandas object (Series, Index, ExtensionArray) is deprecated. The Index constructor will keep the original dtype in the future. Call `infer_objects` on the result to get the old behavior. stacklevel)1pandas.core.indexes.rangermaybe_extract_namerRgetattrrrcr _referencesrsrangeastyperFnpndarrayr`_valuesrkindcomasarray_tuplesafe _dtype_objrN_raise_scalar_data_errorhasattrasarrayrK memoryviewrJlistallpandas.core.indexes.multir from_tuplesrlenarrayrr{ ValueErrorrry_dtype_to_subclass _ensure_array _simple_newobject_warningswarn FutureWarning)rrrrr tupleize_colsr data_dtyperis_pandas_objectresultrarrerrrs rr Index.__new__s 9!$c2   'ET7D1  4)U);<<##D%dY~,NO dUJ/ 0 0dDAF }}U}77M ( / /  ( 4 4  rzz5)< = =$ ..||zzi/ ,,TD t__..t4 4 T; ' 'rzz$'udK Kd##JtZ,H,H..t4 4t$$:DCCdCCCE&11 dT5M22Dz4yA~xxF34yyZQ77,,TD  t5DC-S1&&syy1!!#yyu!=""34"8 =-* 2J||z)  "  / Cs3xO2248cA+s3x7 !CD#M   s: M N+)=N&&N+cURS:a [S5eU[R:Xa [ S5eU(aUR 5nU$)z6 Ensure we have a valid array to pass to _simple_new. rz!float16 indexes are not supported)ndimr6r%float16NotImplementedErrorr)rrrrs rr8Index._ensure_arrayOsH 99q=?@ @ bjj %&IJ J 99;D rcP[U[5(a UR$URS:XaSSKJn U$URS:XaSSKJn U$URS:Xa[$[UR[5(d[U5(a[$[U5e)NMr) DatetimeIndexm)TimedeltaIndexO) rrX index_classr(pandasrMrOrrtyperrLrI)rrrMrOs rr7Index._dtype_to_subclassas e^ , ,$$ $ ::  , ZZ3  -! ! ZZ3 L  C ( (,$v,>0$     !% !0!2F ..v6 rcU"U0UD6nUR[:Xa^UR(dM[R"UR 5nURR S;a[XCRS9$U$)z_ Constructor that uses the 1.0.x behavior inferring numeric dtypes for ndarray[object] inputs. iufbr) rr+ _is_multirmaybe_convert_objectsr'r(rr)rargskwargsrArYs r _with_inferIndex._with_infersc d%f% <<: %f.>.>..v~~>F||  F*V++66 rc[U5$r)rSrs r _constructorIndex._constructors DzrcpUR(d%SnUR5nUSU3- n[U5eg)z Check that an Index has no duplicates. This is typically only called via `NDFrame.flags.allows_duplicate_labels.setter` when it's set to True (duplicates aren't allowed). Raises ------ DuplicateLabelError When the index is not unique. zIndex has duplicates. N) is_unique_format_duplicate_messager0)rmsg duplicatess r_maybe_check_uniqueIndex._maybe_check_uniques?~~-C779J R |$ $C%c* * rcSSKJn XRSS9R5n[ U5(deU"[ R "[ U55SS9RUSS9R[5UnUR(a)[U5RUR5Ul URS:XaURS 5nUR!S S 9$) a\ Construct the DataFrame for a DuplicateLabelError. This returns a DataFrame indicating the labels and positions of duplicates in an index. This should only be called when it's already known that duplicates are present. Examples -------- >>> idx = pd.Index(['a', 'b', 'a']) >>> idx._format_duplicate_message() positions label a [0, 2] rrfirstkeepFr )observedrFlabel positionsr])rRrrrr4r%arangegroupbyaggr0r^rSr3rnlevels rename_axisto_frame)rrrlouts rrjIndex._format_duplicate_messages" "//w/78??A : 299SY'e 4 WTEW * SYz # >>T ..syy9CI <<1 //'*C|||--rcdU[La UROUnURXURS9$)a2 Create a new Index with the same class as the caller, don't copy the data, use the same object attributes with passed in attributes taking precedence. *this is an internal non-public method* Parameters ---------- values : the values to create the new Index, optional name : Label, defaults to self.name rr)rrr9r")rrYrs r _shallow_copyIndex._shallow_copys2"Z/tzzT8H8HIIrcURURURURS9nURUlU$)zB fastpath to make a shallow copy, i.e. new object with same data. r)r9r'rr"rVrrAs r_view Index._views9!!$,,TZZdFVFV!W   rc2UR5nXlU$)z7 fastpath for rename if new name is already validated. )rr)rrrAs r_rename Index._renames   rcXLag[US5(dgURb URcgURURL$)a More flexible, faster check like ``is`` but that works through views. Note: this is *not* the same as ``Index.identical()``, which checks that metadata is also the same. Parameters ---------- other : object Other object to compare against. Returns ------- bool True if both have same underlying data, False otherwise. See Also -------- Index.identical : Works like ``Index.is_`` but also checks metadata. Examples -------- >>> idx1 = pd.Index(['1', '2', '3']) >>> idx1.is_(idx1.view()) True >>> idx1.is_(idx1.copy()) False TrF)r-rrrs ris_ Index.is_sE> =&& XX !288uyy( (rc"[5Ulg)z: Initializes or resets ``_id`` attribute with new object. N)rrrs rrWIndex._reset_identity?s 8rc8URR5 gr)_engine clear_mappingrs r_cleanupIndex._cleanupFs ""$rcLUR5n[UR[5(aURR S;aSSKnURRRnURRU5(a:URR5n[R"UR5$URRU5(a:URR!5n[R""UR5$[U[$5(ar[U[&[45(a$[(URR*"U5$UR.[R0La[R2"U5$[5[6R8U5nUR[::Xa[R<"U5$UR[6R>:Xa[R@"U5$UR[6RB:Xa[RD"U5$[GUR5(aURHRnUR/U5$![,a Nf=f)NMmr)%_get_engine_targetrr'rorr(pyarrow _pa_arrayrStypes is_timestamp_to_datetimearrayrDatetimeEngine_ndarray is_duration_to_timedeltaarrayTimedeltaEnginersrp_masked_enginesrKeyErrorrrExtensionEnginer r%r&r BoolEngine complex64Complex64Engine complex128Complex128EnginerQr)r target_valuespapa_types rr Index._engineJs //1 dll$7 8 8TZZ__PT=T ll,,11Gxx$$W-- $ > > @ ..}/E/EFF%%g.. $ ? ? A // 0F0FGG m^ 4 4-/;N)OPP*=+>+>+C+CD]SS""h&;&;;// >>RZZ7    $ &&&}5 5  BLL 0++M: :  BMM 1,,]; ;  , , !JJ//M  //3 s"J J#"J#cURSS9S[S5Vs1sH3n[U[5(dMUR 5(dM1UiM5 sn$s snf)z Add the string-like labels to the owner dataframe/series dir output. If this is a MultiIndex, it's first level values are used. rrNzdisplay.max_dir_items)rrrr isidentifier)rcs r_dir_additions_for_ownerIndex._dir_additions_for_owner{s[[[q[)*OJ7N,OP P!S! &'nn&6 P   sAAAc,[UR5$)z! Return the length of the Index. )r4rrs r__len__ Index.__len__s4::rc@[R"URUS9$)z( The array interface, return my values. r )r%r.r)rrrs rr Index.__array__szz$**E22rc^[SU55(a[$[R"TX/UQ70UD6nU[LaU$SU;a[R"TX/UQ70UD6$US:Xa&[R "TX/UQ70UD6nU[LaU$UVs/sHofTLaUO UR PM nn[X5"U0UD6nURS:Xa[U4SjU55$US:Xa[R"U5nU$UR[R:XaUR[R 5nTR#U5$s snf)Nc3N# UHn[U[[45v M g7fr)rrcr])rrs rr(Index.__array_ufunc__..sPz%)\!:;;s#%r}reducerc3F># UHnTRU5v M g7fr)__array_wrap__)rxrs rrrs@A,,Q//!)anyNotImplementedri!maybe_dispatch_ufunc_to_dunder_opdispatch_ufunc_with_outdispatch_reduction_ufuncr'r!noutrritem_from_zerodimrr%rHr$float32r)rufuncmethodinputsrarAr new_inputss` r__array_ufunc__Index.__array_ufunc__sb PP P P! !<< % "( ,2   'M F?44e&,06  X 77e&,06F^+ AGHAD=aaii7 H'>v> ::?@@@ @ x **62FM <<2:: %]]2::.F""6**IsEc[R"U5n[U[5(d[ UR 5(d[ R"U5S:aU$[XRS9$)z> Gets called after a ufunc and other functions e.g. np.split. rFr]) rrrrrErr%rGr)rrAcontext return_scalars rrIndex.__array_wrap__s] &&v.65))mFLL.I.Ibgg O O MV)),,rc.URR$)z Return the dtype object of the underlying data. Examples -------- >>> idx = pd.Index([1, 2, 3]) >>> idx Index([1, 2, 3], dtype='int64') >>> idx.dtype dtype('int64') )rrrs rr Index.dtypeszzrc USS$)z Return a view on self. Returns ------- Index See Also -------- numpy.ndarray.ravel : Return a flattened array. Examples -------- >>> s = pd.Series([1, 2, 3], index=['a', 'b', 'c']) >>> s.index.ravel() Index(['a', 'b', 'c'], dtype='object') N)rorders rravel Index.ravels &Awrc|Ub[US5(dUn[U[5(a [U5n[ U5(agUR U5nUR RU5n[U[5(a$URX@RURS9$U$URRU5nOLUb9[R"S[U5R S3["[%5S9 UR'5n[U[(5(aUR*UlU$)NrrzPassing a type in zr.view is deprecated and will raise in a future version. Call view without any argument to retain the old behavior.r)r-rrrRrQr7r5viewrsr9rr"rr;r<rS__name__r=r6rrr)rrridx_clsrBrAs rr Index.views ?73#7#7E#s##$S)"5))11%8jjooe,c>22"..))$2B2B/ ZZ__S)F (d)<)<(=>QQ"/1 ZZ\F fe $ $FJ rcUb [U5nURU:XaU(aUR5$U$URn[ U[ 5(aK[ [U5R[U5R5 URXS9nSSS5 O@[ U[5(a!UR5nURXUS9nO [X1US9n[WURURSS9nU(dTUR bG[#URU5(a,UR UlUR R%U5 U$!,(df  N=f)a\ Create an Index with values cast to dtypes. The class of a new Index is determined by dtype. When conversion is impossible, a TypeError exception is raised. Parameters ---------- dtype : numpy dtype or pandas type Note that any signed integer `dtype` is treated as ``'int64'``, and any unsigned integer `dtype` is treated as ``'uint64'``, regardless of the size. copy : bool, default True By default, astype always returns a newly allocated object. If copy is set to False and internal requirements on dtype are satisfied, the original data is used to create a new Index or the original Index is returned. Returns ------- Index Index with values cast to specified dtype. Examples -------- >>> idx = pd.Index([1, 2, 3]) >>> idx Index([1, 2, 3], dtype='int64') >>> idx.astype('float') Index([1.0, 2.0, 3.0], dtype='float64') Nr rF)rrr)rRrrrrrsr7rSrr$rXconstruct_array_type_from_sequencer8rrr"r9rX)rrrrY new_valuesrrAs rr$ Index.astypes1@   'E :: "&499; 0D 0 fn - -"4<#8#8$t*:M:MN#]]5]< ON~ . .,,.C++DD+IJ&fEJz 9I9IPUV  ,tzz511!%!1!1F     2 26 : -ONs E!! E/a Return a new %(klass)s of the values selected by the indices. For internal compatibility with numpy arrays. Parameters ---------- indices : array-like Indices to be taken. axis : int, optional The axis over which to select values, always 0. allow_fill : bool, default True 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. 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='object') takec U(a[R"SU5 [U5(a [S5e[ U5nUR X4U5nUR n[U[R5(a [R"XaX0RS9nOURXURS9nURRXpRS9$)Nrz!Expected indices to be array-like) allow_fill fill_valuer])nv validate_takerN TypeErrorrC_maybe_disallow_fillr'rr%r&rr _na_valuerer9r)rindicesaxisrrrarYtakens rr Index.takezs    R ( W  ?@ @%g...zwO  fbjj ) )JJJ>>E KK4>> E  ,,U,CCrcU(a^Ub[UR(a&US:R5(a [S5eU$[U5Rn[SUS35eSnU$)zU We only use pandas-style take when allow_fill is True _and_ fill_value is not None. zJWhen allow_fill=True and fill_value is not None, all indices must be >= -1zUnable to fill values because z cannot contain NAF) _can_hold_narr6rSr)rrrrcls_names rrIndex._maybe_disallow_fills *0  bL%%''$4(  :.. 4XJ>PQJra| Repeat elements of a %(klass)s. Returns a new %(klass)s where each element of the current %(klass)s is repeated consecutively a given number of times. Parameters ---------- repeats : int or array of ints The number of repetitions for each element. This should be a non-negative integer. Repeating 0 times will return an empty %(klass)s. axis : None Must be ``None``. Has no effect but is accepted for compatibility with numpy. Returns ------- %(klass)s Newly created %(klass)s with repeated elements. See Also -------- Series.repeat : Equivalent function for Series. numpy.repeat : Similar method for :class:`numpy.ndarray`. Examples -------- >>> idx = pd.Index(['a', 'b', 'c']) >>> idx Index(['a', 'b', 'c'], dtype='object') >>> idx.repeat(2) Index(['a', 'a', 'b', 'b', 'c', 'c'], dtype='object') >>> idx.repeat([1, 2, 3]) Index(['a', 'b', 'b', 'c', 'c', 'c'], dtype='object') repeatc[U5n[R"SSU05 URR U5nUR R X0RS9$)Nrrr])rCrvalidate_repeatr'rrer9r)rrepeatsr res_valuess rr Index.repeatsT%g. 2~.\\((1   ,,Zii,HHrcURXS9SnU(a4URR5n[U5R X1S9nU$UR US9nU$)a Make a copy of this object. Name is set on the new object. Parameters ---------- name : Label, optional Set name for new object. deep : bool, default False Returns ------- Index Index refer to new object which is a copy of this object. Notes ----- In most cases, there should be no functional difference from using ``deep``, but if ``deep`` is passed it will attempt to deepcopy. Examples -------- >>> idx = pd.Index(['a', 'b', 'c']) >>> new_idx = idx.copy() >>> idx is new_idx False )rdeeprr])_validate_namesrrrSr9r)rrrnew_data new_indexs rr Index.copysgD###9!< zz(HT ..x.CI $ /Irc &UR"S0UD6$)Nrr )rras r__copy__Index.__copy__ syy"6""rc URSS9$)zI Parameters ---------- memo, default None Standard signature. Unused Trr )rmemos r __deepcopy__Index.__deepcopy__syydy##rc[U5RnUR5nUR5nUVVs/sH upEUSU3PM nnnSR U5nUSUUS3$s snnf)z1 Return a string representation for this object. =, ())rSr _format_data _format_attrsr)r klass_namerattrskv attrs_strpreprs r__repr__Index.__repr__sy $Z((   """$,12EDAs!A3ZE 2 )$QtfUG1--3sA,c[$)z Return the formatter function. )rrs r_formatter_funcIndex._formatter_func*s rc>SnURS:XaSnOh[UR[5(a3[ SU5n[ UR R5(aSnO[U[5(ag[UURUUURS9$)z0 Return the formatted data as a unicode string. TstringFrr) is_justifyrline_break_each_value) inferred_typerrrVr rM categoriesrbrrr^)rrrs rrIndex._format_data1s     )J  $4 5 5*D1Dt4455" m , ,$   !"&..   rc/nUR(d!URSSURS345 URb'URS[ UR545 OXUR(aG[ SUR 55(a&URS[ UR 545 [S5=(d [U5n[U5U:aURS[U545 U$)z8 Return a list of tuples of the (attr,formatted_value). r'rc3(# UHoSLv M g7frrrrs rr&Index._format_attrs..Vs#F:aTM:srzdisplay.max_seq_itemslength) r^appendrrrrrrr4)rr max_seq_itemss rrIndex._format_attrsKs@B~~ LL'Qtzzl!#45 6 99 LL&.";< = ^^#F4::#F F F LL'>$**#=> ?"#:;Hs4y t9} $ LL(CI. / rcUR(a/[UR5VVs/sH upUcUOUPM snn$URcS$UR$s snnf)zH Return a name or list of names with None replaced by the level number. r)r^ enumeraterr)rrrs r_get_level_namesIndex._get_level_names^s[ >>CLTZZCXCXKE4/CX  )1 8tyy 8 sAc [UR[R5(a>URRS:wa$[ [R UR 5$UR[SS9R$)NrLFr ) rrr%r(r r&rYr$rr'rs r _mpl_reprIndex._mpl_reprjsV djj"(( + + 30F DKK0 0{{6{.666rNaNcF[R"[U5RS3[[ 5S9 /nU(a2UR URb[URSS9OS5 UbU[URU55-$URXCS9$). Render a string representation of the Index. z.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.r  rh escape_charsrheaderna_rep) r;r<rSrr=r6r)rrr0map_format_with_header)rr formatterr=r<s rformat Index.formatqs  Dz""#$, , ')    MM99(TYY5GH   D)!455 5''v'EEr)r@c/nU(a2URURb[URSS9OS5 UbU[UR U55-$UR X0R S9$)r5r6r9rr;)r)rrr0r>r?_default_na_rep)r include_namer@r<s r _format_flatIndex._format_flatsq  MM99(TYY5GH   D)!455 5''v>R>R'SSrcSSKJn URn[UR5(d?[ UR5(d%[ UR[[45(aSnOSnU"USUS9n[U5nX-$)Nr) format_arrayr1r)justify) pandas.io.formats.formatrIr'rMrrPrrYrV trim_front)rr<r=rIrYrJ formattedrAs rr?Index._format_with_headerss9 FLL ) )v||,,$**}6F&GHHGG!w? I&rr.r=decimal float_format date_formatquotingc 2[URUUUUUS9$)NrP)get_values_for_csvr')rr=rQrRrSrTs r_get_values_for_csvIndex._get_values_for_csvs'" LL%#   rch[U5S:aUSn[US5(a&[U[5(dUR 5nO;[ UR 5(a!URU5RSS5nUSn[US5(a&[U[5(dUR 5nO;[ UR 5(a!URU5RSS5nSUSU3nOSnUc[U5RnUS[U5S U3$) z Return a summarized representation. Parameters ---------- name : str name to use in the summary representation Returns ------- String with a summarized representation of the index rrAr$rrr z to z: z entries) r4r-rrrArQrrreplacerSr)rrheadtail index_summarys r_summaryIndex._summarys t9q=7DtX&&z$/D/D{{}$TZZ00++D199#rB8DtX&&z$/D/D{{}$TZZ00++D199#rB d4&1MM <:&&Dr#d)H]O<>> idx = pd.Index(['Ant', 'Bear', 'Cow'], name='animal') By default, the original index and original name is reused. >>> idx.to_series() animal Ant Ant Bear Bear Cow Cow Name: animal, dtype: object To enforce a new index, specify new labels to ``index``: >>> idx.to_series(index=[0, 1, 2]) 0 Ant 1 Bear 2 Cow Name: animal, dtype: object To override the name of the resulting column, specify ``name``: >>> idx.to_series(name='zoo') animal Ant Ant Bear Bear Cow Cow Name: zoo, dtype: object rrp)rr)rRrrrr'r)rrrrs r to_seriesIndex.to_seriess@n " =JJLE <99Ddll'')BBrcSSKJn U[RLaUR 5nU"X 0[ 5(+S9nU(aXlU$)a Create a DataFrame with a column containing the Index. Parameters ---------- index : bool, default True Set the index of the returned DataFrame as the original Index. name : object, defaults to index.name The passed name should substitute for the index name (if it has one). Returns ------- DataFrame DataFrame containing the original Index data. See Also -------- Index.to_series : Convert an Index to a Series. Series.to_frame : Convert Series to DataFrame. Examples -------- >>> idx = pd.Index(['Ant', 'Bear', 'Cow'], name='animal') >>> idx.to_frame() animal animal Ant Ant Bear Bear Cow Cow By default, the original Index is reused. To enforce a new Index: >>> idx.to_frame(index=False) animal 0 Ant 1 Bear 2 Cow To override the name of the resulting column, specify `name`: >>> idx.to_frame(index=False, name='zoo') zoo 0 Ant 1 Bear 2 Cow r)rr )rRrrrr.rr)rrrrrAs rr|Index.to_frameGsDf % 3>> !((*DD<2E2G.GH L rcUR$)z Return Index or MultiIndex name. Examples -------- >>> idx = pd.Index([1, 2, 3], name='x') >>> idx Index([1, 2, 3], dtype='int64', name='x') >>> idx.name 'x' )rrs rr Index.nameszzrctUR(a [S5e[US[U55 Xlg)NzOCannot set name on a level of a MultiIndex. Use 'MultiIndex.set_names' instead.)r RuntimeErrorr rSr)rvalues rrris4  2  5$T 3 rcSSKJn UbUb [S5eUc)Uc&U(aU"UR5O URnO7Ub[ U5(d [S5eUnO[ U5(dU/nOUn[ U5[ UR5:wa-[ S[ UR5S[ U535e[US[U5RS306 U$) zy Handles the quirks of having a singular 'name' parameter for general Index and plural 'names' parameter for MultiIndex. r)deepcopyz*Can only provide one of `names` and `name`Must pass list-like as `names`.zLength of new names must be z, got error_name.name) rrnrrrKr4r6rSrSr)rrrrrn new_namess rrIndex._validate_namess "  !1HI I =T\04,$**I  && ABBId##II y>S_ ,.s4::.?vc)nEUV  yST 8K8K7LE5RSrcFSSKJn Ub[U[[45(aU/n[U[ 5(dUb [ S5eU(dO[X5(a"[R"UR5nU$URcU/O UR/nU$)z Get names of index. Parameters ---------- names : int, str or 1-dimensional list, default None Index names to set. default : str Default name of index. Raises ------ TypeError if names not str or list-like rrz-Index names must be str or 1-dimensional list) r2rrintrr0r6r)fill_missing_namesrr)rrdefaultrs r_get_default_index_namesIndex._get_default_index_namess$ 9  %#s,,%&&5+<LM M$++..tzz: &*YY%6 TYYK rc.[UR45$r)r~rrs r _get_namesIndex._get_namess499,''rrc[U5(d [S5e[U5S:wa[S[U535e[US[ U5R S306 USUlg)ay Set new names on index. Each name has to be a hashable type. Parameters ---------- values : str or sequence name(s) to set level : int, level name, or sequence of int/level names (default None) If the index is a MultiIndex (hierarchical), level(s) to set (None for all levels). Otherwise level must be None Raises ------ TypeError if each name is not hashable. zNames must be a list-likerFz#Length of new names must be 1, got rprqrN)rKr6r4rSrSrrrrYrs r _set_namesIndex._set_namessj F##89 9 v;! B3v;-PQ Q vPT$Z5H5H4I2OPAY r)fsetfget.)rrcgrrrrrrs r set_namesIndex.set_names rcgrrrs rrr rrcgrrrs rrr rrc\Ub [U[5(d [S5eUb+[U5(d[U5(a [ S5e[U5(dUcUR S:a [ S5e[ U5(a [U[5(d [ S5e[ U5(aUb [ S5e[U[5(ao[ U5(a_Uc\//pB[UR5H>upVXaR5;dMURU5 URX5 M@ Un[U5(dU/nUb[U5(dU/nU(aUnOUR5nURXS9 U(dU$g) ac Set Index or MultiIndex name. Able to set new names partially and by level. Parameters ---------- names : label or list of label or dict-like for MultiIndex Name(s) to set. .. versionchanged:: 1.3.0 level : int, label or list of int or label, optional If the index is a MultiIndex and names is not dict-like, level(s) to set (None for all levels). Otherwise level must be None. .. versionchanged:: 1.3.0 inplace : bool, default False Modifies the object directly, instead of creating a new Index or MultiIndex. Returns ------- Index or None The same type as the caller or None if ``inplace=True``. See Also -------- Index.rename : Able to set new names without level. Examples -------- >>> idx = pd.Index([1, 2, 3, 4]) >>> idx Index([1, 2, 3, 4], dtype='int64') >>> idx.set_names('quarter') Index([1, 2, 3, 4], dtype='int64', name='quarter') >>> idx = pd.MultiIndex.from_product([['python', 'cobra'], ... [2018, 2019]]) >>> idx MultiIndex([('python', 2018), ('python', 2019), ( 'cobra', 2018), ( 'cobra', 2019)], ) >>> idx = idx.set_names(['kind', 'year']) >>> idx.set_names('species', level=0) MultiIndex([('python', 2018), ('python', 2019), ( 'cobra', 2018), ( 'cobra', 2019)], names=['species', 'year']) When renaming levels with a dict, levels can not be passed. >>> idx.set_names({'kind': 'snake'}) MultiIndex([('python', 2018), ('python', 2019), ( 'cobra', 2018), ( 'cobra', 2019)], names=['snake', 'year']) Nz%Level must be None for non-MultiIndexz7Names must be a string when a single level is provided.rFroz2Can only pass dict-like as `names` for MultiIndex.z(Can not pass level for dictlike `names`.r) rr`r6rKrrzrer-rkeysr)rr)rrrrnames_adjustediridxs rrrshD  Zm%D%DDE E  \%%8%8\%=P=PUV VE""u}9I=> >   z$ 'F'FPQ Q   5#4FG G dM * *|E/B/Bu}$&>$TZZ0::<'LLO"))%+61#EE""GE  \%%8%8GE C**,C u*Jrrcgrrrrrs rrename Index.renamezrrcgrrrs rrr~rrz3.0rr)version allowed_argsrc$URU/US9$)a) Alter Index or MultiIndex name. Able to set new names without level. Defaults to returning new index. Length of names must match number of levels in MultiIndex. Parameters ---------- name : label or list of labels Name(s) to set. inplace : bool, default False Modifies the object directly, instead of creating a new Index or MultiIndex. Returns ------- Index or None The same type as the caller or None if ``inplace=True``. See Also -------- Index.set_names : Able to set new names partially and by level. Examples -------- >>> idx = pd.Index(['A', 'C', 'A', 'B'], name='score') >>> idx.rename('grade') Index(['A', 'C', 'A', 'B'], dtype='object', name='grade') >>> idx = pd.MultiIndex.from_product([['python', 'cobra'], ... [2018, 2019]], ... names=['kind', 'year']) >>> idx MultiIndex([('python', 2018), ('python', 2019), ( 'cobra', 2018), ( 'cobra', 2019)], names=['kind', 'year']) >>> idx.rename(['species', 'year']) MultiIndex([('python', 2018), ('python', 2019), ( 'cobra', 2018), ( 'cobra', 2019)], names=['species', 'year']) >>> idx.rename('species') Traceback (most recent call last): TypeError: Must pass list-like as `names`. r)rrs rrrsh~~tfg~66rcg)z Number of levels. rFrrs rrz Index.nlevelss rcU$)z Compat with MultiIndex. rrs r_sort_levels_monotonicIndex._sort_levels_monotonics  rc[U[5(a3US:aUS:wa[SUS35eUS:a[SUS-35eg XR:wa[ SUSURS 35eg ) z Validate index level. For single-level Index getting level number is a no-op, but some verification must be done like in MultiIndex. rrz)Too many levels: Index has only 1 level, z is not a valid level numberz-Too many levels: Index has only 1 level, not rFzRequested level (z) does not match index name (rN)rru IndexErrorrrrrs r_validate_index_levelIndex._validate_index_levels eS ! !qyUb[ ?g9;qy CEAI;Oii #E7*G {RST  rc&URU5 g)Nrrrs r_get_level_numberIndex._get_level_numbers ""5)rrqc[U[[45(d [S5e[U[5(a[ U5S:wa [S5eUSn[U[5(d [S5eUR SX$S9$)a For internal compatibility with the Index API. Sort the Index. This is for compat with MultiIndex Parameters ---------- ascending : bool, default True False to sort in descending order na_position : {'first' or 'last'}, default 'first' Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at the end. .. versionadded:: 2.1.0 level, sort_remaining are compat parameters Returns ------- Index zIascending must be a single bool value ora list of bool values of length 1rFz3ascending must be a list of bool values of length 1rzascending must be a bool valueT)return_indexer ascending na_position)rr0rrr4 sort_values)rrrsort_remainingrs r sortlevelIndex.sortlevels8)dD\224  i & &9~" UVV!! I)T**<= =9   rc(URU5 U$)a Return an Index of values for requested level. This is primarily useful to get an individual level of values from a MultiIndex, but is provided on Index as well for compatibility. Parameters ---------- level : int or str It is either the integer position or the name of the level. Returns ------- Index Calling object, as there is only one level in the Index. See Also -------- MultiIndex.get_level_values : Get values for a level of a MultiIndex. Notes ----- For Index, level should be 0, since there are no multiple levels. Examples -------- >>> idx = pd.Index(list('abc')) >>> idx Index(['a', 'b', 'c'], dtype='object') Get level values by supplying `level` as integer: >>> idx.get_level_values(0) Index(['a', 'b', 'c'], dtype='object') rrs r_get_level_valuesIndex._get_level_valuessH ""5) rc^[U[[45(dU/n[U4SjU55SSS2nTR U5$)a Return index with requested level(s) removed. If resulting index has only 1 level left, the result will be of Index type, not MultiIndex. The original index is not modified inplace. Parameters ---------- level : int, str, or list-like, default 0 If a string is given, must be the name of a level If list-like, elements must be names or indexes of levels. Returns ------- Index or MultiIndex Examples -------- >>> mi = pd.MultiIndex.from_arrays( ... [[1, 2], [3, 4], [5, 6]], names=['x', 'y', 'z']) >>> mi MultiIndex([(1, 3, 5), (2, 4, 6)], names=['x', 'y', 'z']) >>> mi.droplevel() MultiIndex([(3, 5), (4, 6)], names=['y', 'z']) >>> mi.droplevel(2) MultiIndex([(1, 3), (2, 4)], names=['x', 'y']) >>> mi.droplevel('z') MultiIndex([(1, 3), (2, 4)], names=['x', 'y']) >>> mi.droplevel(['x', 'y']) Index([5, 6], dtype='int64', name='z') c3F># UHnTRU5v M g7fr)r)rlevrs rr"Index.droplevel..ksF//44rNr)rrr0sorted_drop_level_numbers)rrlevnumss` r droplevelIndex.droplevel;sHZ%%//GEFFFttL''00rcU(d[U[5(dU$[U5UR:a%[ S[U5SURS35e[ SU5n[ UR5n[ UR5n[ UR5nUH6nURU5 URU5 URU5 M8 [U5S:XaUSn[U5S:Xa[[US5S:XaUSSnU$[R"URUSSS 9nURRXSS 9nU$USS :Hn USRUS5nU R!5(a UR#U [$R&5nUSUlU$SS KJn U "UUUS S9$)z5 Drop MultiIndex levels by level _number_, not name. zCannot remove z levels from an index with z) levels: at least one level must be left.rrFrNT)rr]rrFlevelsrrr)rr`r4rzr6r r0rrrrrrr'rer9rputmaskr%nanrr2r) rr new_levels new_codesrrrrrArmaskrs rrIndex._drop_level_numbersos z$ >>K w<4<< ' W.I<<. IK  L$'$++& $ $ A NN1  MM!  MM!  z?a Q-C3x1}y|$)!!WFM"'CKK1RV!WJ --99*UV<9XFM!|r)#A++IaL988::#^^D"&&9F(| M <!!&  rc[UR[5(aURR$URRS;agg)NiubFT)rrrXrr(rs rrIndex._can_hold_nas; djj. 1 1::** * ::??e #rc.URR$)ab Return a boolean if the values are equal or increasing. Returns ------- bool See Also -------- Index.is_monotonic_decreasing : Check if the values are equal or decreasing. Examples -------- >>> pd.Index([1, 2, 3]).is_monotonic_increasing True >>> pd.Index([1, 2, 2]).is_monotonic_increasing True >>> pd.Index([1, 3, 2]).is_monotonic_increasing False )ris_monotonic_increasingrs rrIndex.is_monotonic_increasing,||333rc.URR$)ab Return a boolean if the values are equal or decreasing. Returns ------- bool See Also -------- Index.is_monotonic_increasing : Check if the values are equal or increasing. Examples -------- >>> pd.Index([3, 2, 1]).is_monotonic_decreasing True >>> pd.Index([3, 2, 2]).is_monotonic_decreasing True >>> pd.Index([3, 1, 2]).is_monotonic_decreasing False )ris_monotonic_decreasingrs rrIndex.is_monotonic_decreasingrrc@UR=(a UR$)a Return if the index is strictly monotonic increasing (only increasing) values. Examples -------- >>> Index([1, 2, 3])._is_strictly_monotonic_increasing True >>> Index([1, 2, 2])._is_strictly_monotonic_increasing False >>> Index([1, 3, 2])._is_strictly_monotonic_increasing False )rirrs r!_is_strictly_monotonic_increasing'Index._is_strictly_monotonic_increasing ~~>$">">>rc@UR=(a UR$)a Return if the index is strictly monotonic decreasing (only decreasing) values. Examples -------- >>> Index([3, 2, 1])._is_strictly_monotonic_decreasing True >>> Index([3, 2, 2])._is_strictly_monotonic_decreasing False >>> Index([3, 1, 2])._is_strictly_monotonic_decreasing False )rirrs r!_is_strictly_monotonic_decreasing'Index._is_strictly_monotonic_decreasingrrc.URR$)a Return if the index has unique values. Returns ------- bool See Also -------- Index.has_duplicates : Inverse method that checks if it has duplicate values. Examples -------- >>> idx = pd.Index([1, 5, 7, 7]) >>> idx.is_unique False >>> idx = pd.Index([1, 5, 7]) >>> idx.is_unique True >>> idx = pd.Index(["Watermelon", "Orange", "Apple", ... "Watermelon"]).astype("category") >>> idx.is_unique False >>> idx = pd.Index(["Orange", "Apple", ... "Watermelon"]).astype("category") >>> idx.is_unique True )rrirs rriIndex.is_unique sB||%%%rc$UR(+$)a[ Check if the Index has duplicate values. Returns ------- bool Whether or not the Index has duplicate values. See Also -------- Index.is_unique : Inverse method that checks if it has unique values. Examples -------- >>> idx = pd.Index([1, 5, 7, 7]) >>> idx.has_duplicates True >>> idx = pd.Index([1, 5, 7]) >>> idx.has_duplicates False >>> idx = pd.Index(["Watermelon", "Orange", "Apple", ... "Watermelon"]).astype("category") >>> idx.has_duplicates True >>> idx = pd.Index(["Orange", "Apple", ... "Watermelon"]).astype("category") >>> idx.has_duplicates False rirs rhas_duplicatesIndex.has_duplicates, sF>>!!rc[R"[U5RS3[[ 5S9 UR S;$)a Check if the Index only consists of booleans. .. deprecated:: 2.0.0 Use `pandas.api.types.is_bool_dtype` instead. Returns ------- bool Whether or not the Index only consists of booleans. See Also -------- is_integer : Check if the Index only consists of integers (deprecated). is_floating : Check if the Index is a floating type (deprecated). is_numeric : Check if the Index only consists of numeric data (deprecated). is_object : Check if the Index is of the object dtype (deprecated). is_categorical : Check if the Index holds categorical data. is_interval : Check if the Index holds Interval objects (deprecated). Examples -------- >>> idx = pd.Index([True, False, True]) >>> idx.is_boolean() # doctest: +SKIP True >>> idx = pd.Index(["True", "False", "True"]) >>> idx.is_boolean() # doctest: +SKIP False >>> idx = pd.Index([True, False, "True"]) >>> idx.is_boolean() # doctest: +SKIP False zE.is_boolean is deprecated. Use pandas.api.types.is_bool_type instead.r)rr;r<rSrr=r6r rs r is_booleanIndex.is_booleanQ sGH  Dz""#$9 9 ')  !![00rc[R"[U5RS3[[ 5S9 UR S;$)a Check if the Index only consists of integers. .. deprecated:: 2.0.0 Use `pandas.api.types.is_integer_dtype` instead. Returns ------- bool Whether or not the Index only consists of integers. See Also -------- is_boolean : Check if the Index only consists of booleans (deprecated). is_floating : Check if the Index is a floating type (deprecated). is_numeric : Check if the Index only consists of numeric data (deprecated). is_object : Check if the Index is of the object dtype. (deprecated). is_categorical : Check if the Index holds categorical data (deprecated). is_interval : Check if the Index holds Interval objects (deprecated). Examples -------- >>> idx = pd.Index([1, 2, 3, 4]) >>> idx.is_integer() # doctest: +SKIP True >>> idx = pd.Index([1.0, 2.0, 3.0, 4.0]) >>> idx.is_integer() # doctest: +SKIP False >>> idx = pd.Index(["Apple", "Mango", "Watermelon"]) >>> idx.is_integer() # doctest: +SKIP False zI.is_integer is deprecated. Use pandas.api.types.is_integer_dtype instead.r)integerrrs rrIIndex.is_integer} sGH  Dz""#$= = ')  !![00rc[R"[U5RS3[[ 5S9 UR S;$)a Check if the Index is a floating type. .. deprecated:: 2.0.0 Use `pandas.api.types.is_float_dtype` instead The Index may consist of only floats, NaNs, or a mix of floats, integers, or NaNs. Returns ------- bool Whether or not the Index only consists of only consists of floats, NaNs, or a mix of floats, integers, or NaNs. See Also -------- is_boolean : Check if the Index only consists of booleans (deprecated). is_integer : Check if the Index only consists of integers (deprecated). is_numeric : Check if the Index only consists of numeric data (deprecated). is_object : Check if the Index is of the object dtype. (deprecated). is_categorical : Check if the Index holds categorical data (deprecated). is_interval : Check if the Index holds Interval objects (deprecated). Examples -------- >>> idx = pd.Index([1.0, 2.0, 3.0, 4.0]) >>> idx.is_floating() # doctest: +SKIP True >>> idx = pd.Index([1.0, 2.0, np.nan, 4.0]) >>> idx.is_floating() # doctest: +SKIP True >>> idx = pd.Index([1, 2, 3, 4, np.nan]) >>> idx.is_floating() # doctest: +SKIP True >>> idx = pd.Index([1, 2, 3, 4]) >>> idx.is_floating() # doctest: +SKIP False zH.is_floating is deprecated. Use pandas.api.types.is_float_dtype instead.r)floatingzmixed-integer-floatz integer-narrs r is_floatingIndex.is_floating sHX  Dz""#$; ; ')  !!%VVVrc[R"[U5RS3[[ 5S9 UR S;$)aJ Check if the Index only consists of numeric data. .. deprecated:: 2.0.0 Use `pandas.api.types.is_numeric_dtype` instead. Returns ------- bool Whether or not the Index only consists of numeric data. See Also -------- is_boolean : Check if the Index only consists of booleans (deprecated). is_integer : Check if the Index only consists of integers (deprecated). is_floating : Check if the Index is a floating type (deprecated). is_object : Check if the Index is of the object dtype. (deprecated). is_categorical : Check if the Index holds categorical data (deprecated). is_interval : Check if the Index holds Interval objects (deprecated). Examples -------- >>> idx = pd.Index([1.0, 2.0, 3.0, 4.0]) >>> idx.is_numeric() # doctest: +SKIP True >>> idx = pd.Index([1, 2, 3, 4.0]) >>> idx.is_numeric() # doctest: +SKIP True >>> idx = pd.Index([1, 2, 3, 4]) >>> idx.is_numeric() # doctest: +SKIP True >>> idx = pd.Index([1, 2, 3, 4.0, np.nan]) >>> idx.is_numeric() # doctest: +SKIP True >>> idx = pd.Index([1, 2, 3, 4.0, np.nan, "Apple"]) >>> idx.is_numeric() # doctest: +SKIP False zQ.is_numeric is deprecated. Use pandas.api.types.is_any_real_numeric_dtype insteadr)rrrrs r is_numericIndex.is_numeric sJX  Dz""#$E E ')  !!%<<>> idx = pd.Index(["Apple", "Mango", "Watermelon"]) >>> idx.is_object() # doctest: +SKIP True >>> idx = pd.Index(["Apple", "Mango", 2.0]) >>> idx.is_object() # doctest: +SKIP True >>> idx = pd.Index(["Watermelon", "Orange", "Apple", ... "Watermelon"]).astype("category") >>> idx.is_object() # doctest: +SKIP False >>> idx = pd.Index([1.0, 2.0, 3.0, 4.0]) >>> idx.is_object() # doctest: +SKIP False zE.is_object is deprecated.Use pandas.api.types.is_object_dtype insteadr)r;r<rSrr=r6rMrrs r is_objectIndex.is_object sER  Dz""#$; ; ')  tzz**rc[R"[U5RS3[[ 5S9 UR S;$)an Check if the Index holds categorical data. .. deprecated:: 2.0.0 Use `isinstance(index.dtype, pd.CategoricalDtype)` instead. Returns ------- bool True if the Index is categorical. See Also -------- CategoricalIndex : Index for categorical data. is_boolean : Check if the Index only consists of booleans (deprecated). is_integer : Check if the Index only consists of integers (deprecated). is_floating : Check if the Index is a floating type (deprecated). is_numeric : Check if the Index only consists of numeric data (deprecated). is_object : Check if the Index is of the object dtype. (deprecated). is_interval : Check if the Index holds Interval objects (deprecated). Examples -------- >>> idx = pd.Index(["Watermelon", "Orange", "Apple", ... "Watermelon"]).astype("category") >>> idx.is_categorical() # doctest: +SKIP True >>> idx = pd.Index([1, 3, 5, 7]) >>> idx.is_categorical() # doctest: +SKIP False >>> s = pd.Series(["Peter", "Victor", "Elisabeth", "Mar"]) >>> s 0 Peter 1 Victor 2 Elisabeth 3 Mar dtype: object >>> s.index.is_categorical() # doctest: +SKIP False zO.is_categorical is deprecated.Use pandas.api.types.is_categorical_dtype insteadr) categoricalrrs ris_categoricalIndex.is_categoricalB sIX  Dz""#$@ @ ')  !!_44rc[R"[U5RS3[[ 5S9 UR S;$)a Check if the Index holds Interval objects. .. deprecated:: 2.0.0 Use `isinstance(index.dtype, pd.IntervalDtype)` instead. Returns ------- bool Whether or not the Index holds Interval objects. See Also -------- IntervalIndex : Index for Interval objects. is_boolean : Check if the Index only consists of booleans (deprecated). is_integer : Check if the Index only consists of integers (deprecated). is_floating : Check if the Index is a floating type (deprecated). is_numeric : Check if the Index only consists of numeric data (deprecated). is_object : Check if the Index is of the object dtype. (deprecated). is_categorical : Check if the Index holds categorical data (deprecated). Examples -------- >>> idx = pd.Index([pd.Interval(left=0, right=5), ... pd.Interval(left=5, right=10)]) >>> idx.is_interval() # doctest: +SKIP True >>> idx = pd.Index([1, 3, 5, 7]) >>> idx.is_interval() # doctest: +SKIP False zI.is_interval is deprecated.Use pandas.api.types.is_interval_dtype insteadr)intervalrrs r is_intervalIndex.is_intervalw sGD  Dz""#$= = ')  !!\11rc URS;$)z& Whether the type is an integer type. )rrr rs r_holds_integerIndex._holds_integer s !!%AAArc[R"[U5RS3[[ 5S9 UR 5$)zl Whether the type is an integer type. .. deprecated:: 2.0.0 Use `pandas.api.types.infer_dtype` instead zG.holds_integer is deprecated. Use pandas.api.types.infer_dtype instead.r)r;r<rSrr=r6rrs r holds_integerIndex.holds_integer sD  Dz""#$8 8 ')  ""$$rc@[R"URSS9$)z Return a string of the type inferred from the values. Examples -------- >>> idx = pd.Index([1, 2, 3]) >>> idx Index([1, 2, 3], dtype='int64') >>> idx.inferred_type 'integer' Fskipna)r infer_dtyper'rs rr Index.inferred_type st||E::rc[UR5(agUR[:wagUR(ag[ [ UR 55$)z8 Whether or not the index values only consist of dates. TF)rQrr+r^rrBr'rs r _is_all_datesIndex._is_all_dates sE tzz * * ZZ: % ^^ t||!<==rc"[U[5$)z9 Cached check equivalent to isinstance(self, MultiIndex) )rr`rs rr^Index._is_multi s $ ..rc\URURS.n[[U5U4S4$)N)rr)rrrrS)rrs r __reduce__Index.__reduce__ s)ZZ 3DJ?D00rcURn[U[R5(a&URS;a[$[R $UR $)z-The expected NA value to use with this index.mM)rrr%r(rrna_valuerrs rrIndex._na_value sB  eRXX & &zzT! 66M~~rcUR(a [U5$[R"[ U5[R S9nUR S5 U$)z Return if each value is NaN. r F)rrhr%emptyr4bool_fill)rrYs r_isnan Index._isnan sB   : XXc$irxx8F KK MrclUR(a#[URR55$g)z Return True if there are any NaNs. Enables various performance speedups. Returns ------- bool Examples -------- >>> s = pd.Series([1, 2, 3], index=['a', 'b', None]) >>> s a 1 b 2 None 3 dtype: int64 >>> s.index.hasnans True F)rrrrrs rhasnans Index.hasnans s&,    )* *rcUR$)aq Detect missing values. Return a boolean same-sized object indicating if the values are NA. NA values, such as ``None``, :attr:`numpy.NaN` or :attr:`pd.NaT`, get mapped to ``True`` values. Everything else get mapped to ``False`` values. Characters such as empty strings `''` or :attr:`numpy.inf` are not considered NA values. Returns ------- numpy.ndarray[bool] A boolean array of whether my values are NA. See Also -------- Index.notna : Boolean inverse of isna. Index.dropna : Omit entries with missing values. isna : Top-level isna. Series.isna : Detect missing values in Series object. Examples -------- Show which entries in a pandas.Index are NA. The result is an array. >>> idx = pd.Index([5.2, 6.0, np.nan]) >>> idx Index([5.2, 6.0, nan], dtype='float64') >>> idx.isna() array([False, False, True]) Empty strings are not considered NA values. None is considered an NA value. >>> idx = pd.Index(['black', '', 'red', None]) >>> idx Index(['black', '', 'red', None], dtype='object') >>> idx.isna() array([False, False, False, True]) For datetimes, `NaT` (Not a Time) is considered as an NA value. >>> idx = pd.DatetimeIndex([pd.Timestamp('1940-04-25'), ... pd.Timestamp(''), None, pd.NaT]) >>> idx DatetimeIndex(['1940-04-25', 'NaT', 'NaT', 'NaT'], dtype='datetime64[ns]', freq=None) >>> idx.isna() array([False, True, True, True]) )rrs rrh Index.isna sj{{rc$UR5)$)a Detect existing (non-missing) values. Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to ``True``. Characters such as empty strings ``''`` or :attr:`numpy.inf` are not considered NA values. NA values, such as None or :attr:`numpy.NaN`, get mapped to ``False`` values. Returns ------- numpy.ndarray[bool] Boolean array to indicate which entries are not NA. See Also -------- Index.notnull : Alias of notna. Index.isna: Inverse of notna. notna : Top-level notna. Examples -------- Show which entries in an Index are not NA. The result is an array. >>> idx = pd.Index([5.2, 6.0, np.nan]) >>> idx Index([5.2, 6.0, nan], dtype='float64') >>> idx.notna() array([ True, True, False]) Empty strings are not considered NA values. None is considered a NA value. >>> idx = pd.Index(['black', '', 'red', None]) >>> idx Index(['black', '', 'red', None], dtype='object') >>> idx.notna() array([ True, True, True, False]) )rhrs rnotna Index.notnaU sT |rc[U5(d![S[U5R35eU[R La:[ R"S[U5RS3[[5S9 OSnUR(a]URURU5nUc[RX0RS9$[![U5RS35eUR#5$)a Fill NA/NaN values with the specified value. Parameters ---------- value : scalar Scalar value to use to fill holes (e.g. 0). This value cannot be a list-likes. downcast : dict, default is None A dict of item->dtype of what to downcast if possible, or the string 'infer' which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible). .. deprecated:: 2.1.0 Returns ------- Index See Also -------- DataFrame.fillna : Fill NaN values of a DataFrame. Series.fillna : Fill NaN Values of a Series. Examples -------- >>> idx = pd.Index([np.nan, np.nan, 3]) >>> idx.fillna(0) Index([0.0, 0.0, 3.0], dtype='float64') z"'value' must be a scalar, passed: zThe 'downcast' keyword in zb.fillna is deprecated and will be removed in a future version. It was previously silently ignored.rNr]zF.fillna does not support 'downcast' argument values other than 'None'.)rNrrSrrrr;r<r=r6rrrrrbrrIr)rrldowncastrAs rfillna Index.fillna s>@eAUAU@VWX X 3>> ) MM,T$Z-@-@,AB66+-  H <<\\$++u5F((ii(@@%:&&'(55 zz|rcUS;a[SU35eUR(a<URUR)n[ U5R X R S9$UR5$)a6 Return Index without NA/NaN values. Parameters ---------- how : {'any', 'all'}, default 'any' If the Index is a MultiIndex, drop the value when any or all levels are NaN. Returns ------- Index Examples -------- >>> idx = pd.Index([1, np.nan, 3]) >>> idx.dropna() Index([1.0, 3.0], dtype='float64') )rr1zinvalid how option: r])r6rr'rrSr9rr)rrrs rdropna Index.dropna sb( n $3C59: : <<t{{l3J:))*99)E Ezz|rc>UbURU5 UR(aUR5$[TU]5nUR U5$)a& Return unique values in the index. Unique values are returned in order of appearance, this does NOT sort. Parameters ---------- level : int or hashable, optional Only return values from specified level (for MultiIndex). If int, gets the level by integer position, else by level name. Returns ------- Index See Also -------- unique : Numpy array of unique values in that column. Series.unique : Return unique values of Series object. Examples -------- >>> idx = pd.Index([1, 1, 2, 3, 3]) >>> idx.unique() Index([1, 2, 3], dtype='int64') )rrirsuperrr)rrrA __class__s rr Index.unique sI6    & &u - >>::< !!!&))rrrc`>UR(aUR5$[TU] US9$)a Return Index with duplicate values removed. Parameters ---------- keep : {'first', 'last', ``False``}, default 'first' - 'first' : Drop duplicates except for the first occurrence. - 'last' : Drop duplicates except for the last occurrence. - ``False`` : Drop all duplicates. Returns ------- Index See Also -------- Series.drop_duplicates : Equivalent method on Series. DataFrame.drop_duplicates : Equivalent method on DataFrame. Index.duplicated : Related method on Index, indicating duplicate Index values. Examples -------- Generate an pandas.Index with duplicate values. >>> idx = pd.Index(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo']) The `keep` parameter controls which duplicate values are removed. The value 'first' keeps the first occurrence for each set of duplicated entries. The default value of keep is 'first'. >>> idx.drop_duplicates(keep='first') Index(['lama', 'cow', 'beetle', 'hippo'], dtype='object') The value 'last' keeps the last occurrence for each set of duplicated entries. >>> idx.drop_duplicates(keep='last') Index(['cow', 'beetle', 'lama', 'hippo'], dtype='object') The value ``False`` discards all sets of duplicated entries. >>> idx.drop_duplicates(keep=False) Index(['cow', 'beetle', 'hippo'], dtype='object') rr)rirr"drop_duplicates)rrsr#s rr&Index.drop_duplicates s,\ >>::< w&D&11rcUR(a"[R"[U5[S9$UR US9$)a Indicate duplicate index values. Duplicated values are indicated as ``True`` values in the resulting array. Either all duplicates, all except the first, or all except the last occurrence of duplicates can be indicated. Parameters ---------- keep : {'first', 'last', False}, default 'first' The value or values in a set of duplicates to mark as missing. - 'first' : Mark duplicates as ``True`` except for the first occurrence. - 'last' : Mark duplicates as ``True`` except for the last occurrence. - ``False`` : Mark all duplicates as ``True``. Returns ------- np.ndarray[bool] See Also -------- Series.duplicated : Equivalent method on pandas.Series. DataFrame.duplicated : Equivalent method on pandas.DataFrame. Index.drop_duplicates : Remove duplicate values from Index. Examples -------- By default, for each set of duplicated values, the first occurrence is set to False and all others to True: >>> idx = pd.Index(['lama', 'cow', 'lama', 'beetle', 'lama']) >>> idx.duplicated() array([False, False, True, False, True]) which is equivalent to >>> idx.duplicated(keep='first') array([False, False, True, False, True]) By using 'last', the last occurrence of each set of duplicated values is set on False and all others on True: >>> idx.duplicated(keep='last') array([ True, False, True, False, False]) By setting keep on ``False``, all duplicates are True: >>> idx.duplicated(keep=False) array([ True, False, True, False, True]) r rr)rir%zerosr4r _duplicated)rrss rrIndex.duplicated2 s5l >>88CIT2 2T**rc X-$rrrs r__iadd__Index.__iadd__p s |rcF[S[U5RS35e)NzThe truth value of a zC is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().)r6rSrrs r __nonzero__Index.__nonzero__t s-#DJ$7$7#89C C  rc\[X5nURULaURU5$U$)z If the result of a set operation will be self, return self, unless the name changes, in which case make a shallow copy of self. )rrr)rrrs r_get_reconciled_name_object!Index._get_reconciled_name_object s. "$. 99D ;;t$ $ rc.US;a[SUS35eg)N)NFTzBThe 'sort' keyword only takes the values of None, True, or False; z was passed.r6)rrs r_validate_sort_keywordIndex._validate_sort_keyword s. * *))-l<  +rc[U[5(aT[U[5(a?URb2URb%URS5nURS5nX44$X4$)z. With mismatched timezones, cast both to UTC. UTC)rr^tz tz_convert)rrsetoprrights r_dti_setop_align_tzsIndex._dti_setop_align_tzs sd t- . .5"233#$??5)D$$U+E; {rc$URU5 URU5 URU5upURUR:wa[ U[ 5(a3[ [U55(d[U5S:a [S5eURUS5upURU5nURUSS9nURUSS9nURXbS9$[U5(aURU5(a(URU5nUSLaUR!5$U$[U5(d(URU5nUSLaUR!5$U$UR#XS9nUR%X5$)a Form the union of two Index objects. If the Index objects are incompatible, both Index objects will be cast to dtype('object') first. Parameters ---------- other : Index or array-like sort : bool or None, default None Whether to sort the resulting Index. * None : Sort the result, except when 1. `self` and `other` are equal. 2. `self` or `other` has length 0. 3. Some values in `self` or `other` cannot be compared. A RuntimeWarning is issued in this case. * False : do not sort the result. * True : Sort the result (which may raise TypeError). Returns ------- Index Examples -------- Union matching dtypes >>> idx1 = pd.Index([1, 2, 3, 4]) >>> idx2 = pd.Index([3, 4, 5, 6]) >>> idx1.union(idx2) Index([1, 2, 3, 4, 5, 6], dtype='int64') Union mismatched dtypes >>> idx1 = pd.Index(['a', 'b', 'c', 'd']) >>> idx2 = pd.Index([1, 2, 3, 4]) >>> idx1.union(idx2) Index(['a', 'b', 'c', 'd', 1, 2, 3, 4], dtype='object') MultiIndex case >>> idx1 = pd.MultiIndex.from_arrays( ... [[1, 1, 2, 2], ["Red", "Blue", "Red", "Blue"]] ... ) >>> idx1 MultiIndex([(1, 'Red'), (1, 'Blue'), (2, 'Red'), (2, 'Blue')], ) >>> idx2 = pd.MultiIndex.from_arrays( ... [[3, 3, 2, 2], ["Red", "Green", "Red", "Green"]] ... ) >>> idx2 MultiIndex([(3, 'Red'), (3, 'Green'), (2, 'Red'), (2, 'Green')], ) >>> idx1.union(idx2) MultiIndex([(1, 'Blue'), (1, 'Red'), (2, 'Blue'), (2, 'Green'), (2, 'Red'), (3, 'Green'), (3, 'Red')], ) >>> idx1.union(idx2, sort=False) MultiIndex([(1, 'Red'), (1, 'Blue'), (2, 'Red'), (2, 'Blue'), (3, 'Red'), (3, 'Green'), (2, 'Green')], ) rzjCan only union MultiIndex with MultiIndex or Index of tuples, try mi.to_flat_index().union(other) instead.unionFr rT)r7_assert_can_do_setop_convert_can_do_setoprrr`rM_unpack_nested_dtyper4rIr?_find_common_type_compatr$rBequalsr3r_union_wrap_setop_result)rrr result_namerrr>rAs rrB Index.union stf ##D) !!%(!77> :: $4//'(>[[G!45F7|a"ZZ0 &':;++53P3P$V2F S45 :!%[  J !!e"Je 7I1e"JKxx &99 :s$F==5H.2 H ?H &H.-H.c[X5n[U[5(a#URU:waUR U5nU$UR X#S9nU$Nr])rrrrrr)rrrArs rrJIndex._wrap_setop_resulto sT!$. fe $ ${{d"t, ''':F rcURU5 URU5 URU5upURUR:waUR US5upUR U5(aYUR (d UR5RU5nOURU5nUSLaUR5nU$[U5S:Xd[U5S:XaUR(dUR(aUSSRU5$URU5nURU:Xa7[U5S:XaUSSRU5$USSRU5$[/XSS9$URU5(d3[!U["5(aUSSRU5$[/US9$URUR:wa@URU5nUR%USS9nUR%USS9nUR'XS 9$UR)XS 9nUR+X5$) ax Form the intersection of two Index objects. This returns a new Index with elements common to the index and `other`. Parameters ---------- other : Index or array-like sort : True, False or None, default False Whether to sort the resulting index. * None : sort the result, except when `self` and `other` are equal or when the values cannot be compared. * False : do not sort the result. * True : Sort the result (which may raise TypeError). Returns ------- Index Examples -------- >>> idx1 = pd.Index([1, 2, 3, 4]) >>> idx2 = pd.Index([3, 4, 5, 6]) >>> idx1.intersection(idx2) Index([3, 4], dtype='int64') intersectionTrN)rrr]Fr rC)r7rDrErr?rHrirr3rr4r^rrGr_should_comparerr`r$rf _intersection_wrap_intersection_result)rrrrKrArthiss rrfIndex.intersectiony s> ##D) !!%(!77> :: $33E>JKD ;;u  >>BB5I99%@t|++-M t9>SZ1_~~BQx{3311%8EzzU"t9>8??;77 !9++K885; ;%%e,,$ ..BQx{33+. . ZZ5;; &11%8E;;u5;1DLLUL3E$$U$6 6##E#5--e<>> idx1 = pd.Index([2, 1, 3, 4]) >>> idx2 = pd.Index([3, 4, 5, 6]) >>> idx1.difference(idx2) Index([1, 2], dtype='int64') >>> idx1.difference(idx2, sort=False) Index([2, 1], dtype='int64') NrTrC) r7rDrErHrr4rrrg _difference_wrap_difference_result)rrrrKrAs r differenceIndex.differencesB ##D) !!%(!77> ;;u  8??;/ / u:?[[]))+6Ft|))++M##E**[[]))+6Ft|))++M!!%!3++E::rc@Un[U[5(a2UR(a!UR(aUR5nUR 5nX1R U5S:HnUR (aUOUR 5n[XB5nU$Nr)rr\rrrrxrirS)rrrrjthe_diffs rr~Index._differencePsr d/ 0 0T\\emm;;=D --d3r9:#~~88??3D"82rc$URX5$rrsrts rrIndex._wrap_difference_result[rvrcURU5 URU5 URU5upUcUnURUR:waUR US5upUR U5(dUR XS9RU5$URUR:waOURU5nURUSS9nURUSS9nURXsS9RU5$UR5nUR5nURU5nURUS:gR5S5n [R "[R""UR$5U SS9n URU 5n US:HR5Sn URU 5n U R'U 5n[)X5nUR*(d[-XURS 9$[/S U 5n [1U5S:XaU R35R5U5$UR5U5$) a Compute the symmetric difference of two Index objects. Parameters ---------- other : Index or array-like result_name : str sort : bool or None, default None Whether to sort the resulting index. By default, the values are attempted to be sorted, but any TypeError from incomparable elements is caught by pandas. * None : Attempt to sort the result, but catch any TypeErrors from comparing incomparable elements. * False : Do not sort the result. * True : Sort the result (which may raise TypeError). Returns ------- Index Notes ----- ``symmetric_difference`` contains elements that appear in either ``idx1`` or ``idx2`` but not both. Equivalent to the Index created by ``idx1.difference(idx2) | idx2.difference(idx1)`` with duplicates dropped. Examples -------- >>> idx1 = pd.Index([1, 2, 3, 4]) >>> idx2 = pd.Index([2, 3, 4, 5]) >>> idx1.symmetric_difference(idx2) Index([1, 5], dtype='int64') symmetric_differencerCFr rrT) assume_uniquerrr)r7rDrErr?rgrBrrGr$rrrxrrVr% setdiff1drwsizer)rSr^rr r4remove_unused_levelsr)rrrKrresult_name_updaterrjthatr^common_indexer left_indexer left_diff right_indexer right_diffrrAs rrIndex.symmetric_difference_s H ##D) !!%($($>$>u$E!  ,K :: $33E;QRKD##E**::e:/66{C C ZZ5;; &11%8E;;u5;1D<<E<2D,,T,=DD[Q Q{{} &&u-!w"}&=&=&?&BC|| IIdii . IIl+ !B//1!4 ZZ . %%j1  2~~9I9IJ J\95I6{a 557AA+NN##K0 0rc:[U5(d [S5eg)Nz!Input must be Index or array-likeT)rKrrs rrDIndex._assert_can_do_setopsE""?@ @rc[U[5(d"[XRS9nURnX4$[X5nX4$rc)rrrr)rrrKs rrEIndex._convert_can_do_setopsF%''%ii0E))K!!-T9K!!rczURU5nURRU5$![agn[ U[ 5(d6[ U[ R5(a"[SU55(a [U5e[U5UeSnAf[a URU5 ef=f)a Get integer location, slice or boolean mask for requested label. Parameters ---------- key : label Returns ------- int if unique index, slice if monotonic index, else mask Examples -------- >>> unique_index = pd.Index(list('abc')) >>> unique_index.get_loc('b') 1 >>> monotonic_index = pd.Index(list('abbc')) >>> monotonic_index.get_loc('b') slice(1, 3, None) >>> non_monotonic_index = pd.Index(list('abcb')) >>> non_monotonic_index.get_loc('b') array([False, True, False, True]) c3B# UHn[U[5v M g7fr)rslicer&s rr Index.get_loc..sAj 1e,,jrN) _maybe_cast_indexerrget_locrrrrrrr1r_check_indexing_error)rkey casted_keyrCs rr Index.get_locs4--c2  <<'' 3 3 )*e,,:s||44AjAAA',,3-S (   & &s +   s. B:A"B B:c[U5nUnURU5nURX#U5 UR(d[ UR 5e[ U5S:Xa#[R"/[RS9$URU5(d&URU5(dURXSS9$[UR[5(aURUR:XdeUR R#UR$5nUR&(aWUR&(aF[)U5nUR+[R,5nUR)5n XU'SXiU)-'U$[UR[5(aUR#UR.5n [0R2"XR$SS9nUR4(dUUR&(aDUR&(a3UR+[R,5nUR)5n XU '[7U5$UR9U5upXLdXLaU R#XX4S9$URUR:XaBUR;U5(a,[R<"[ U5[RS9$URUR:waWURU5(dAUR?U5n URAU SS 9nURAU SS 9nURCXX4S9$URCXX45$) a Compute indexer and mask for new index given the current index. The indexer should be then used as an input to ndarray.take to align the current data to the new index. Parameters ---------- target : Index method : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional * default: exact matches only. * pad / ffill: find the PREVIOUS index value if no exact match. * backfill / bfill: use NEXT index value if no exact match * nearest: use the NEAREST index value if no exact match. Tied distances are broken by preferring the larger index value. limit : int, optional Maximum number of consecutive labels in ``target`` to match for inexact matches. tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index's type. Returns ------- np.ndarray[np.intp] Integers from 0 to n - 1 indicating that the index at these positions matches the corresponding target values. Missing values in the target are marked by -1. Notes ----- Returns -1 for unmatched values, for further explanation see the example below. Examples -------- >>> index = pd.Index(['c', 'a', 'b']) >>> index.get_indexer(['a', 'b', 'x']) array([ 1, 2, -1]) Notice that the return value is an array of locations in ``index`` and ``x`` is marked by -1, as it is not in ``index``. rr Trrrrrlimit toleranceFr )"r_maybe_cast_listlike_indexer_check_indexing_methodrTr1_requires_unique_msgr4r%r5intprg_should_partial_index_get_indexer_non_comparablerrrVrrUrrrhrrr!rtake_ndr^rC_maybe_downcast_for_indexingrHrwrGr$ _get_indexer)rtargetrrr orig_targetr^ target_nanslocrcategories_indexerpselfptargetrrjs rrUIndex.get_indexerst+62 226: ##F9=$$#D$=$=> > v;! 88Bbgg. .##F++D4N4Nv4V4V33FRV3W W djj"2 3 3::- --ll..v||>")<,,(-F djj=2B"C D D!)fX%>tDz?R?R>ST >$ 5  5! rc [R"U5nURUR:waURS:a [S5e[ U5(a[R "UR [R5(dyURS:a/[S[U5RSUR S35e[S[U5RSUR S[U535eU$)NrFz5list-like tolerance size must match target index sizerztolerance argument for z with dtype z1 must contain numeric elements if it is list typez$ must be numeric if it is a scalar: ) r%r.rr6rL issubdtypernumberrGrSrrepr)rrrs rrIndex._convert_tolerancesJJy) ;;).. (Y^^a-?TU U d # #BMM)//299,U,U~~! -d4j.A.A-B,zzl"SU )$t**=*=)>l4::,W66:9o5FH rc*UR(aUR(dUR(d [S5eUR U5R R n[US[U55n[U[U5S5nURXrX45$UR(aUR(aUR5nUR5n [U[R5(a[U [R5(d[eUS:Xa[R "XUS9n O([R""XUS9n OUR%XU5n Ub"[U5(aUR'XU5n U $)N0index must be monotonic increasing or decreasingrr)r^rrr6r)rrYrr4rrrr%r&rIlibalgosrr_get_fill_indexer_searchsorted_filter_indexer_tolerance) rrrrrencoded self_encodedtarget_encodedr own_valuesr^s rrIndex._get_fill_indexersE >>00D4P4P !STTkk&)1188G 3t9!56L"73t9;#78N11   ' 'F,J,J"557M002JmRZZ88 BJJAA*)",,zN#++JUS99&%PG  SYY44ViPGrcUb[S[U5S35eUS:XaSOSnURU5nUS:HnURXU5XV'US:XaXV==S-ss'U$SXU[ U5:H'U$)zj Fallback pad/backfill get_indexer that works for monotonic decreasing indexes and non-monotonic targets. zlimit argument for z; method only well-defined if index and target are monotonicrrr>rrF)r6rrU_searchsorted_monotonicr4)rrrrsider^nonexacts rr$Index._get_fill_indexer_searchsorteds  %d6l^444  4:U?""6*b= 889I4P 6>   " -/Gs4y( )rc[U5(dURUS5$URUSUS9nURUSUS9nURX5nURX5nUR(a[ R O[ Rn[R"U"Xg5US:H-UU5n UbURXU5n U $)z Get the indexer for the nearest index labels; requires an index with values that can be subtracted from each other (e.g., not strings or tuples). rrrr) r4rrU_difference_compatroperatorltler%wherer) rrrrrrleft_distancesright_distancesopr^s rrIndex._get_nearest_indexers4yy))&%8 8''U'C ((5(I 00F11&H 88X[[hkk(( ~ /" $     44ViPGrcXURX5n[R"XC:*US5$r)rr%r)rrr^rdistances rrIndex._filter_indexer_tolerance?s***6;xx-w;;rc[UR[5(aH[SUR5R n[SUR5R nX2U- nOUR UUR - n[U5$)Nr)rrrZr rrr'abs)rrr^rrdiffs rrIndex._difference_compatJsq djj+ . .mTZZ8AAJ  =FFM&6D<<(6>>9D4yrcURSURS5 URSURS5 URSURS5 g)zb For positional indexing, a slice must have either int or None for each of start, stop, and step. positionalilocN)_validate_indexerrstopsteprrs r_validate_positional_slice Index._validate_positional_slice_sF |SYY? |SXXv> |SXXv>rcURURURpTn[U5n[R "UR S5(aLUS:Xa4U(a-X4:Xd(US:wa"[R"S[[5S9 URX4U5$US:Xa|U(aU$UR RS;aYURSURS5 URSURS5 URSURS5 U$U=(a URnU(a+UbURU5 UbURU5 Sn["R$"U5(aUnU$U(aUS :Xa ['S 5eUnU$URX4U5nU$![ a N[f=f) z Convert a slice indexer. By definition, these are labels unless 'iloc' is passed in. Floats are not allowed as the start, step, or stop of the slice. Parameters ---------- key : label of the slice bound kind : {'loc', 'getitem'} fgetitemrzThe behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] insteadriurFrzjSlicing a positional slice with .loc is not allowed, Use .loc with labels or .iloc with positions instead.)rrrr}r is_np_dtyperr;r<r=r6 slice_indexerr(r_should_fallback_to_positionalrrr) is_null_slicer) rrr(rrris_index_slice is_positionalr^s r_convert_slice_indexerIndex._convert_slice_indexeris IIsxxT337 ??4::s + +y ^EMdVWi  /"/1%%e48 8 9  D(&&w 9E&&w)D&&w)D 'N4+N+N   $LL'#LL& %    S ! !Gu}LG((d;G#  s*F99 GGc SUS[U5RSUS[U5R3nU[RLa [ U5Ue[ U5e)z+ Raise consistent invalid indexer message. z cannot do z indexing on z with these indexers [z ] of type )rSrrrr)rformrreraiserks r_raise_invalid_indexerIndex._raise_invalid_indexersfmDJ,?,?+@AZS (:(:'; =  #.. (C.g -nrc^UR(d[U5(a [S5egg)z Check if we are allowing reindexing with this particular indexer. Parameters ---------- indexer : an integer ndarray Raises ------ ValueError if its a duplicate axis /cannot reindex on an axis with duplicate labelsN)rTr4r6)rr^s r_validate_can_reindexIndex._validate_can_reindexs)$$WNO O*6$rc[US5(+n[U5n[U[5(d;[ U5S:Xa,Ub!UR (aUR UnOUnUSSnO [U5nUb\[U[5(d[U[5(a2Ub [S5eURXSUR (+S9upn OURU5(aSnOmUR(aURXXES9nOKUR (a [S5eUR(d [S 5eUR!U5upUR#XU5nX4$) aX Create index with target's values. Parameters ---------- target : an iterable method : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional * default: exact matches only. * pad / ffill: find the PREVIOUS index value if no exact match. * backfill / bfill: use NEXT index value if no exact match * nearest: use the NEAREST index value if no exact match. Tied distances are broken by preferring the larger index value. level : int, optional Level of multiindex. limit : int, optional Maximum number of consecutive labels in ``target`` to match for inexact matches. tolerance : int or float, optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index's type. Returns ------- new_index : pd.Index Resulting index. indexer : np.ndarray[np.intp] or None Indices of output values in original index. Raises ------ TypeError If ``method`` passed along with ``level``. ValueError If non-unique multi-index ValueError If non-unique index and ``method`` or ``limit`` passed. See Also -------- Series.reindex : Conform Series to new index with optional filling logic. DataFrame.reindex : Conform DataFrame to new index with optional filling logic. Examples -------- >>> idx = pd.Index(['car', 'bike', 'train', 'tractor']) >>> idx Index(['car', 'bike', 'train', 'tractor'], dtype='object') >>> idx.reindex(['car', 'bike']) (Index(['car', 'bike'], dtype='object'), array([0, 1])) rrNz)Fill method not supported if level passedr>)r keep_orderrz'cannot handle a non-unique multi-index!r)r-ensure_has_lenrrr4r^r ensure_indexr`r _join_levelrHrTrUr6rirX_wrap_reindex_result) rrrrrrpreserve_namesrr^ros rreindex Index.reindexsTF%VV44 '&%((S[A-= T^^kk%(!WF!&)F   t] + +z&-/P/P! KLL"&!1!174>>7I"2" FQ {{6""(("..U/G^^$%NOO$%VWW!%! >66v>2 )-7>)B  w<<YYs7|277;F)'2G#[[1N$VnO7>299J -K3w(?rwwO &( F#$ ..jyy9IT ..z.LI;..rrcgrrrrrrrrs rr Index.join rrcgrrr,s rrr-r.rcgrrr,s rrr-r.rrc[U5nU=(d US:Hn[U[5(aB[U[5(a-URSLURSL- (a [ S5eUR (d>UR (d-UR U5upgX`LdXqLaURXrUSUS9$UcLUR (dUR (a*URUR:XaOURXS9$Ub2UR (dUR (aURXUS9$[U5S:Xd[U5S:XaURXU5$URUR:waAURU5nURUSS 9n URUSS 9nU RXSS 9$[U[ 5(a~[U[ 5(aiUR"(dXUR$R'UR$5(d.[)UR*R-UR$55n[/U5 UR0(aeUR0(aTUR2(aCUR2(a2UR4(dUR4(aUR7XS9$UR4(aUR4(dUR9XUS 9$UR;XU5$![a GNf=f![a N/f=f) ak Compute join_index and indexers to conform data structures to the new index. Parameters ---------- other : Index how : {'left', 'right', 'inner', 'outer'} level : int or level name, default None return_indexers : bool, default False sort : bool, default False Sort the join keys lexicographically in the result Index. If False, the order of the join keys depends on the join type (how keyword). Returns ------- join_index, (left_indexer, right_indexer) Examples -------- >>> idx1 = pd.Index([1, 2, 3]) >>> idx2 = pd.Index([4, 5, 6]) >>> idx1.join(idx2, how='outer') Index([1, 2, 3, 4, 5, 6], dtype='int64') outerNz0Cannot join tz-naive with tz-aware DatetimeIndexTrrrFr rrrr)rrr^r;rr^rrr _join_multirr4 _join_emptyrrGr$r\orderedr!rHrr'reorder_categories_validate_join_methodrrOri_join_monotonic_join_non_unique_join_via_get_indexer) rrrrrrrpotherrrjs rrr-sFU#%sg~ d, - -*UDT2U2U4EHH$45 RSS~~eoo ==eDME F$7zz5$T" =dnnzzU[[('''77  $..EOO##Ec#: : t9>SZ1_ ''D99 :: $11%8E;;u5;1DLLUL3E99UT9B B t0 1 15"566LLOO**5+;+;<<%--::4??KLEc"  ( (--%%&&5?? ++E+;;u((d(C C))%d;;G  :  s$#L(L9( L65L69 MMch[U5S:Xd[U5S:Xde[U5 [U5(a8[[SSS.R X"55nUR XU5upEnOUS;aU(a3UR (d"UR5nURU5nOSnUR5n[R"[R"S5[U55nO5UR5n[R"/[RS9nSnXFU4$)Nrr>rrr>)rr2rr )r4r:r r(rr7rargsortrrr% broadcast_torr5)rrrrrrrs rr7Index._join_empty5s4yA~Uq00c" u::w6 B F Fs PQC%*%6%6t$%G "Jd % %D88||~!YYt_ !ZZ\ ??2772;J@DJ88Bbgg.DD%%rcUS:XaU(aUR5OUnOLUS:XaU(aUR5OUnO,US:XaURXS9nOUS:XaURXS9nWULaSnOUR U5nXALaSnOUR U5nXEU4$![a1 URU5n[ XC5nN^![a Nkf=ff=f)Nrr>innerrCr2)rrfrBrrSrx)rrrrrlindexerrindexers rr=Index._join_via_get_indexerPs &=/3))+J G^04**,%J G^**5* C  CCCCc"SSKJn SSKJn [ [ R "UR65n[ [ R "UR65nURnURn[U5n [U5n X-n U (d [S5e[X5(a[X5(a[X- US9n [X- US9n [X-5(dUnURUR5nO"URU 5nURU 5nUR!XSS9unnnX-nU"UUUUUU5unnnU"UUUSS 9nUR#5nUS :XaXl-nOX]-nURU5nUUU4$[%['U 55nSn[X5(aXpSnS S S .nUR)X"5nURRU5nUR+UUUS 9nU(aUSUSUS4$U$)Nrr) restore_dropped_levels_multijoinz+cannot join with no overlapping index names)rTr4Frr>rr>rr3rrF)r2rpandas.core.reshape.mergerJr0r)not_nonerrrPr6rrr4reorder_levelsrrrnextiterrr)rrrrrJself_names_listother_names_listself_names_orderother_names_order self_names other_namesoverlap ldrop_names rdrop_names self_jnlevelsother_jnlevelsjoin_idxrr dropped_namesrrrmulti_join_idx level_orderjl flip_orderfliprrAs rr6Index._join_multiss58Ns||TZZ89 ekk :;*00,22) *+ *JK K d ' 'Ju,I,I !5;KLK !6rs rr<Index._join_non_uniques KzzU[[(((: LL%--S 2~99X& 9%%%d2 j- 0 0SG^#::._get_leaf_sortersay~~"xx116{a-.A&).LMM)CR.F1IabM1Ca|3Bx3qr7**$YYdV45==?BFvbz*C''-@-HI Irz9Join on level between two MultiIndex objects is ambiguousrr>rKz8Index._join_level on non-unique index is not implementedTr4NrFrr Fr)rzlist[np.ndarray]returnnpt.NDArray[np.intp])r2rrrrrrrirIrr4rrCrget_reverse_indexerrr0r%rwrr r&r1maxrgroupsort_indexerrVr)rrrrrrrprr>rarb old_level new_levelleft_lev_indexerright_lev_indexerrr rev_indexer old_codesr{ new_lev_codesrrrrn max_new_levngroupscountsmask_allrs rrIndex._join_levels 9 J< d ' 'Ju,I,IWX Xe#D55 %5;W+MD((3$C$++++&&u-KK& %J :C D:H: 6 %6  #SY!^# ! / ;UQY0GH !/  33CD 112BC NSK 5)Ii2o.E',,U3MTZZ(I,Ie dkk*J )Ju !yyT"''B #BJJ = $*xxzz6? @isTiI @#/#5LA:'*='9Q'>!MDUDUDWK+oG+3+E+E%w,(L& $0q #G His\!2iI HI)B.D#xxzH#:C$D)3SY) $D#3Ik 4J#KL>G His\!2iI H$'+||~a'8'F #!jj!& J  (-22:3C3CE3JKM&,,U3M *7-L!(D.A,.O ")D/B=/Q <66e!A!I %E!Is8M!MM"6M'cURUR:XdeUR(aUR(deURU5(aUS:XaUOUnUSS4$UR(aUR(aUS:XaUnSnUR U5nOUS:XaUnUR U5nSnOUS:Xa'UR U5upunUR XqXV5nOUS:Xa&URU5upunUR XqXV5nOUS:XaURU5upunOPUS:XaURU5upvnO5US:XaUR U5upunOUS:XaURU5upunWceWceUR WXU5nWcSO [U5nWcSO [U5nWXV4$)Nr>rrEr2) rrOrHrirr_wrap_joined_indexrrrC)rrr ret_indexrrr join_arrays rr;Index._join_monotonicws {{djj((($$)?)??? ;;u  "%TIdD( ( >>eoof}! 007" 11$7)-)<)rs rrIndex._wrap_joined_indexs{{djj((( dM * *!%u{{!:4::D2:DyyHuzz$'78E!))$6MMOJ''- -%d2D$$00$**0U Urc[U5[Laa[UR[R5=(d6 [UR [ [45=(d URS:H$[U[[45(+$)a Whether we can use the fastpaths implemented in _libs.join. This is driven by whether (in monotonic increasing cases that are guaranteed not to have NAs) we can convert to a np.ndarray without making a copy. If we cannot, this negates the performance benefit of using libjoin. zstring[python]) rSrrrr%r'rorpr_r`rs rrOIndex._can_use_libjoinsl : 4::rxx02dll-@/,RS2::!11 d%5}$EFFFrc[5(aNURn[U[R5(a!UR 5nSUR lU$UR$)a Return an array representing the data in the Index. .. warning:: We recommend using :attr:`Index.array` or :meth:`Index.to_numpy`, depending on whether you need a reference to the underlying data or a NumPy array. Returns ------- array: numpy.ndarray or ExtensionArray See Also -------- Index.array : Reference to the underlying data. Index.to_numpy : A NumPy array representing the underlying data. Examples -------- For :class:`pandas.Index`: >>> idx = pd.Index([1, 2, 3]) >>> idx Index([1, 2, 3], dtype='int64') >>> idx.values array([1, 2, 3]) For :class:`pandas.IntervalIndex`: >>> idx = pd.interval_range(start=0, end=5) >>> idx.values [(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]] Length: 5, dtype: interval[int64, right] F)rrrr%r&rflags writeable)rrs rrY Index.valuessNL  ::D$ ++yy{', $KzzrcxURn[U[R5(aSSKJn U"U5nU$)Nr)NumpyExtensionArray)rrr%r&pandas.core.arrays.numpy_r)rr5rs rr5 Index.arrays0  eRZZ ( ( E'.E rcUR$)a The best array representation. This is an ndarray or ExtensionArray. ``_values`` are consistent between ``Series`` and ``Index``. It may differ from the public '.values' method. index | values | _values | ----------------- | --------------- | ------------- | Index | ndarray | ndarray | CategoricalIndex | Categorical | Categorical | DatetimeIndex | ndarray[M8ns] | DatetimeArray | DatetimeIndex[tz] | ndarray[M8ns] | DatetimeArray | PeriodIndex | ndarray[object] | PeriodArray | IntervalIndex | IntervalArray | IntervalArray | See Also -------- values : Values )rrs rr' Index._valuess0zzrcURn[U[5(a UR$[U[5(aUR R S;aSSKnURRnURRU5(a+UR5nURRS5$URRU5(a+UR5nURRS5$[U5[ La[UR["5(a[UR[$5(dr[UR[5(a4['UR 5(aUR R S:wdURR)[*5$U$)zT Get the ndarray or ExtensionArray that we can pass to the IndexEngine constructor. rrNi8rP)r'rrurrorr(rrrSrrrrrrrrsrprLr$r)rvalsrrs rrIndex._get_engine_target2s> || dK ( (== d/ 0 0TZZ__5L nn))Gxx$$W----/}}))$//%%g....0}}))$// J% 4<<88t||_==4<<)<==$TZZ00JJOOs*<<&&v. . rcV[UR[5(aURR$[UR[5(aURR 5$UR 5n[U[R5(d [S5eU$)zK Get the ndarray or ExtensionArray that we can pass to the join functions. z%_can_use_libjoin should return False.) rr'rprroto_numpyrr%r&r6rs rrIndex._get_join_targetTs~ dllO 4 4<<%% %  &9 : :<<((* *((*&"**--DE E rct[UR[5(aG[UR5"U[R "UR [RS95$[UR[[45(a,[UR5RXRS9$U$)zc Cast the ndarray returned from one of the libjoin.foo_indexer functions back to type(self._data). r ) rrYrprSr%r)shaperrorurrrs rrIndex._from_join_targeths{ dkk? 3 3 $VRXXfll"((-ST T  &9;%G H H $33F**3M M rcZURUS9nX RRUS9- nU$)Nr) _memory_usagersizeof)rrrAs r memory_usageIndex.memory_usagess6###. ,,%%4%00 rc[U[5(a [S5e[R"U[ S9nUR U)U5$)a Replace values where the condition is False. The replacement is taken from other. Parameters ---------- cond : bool array-like with the same length as self Condition to select the values on. other : scalar, or array-like, default None Replacement if the condition is False. Returns ------- pandas.Index A copy of self with values replaced from other where the condition is False. See Also -------- Series.where : Same method for Series. DataFrame.where : Same method for DataFrame. Examples -------- >>> idx = pd.Index(['car', 'bike', 'train', 'tractor']) >>> idx Index(['car', 'bike', 'train', 'tractor'], dtype='object') >>> idx.where(idx.isin(['car', 'train']), 'other') Index(['car', 'other', 'train', 'other'], dtype='object') z1.where is not supported for MultiIndex operationsr )rr`rIr%r.rr)rcondrs rr Index.where{sGB dM * *%C zz$d+||TE5))rc[URS[U[R5(d [ U5O [ U5S35e)Nz5(...) must be called with a collection of some kind, z was passed)rrrr%genericrr)rrs rr,Index._raise_scalar_data_errorsJ ||n'1$ 'C'CT$ZTST   rcURn[U[R5(aURS;a [ X!5$[URU5(d[ eU$![ a n[ UeSnAff=f)z Check if the value can be inserted into our array without casting, and convert it to an appropriate native type if necessary. Raises ------ TypeError If the value cannot be inserted into an array of this dtype. rN) rrr%r(r@r:rr;r')rrlrrCs r_validate_fill_valueIndex._validate_fill_valuesp  eRXX & &5::T+A )*588"$,,66O % )S( )s A++ B5A<<Bc,[UR5$)z8 Return a boolean if we need a qualified .info display. )rMrrs r_is_memory_usage_qualified Index._is_memory_usage_qualifiedstzz**rcl[U5 XR;$![[[4a gf=f)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 F)hashr OverflowErrorrr6rs r __contains__Index.__contains__s7F S  ,,& &y*5  s 33zClassVar[None]__hash__c[S5e)Nz)Index does not support mutable operationsr)rrrls r __setitem__Index.__setitem__sCDDrc$URRn[U5(d[U5(a[R "U5nU"U5$[ U[5(aURU5$[R"U5(a[ [USS5[5(aUR[SS9nO[R"U[S9n[ UR [5(dI[#U5S:Xa:[#U5[#U5:wa"[$R&"S[([+5S9 U"U5nUR,S :a [/U5 UR0R3X0R4S 9$) a Override numpy.ndarray's __getitem__ method to work as desired. This function adds lists and Series as valid boolean indexers (ndarrays only supports ndarray with dtype=bool). If resulting ndim != 1, plain ndarray is returned instead of corresponding `Index` subclass. rNF)rr r rz~Using a boolean indexer with length 0 on an Index with length greater than 0 is deprecated and will raise in a future version.rrFr])r __getitem__rIrGr)cast_scalar_indexerrr_getitem_sliceis_bool_indexerr!rXrrr%r.rr4r;r<r=r6rGr|rer9r)rrrrAs rrIndex.__getitem__s.**(( c??hsmm))#.C3<  c5 ! !&&s+ +   s # # '#w5~FFlll>jjD1djj.99s8q=SXT%:MM*#5  ;;? "6 *  ,,V**,EErc.URUn[U5RX RURS9nSUR ;aHUR SL=(a UR S:nURRURUS9 U$)z8 Fastpath for __getitem__ when we know we have a slice. rrNr)reverse) rrSr9rr"rVrr_update_from_sliced)rslobjrprArs rrIndex._getitem_slice1s|jjd''**4CSCS'T  #jj,?aG NN . .t||W . M rc[UR5(d9[UR5(d[UR[5(aX;$g)a- Faster check for ``name in self`` when we know `name` is a Python identifier (e.g. in NDFrame.__getattr__, which hits this to support . key lookup). For indexes that can't hold identifiers (everything but object & categorical) we just return False. https://github.com/pandas-dev/pandas/issues/19764 F)rMrrPrrV)rrs r$_can_hold_identifiers_and_holds_name*Index._can_hold_identifiers_and_holds_name=s? DJJ ' 'tzz**$**&677< rclU/n[U[[45(aU[U5- nOURU5 UH#n[U[5(aM[ S5e UVs1sHo3R iM nn[U5S:aSO UR nURX%5$s snf)z Append a collection of Index options together. Parameters ---------- other : Index or list/tuple of indices Returns ------- Index Examples -------- >>> idx = pd.Index([1, 2, 3]) >>> idx.append(pd.Index([4])) Index([1, 2, 3, 4], dtype='int64') zall inputs must be IndexrFN) rr0rr)rrrr4_concat)rr to_concatobjrrs rr) Index.appendOs$F edE] + + e $I   U #Cc5)) :;;&//YcY/5zA~t499||I,,0s,B1c~UVs/sHo3RPM nn[U5n[RXRS9$s snf)z% Concatenate multiple Index objects. r])r'rTrrb)rrrrto_concat_valsrAs rr Index._concatss=.77Y))Y7~.   33 8s:c[URU5upU(aUR5$UR[:wa&[ X R5(a UR nURU5nURR5n[U[ R"5(a2[%XqR'5U5n[ R"XqU5 OUR)X5 UR+U5$![[[4aWn[UR5(aUeURU5nURU5RX5sSnA$SnAff=f)a] Return a new Index of the values set with the mask. Returns ------- Index See Also -------- numpy.ndarray.putmask : Changes elements of an array based on conditional and input values. Examples -------- >>> idx1 = pd.Index([1, 2, 3]) >>> idx2 = pd.Index([5, 6, 7]) >>> idx1.putmask([True, False, False], idx2) Index([5, 2, 3], dtype='int64') N)rnr'rrrrgrrr:r6rrMrGr$rrr%r&rmsum_putmaskr)rrrlnoop convertedrCrrYs rr Index.putmask}s(&dllD9  99;  :: $9%$L$LNNE ;11%8I""$ fbjj ) )3FHHJ RI JJvY / OOD (!!&))'":y9 ;tzz** 11%8E;;u%--d: :  ;s+D E5A E0*E50E5cURU5(ag[U[5(dg[U5[U5:wag[UR[ 5(aXURR S:Xa>URUR:wa$URUR[55$[UR5(a+[UR5(dURU5$[U[5(aURU5$[UR[5(aP[U[U55(dg[[UR 5nURUR 5$[UR["5(aURU5$[%URUR5$)a Determine if two Index object are equal. The things that are being compared are: * The elements inside the Index object. * The order of the elements inside the Index object. Parameters ---------- other : Any The other object to compare against. Returns ------- bool True if "other" is an Index and it has the same elements and order as the calling index; False otherwise. Examples -------- >>> idx1 = pd.Index([1, 2, 3]) >>> idx1 Index([1, 2, 3], dtype='int64') >>> idx1.equals(pd.Index([1, 2, 3])) True The elements inside are compared >>> idx2 = pd.Index(["1", "2", "3"]) >>> idx2 Index(['1', '2', '3'], dtype='object') >>> idx1.equals(idx2) False The order is compared >>> ascending_idx = pd.Index([1, 2, 3]) >>> ascending_idx Index([1, 2, 3], dtype='int64') >>> descending_idx = pd.Index([3, 2, 1]) >>> descending_idx Index([3, 2, 1], dtype='int64') >>> ascending_idx.equals(descending_idx) False The dtype is *not* compared >>> int64_idx = pd.Index([1, 2, 3], dtype='int64') >>> int64_idx Index([1, 2, 3], dtype='int64') >>> uint64_idx = pd.Index([1, 2, 3], dtype='uint64') >>> uint64_idx Index([1, 2, 3], dtype='uint64') >>> int64_idx.equals(uint64_idx) True TF pyarrow_numpy)rrrr4rrvstoragerHr$rrMr`r'rsrSr rrXrf)rrearrs rrH Index.equalssLv 88E??%'' t9E " tzz; / / ""o5 tzz)<< F 34 4 4:: & &u{{/K/K<<% % e] + +<<% % dllN 3 3eT$Z00 3D;;u{{+ + ekk> 2 2<<% % emm<>> idx1 = pd.Index(['1', '2', '3']) >>> idx2 = pd.Index(['1', '2', '3']) >>> idx2.identical(idx1) True >>> idx1 = pd.Index(['1', '2', '3'], name="A") >>> idx2 = pd.Index(['1', '2', '3'], name="B") >>> idx2.identical(idx1) False c3Z># UH n[TUS5[TUS5:Hv M" g7fr)r!)rrrrs rr"Index.identical..,s/*Aa&'%D*AA*s(+)rHr1rrSrrs``r identicalIndex.identicalsd0 KK  *** * T d5k)  *  ekk) rcURU5 URU5n[U[5(aUR [ U55SnX$![ [4af URU/SS9nURS:dURS:a [S5eUR5nUS:XaURs$X$f=f)a Return the label from the index, or, if not present, the previous one. Assuming that the index is sorted, return the passed index label if it is in the index, or return the previous index label if the passed one is not in the index. Parameters ---------- label : object The label up to which the method returns the latest index label. Returns ------- object The passed label if it is in the index. The previous label if the passed label is not in the sorted index or `NaN` if there is no such label. See Also -------- Series.asof : Return the latest value in a Series up to the passed index. merge_asof : Perform an asof merge (similar to left join but it matches on nearest key rather than equal key). Index.get_loc : An `asof` is a thin wrapper around `get_loc` with method='pad'. Examples -------- `Index.asof` returns the latest index label up to the passed label. >>> idx = pd.Index(['2013-12-31', '2014-01-02', '2014-01-03']) >>> idx.asof('2014-01-01') '2013-12-31' If the label is in the index, the method returns the passed label. >>> idx.asof('2014-01-02') '2014-01-02' If all of the labels in the index are later than the passed label, NaN is returned. >>> idx.asof('1999-01-02') nan If the index is not sorted, an error is raised. >>> idx_not_sorted = pd.Index(['2013-12-31', '2015-01-02', ... '2014-01-03']) >>> idx_not_sorted.asof('2013-12-31') Traceback (most recent call last): ValueError: index must be monotonic increasing or decreasing rrrrFz!asof requires scalar valued input) rrrrrr4rrrUrGritemr)rrurr^s rasof Index.asof4sr $$U+ 1,,u%C#u%%kk#d),R0y)$ &&&wu&=G||a7<>> idx = pd.date_range('2023-06-01', periods=3, freq='D') >>> where = pd.DatetimeIndex(['2023-05-30 00:12:00', '2023-06-01 00:00:00', ... '2023-06-02 23:59:59']) >>> mask = np.ones(3, dtype=bool) >>> idx.asof_locs(where, mask) array([-1, 0, 1]) We can use ``mask`` to ignore certain values in the index during calculation. >>> mask[1] = False >>> idx.asof_locs(where, mask) array([-1, 0, 0]) r>rrrFr r) r' searchsortedr%rrwr4rrargmax)rrrlocsrA first_values r asof_locsIndex.asof_locssj||D!.. MM/ xxq$(A.3t9BGG4T:??Ell4;;=1 >@ emmk9:; r)rrrrcgrrrrrrrs rrIndex.sort_values r)rrrcgrrrs rrrrrcgrrrs rrrrrrcUcU(aUR(dU(dfUR(aUU(a>[R"[ U5[R S9nUR 5U4$UR 5$[U[5(d [XX4S9nO7[[[X55nURUS9nU(dUSSS2nURU5nU(aX4$U$)a Return a sorted copy of the index. Return a sorted copy of the index, and optionally return the indices that sorted the index itself. Parameters ---------- return_indexer : bool, default False Should the indices that would sort the index be returned. ascending : bool, default True Should the index values be sorted in an ascending order. na_position : {'first' or 'last'}, default 'last' Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at the end. key : callable, optional If not None, apply the key function to the index values before sorting. This is similar to the `key` argument in the builtin :meth:`sorted` function, with the notable difference that this `key` function should be *vectorized*. It should expect an ``Index`` and return an ``Index`` of the same shape. Returns ------- sorted_index : pandas.Index Sorted copy of the index. indexer : numpy.ndarray, optional The indices that the index itself was sorted by. See Also -------- Series.sort_values : Sort values of a Series. DataFrame.sort_values : Sort values in a DataFrame. Examples -------- >>> idx = pd.Index([10, 100, 1, 1000]) >>> idx Index([10, 100, 1, 1000], dtype='int64') Sort values in ascending order (default behavior). >>> idx.sort_values() Index([1, 10, 100, 1000], dtype='int64') Sort values in descending order, and also get the indices `idx` was sorted by. >>> idx.sort_values(ascending=False, return_indexer=True) (Index([1000, 100, 10, 1], dtype='int64'), array([3, 1, 0, 2])) Nr )itemsrrr)rr)rrr%rwr4rrrr`rr rrrAr) rrrrrr^_asr sorted_indexs rrrsz ; 477$">">))CIRWW=yy{G++yy{"$ ..[Cu/:;C++++6C$B$iyy~ $ $ rc[S5e)z Use sort_values instead. z=cannot sort an Index object in-place, use sort_values insteadrrr`ras rr Index.sort<s WXXrcD[S[U5R35e)a3 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 str, 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.Index Shifted index. See Also -------- Series.shift : Shift values of Series. Notes ----- This method is only implemented for datetime-like index classes, i.e., DatetimeIndex, PeriodIndex and TimedeltaIndex. Examples -------- Put the first 5 month starts of 2011 into an index. >>> month_starts = pd.date_range('1/1/2011', periods=5, freq='MS') >>> month_starts DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01', '2011-04-01', '2011-05-01'], dtype='datetime64[ns]', freq='MS') Shift the index by 10 days. >>> month_starts.shift(10, freq='D') DatetimeIndex(['2011-01-11', '2011-02-11', '2011-03-11', '2011-04-11', '2011-05-11'], dtype='datetime64[ns]', freq=None) The default value of `freq` is the `freq` attribute of the index, which is 'MS' (month start) in this example. >>> month_starts.shift(10) DatetimeIndex(['2011-11-01', '2011-12-01', '2012-01-01', '2012-02-01', '2012-03-01'], dtype='datetime64[ns]', freq='MS') z\This method is only implemented for DatetimeIndex, PeriodIndex and TimedeltaIndex; Got type )rIrSr)rperiodsfreqs rshift Index.shiftCs,p"((,T (;(;'< >  rc:URR"U0UD6$)as Return the integer indices that would sort the index. Parameters ---------- *args Passed to `numpy.ndarray.argsort`. **kwargs Passed to `numpy.ndarray.argsort`. Returns ------- np.ndarray[np.intp] Integer indices that would sort the index if used as an indexer. See Also -------- numpy.argsort : Similar method for NumPy arrays. Index.sort_values : Return sorted copy of Index. Examples -------- >>> idx = pd.Index(['b', 'a', 'd', 'c']) >>> idx Index(['b', 'a', 'd', 'c'], dtype='object') >>> order = idx.argsort() >>> order array([1, 0, 3, 2]) >>> idx[order] Index(['a', 'b', 'c', 'd'], dtype='object') )rrArs rrA Index.argsortsJzz!!42622rc:[U5(d [U5egr)rNr1rs rrIndex._check_indexing_errors~~$C( (rc URS;$)z1 Should an integer key be treated as positional? >complexrrrrrs rr$Index._should_fallback_to_positionals !!*   ra: Compute indexer and mask for new index given the current index. The indexer should be then used as an input to ndarray.take to align the current data to the new index. Parameters ---------- target : %(target_klass)s Returns ------- indexer : np.ndarray[np.intp] Integers from 0 to n - 1 indicating that the index at these positions matches the corresponding target values. Missing values in the target are marked by -1. missing : np.ndarray[np.intp] An indexer into the target of the values not found. These correspond to the -1 in the indexer array. Examples -------- >>> index = pd.Index(['c', 'b', 'a', 'b', 'b']) >>> index.get_indexer_non_unique(['b', 'b']) (array([1, 3, 4, 1, 3, 4]), array([], dtype=int64)) In the example below there are no matched values. >>> index = pd.Index(['c', 'b', 'a', 'b', 'b']) >>> index.get_indexer_non_unique(['q', 'r', 't']) (array([-1, -1, -1]), array([0, 1, 2])) For this reason, the returned ``indexer`` contains only integers equal to -1. It demonstrates that there's no match between the index and the ``target`` values at these positions. The mask [0, 1, 2] in the return value shows that the first, second, and third elements are missing. Notice that the return value is a tuple contains two items. In the example below the first item is an array of locations in ``index``. The second item is a mask shows that the first and third elements are missing. >>> index = pd.Index(['c', 'b', 'a', 'b', 'b']) >>> index.get_indexer_non_unique(['f', 'b', 's']) (array([-1, 1, 3, 4, -1]), array([0, 2])) rXc[U5nURU5nURU5(d'URU5(dUR USSS9$UR U5up#X LdX1LaUR U5$URUR:waBURU5nURUSS9nURUSS9nUR U5$UR(a/UR(aURnURU5nOUR5nURR U5up[U 5[U 54$)NFrr )rrrgrrrrXrrGr$r^rrrrC) rrrrrrjrrrr^r_s rrXIndex.get_indexer_non_uniquesFf%226:##F++D4N4Nv4V4V33F4PU3V V::6B   5//8 8 :: %11&9E;;u5;1D==U=3D..t4 4 >>f..\\F 44V>zJ"7+-@-IIIrcpUR(aURU5$URU5up#U$)a+ Guaranteed return of an indexer even when non-unique. This dispatches to get_indexer or get_indexer_non_unique as appropriate. Returns ------- np.ndarray[np.intp] List of indices. Examples -------- >>> idx = pd.Index([np.nan, 'var1', np.nan]) >>> idx.get_indexer_for([np.nan]) array([0, 2]) )rTrUrX)rrr^ros rrxIndex.get_indexer_fors5&  ##F+ +008 rcUn[U[5(d[R"U5nUR(a&UR U5nUR U5SnOURU5up4nURX4U5 URU5n[U[5(aURUl [R"URS5(d[UR[5(aM[U[5(d'[U[!U55(aUR"cUR%S5nX44$)zB Analogue to get_indexer that raises if any elements are missing. rrN)rrr)r*rTrxrr)_raise_if_missingrrrrrrWr0rSr _with_freq)rr axis_namekeyarrr^r$s r_get_indexer_strictIndex._get_indexer_strict*s &%((**62F  **62G\\&)!,F+/+C+CF+K (F[ v :7# c5 ! !((FK ??6<< . .* LL/3 3 #t$$3T ++HH$**40rc&[U5S:XagUS:nUR5nU(aeU[U5:Xa[SUSUS35e[[ U5UR 5SR 55n[US35eg)a Check that indexer can be used to return a result. e.g. at least one element was found, unless the list of keys was actually empty. Parameters ---------- key : list-like Targeted labels (only used to show correct error message). indexer: array-like of booleans Indices corresponding to the key, (with -1 indicating not found). axis_name : str Raises ------ KeyError If at least one key was requested but none was found. rNz None of [z] are in the []z not in index)r4rrr0rrVr)rrr^r missing_masknmissing not_founds rr Index._raise_if_missingKs* s8q= { ##% 3w<'3%~i[JKK\#.|/C/C/Ea/HIPPRSIi[ 67 7 rcgrrrrrrs rr!Index._get_indexer_non_comparablen rcgrrrs rrrtrrcgrrrs rrrzrrc$Ub&[U5n[SURSU35eS[R"UR [R S9-nU(aU$[R"[U5[R S9nXV4$)a Called from get_indexer or get_indexer_non_unique when the target is of a non-comparable dtype. For get_indexer lookups with method=None, get_indexer is an _equality_ check, so non-comparable dtypes mean we will always have no matches. For get_indexer lookups with a method, get_indexer is an _inequality_ check, so non-comparable dtypes mean we will always raise TypeError. Parameters ---------- target : Index method : str or None unique : bool, default True * True if called from get_indexer. * False if called from get_indexer_non_unique. Raises ------ TypeError If doing an inequality check, i.e. method is not None. zCannot compare dtypes z and rr ) rFrrr%onesrrrwr4)rrrr other_dtype no_matchesr_s rrrsz6  .v6K4TZZLk]ST T"''&,,bgg>>  iiF 277;G& &rcUR$)zz Whether we should treat this as unique for the sake of get_indexer vs get_indexer_non_unique. For IntervalIndex compat. rrs rrTIndex._index_as_uniques~~rz8Reindexing only valid with uniquely valued Index objectscR[U[5(ax[U[5(acURbTURbG[URUR5(d"UR S5UR S54$GOUR S:Xa)[U[5(a[ U5"U5U4$UR S:Xa([U[5(a[ U5"U5U4$URRS:XaKURRS:Xa1UR5S:aXRUR54$O=UR(a,UR(d[ U5RU5n['UR5(d-['UR5(aUR)U5upX4$![a X4s$f=f![[ 4a [#UR$5nNf=f)z When dealing with an object-dtype Index and a non-object Index, see if we can upcast the object-dtype one to improve performance. r:date timedeltaurr)rr^r;rr<r rSrrdrr(minr$r^r3rr6rr'rMrrs rr"Index._maybe_downcast_for_indexings d, - -*UDT2U2U#HH("477EHH55u-u/?/?/FFF   6 )j@P.Q.Q #E{4(%//  ; .:eEV3W3W;t$e+ + ZZ__ # (8(8C(?yy{a\\$**555 ^^EOO +T ..u5 tzz**u{{/K/K<+ G;:G;>%H&%H&c[U5up#URS:XdUS:Xa0[UR5(d[U5(a[$[ URU5n[ X/U5nU$)zS Implementation of find_common_type that adjusts for Index-specific special cases. uint64)r>rrOr+r=r<)rr target_dtyperors rrGIndex._find_common_type_compatso +62  :: !\X%=&tzz226M77"! V4/F rcURS:Xa[UR5(d*URS:Xa[UR5(ag[U5nUR U5=(d [ U5$)z; Check if `self == other` can ever have non-False entries. rF)r rDrrF_is_comparable_dtyperM)rrrs rrgIndex._should_comparesh   9 ,1J4::1V1V   ) +0I%++0V0V $U+((/I?53IIrcURRS:XaURS:H$[UR5(a [U5$g)z6 Can we compare values of the given dtype to our own? bT)rr(rLr s rr/Index._is_comparable_dtypesB ::??c !::$ $ djj ) )#E* *rc[U[5(a URn[U5nUR 5nUR 5VVs0sHup4X0R U5_M nnn[U5$s snnf)z Group the index labels by a given array of values. Parameters ---------- values : array Values used to determine the groups. Returns ------- dict {group name -> group labels} )rr`r'rq_reverse_indexerrrr)rrYrArrs rrx Index.groupbysj" fm , ,^^FV$((*/5lln=nda!YYq\/n=&!!>sA;c.SSKJn URXS9nUR(au[ US[ 5(a][ X5(a UR nO0UR(aUR/[US5-nOSnUR"XES9$SnUR(d URn[R"USS9UR:HnU(a[X@RUS9n[R!XFSURS 9$) aN Map values using an input mapping or function. Parameters ---------- mapper : function, dict, or Series Mapping correspondence. na_action : {None, 'ignore'} If 'ignore', propagate NA values, without passing them to the mapping correspondence. Returns ------- Union[Index, MultiIndex] The output of the mapping function applied to the index. If the function returns a tuple with more than one element a MultiIndex will be returned. Examples -------- >>> idx = pd.Index([1, 2, 3]) >>> idx.map({1: 'a', 2: 'b', 3: 'c'}) Index(['a', 'b', 'c'], dtype='object') Using `map` with a function: >>> idx = pd.Index([1, 2, 3]) >>> idx.map('I am a {}'.format) Index(['I am a 1', 'I am a 2', 'I am a 3'], dtype='object') >>> idx = pd.Index(['a', 'b', 'c']) >>> idx.map(lambda x: x.upper()) Index(['A', 'B', 'C'], dtype='object') rr) na_actionNrFr) same_dtyper )r2r _map_valuesrrrrrr4r3rrrr r?rrb)rmapperr8rrrrr9s rr> Index.map6sF 9%%f%B  ??z*Q-??$++  c*Q-&88))*B BJJE __Z>$BTBTT 4JJ:J  u499 UUrc[U[5(av[UR5Vs/sH<nX2:XdUc UR U5R U5OUR U5PM> nn[ U5RU5$UVs/sH oQ"U5PM nn[X`RSS9$s snfs snf)z Apply function to all values found in index. This includes transforming multiindex entries separately. Only apply function to one level of the MultiIndex if level is specified. F)rr>) rr`r#rzget_level_valuesr>rS from_arraysrr)rfuncrrrYrrs r_transform_indexIndex._transform_indexxs dM * * t||, -A:%%a(,,T2**1-.-  :))&1 1&*+dT!WdE+YYeD D,s AB7B<clUbURU5 [R"URU5$)a Return a boolean array where the index values are in `values`. Compute boolean array of whether each index value is found in the passed set of values. The length of the returned boolean array matches the length of the index. Parameters ---------- values : set or list-like Sought values. level : str or int, optional Name or position of the index level to use (if the index is a `MultiIndex`). Returns ------- np.ndarray[bool] NumPy array of boolean values. See Also -------- Series.isin : Same for Series. DataFrame.isin : Same method for DataFrames. Notes ----- In the case of `MultiIndex` you must either specify `values` as a list-like object containing tuples that are the same length as the number of levels, or specify `level`. Otherwise it will raise a ``ValueError``. If `level` is specified: - if it is the name of one *and only one* index level, use that level; - otherwise it should be a number indicating level position. Examples -------- >>> idx = pd.Index([1,2,3]) >>> idx Index([1, 2, 3], dtype='int64') Check whether each index value in a list of values. >>> idx.isin([1, 4]) array([ True, False, False]) >>> midx = pd.MultiIndex.from_arrays([[1,2,3], ... ['red', 'blue', 'green']], ... names=('number', 'color')) >>> midx MultiIndex([(1, 'red'), (2, 'blue'), (3, 'green')], names=['number', 'color']) Check whether the strings in the 'color' level of the MultiIndex are in a list of colors. >>> midx.isin(['red', 'orange', 'yellow'], level='color') array([ True, False, False]) To check across the levels of a MultiIndex, pass a list of tuples: >>> midx.isin([(1, 'red'), (3, 'red')]) array([ True, False, False]) )rrisinr'r~s rrD Index.isins.J    & &u -zz$,,//rc[er)rIrs r_get_string_sliceIndex._get_string_slices "!rcURXUS9upE[U5(d [S5e[U5(d [S5e[XEU5$)a Compute the slice indexer for input labels and step. Index needs to be ordered and unique. Parameters ---------- start : label, default None If None, defaults to the beginning. end : label, default None If None, defaults to the end. step : int, default None Returns ------- slice Raises ------ KeyError : If key does not exist, or key is not unique and index is not ordered. Notes ----- This function assumes that the data is sorted, so use at your own peril Examples -------- This is a method on all index types. For example you can do: >>> idx = pd.Index(list('abcd')) >>> idx.slice_indexer(start='b', end='c') slice(1, 3, None) >>> idx = pd.MultiIndex.from_arrays([list('abcd'), list('efgh')]) >>> idx.slice_indexer(start='b', end=('c', 'g')) slice(1, 3, None) )rzStart slice bound is non-scalarzEnd slice bound is non-scalar) slice_locsrNAssertionErrorr)rrendr start_slice end_slices rrIndex.slice_indexersWX"&$!G %% !BC C## !@A A[T22rcU$)z` If we have a float key and are not a floating index, then try to cast to an int if equivalent. rrs rrIndex._maybe_cast_indexers  rc[U5$)zD Analogue to maybe_cast_indexer for get_indexer instead of get_loc. )rrs rr"Index._maybe_cast_listlike_indexersF##rc^[R"U5(dURX5 gg)zb If we are positional indexer, validate that we have appropriate typed bounds must be an integer. N)ris_int_or_noner )rr rr(s rrIndex._validate_indexers(!!#&&  ' ' 2'rc[UR5(aURU5$[U5(d[ U5(aX;aUR SU5 U$)aY This function should be overloaded in subclasses that allow non-trivial casting on label-slice bounds, e.g. datetime-like indices allowing strings containing formatted datetimes. Parameters ---------- label : object side : {'left', 'right'} Returns ------- label : object Notes ----- Value of `side` parameter should be validated in caller. r)rLrrrGrIr )rrurs r_maybe_cast_slice_boundIndex._maybe_cast_slice_bound+sS. DJJ ' '++E2 2 UOOz%00e6G  ' ' 7 rcUR(aURXS9$UR(a+USSS2RXS:XaSOSS9n[U5U- $[ S5e)Nrrrr>r)rrrr4r6)rrurposs rrIndex._searchsorted_monotonicKsn  ' '$$U$6 6  ) )tt*))v~G6*Ct9s? "KLLrcUS;a[SU35eUnURX5nURU5n[ U[ R5(az[UR5(de[R"URS55n[ U[ R5(a[SUS[U535e[ U[5(aUS:Xa UR$UR $US:XaUS -$U$![a,nUR X5sSnA$![a Uef=fSnAff=f) a Calculate slice bound that corresponds to given label. Returns leftmost (one-past-the-rightmost if ``side=='right'``) position of given label. Parameters ---------- label : object side : {'left', 'right'} Returns ------- int Index of label. See Also -------- Index.get_loc : Get integer location, slice or boolean mask for requested label. Examples -------- >>> idx = pd.RangeIndex(5) >>> idx.get_slice_bound(3, 'left') 3 >>> idx.get_slice_bound(3, 'right') 4 If ``label`` is non-unique in the index, an error will be raised. >>> idx_duplicate = pd.Index(['a', 'b', 'a', 'c', 'd']) >>> idx_duplicate.get_slice_bound('a', 'left') Traceback (most recent call last): KeyError: Cannot get left slice bound for non-unique label: 'a' r@z@Invalid value for side kwarg, must be either 'left' or 'right': Nu1z Cannot get z# slice bound for non-unique label: rr>rF)r6rXrrrrr%r&rErrmaybe_booleans_to_slicerrrrr)rruroriginal_labelslcrCs rget_slice_boundIndex.get_slice_boundYsKN ( (&&*V-  ,,U9 ,,u%C c2:: & &!++ ++--chhtn=C#rzz**!$(">235 c5 ! !v~yy xxwQw 7  33E@@    s)D E D4.E 4 EEE cTUSL=(d US:nU(dX!p![U[[45(ac[U[[45(aH[U5n[U5n[ UR UR 5(d [ S5eSnUbURUS5nUcSnSnUbURUS5nUc [U5nU(d1US- US- pxUS:XaU[U5-nUS:XaU[U5-nXx4$![ [4a Nf=f)a Compute slice locations for input labels. Parameters ---------- start : label, default None If None, defaults to the beginning. end : label, default None If None, defaults to the end. step : int, defaults None If None, defaults to 1. Returns ------- tuple[int, int] See Also -------- Index.get_loc : Get location for a single label. Notes ----- This method only works if the index is monotonic or unique. Examples -------- >>> idx = pd.Index(list('abcd')) >>> idx.slice_locs(start='b', end='c') (1, 3) Nrz(Both dates must have the same UTC offsetrr>rFr) rrrrrtzinfor6rrbr4) rrrLrincts_startts_endrMrNs rrJIndex.slice_locss<>dl'dai3 ec8_ - -*S3/2R2R Q$U+"3"(//6==AA$%OPPB  ..uf=K  K ?,,S':I  D I&11_i!m{ BSY& b s4y( %%W *  sDD'&D'cURn[U[R5(a[R"X!5nOUR U5nUR R X0RS9$)aH Make new Index with passed location(-s) deleted. Parameters ---------- loc : int or list of int Location of item(-s) which will be deleted. Use a list of locations to delete more than one value at the same time. Returns ------- Index Will be same type as self, except for RangeIndex. See Also -------- numpy.delete : Delete any rows and column from NumPy array (ndarray). Examples -------- >>> idx = pd.Index(['a', 'b', 'c']) >>> idx.delete(1) Index(['a', 'c'], dtype='object') >>> idx = pd.Index(['a', 'b', 'c']) >>> idx.delete([0, 2]) Index(['b'], dtype='object') r])r'rr%r&deleterer9r)rrrYrs rrk Index.deletesZ: fbjj ) )6/Js+J  ,,Zii,HHrc[R"U5n[X R5(a UR[:wa UR nUR n[U[5(a3URX5n[U5RX@RS9$URU5nUR[:wd4[U[&[(R*[(R,45(d3URRU5n[(R"X1U5nO([(R"X1S5nUS:aUOUS- nX'U'[.R1XpRS9n[35(aI[5UR5(a/UR[:XaUR%UR5nUR[:Xa6UR[:wa"[6R8"S[:[=5S9 U$![[[ 4a4 UR#U5nUR%U5RX5s$f=f)a, Make new Index inserting new item at location. Follows Python numpy.insert semantics for negative values. Parameters ---------- loc : int item : object Returns ------- Index Examples -------- >>> idx = pd.Index(['a', 'b', 'c']) >>> idx.insert(1, 'x') Index(['a', 'x', 'b', 'c'], dtype='object') r]NrrFzThe behavior of Index.insert with object-dtype is deprecated, in a future version this will return an object-dtype Index instead of inferring a non-object dtype. To retain the old behavior, do `idx.insert(loc, item).infer_objects(copy=False)`r)rrrgrrrr'rrsinsertrSr9rrrr6r:rGr$rr% datetime64 timedelta64rrbrrPr;r<r=r6) rrrrBrrcastedrr}s rrn Index.insert,s*$$T* zz 2 2tzzV7K>>Dll 8#~.. ZZ2 Dz--jyy-II006 99 j 5"--8' ' YY^^D)F3V4J 3T2J#sQwC"sO ; & ( ( **  F***Z--.C :: CII$7 MMQ+-   Q:'89 811$7E;;u%,,S7 7  8sAH&HAI%$I%cF[U[5(d(URS:XaSOSn[R"XS9nUR U5nUS:HnUR 5(a)US:wa[XR5S35eXE)nURU5$)a Make new Index with passed list of labels deleted. Parameters ---------- labels : array-like or scalar errors : {'ignore', 'raise'}, default 'raise' If 'ignore', suppress error and existing labels are dropped. Returns ------- Index Will be same type as self, except for RangeIndex. Raises ------ KeyError If not all of the labels are found in the selected axis Examples -------- >>> idx = pd.Index(['a', 'b', 'c']) >>> idx.drop(['a']) Index(['b', 'c'], dtype='object') rNr rignorez not found in axis) rrrr)index_labels_to_arrayrxrrtolistrk)rrerrors arr_dtyper^rs rdrop Index.dropws<&%(($(JJ($:I..vGF&&v."} 88::!&,"5"5"7!88JKLLenG{{7##rcUR(a [S5eUR[:waU(aUR 5$U$UR n[ SU5n[R"USS9nU(aX2LaUR 5$[X0RS9nU(d=X2La9URb,URUl URRU5 U$)z If we have an object dtype, try to infer a non-object dtype. Parameters ---------- copy : bool, default True Whether to make a copy in cases where no inference occurs. z^infer_objects is not implemented for MultiIndex. Use index.to_frame().infer_objects() instead.npt.NDArray[np.object_]T)convert_non_numericr]) r^rIrrrr'r rr_rrr"rX)rrrYrrAs r infer_objectsIndex.infer_objectss >>%@  :: "&499; 0D 0/8..  $  J(99; z 2 ,1A1A1M!%!1!1F     2 26 : rcR[UR5RU55$)aH Computes the difference between consecutive values in the Index object. If periods is greater than 1, computes the difference between values that are `periods` number of positions apart. Parameters ---------- periods : int, optional The number of positions between the current and previous value to compute the difference with. Default is 1. Returns ------- Index A new Index object with the computed differences. Examples -------- >>> import pandas as pd >>> idx = pd.Index([10, 20, 30, 40, 50]) >>> idx.diff() Index([nan, 10.0, 10.0, 10.0, 10.0], dtype='float64') )rrdr)rrs rr Index.diffs!6T^^%**7344rc^URUR5RU55$)a Round each value in the Index to the given number of decimals. Parameters ---------- decimals : int, optional Number of decimal places to round to. If decimals is negative, it specifies the number of positions to the left of the decimal point. Returns ------- Index A new Index with the rounded values. Examples -------- >>> import pandas as pd >>> idx = pd.Index([10.1234, 20.5678, 30.9123, 40.4567, 50.7890]) >>> idx.round(decimals=2) Index([10.12, 20.57, 30.91, 40.46, 50.79], dtype='float64') )rerdround)rdecimalss rr Index.rounds'0  !1!7!7!ABBrcURU5(aU[R[R[R1;a\[ R "[U5[S9nUR(a'[U[5(dSX0R5'U$U[RLa\[ R"[U5[S9nUR(a'[U[5(dSX0R5'U$[U[ R[ ["[$45(a#[U5[U5:wa ['S5e[U[5(d [)USS9nO[ R*"U5n[-UR.5(a*[U[$5(aU"UR0U5nU$[UR0[$5(aU"UR0U5nU$[-UR.5(a8[U[5(d#[2R4"X R0U5nU$[2R6"UR0X5nU$)z1 Wrapper used to dispatch comparison operations. r FTzLengths must match to compare) extract_numpy)rreqrger%rr4rrrr`rhner)r&rrcrsr6rzr.rMrr'rkcomp_method_OBJECT_ARRAY comparison_op)rrrrBrAs r _cmp_methodIndex._cmp_methods 88E??hkk8;; <<ggc$it4$$Zm-L-L',C $ x{{"hhs4y5$$Zm-L-L'+C $ ebjj%NK L LQT R ZR<= =%//!%t[U[5(a2[UR5(a[ U5[La[ $[ TU]X5$r)rrrMrrSrr" _arith_method)rrrr#s rrIndex._arith_method;sC ue $ $ ,,U 5( " !w$U//rcLU"UR5n[X RS9$rc)r'rr)rrrAs r _unary_methodIndex._unary_methodHsDLL!V)),,rc@UR[R5$r)rrrrs r__abs__ Index.__abs__M!!(,,//rc@UR[R5$r)rrnegrs r__neg__ Index.__neg__Prrc@UR[R5$r)rrr[rs r__pos__ Index.__pos__Srrc@UR[R5$r)rrinvrs r __invert__Index.__invert__Vs!!(,,//rc[R"X5 URS5 URn[ U[ R 5(dURS5$[ R"U5$)a Return whether any element is Truthy. Parameters ---------- *args Required for compatibility with numpy. **kwargs Required for compatibility with numpy. Returns ------- bool or array-like (if axis is specified) A single element array-like may be converted to bool. See Also -------- Index.all : Return whether all elements are True. Series.all : Return whether all elements are True. Notes ----- Not a Number (NaN), positive infinity and negative infinity evaluate to True because these are not equal to zero. Examples -------- >>> index = pd.Index([0, 1, 2]) >>> index.any() True >>> index = pd.Index([0, 0, 0]) >>> index.any() False r) r validate_any_maybe_disable_logical_methodsr'rr%r&_reducerrr`rars rr Index.any]sXH % ++E2||$ ++<<& &vvd|rc[R"X5 URS5 URn[ U[ R 5(dURS5$[ R"U5$)a Return whether all elements are Truthy. Parameters ---------- *args Required for compatibility with numpy. **kwargs Required for compatibility with numpy. Returns ------- bool or array-like (if axis is specified) A single element array-like may be converted to bool. See Also -------- Index.any : Return whether any element in an Index is True. Series.any : Return whether any element in a Series is True. Series.all : Return whether all elements in a Series are True. Notes ----- Not a Number (NaN), positive infinity and negative infinity evaluate to True because these are not equal to zero. Examples -------- True, because nonzero integers are considered True. >>> pd.Index([1, 2, 3]).all() True False, because ``0`` is considered False. >>> pd.Index([0, 1, 2]).all() False r1) r validate_allrr'rr%r&rr1rs rr1 Index.allsXN % ++E2||$ ++<<& &vvd|rc[U[5(d4[UR5(a-URRS:wa[ U5"U5 ggg)z; raise if this Index subclass does not support any or all. rNN)rr`rQrr(r)ropnames rr$Index._maybe_disable_logical_methodssI t] + +$DJJ//DJJOOs4J F #D )5K/rc~>[R"X45 [R"U5 UR(dsUR(abUR nU(aUR 5(a:[R"S[U5RS3[[5S9 g[TU]9US9$NzThe behavior of zx.argmax/argmin with skipna=False and NAs, or with all-NAs is deprecated. In a future version this will raise ValueError.rrr)rvalidate_argminvalidate_minmax_axisr^rrr1r;r<rSrr=r6r"argminrrrr`rarr#s rr Index.argmin 4( %~~$,,;;DTXXZZ &tDz':':&;[R"X45 [R"U5 UR(dsUR(abUR nU(aUR 5(a:[R"S[U5RS3[[5S9 g[TU]9US9$r)rvalidate_argmaxrr^rrr1r;r<rSrr=r6r"rrs rr Index.argmaxrrc[R"X45 [R"U5 [U5(d UR$[U5(a(UR (aUSn[ U5(dU$UR(dEUR(a4URnU(aUR5(a UR$UR(dC[UR[R5(dURRSUS9$[ R""URUS9$)a  Return the minimum value of the Index. Parameters ---------- axis : {None} Dummy argument for consistency with Series. skipna : bool, default True Exclude NA/null values when showing the result. *args, **kwargs Additional arguments and keywords for compatibility with NumPy. Returns ------- scalar Minimum value. See Also -------- Index.max : Return the maximum value of the object. Series.min : Return the minimum value in a Series. DataFrame.min : Return the minimum values in a DataFrame. Examples -------- >>> idx = pd.Index([3, 2, 1]) >>> idx.min() 1 >>> idx = pd.Index(['c', 'b', 'a']) >>> idx.min() 'a' For a MultiIndex, the minimum is determined lexicographically. >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)]) >>> idx.min() ('a', 1) rr(rrr)r validate_minrr4rrrhr^rrr1rr'r%r&rrjnanmin)rrrr`rarqrs rr( Index.minsP % %4yy>> ! t9955GE;; ~~$,,;;DTXXZZ~~%~~jrzz&J&J<<''U6'B B}}T\\&99rc[R"X45 [R"U5 [U5(d UR$[U5(a(UR (aUSn[ U5(dU$UR(dEUR(a4URnU(aUR5(a UR$UR(dC[UR[R5(dURRSUS9$[ R""URUS9$)a Return the maximum value of the Index. Parameters ---------- axis : int, optional For compatibility with NumPy. Only 0 or None are allowed. skipna : bool, default True Exclude NA/null values when showing the result. *args, **kwargs Additional arguments and keywords for compatibility with NumPy. Returns ------- scalar Maximum value. See Also -------- Index.min : Return the minimum value in an Index. Series.max : Return the maximum value in a Series. DataFrame.max : Return the maximum values in a DataFrame. Examples -------- >>> idx = pd.Index([3, 2, 1]) >>> idx.max() 3 >>> idx = pd.Index(['c', 'b', 'a']) >>> idx.max() 'c' For a MultiIndex, the maximum is determined lexicographically. >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)]) >>> idx.max() ('b', 2) rrurr)r validate_maxrr4rrrhr^rrr1rr'r%r&rrjnanmax)rrrr`ralastrs rru Index.max-sR % %4yy>> ! t99558D:: ~~$,,;;DTXXZZ~~%~~jrzz&J&J<<''U6'B B}}T\\&99rc[U54$)z Return a tuple of the shape of the underlying data. Examples -------- >>> idx = pd.Index([1, 2, 3]) >>> idx Index([1, 2, 3], dtype='int64') >>> idx.shape (3,) )r4rs rr Index.shapeosD |r)rr)rr,rrrs)rr,rrzrrrr,)rr)rr$)NN)rYr rHashable | Nonerrr,)rrz type[Self])rrNone)rrr)rrrrr,)rrr,)rrzLlibindex.IndexEngine | libindex.ExtensionEngine | libindex.MaskedIndexEngine)rrz set[str_t])rrru)rrr)rznp.ufuncrstr_t)NF)rrr$)C)rrrrr,r)T)rTN)rr"rrrrr,)rrrrr)rrrrr,)rrrrrrr,)rrr)rrz-list[tuple[str_t, str_t | int | bool | None]])rrzHashable | Sequence[Hashable])FNr3)rrr@Callable | Noner=rrr list[str_t])rErr@rrrr)r<rr=rrrr)r=rrQrrrr|)rrrrr)rrrrrrrrrr)rlrrrr)NNF)rrrrlist[Hashable])rz$Hashable | Sequence[Hashable] | Nonerrr)rrr~)rLiteral[False]rrr,)r Literal[True]rrr)rrrrz Self | NoneF)NTNrq)rzbool | list[bool]rr*rrr)r)rr')rz list[int])rrnpt.NDArray[np.bool_])r)rrrrr,)rrrrr,)rsr#rrr,)rq)rsr#rrr)rrr )rrr=rrrtuple[Index, Index])rrr bool | None)rrrrr)rr)rrrr)rzIndex | MultiIndexrrzArrayLike | MultiIndex)rrztuple[Index, Hashable])NNN)rReindexMethod | Noner int | Nonerrrs)rrr str_t | Nonerrrrrs)rrrrr)rrrrrrr)rznp.ndarray | Indexrrr)rrrrrrrrrs)rrrrrrrs)rrr^rsrrrs)rrr^rsrrr )rrrrr)rrr(zLiteral['loc', 'getitem'])r zLiteral['slice', 'positional']r z lib.NoDefault | None | Exceptionrrr)r^rrrr)NNNN)rrrrrz float | Nonerrz)tuple[Index, npt.NDArray[np.intp] | None])rr)rrrr)rrrrz?tuple[Index, npt.NDArray[np.intp], npt.NDArray[np.intp] | None]) rrrr(rr)rrrrrrFtuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]) rrrr(rr)rrrrrrr) rrrr(rr)rrrrrrNIndex | tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]) rrrr(rz Level | Nonerrrrrrr)rrrr(rrrrr)rrrr()rF)rrrr(rrrrz8tuple[Index, npt.NDArray[np.intp], npt.NDArray[np.intp]])rT)rrrr(rrrrzKtuple[MultiIndex, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None])r)rrrr(rrr) rr rr,rrsrrsrrr,)rrr )rrrs)rrr)rArrrr )rrrrru)rrrrr)rrrrr,)rzIndex | Sequence[Index]rrr)rz list[Index]rrrrr)rrrrr)rrrrrrrs) rrrrrr*rrrrr,) rrrrrr*rrrrztuple[Self, np.ndarray]) rrrrrr*rrrrzSelf | tuple[Self, np.ndarray])FTrNrFN)rru)rrrs)rr1tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]])rrrrztuple[Index, np.ndarray])rrrrr).)rrrrrrrs)rrrrrrr)rrrrrrzHnpt.NDArray[np.intp] | tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]])rrrrr)rrrrr)rr$rrr)rrz PrettyDict[Hashable, np.ndarray])r8zLiteral['ignore'] | None)rr)rrrLrrrrrr)r zLiteral['positional', 'slice']r(zLiteral['getitem', 'iloc']rrr)rr)rLiteral['left', 'right'])rrrrru)rrztuple[int, int])rrurrr)raise)rz'Index | np.ndarray | Iterable[Hashable]rwr&rrr)rrrrr)rF)rrurrr)rrurrr,)rrrrrrN)rrrrru)rr)rrr-("r __module__ __qualname____firstlineno____doc____pandas_priority__rrrrrr__annotations__r%r&rsrrrrrrr3rrint8r Int8Engineint16 Int16Engineint32 Int32Engineint64 Int64Engineuint8 UInt8Engineuint16 UInt16Engineuint32 UInt32Enginer+ UInt64Enginer Float32Enginefloat64 Float64Enginerrrrrpropertyr!_supports_partial_string_indexing _accessorsrlrrr"r classmethodr8r7r9rbrermrjrrrrrrWrrrrr rrrrr$rr2rrrrrrrrrrrr.r1rArDrFr?rWr^rardrr|rsetterrrxr{rrrrrr4rzrrrrrr>rrrrrrrrirrrIrrrrrrrr rr^rrrrrhisnullrnotnullrrrr&rr-r0__bool__r3r7r?rBrIrJrfrhrirmrr~rrrDrErrUrrrrrrrrrrrr rrrrr)rrr7r=r6r<rr;rrOrYr5rwr5r'rrrrrrr,rrrrrrrr)rrrHrrrrrrrArrrXrxrr rrTrrrGrgr/rxr>rArDrGrrrrrXrrbrJrkrnryr~rrrrrrrrrrrrr1rrrr(rur__static_attributes__ __classcell__)r#s@rrr@s1j 8 8 "" E" " "" E" " "" E" "D# && WISCE8#d"%hL)&$XK%00 8.. H00 H00 H00 H00 X22 X22 X22 h44 h44  8 8 !:!: RMN I >II).%J  .CK " r r  r rh" ) )LCG&5 0" + +( !. !.L6@J"   %) %)N    % %.0 U.0.0`       33 #+J  -  -      (!F??F H (+<<= DD D D>D:  0# N *->>?I@I!%(((  (T # # $ $  .  .     2&  9  9 7 7%) FF#F F  F<O &* TT# T  T T,0     ! $!=!=L$ =C =C@!3>>;;(0; ;@   [[ 27+/  BKO!9! !F(+/6 *: 6E (+s  (+  (+S  )-eggR8;    $VV$481717l    . '+") , %,  , \%N) 11 11f 7 7x  44.44.  ? ?   ? ?  & &D  !" !"F )1 )1V )1 )1V 1W 1Wf 1= 1=f .+ .+` 25 25h '2 '2R B B   %  % ; ;  >  > / /1  4 4 4lF ) )VG#..7r>"*"*H3:1212f9+9+|    H     $ w6 w6rM^   S= S=j<6 '  @ ;; ;;z 6O1b   ")V (,  DC%DC DC  DC DCR $  ,,, ,  ,:     ! +++  + +Z$ PT%*3=  @ @D%*3=  B $.  > <<&<  < < &:  ( ? ?Rh 58NN ,2    ( P P&(, "& o%o  o  o 3ob  E/E/ HE/ E/T         '     P     *-        (           #             X      " %d<d< d<  d<  d<d< Xd< d 55 58 CC C8*X A A < < 0 - -0000+Z.`  *  *m""**+--,-$m""**+--,-$=:=:~>:>:D      rcvSSKJn [U5S:XaUbUSn[USUS9$UR"XS9$)a0 Construct an index from sequences of data. A single sequence returns an Index. Many sequences returns a MultiIndex. Parameters ---------- sequences : sequence of sequences names : sequence of str Returns ------- index : Index or MultiIndex Examples -------- >>> ensure_index_from_sequences([[1, 2, 3]], names=["name"]) Index([1, 2, 3], dtype='int64', name='name') >>> ensure_index_from_sequences([["a", "a"], ["a", "b"]], names=["L1", "L2"]) MultiIndex([('a', 'a'), ('a', 'b')], names=['L1', 'L2']) See Also -------- ensure_index rrrFr]r)r2rr4rr?) sequencesrrs rensure_index_from_sequencesrsD<5 9~  !HEYq\..%%i==rc[U[5(aU(aUR5nU$[U[5(aURn[XUS9$[ U5(a [ U5n[U[ 5(aj[U5[ La [ U5n[U5(a3[R"U5(aSSK J n UR"U5$[XSS9$[XS9$)a Ensure that we have an index from some index-like object. Parameters ---------- index_like : sequence An Index or other sequence copy : bool, default False Returns ------- index : Index or MultiIndex See Also -------- ensure_index_from_sequences Examples -------- >>> ensure_index(['a', 'b']) Index(['a', 'b'], dtype='object') >>> ensure_index([('a', 'a'), ('b', 'c')]) Index([('a', 'a'), ('b', 'c')], dtype='object') >>> ensure_index([['a', 'a'], ['b', 'c']]) MultiIndex([('a', 'b'), ('a', 'c')], ) )rrrrF)rr>r )rrrrcrrJr0rSr4ris_all_arrayliker2rr?) index_likerrrs rrrs>*e$$ #*J*i((Z66:*% *d##  4 'j)J z??s33J?? <))*5 5eD DZ++rcT[U5 U$![a [U5s$f=f)z4 If seq is an iterator, put its values into a list. )r4rr0)seqs rrrs0 C  Cys ''cU(dU$[U5(aT[SU55(a=UVs/sHoSSPM nn[U5(a[SU55(aM=U$s snf)z Trims zeros and decimal points. Examples -------- >>> trim_front([" a", " b"]) ['a', 'b'] >>> trim_front([" a", " "]) ['a', ''] c30# UH oSS:Hv M g7f)r Nrr&s rrtrim_front..srEr2zdo not recognize join method r6rs rr:r:s# 888ABB9rcUc'[U[[45(a URn[ U5(d[ UR S35eU$)zJ If no name is passed, then extract it from data, validating hashability. z.name must be a hashable type)rrrcrrHrr)rrrs rr r sN | 3 (:;;xx t  3<<.(EFGG KrcUVs/sHn[UR5PM nn[U6Vs/sHn1UkPM nn[SU55nU$s snfs snf)z Return common name if all indices agree, otherwise None (level-by-level). Parameters ---------- indexes : list of Index objects Returns ------- list A list representing the unanimous 'names' found. c3d# UH&n[U5S:XaUR5OSv M( g7fr)r4r)rnss rr&get_unanimous_names..'s%Iyc"gl"&&(4ys.0)rrr)indexesr name_tupsr name_setsrs rget_unanimous_namesr"s\*11AqwwI1!,i!89!822!8I9 IyI IE L29s AAcpURn[U[5(aURR$[U[5(a_SSKnUR RUR5(a1USSR[ URR55nUR$)z When checking if our dtype is comparable with another, we need to unpack CategoricalDtype to look at its categories.dtype. Parameters ---------- other : Index Returns ------- np.dtype or ExtensionDtype rN) rrrVr!rUrr is_dictionary pyarrow_dtyper$ value_type)rrrs rrFrF+s KKE%)**%%% E: & & 88 ! !%"5"5 6 6"1I$$Z0C0C0N0N%OPE ;;rcUSLa[R"U5nU$U$![a6nUSLae[R"US3[ [ 5S9 SnAU$SnAff=f)NFTz3, sort order is undefined for incomparable objects.r)r safe_sortrr;r<RuntimeWarningr6)rArrCs rrSrSFsm 5  __V,F M6M t| MM%JK+-  M s! A!+AA!rrO)r=rTrRrQc [U[5(aaURRRS;a=[ R "URR[UR5US9n[U5n[U[[45(aURS:Xa%URX!S9nUR[ SS9nU$/n[#[%U55H;nXSS24RX!S9nUR'UR[ SS95 M= [(R*"U5$[UR[,5(a[/SU5nURX!S9n U $[UR[05(am[/S U5nUR35n U(d*[(R4"U5R[65nO[(R8"U[ S S 9nX&U 'U$URRS :Xa[UR[:5(dUc]US :XaW[3U5n U(dUR[65nO[(R8"USS9nX U 'UR[ SS9nU$SSKJn U "UUUUUSS9n U RA5n U R[ SS9n U $[U[B5(a:[3U5n [(R4"UR[ 55n X-U 'U $[3U5n [DRF"U5nUR[H:wasU(dlU(aeUR[65nURRJ[(R"S5RJ- U:aURSU35nO[(R8"USS9nX U 'UR[ SS9nU$)zX Convert to types which can be consumed by the standard library's csv.writer.writerows. rrrF)r=rSFr NrrTrrrOrr r)FloatArrayFormatter)r=rRrQrT fixed_widthU1zFt    c !*V\\;*O*O  GsN'>>IH:7XXfH5Ft vE2 r)rr%rrr%rrr)rr!rrrrr)rrrrr)rrrrrr)rrrrztuple[Hashable, ...])rrrrr$)rAzIndex | ArrayLikerr)rYr r=rrQrrrr|) __future__r collectionsrrr itertoolsrrtypingrrr r r r r rrr;numpyr%pandas._configrrr pandas._libsrrrrrrrpandas._libs.internalsrpandas._libs.join_libsrrpandas._libs.librrpandas._libs.tslibsrrrrpandas._typingrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.pandas.compat.numpyr/r pandas.errorsr0r1pandas.util._decoratorsr2r3r4r5pandas.util._exceptionsr6r7pandas.core.dtypes.astyper8r9pandas.core.dtypes.castr:r;r<r=r>r?r@pandas.core.dtypes.commonrArBrCrDrErFrGrHrIrJrKrLrMrNrOrPrQrRrSpandas.core.dtypes.concatrTpandas.core.dtypes.dtypesrUrVrWrXrYrZr[pandas.core.dtypes.genericr\r]r^r_r`rarbrcrdpandas.core.dtypes.inferencerepandas.core.dtypes.missingrfrgrh pandas.corerirjrkpandas.core.accessorrlpandas.core.algorithmscore algorithmspandas.core.array_algos.putmaskrmrnpandas.core.arraysrorprqrrrsrtpandas.core.arrays.string_rurvpandas.core.baserwrxpandas.core.commoncommonr)pandas.core.constructionryrzr{pandas.core.indexersr|r}pandas.core.indexes.frozenr~pandas.core.missingrpandas.core.opsrpandas.core.ops.invalidrpandas.core.sortingrrrpandas.core.strings.accessorrpandas.io.formats.printingrrrrcollections.abcrrrrRrrrrrr__all__ frozenset_unsortable_typesrrrrrrr+MaskedComplex128EngineMaskedComplex64EngineMaskedFloat64EngineMaskedFloat32EngineMaskedUInt64EngineMaskedUInt32EngineMaskedUInt16EngineMaskedUInt8EngineMaskedInt64EngineMaskedInt32EngineMaskedInt16EngineMaskedInt8EngineMaskedBoolEnginerrrrrrrrLr:r r"rFrSrVrrrrss"!    3## $/  *4   6  0&&!  29.3 7   )89 %>&(N'  XXh  (11//x++x++   h))   h))  h)) X ' ' X ' ' X ' ' X ' ' H % %x((x3344 44!"h22#$ 22222200000000....5<8!8~pM<~pBb%>P8,v &C  &60 k k  kkkr