+ nDj\^RIt^RIHt^RIHt^RIt^RIHt^RIt^RI H t ^RI t ^RI t ^RI t ^RIt^RIt^RIt^RIHtHtHt/s/s/sRtRtR tR tR tR t R*R lt!Rt"Rt#Rt$Rt%Rt&Rt'Rt(Rt)Rt*Rt+R*Rlt,R+Rlt-Rt.R,Rlt/Rt0R-Rlt10R.mt2Rt3Rt4R t5R*R!lt6R*R"lt7R#t8R$t9R%t:R&t;R'tR# ]dRtLi;i)/N)Sequence)futures)deepcopy) zip_longest) _pandas_api frombytesis_threading_enabledc \'Eg1\P/\PPRb\PP Rb\PP Rb\PPRb\PPRb\PPRb\PPRb\PPRb\PPRb\PPR b\PPR b\PPR b\PP R b\PP"R b\PP$R b\PP&Rb\PP(Rb\PP*R\PP,R\PP.R/C4\#)emptyboolint8int16int32int64uint8uint16uint32uint64float16float32float64datetimebytesunicode)_logical_type_mapupdatepalibType_NA Type_BOOL Type_INT8 Type_INT16 Type_INT32 Type_INT64 Type_UINT8 Type_UINT16 Type_UINT32 Type_UINT64Type_HALF_FLOAT Type_FLOAT Type_DOUBLE Type_DATE32 Type_DATE64 Type_TIME32 Type_TIME64 Type_BINARYType_FIXED_SIZE_BINARY Type_STRINGO/data/cameron/venvs/s3viz/lib/python3.14/site-packages/pyarrow/pandas_compat.pyget_logical_type_mapr7.s    " FFNNG" FF  f"  FF  f"  FF  w " FF  w " FF  w "  FF  w"  FF  "  FF  "  FF  "  FF " "I"  FF  y"  FF   "  FF  "  FF  " FF  !" " FF  #" $ FF   FF ) )7 FF   )"  , r5c\4pWP,# \d\T\P P 4'dR#\T\P P4'dR\TP4 R2u#\T\P P4'dTPeRu#Ru#\PPT4'dR#R#i;i) categoricalzlist[] datetimetzdatetimedecimalobject)r7idKeyError isinstancerrDictionaryTypeListTypeget_logical_type value_type TimestampTypetztypes is_decimal) arrow_typelogical_type_maps& r6rDrDKs+-  ..   j"&&"7"7 8 8  BFFOO 4 4+J,A,ABC1E E  BFF$8$8 9 9#-==#<< L* L XX  , , s(5DAD:DD$DDc\'g\P\PR\PR\P R\P R\PR\PR\PR\PR\PR\PR \PR R R \PR \PR/4\#)r r rrrrrrrrr datetime64[D]rstringr)_numpy_logical_type_maprnpbool_r rrrrrrrrrstr_bytes_r4r5r6get_numpy_logical_type_maprT\s " "&& HHf GGV HHg HHg HHg HHg IIx IIx IIx JJ JJ V GGX IIw(  #"r5cv\4pWPP,# \d\ TPR4'dR#\ TP4P R4'd\ TP4u#\P!T4pTR8XdR#Tu#i;i)rGr; datetime64rNr) rTdtypetyper@hasattrstr startswithr infer_dtype)pandas_collectionnumpy_logical_type_mapresults& r6get_logical_type_from_numpyr`rs79 %&=&=&B&BCC   $**D 1 1  && ' 2 2< @ @(../ /(():; X   s!('B8?B8B83B87B8cVPp\V4R8XdY\VRV4pVfQhR\VP4RVP /p\VP P4pWC3#\VR4'd?R\PPVP4/pRVP R2pWC3#Rp\V4pWC3#) categorycatNnum_categoriesorderedrGtimezonez datetime64[r:) rWrZgetattrlen categoriesrecodesrYrrtzinfo_to_stringrGunit)columnrWcatsmetadataphysical_dtypes& r6get_extension_dtype_inforqs LLE 5zZvuf- c$//2 t|| TZZ--.  ##    7 7 AB&uzzl!4  ##U  ##r5c \V4p\V4wrVVR8XdRVPRVP/pRpVem\ V\ 4'd\ P!V4'g;\ V\4'g%\RV R\V4P 24h\ V\4'gQ\\V444hRVRVR VR VR V/#) aConstruct the metadata for a given column Parameters ---------- column : pandas.Series or pandas.Index name : str arrow_type : pyarrow.DataType field_name : str Equivalent to `name` when `column` is a `Series`, otherwise if `column` is a pandas Index then `field_name` will not be the same as `name`. This is the name of the field in the arrow Table's schema. Returns ------- dict r= precisionscaler>z)Column name must be a string. Got column z of type name field_name pandas_type numpy_typero) rDrqrsrtrAfloatrPisnanrZ TypeErrorrX__name__)rmrurJrv logical_type string_dtypeextra_metadatas&&&& r6get_column_metadatars"$J/L#;F#C Ly -- Z%%   D%((RXXd^^4%%7vYDz""# %  j# & &=D,<(== &j|lN  r5cVfVUu.uFp\V4NK pp\W44U U u.uF wr\V \4'dKW3NK" p p p \ V 4p \ V4p VRW, pWmV , Rp.p\WW~4F(wpppp\ VVVVR7pVP V4K* .pVRJEdI.p\W4FwwrpV Pe<\V P\4'gVP V P4\ V \V P4VV R7pVP V4K \ V4^8d"\P!RV R2\^R7.p\VPRVP.4p\VPRVPP.4p\VV4F!wr\W4pVP V4K# M.;p;pp\VR 4'd VP M/p\"P$!V4R \"P$!R VRVRVV,RVRRRR\(P*/R\,P./4P1R4/#uupiuup p i \&d.p/p\P!R T R 2\^R7Rp?LRp?ii;i)aReturns a dictionary containing enough metadata to reconstruct a pandas DataFrame as an Arrow Table, including index columns. Parameters ---------- columns_to_convert : list[pd.Series] df : pandas.DataFrame column_names : list[str | None] column_field_names: list[str] index_levels : List[pd.Index] index_descriptors : List[Dict] preserve_index : bool types : List[pyarrow.DataType] Returns ------- dict N)rurJrvFz&The DataFrame has non-str index name `z@` which will be converted to string and not roundtrip correctly. stacklevellevelsnamesattrsz(Could not serialize pd.DataFrame.attrs: z!, defaulting to empty attributes.spandas index_columnscolumn_indexescolumns attributescreatorlibrarypyarrowversionpandas_versionutf8)rZziprAdictrhrappendru_column_name_to_stringswarningswarn UserWarningrgr_get_simple_index_descriptorrYrjsondumps Exceptionr __version__rrencode)columns_to_convertdf column_names index_levelsindex_descriptorspreserve_indexrHcolumn_field_namesrulevel descriptorserialized_index_levelsnum_serialized_index_levelsntypesdf_types index_typescolumn_metadatacolrvrJroindex_column_metadatanon_str_index_namesrrrres&&&&&&&& r6construct_metadatars*!5AALDc$iLA"%\!E!E E*d+ !E #&&=">ZF:f:;H!<<=>KO-01C1C.O)T:z&s2<2<> x( .OU" /2 #0 + Uzz%jS.I.I#**5::6*,UZZ8%% H " ( ( 20  " #a ' MM89L8MN00  + X |< Gbjjoo->?vu-KE3E@H  ! !( +.FHGG1N$R11rJ' : 4:: . n )>> * 92>> k11    6&>  KBx '  6qc:/ 0 A ' ''s(J$J) J)4J// K':#K""K'c \V4wr#\V4pRV9d\P!R\^R7VR8XdV'dQhRR/pRVRVRVR VR V/#) mixedzlThe DataFrame has column names of mixed type. They will be converted to strings and not roundtrip correctly.rrencodingUTF-8rurvrwrxro)rqr`rrr)rrur~rrws&& r6rr2sx#;E#B L-e4K+  @ A 'i!!!$g.d{lN  r5c\V\4'dV#\V\4'dVPR4#\V\4'd#\\ \ \ V444#\V\4'd \R4hVe3\V\4'd\P!V4'dV#\V4#)aConvert a column name (or level) to either a string or a recursive collection of strings. Parameters ---------- name : str or tuple Returns ------- value : str or tuple Examples -------- >>> name = 'foo' >>> _column_name_to_strings(name) 'foo' >>> name = ('foo', 'bar') >>> _column_name_to_strings(name) "('foo', 'bar')" >>> import pandas as pd >>> name = (1, pd.Timestamp('2017-02-01 00:00:00')) >>> _column_name_to_strings(name) "('1', '2017-02-01 00:00:00')" rz%Unsupported type for MultiIndex level) rArZrdecodetuplemaprrr{ryrPrzrus&r6rrFs2$ D% {{6"" D% 54d;<== D( # #?@@ *T511bhhtnn t9r5cxVPe'VPV9d\VP4#RVR R2#)zReturn the name of an index level or a default name if `index.name` is None or is already a column name. Parameters ---------- index : pandas.Index i : int Returns ------- name : str __index_level_d__)rur)indexirs&&&r6_index_level_namerms9 zz%**L"@&uzz22!uB''r5c\WV4pVPP'g"\R\ VP4 24hVe \ WV4#.p.pVRJd\ VP4M.p.p.pVFp W ,p \V 4p \P!V 4'd\RV R24hVPV 4VPR4VPV 4VP\V 44K .p .p \V4Fwr\WV4p \!V\P"P$4'dVf \'V4pM5VPV4VPR4T pV PV 4V PV4K W\,pVWEV WWx3#)zDuplicate column names found: NFSparse pandas data (column ) not supported.)_resolve_columns_of_interestr is_unique ValueErrorlist$_get_columns_to_convert_given_schema_get_index_level_valuesrrr is_sparser{rrZ enumeraterrApd RangeIndex_get_range_index_descriptor)rschemarrrrrrconvert_fieldsrurrindex_column_namesr index_leveldescr all_namess&&&& r6_get_columns_to_convertrs*2w?G ::   ,T"**-=,> ?  3BOOL.<5-H)  Nh&t,   % %-dV3CDF F !!#&d#D!!!#d),#L1 > {KNN$=$= > >&/ > TF#''((H- s* C%% E91 C=<E9=!DAE98E9cTpWPP9d)\V4'd\V\ R4R4pVPP V4#)zS Get the index level of a DataFrame given 'name' (column name in an arrow Schema). r)rr_is_generated_index_nameintrhget_level_values)rrukeys&& r6rrsP C 88>>!&>t&D&D$s+,R01 88 $ $S ))r5cl\P!V4V# \d\T4u#i;iN)rrr{rZrs&r6 _level_namer s1 4 4ys 33c RRR\VP4R\P!VR4R\P!VR4R\P!VR4/#)kindrangerustartstopstep)rrurget_rangeindex_attribute)rs&r6rrsW  EJJ'55eWE 44UFC 44UFC  r5c\\VRV.44p\V4Uu.uFq PV4NK up#uupi)r)rhrgrr)rnrs& r6rr!s< GE8eW -.A/4Qx 8x! " "1 %x 88 8sAcVeVe \R4hVeVPpV#Ve&VUu.uFq3VP9gKVNK ppV#VPpV#uupi)NzJSchema and columns arguments are mutually exclusive, pass only one of them)rrr)rrrcs&&& r6rr&sw g1<= =  ,, N  %9gbjj11g9 N** N :s AAc \VRW4wppppppp p.p V EF@p V Pp \P!V 4'd$\P !V RR7P p M\P!V 4'dh\V \PP4'dV P^4MV R,p\P !VRR7P p M]\WPR4wr\PPW4p V f#\P !V RR7P p V P!V 4EKC \#WWHVWVR7pW:V3#)NT) from_pandas:NrNr)rvaluesris_categoricalrarrayrXis_extension_array_dtyperArSeriesheadget_datetimetz_typerWr_ndarray_to_arrow_typerr)rrrrrr_rrrrHrrtype_r ros&&& r6dataframe_to_typesr4s8 ""dN DY E   % %f - -HHQD166E  1 1& 9 9!+;>>(("*"*AFF1I/0u HHU5::E/FMFFF11&@E}5:: U " =N2DH X %%r5c a\WVV4wpppp p p p p VfL\V4\VP4rW^d,8dV^8d\P!4pM^p\ 4'g^pV3RlpRpV^8Xd(\ W4UUu.uFwppV!VV4NK pppM.p\P!V4;_uu_4p\ W4FZwppV!VP4'dVPV!VV44K8VPVPVVV44K\ RRR4\V4F;wpp\V\P4'gK(VP4VV&K= VUu.uFpVP NK ppVfU.p\ VV4F,wppVP\P"!VV44K. \P$!V4p\'WW{V VVVR7pVP('d\+VP(4M \-4pVP/V4VP1V4pRp\V4^8Xd_V ^,R,p V R8XdGV ^,R,p!V ^,R,p"V ^,R,p#\\3V!V"V#44pVVV3#uuppi +'giEL;iuupi \4dL1i;i) Nc<VfRpRpMVPpVPp\P!WRSR7pT'g.TP^8d\RT RTP R24hT# \P\P \P 3d<pT;PRTP RTP 23, unThRp?ii;i)NT)rXrsafezConversion failed for column z with type zField z( was non-nullable but pandas column had z null values) nullablerXrr ArrowInvalidArrowNotImplementedErrorArrowTypeErrorargsrurW null_countr)rrfield_nullablerr_rrs&& r6convert_column+dataframe_to_arrays..convert_columnps =!NE"^^NJJE XXc4dKF&"3"3a"7veW-$$*$5$5#6lDE E ++!!#  FF/zSYYKPS SFG  sA444C#(6CC#c\V\P4;'dMVPP;'d/\ VP P\P4#r) rArPndarrayflags contiguous issubclassrWrXinteger)arrs&r6_can_definitely_zero_copy6dataframe_to_arrays.._can_definitely_zero_copysL3 +77 $$77399>>2::6 8r5rrrrrr)rrhrr cpu_countr rrThreadPoolExecutorrrsubmitrrAFuturer_rXrrrrorrr with_metadatarr)$rrrnthreadsrrrrrrrrrrnrowsncolsrrrfarraysexecutorr maybe_futxrHfieldsrurpandas_metadataron_rowsrrrrs$&&&&&f r6dataframe_to_arraysr#Ws/r>/68Y 2wBJJu 3; 519||~HH  ! !*8 1}!"4EGEda!A&E G  ' ' 1 1X.?1,QXX66MM.A"67MM(//.!Q"GH @2&f-LAy)W^^44%,,.q .$ $VQVVVE $ ~y%0KD% MM"((4/ 016"( =N2DO-3OOOx(H OOO$  ! !( +FF 6{a $Q'/Dw)!,W5(+F3(+F3U5$56 66 !![G2 1 1 %6   s,KA*KK26AK7 K/ 7 LLcJVPP\P8wdW3#\P !V4'd6Vf2VP pVPp\P!WC4pW3#Vf!\P!VP4pW3#r) rWrXrPrVr is_datetimetzrGrlr timestampfrom_numpy_dtype)rrWrrGrls&&& r6rrs ||BMM)}  ''EM XXzz T& = ##FLL1 =r5c.^RIHuHpVPRR4pVR,pRV9d2\P P WPR,VR,R7pEMRV9d\P!VP4wr\WR,4p \P!4'd3\PPVPR4V R R 7pMTpV'd!VPWVVP V R 7p V #MjR V9dbVR ,p\#V4^8XgQhW^,,p W,,p \%V R 4'g \'R4hV P)V4pMTpV'dVPWvR7#Wv3#)a\ Construct a pandas Block from the `item` dictionary coming from pyarrow's serialization or returned by arrow::python::ConvertTableToPandas. This function takes care of converting dictionary types to pandas categorical, Timestamp-with-timezones to the proper pandas Block, and conversion to pandas ExtensionBlock Parameters ---------- item : dict For basic types, this is a dictionary in the form of {'block': np.ndarray of values, 'placement': pandas block placement}. Additional keys are present for other types (dictionary, timezone, object). columns : Column names of the table being constructed, used for extension types extension_columns : dict Dictionary of {column_name: pandas_dtype} that includes all columns and corresponding dtypes that will be converted to a pandas ExtensionBlock. Returns ------- pandas Block Nblock placement dictionaryre)rirerfrF)rWcopy)r*klassrWpy_array__from_arrow__zGThis column does not support to be converted to a pandas ExtensionArray)r*)pandas.core.internalscore internalsgetrcategorical_type from_codesrP datetime_datarWmake_datetimetz is_ge_v21rrview make_blockDatetimeTZBlockrhrYrr/)itemrextension_columns return_block_int block_arrr*rrlrrWr)ru pandas_dtypes&&&& r6_reconstruct_blockrBs8)($'I[!It**55 |"4O6% t ""9??3:&67  " "..&&w'u5'CC .2.B.B.3(5  t :9~"""|$(. |%566:; ;))#.s88~r5c\P!4'dRp\PP V4p\P !WR7#)nsrG)ris_v1rrstring_to_tzinfodatetimetz_type)rlrGs&&r6r7r7s;   $B  & &t 33r5c .p.p/pVPPpV'gbVe^VR,pVPR.4pVPR/4pVR,p \W4p\ WWT4wr\ WW@V4p M7\ PPVP4p \ V.W@V4p \V4\WV4p VPp \PPWV\!V P#444p\ P$!4'd7^RIHpVUu.uFp\+VWRR7NK ppV!VWR7pVVnV#^R IHp^R IHpVUu.uFp\+VW4NK ppW.pV!VV4p\ P6!4'dVP9VVP:4pMV!V4pVVnV#uupiuupi) Nrrrr)create_dataframe_from_blocksF)r>)rr) BlockManager) DataFrame)rr!r3_add_any_metadata_reconstruct_index_get_extension_dtypesrrrnum_rows'_check_data_column_metadata_consistency_deserialize_column_indexrrrtable_to_blocksrkeysis_ge_v3pandas.api.internalsrJrBrr0rKpandasrLr8 _from_mgraxes)optionstableriignore_metadata types_mapper all_columnsrrr!rrext_columns_dtypesrrr_rJr<blocksrrKrLrYmgrs&&&&& r6table_to_dataframerbsKNJll22O :%i0 (,,-=rB$((r: +O<!%9)%*5E 2  zC))%..92 2|j ,K8'NKG%%L VV # #GJ$();)@)@)B$CEFE   lU L  *& O 6$  t\ F  64(  " "$$S#((3B3B 5  s =G%9G*cVR,pT;'g.p/p\PfV#V'd<VPF+pVPpV!V4p V fKWVP&K- VPFcpVPpVPV9gK"\ V\ P4'gKDVP4p WVP&Ke VEFp V R,p V R,p W9gKV \9gK,\P!V 4p \ V \P4'gKd\ V \PP4'd]V'gW9dK\ PP!VPP#V 4P4'dK\%V R4'gKWV &EK \P&!4'Ed V'EgVPFpVPV9gK\ PP)VP4'ga\ PP+VP4'g2\ PP-VP4'gKVPV9gK\PP\.P0R7WgP&K V# \dEKi;i \dT R,p ELLi;i \dELi;i)a Based on the stored column pandas metadata and the extension types in the arrow schema, infer which columns should be converted to a pandas extension dtype. The 'numpy_type' field in the column metadata stores the string representation of the original pandas dtype (and, despite its name, not the 'pandas_type' field). Based on this string representation, a pandas/numpy dtype is constructed and then we can check if this dtype supports conversion from arrow. strings_to_categoricalrvrurxr/)na_value)rextension_dtyperrXrurArBaseExtensionTypeto_pandas_dtypeNotImplementedErrorr@_pandas_supported_numpy_typesrAr StringDtyperH is_dictionaryrrYuses_string_dtype is_stringis_large_stringis_string_viewrPnan) r[columns_metadatar]rZrird ext_columnsrtyprAcol_metarurWs &&&&& r6rOrObs%%=>!!rJK""*\\E**C',L'*6EJJ' "jj ::[ (ZR=Q=Q-R-R 7"224 +7EJJ'% $L)D&  "u4Q'Q'33E:L, (C(CDDlKNN,F,FGG .1C 8811%,,2D2DT2J2O2OPP$Q<)9::(4%5%:$$&&/E/E\\Ezz,""5::..88++EJJ7788**5::66**J.*5..*D*Dbff*D*U JJ' " Y'   $F#D $($s7L) L'AM L$#L$'L?>L? MMc~\;QJdRV4F 'dK RM RM !RV44'gQhR#)c3x"TF0pVR,RJ;'dRV9;'g VR,RJxK2 R#5i)ruNrvr4).0rs& r6 :_check_data_column_metadata_consistency..sAA 6d  0 0|q0JJQvYd5JJs : ::FTN)all)r^s&r6rQrQs= 3333   r5c V'ddVUu/uF-pVPR\VR,44VR,bK/ ppVPUu.uFqTPWU4NK ppM VPp\V4^8dd\P P P\\\PV44VUu.uF qwR,NK upR7pM8\P PYb'dV^,R,MRR7p\V4^8d \W4pV#uupiuupiuupi)rvrurNr)r3rrrhrr MultiIndex from_tuplesrrast literal_evalIndex"_reconstruct_columns_from_metadata) block_tabler^rrcolumns_name_dictrucolumns_values col_indexrs &&& r6rRrRs=!   EE, 7& B CQvY N   ;F:R:R :R$ ! !$ -:R  %11 >Q..++77 S%%~6 76DEnV$$nE8  ..&& n!26!:RV'   >Q4WM N9  Fs3EEE ctVUu/uFpVPRVR,4VbK pp.p.pTpVFp \V \4'd\WWV4wrp V fK/MV R,R8Xd^V R,p \P P V R,V R,V R,V R7p \V 4\V48wdKM\RV R, 24hVPV 4VPV 4K \P p \V4^8d V PPWgR 7p W3#\V4^8XdAV^,p \WP4'gV PW^,R 7p W3#V P VP4p W3#uupi) rvrurrrrr)rruzUnrecognized index kind: r}r)r3rArZ_extract_index_levelrrrrhrrr~ from_arraysrrP)r[rr^r]rfield_name_to_metadata index_arrays index_names result_tablerr index_namerrs&&&& r6rNrNsA lAfI&)LKL" eS ! !4HUL5R 1Lz"#6]g %vJ%..33E'N49&M9>v9C4EK;3u:-.8vHI IK(:&'#* B <1 )),)J   \ a Q%**HHUQH8E   enn-  Ys#F5c4W2,R,p\W%4pVPPV4pVR8XdVRR3#VPV4pVP VR7p RV nVP VPPV44pWV3#)ruN)r]) _backwards_compatible_index_namerget_field_indexrm to_pandasru remove_column) r[rrvrr] logical_namerrrrs &&&&& r6rrs)5f=L1*KJ $$Z0ABwT4'' ,,q/C--\-:KK--++J7L j 00r5c8W8Xd\V4'dR#V#)aCompute the name of an index column that is compatible with older versions of :mod:`pyarrow`. Parameters ---------- raw_name : str logical_name : str Returns ------- result : str Notes ----- * Part of :func:`~pyarrow.pandas_compat.table_to_blockmanager` N)r)raw_namers&&r6rr)s$$ ppp.p\P!RR4p VEFwrZp \V 4p V \P8XdVPV 4pEMLV R 8Xd\PPV^,R ,R ,4p VP!VR R 7P#V 4p\P$!4'd-VP'\P(!V 4^,4pMV R8XdE\PP+VUu.uFp\,P.!V4NK up4pM\VP R8Xd*V R8Xd#RV 9gV R9dVP1V4EKdVP V 8wdVP3V 4pVP V 8wdV R 8wdVP3V 4pVP1V4EK \5V4^8dVP7WVP8R7#VP+V^,V^,P VP:R7#uuppiuupi)aConstruct a pandas MultiIndex from `columns` and column index metadata in `column_indexes`. Parameters ---------- columns : List[pd.Index] The columns coming from a pyarrow.Table column_indexes : List[Dict[str, str]] The column index metadata deserialized from the JSON schema metadata in a :class:`~pyarrow.Table`. Returns ------- result : MultiIndex The index reconstructed using `column_indexes` metadata with levels of the correct type. Notes ----- * Part of :func:`~pyarrow.pandas_compat.table_to_blockmanager` rNrj) fillvaluerwrxrrr;rorfT)utcr=rZr>rr})rWru)rrN)rrrgrr3rZrWoperator methodcallerrrPrSrrrrG to_datetime tz_convertrUas_unitr6rr=Decimalrastyperhr~rru)rrrrlabelsrr levels_dtypes new_levelsencoderrA numpy_dtyperWrGrs&& r6rrpso, BWh - : :'F Wgt , 6 6F !, b! ! E  mS-=> |T * ,! J##Hg6G,9([*<8 BII IIg&E \ )((q!*-j9;BNN5dN3>>rBE##%% b&6&6{&CA&FG Y &NN((e)Le'//!*K%9%MM,.88FK4D4D4A,CO(,9$)==@ q s5<<()A$/23 o01ux( ell1o. *xx##GBIIf4E#FF e2s KKc\PPV4pVPP R4PP V4pV#)z: Make a datetime64 Series timezone-aware for the given tz r)rrrGdt tz_localizer)seriesrGs&&r6 make_tz_awarersA   $Bii##E*R 2 Mr5r)NT)NNT)NFN> r r rrrrr>rrrrrr)?rcollections.abcr concurrentrconcurrent.futures.threadr,rr= itertoolsrrrrrnumpyrP ImportErrorrr pyarrow.librrr rrOrr7rDrTr`rqrrrrrrrrrrrrrr#rrBr7rbrjrOrQrRrNrrrrrrrMrr4r5r6rs5& $!!  DD:"#,"$&,^jZ($N(&?QD;Q| *9  &Fa"H &BJ4;@!PfB2j1&0/ $&%.TUn:BK' BsC CC