IjЃzSrSSKJr SSKJr SSKJrJr SSKr SSKJ r SSK J r SSK Jr SS KJr SS KJrJrJrJr SS KJrJrJrJrJrJr SS KJrJr SS K J!r!J"r" SSK#J$r$ SSK%J&r&J'r( SSK)J*r* SSK+J,r, SSK-J.r/J0r0J1r1J2r2 SSK3J4r4J5r5J6r6J7r7J8r8J9r9J:r:J;r;Jr>J?r?J@r@JArA SSKBJCrCJDrD \(aSSKEJFrFJGrG SSKHJIrIJJrJJKrKJLrL SSSS.S1SjjrMS2SjrNS3SjrOS4SjrPSSS.S5SjjrQS6S jrRS7S!jrSS8S9S"jjrTS:S#jrUS;S$jrVSS(jrZS)r[S?S@S*jjr\SAS+jr]SBS,jr^SCS-jr_SDS.jr`SES/jraSFSGS0jjrbg)Hz~ Functions for preparing various inputs passed to the DataFrame or Series constructors before passing them to a BlockManager. ) annotations)abc) TYPE_CHECKINGAnyN)ma)using_string_dtype)lib)astype_is_view)"construct_1d_arraylike_from_scalar dict_compatmaybe_cast_to_datetimemaybe_convert_platform)is_1d_only_ea_dtypeis_integer_dtype is_list_likeis_named_tupleis_object_dtype is_scalar)BaseMaskedDtypeExtensionDtype) ABCDataFrame ABCSeries)isna) algorithmscommon)ExtensionArray) StringDtype)array extract_arrayrange_to_ndarraysanitize_array) DatetimeIndexIndex MultiIndexTimedeltaIndex default_index ensure_indexget_objs_combined_axismaybe_sequence_to_range union_indexes)BlockPlacementensure_block_shape new_block new_block_2d) create_block_manager_from_blocks'create_block_manager_from_column_arrays)HashableSequence) ArrayLikeDtypeObjManagernptT)dtypeverify_integrity consolidatec U(a)Uc [U5nO [U5n[XU5upO[U5nUVs/sH n[USS9PM nnS/[ U5-nUH[n[ U[ R[45(a*URS:wd[ U5[ U5:wdMR[S5e [U5n[ U5[ U5:wa [S5eX/n [X XVS9$s snf)zg Segregate Series based on type and coerce into matrices. Needs to handle a lot of exceptional cases. NT extract_numpyzYArrays must be 1-dimensional np.ndarray or ExtensionArray with length matching len(index)z#len(arrays) must match len(columns))r9refs) _extract_indexr' _homogenizerlen isinstancenpndarrayrndim ValueErrorr0) arrayscolumnsindexr7r8r9r>xarraxess s/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/pandas/core/internals/construction.py arrays_to_mgrrN`s ="6*E 'E#6%8 U#@FG1-6GvF #CsRZZ$@AA88q=s8s5z) 6 7#G 7|s6{">??  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Tc+d&ST T T )88GA/a8NSTDUV8 L  c'l 34Bf>B4L 6<<,,38J8L8LRVV,6l %[1  24 **,;;D;NuQA/  2  E#g,/ 0 &T :t  7|q +& CV   s 8WA:W'6W )W$A W*c<URS[U5:wdURS[U5:wadURSSs=:Xa[U5:aO O [S5eURn[U5[U54n[SUSU35eg)zP Check that the shape implied by our axes matches the actual shape of the data. r=rz)Empty data passed with indices specified.zShape of passed values is z, indices imply N)r|rArF)rrIrHpassedimplieds rMrres||A#g,&&,,q/SZ*G <<?a ,#e* ,HI Iu:s7|,5fX=MgYWXX+Hr\r7rVcUGb[U5nUb9[U[R5(dUR/[ U5-nO[R /[ U5-n[5n[UR55n[UR55n[U5HTupURU 5n Xn XU '[U 5(dM1[!U 5(dMCURU 5 MV UcDU(a1[#[U5V V s/sHupX;dM U PM sn n 5nO[#U5nO [U5nU(aN[%U5(d>UH8n ['XY[ U5UbUO[R"S55n XU 'M: Og[)[UR555nU(a [+U5O [-S5nUVs/sHn[.R0"X5PM nnU(aUVs/sH}n[U[25(aUR55OT[U[*5(d/[U[65(a)[9UR5(aUR5SS9OUPM nn[;XRXUS9$![a URU 5 GM"f=fs sn n fs snfs snf)z Segregate Series based on type and coerce into matrices. Needs to handle a lot of exceptional cases. Used in DataFrame.__init__ ryrTrQr^)r'rBrCr7rurArsetkeysrrrget_locKeyErroraddrrr?rr r)r#r&commaybe_iterable_to_listrrVrrrN)rWrIrHr7rVrGmidxs data_keys data_valuesrcolumnidxrrKrkrJs rM dict_to_mgrrwsu w'  Zrxx%@%@nn%G 4FffXG ,F - 4;;=) "7+IA ''/ $E1IDKK ! , =&+4V+<O+.converts9A*Q "="=H !4 0$Q' r\) rArCrxryrBr{r newaxisrrrDrEr)rrVrKrrs rMrrs 6{axxf-- FE " "v&3 ?##F1Iv6v!71:v67 F1Irzz * *vay~~/Bv6v!71:v67 f 77s D*DcURS:Xa"URURSS45nU$URS:wa[SUR35eU$)z: Reshape 1D values, raise on anything else other than 2D. r=rrrzMust pass 2-d input. shape=)rEr~r|rF)rs rMrrs\{{aa! 45 M  6v||nEFF Mr\cvSn/n/nUGHn[U[[45(arUbURU5n[U[5(a URULaUR U5nUR UR5 URnGO[U[5(aUcURS5n[U[[45(a [U5nO [U5n[U[5(d9UR(a(SSKJn U"U5R U5RnO-["R$"XcR[&R(S9n[+XaUSS9n[,R."Xa5 UR S5 UR U5 GM XE4$)NOr)Series)defaultFr)rBrr#astyperIrwappendr}rdictr"r%r r$hasnanspandasrr fast_multigetrCrr!rrequire_length_match)rWrIr7oindex homogenizedr>valrs rMr@r@)s^FKD cIu- . . jj'#y))ciiu.Dkk%( KK (++C#t$$>"\\#.Fem^%DEE%c*Cs)C!%44. +--e4<.sKd640w' >r\c6SSKJn [[X55$)as Converts a list of dataclass instances to a list of dictionaries. Parameters ---------- data : List[Type[dataclass]] Returns -------- list_dict : List[dict] Examples -------- >>> from dataclasses import dataclass >>> @dataclass ... class Point: ... x: int ... y: int >>> dataclasses_to_dicts([Point(1, 2), Point(2, 3)]) [{'x': 1, 'y': 2}, {'x': 2, 'y': 3}] r)asdict) dataclassesrrmap)rWrs rMdataclasses_to_dictsrs0# F! ""r\c[U5(d[U[R5(aURR bv[ URR 5nUVs/sHo0UPM nn[U5S:Xa1[U5H"upVURS:XdMUSS2S4XE'M$ XA4$/[ /54$[U[R5(aPURR b9Uc[URR 5nUVs/sHopUPM nnXA4$[US[[45(a [U5nO|[US[R5(a[X5upaOL[US[ 5(a[#X5upaO&UVs/sHn[U5PM nn[U5n[%XaU5upX4$s snfs snfs snf)z Return list of arrays, columns. Returns ------- list[ArrayLike] These will become columns in a DataFrame. Index This will become frame.columns. Notes ----- Ensures that len(result_arrays) == len(result_index). Nrrr)rArBrCrDr7namesr'rrEr#rtuple_list_to_arraysrMapping_list_of_dict_to_arraysr_list_of_series_to_arrays_finalize_columns_and_data) rWrHr7rvrGrrKrrJcontents rMrTrTs$ t99 dBJJ ' 'zz+&tzz'7'78189t*9t9>#,F"388q=(+AqD FI#4&<### D"** % %$***:*:*F ?DJJ,,-G#*+7aq'7+$q'D%=))d# DGS[[ ) ).t= W DGY ' '0? W#''$Qa$'d#1#FG  ?: ,(s*G0&G5=G:c[US[5(a[R"U5nU$[R"U5nU$)Nr)rBrr to_object_array_tuplesto_object_array)rWrs rMrr0sC$q'5!!,,T2 N%%d+ Nr\cUc9UVs/sH"n[U[[45(dM UPM$ nn[USS9n0n/nUHn[ USS5nUc[ [ U55n[U5U;aU[U5nOURU5=o[U5'[USS9n UR[R"X55 M [R"U5n X4$s snf)NFrrITr;)rBrrr(rzr&rAidrrrrtake_ndrCvstack) rWrHrJ pass_data indexer_cachealigned_valuesrrIrrrs rMrr;s  $Q1 1y,6O(PQ Q(?+-MN 7D) =!#a&)E e9 %#BuI.G161B1B71K KGBuI.q5j00ABii'G  )Rs C2C2cfUVs/sH"n[U5[LaUO [U5PM$ nnUcXSU5n[SU55(+n[R"X4S9n[ U5n[R "X5nXa4$[R "U[U55nXa4$s snf)a Convert list of dicts to numpy arrays if `columns` is not passed, column names are inferred from the records - for OrderedDict and dicts, the column names match the key insertion-order from the first record to the last. - For other kinds of dict-likes, the keys are lexically sorted. Parameters ---------- data : iterable collection of records (OrderedDict, dict) columns: iterables or None Returns ------- content : np.ndarray[object, ndim=2] columns : Index c3T# UHn[UR55v M g7frk)rr)rmrJs rMro*_list_of_dict_to_arrays..vs,t!tAFFH~~ts&(c3B# UHn[U[5v M g7frk)rBr)rmds rMrorws9Dqz!T**Dr)typerrr fast_unique_multiple_list_genr'dicts_to_arrayr)rWrHrgenrpre_colsrs rMrrZs28<    'o3&'s A A1 A,,A1cUc[[U55nU$[U[5=(a [ SU55nU(d;[U5[U5:wa#[ [U5S[U5S35eU(a|[UVs1sHn[U5iM sn5S:a [ S5eU(aA[US5[U5:wa&[ [US5S[U5S35eU$s snf)ah If columns is None, make numbers as column names; Otherwise, validate that columns have valid length. Parameters ---------- content : list of np.ndarrays columns : Index or None Returns ------- Index If columns is None, assign positional column index value as columns. Raises ------ 1. AssertionError when content is not composed of list of lists, and if length of columns is not equal to length of content. 2. ValueError when content is list of lists, but length of each sub-list is not equal 3. ValueError when content is list of lists, but length of sub-list is not equal to length of content c3B# UHn[U[5v M g7frk)rBr)rmcols rMro0_validate_or_indexify_columns..s7 -4cJsD ! !Wrz! columns passed, passed data had z columnsr=zT[R"S5:wGaTS:g=(d [T[5n[R "UTUS9nTcUR[R"S5:XaTS:gn[R "USSU[R"S5S9nU(aMUR[R"S5:Xa)[ 5nUR5nURXS9nU$TS:waC[U[R5(a$URRS ;a [USS 9nU$[T[5(a#TR5nURUTSS 9nU$TRS ;a [UT5nU$) Nrnumpy) try_floatrhFTrdrerPiufbrbrmM)rCr7rBrr rrrrrDrpd_arrayrr ) rK to_nullablerh new_dtypearr_clscls coerce_floatr7 dtype_backends rMr%convert_object_array..convertsv BHHSM !(72Xj6XK++&*5C}99 -0=0H-33).,02K)+'):C1SYY"((3-5O$/M "+"@"@"B%44S4J& %#g-*S"**2M2Myy~~/&s7  E>22002((E(F t# -S%8 r\rl)rr7r"r!rrKrGs ``` rMr r s-,4l'. .gsgclgF . 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