uj\dZddlmZddlmZddlmZmZddlZddl Z ddl m Z ddl m Z ddlmZdd lmZdd lmZdd lmZdd lmZmZmZmZdd lmZmZmZmZm Z m!Z!ddl"m#Z#m$Z$ddl%m&Z&m'Z'ddl(m)Z)ddl*m+Z+m,Z-ddl.m/Z/ddl0m1Z1ddl2m3Z4m5Z5m6Z6m7Z7ddl8m9Z9m:Z:m;Z;mZ>m?Z?m@Z@mAZAddlBmCZCmDZDmEZEmFZFddlGmHZHmIZIerddlJmKZKmLZLddlMmNZNmOZOmPZPmQZQdddd d3dZR d4dZS d5dZT d6dZUddd  d7d!ZV d8d"ZWd9d#ZXd:d;d$ZYdd'Z\ d?d(Z]d>d)Z^ d@d*Z_d+Z` dA dBd,ZadCd-Zb dDd.Zc dEd/Zd dFd0Ze dGd1Zf dH dId2Zgy)Jz~ Functions for preparing various inputs passed to the DataFrame or Series constructors before passing them to a BlockManager. ) annotations)abc) TYPE_CHECKINGAnyN)ma)using_string_dtype)lib)Pandas4Warning)find_stack_level)astype_is_view)"construct_1d_arraylike_from_scalar'construct_1d_object_array_from_listlike dict_compatmaybe_cast_to_datetime)is_1d_only_ea_dtypeis_integer_dtype is_list_likeis_named_tupleis_object_dtype is_scalar)BaseMaskedDtypeExtensionDtype) ABCDataFrame ABCSeries)isna) algorithmscommon)ExtensionArray) StringDtype)array extract_arrayrange_to_ndarraysanitize_array) DatetimeIndexIndex MultiIndexTimedeltaIndex default_index ensure_indexget_objs_combined_axismaybe_sequence_to_range union_indexes)BlockPlacementensure_block_shape new_block new_block_2d) create_block_manager_from_blocks'create_block_manager_from_column_arrays)HashableSequence) ArrayLikeDtypeObjManagernptT)dtypeverify_integrity consolidatec|r*| t|}n t|}t|||\}}nt|}|Dcgc]}t|d}}dgt |z}|D]S}t |t jtfr'|jdk7st |t |k7sJtdt|}t |t |k7r td||g} t|| ||Scc}w)zs Segregate Series based on type and coerce into matrices. 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U D 2+D /HsC9c.tj|}|tt|}n t |}| t |}t ||\}}t |||t|\}}||}t||||}|r|jd}|S)zA Extract from a masked rec array and create the manager. r9Tdeep) rgetdatar(rCr) to_arraysreorder_arraysrPcopy) datarKrJr9rXfdatarI arr_columnsmgrs rOrec_array_to_mgrr]s JJt E }c%j)U#w'#E73FK)gs5zRFK e z!ndarray_to_mgr..DsTdaqzTs)strict) placement)rtr@U)na_value)rG)r:)6rDrr%rnamerKreindexrCrEemptyobjectgetattrrrFrGrangeshaper9rXr)rPr _referencesrr!reshape_values _ensure_2dr asarray_prep_ndarrayliker# _get_axes!_check_values_indices_shape_matchTrlistr maybe_convert_objectsanyzip enumerater0r.r-slicekindrrnanr/construct_array_type_from_sequencer1)valuesrKrJr9rX infer_objectvdtyper@nrL obj_columnsmaybe_datetimedval block_valuesbpnbirYs rOndarray_to_mgrrs "&9e^*LMML&)$ ?{{& . =LLE^^E*F6{w2s7|XXfF3F VWd +F D6"&9%&@ frzz>: ; av||A/q!t F XF    a.%!CD'0'1E&:T"F ?E#f+./G"7+GVWe5dSS&9e,-.u*M%%D&.)vT: [[]F ;;! ^^B*F FY. / ^^((*F^^FF# FRZZ8 9 U]nV\\5&IXXf4s;FZZ'FF# #65 V\\U2     QawNE7&feW= XXF  }/&,,*G6l !    % %!&$(*/!#'!2      Tc+~d&ST T )8At/a8NSTDUVL  c'l 34Bf>B4L 6<<,,38J8LRVV,6l %[1  4 **,;;D;NuQA/   E#g,/ 0 &BT :t  7|q +w& CV   s U5/A+U:>4U? *VA V c0|jdt|k7s|jdt|k7r`|jddcxk(rt|kr td|j}t|t|f}td|d|y)z\ Check that the shape implied by our axes matches the actual shape of the data. r?rz)Empty data passed with indices specified.zShape of passed values is z, indices imply N)r}rCrH)rrKrJpassedimplieds rOrrhs||A#g,&&,,q/SZ*G <<?a ,#e* ,HI I -u:s7|,5fX=MgYWXX+Hr^r9rXc@|t|}|4t|tjs|jgt |z}ntj gt |z}t}t|j}t|j}t|D]J\} } |j| } || } | || <t| s.t!| s:|j| L|;|r-t#t|D cgc] \} } | |vs | c} } }nt#|}n t|}|rt%|s|D]7} t'|| t |||ntjd} | || <9n_t)t|j}|r t+|n t-d}|Dcgc]}t/j0||}}|rv|Dcgc]k}t|t2r|j5nHt|t*s%t|t6r't9|jr|j5dn|m}}t;|||||S#t$r|j| YwxYwcc} } wcc}wcc}w)z Segregate Series based on type and coerce into matrices. Needs to handle a lot of exceptional cases. Used in DataFrame.__init__ rzrTrSr`)r)rDrEr9rvrCrsetkeysrrrget_locKeyErroraddrrrArr r+r%r(commaybe_iterable_to_listrrXrrrP)rYrKrJr9rXrImidxs data_keys data_valuesrcolumnidxr rMrkrLs rO dict_to_mgrrzso w'  Zrxx%@nn%G 4FffXG ,F - 4;;=) "7+ IAv ''/ $EF1IDK !   =&+4V+<Oxq%UO'v. 'E )%0 81IJ".EBHHX4F  q  'tDIIK'89!%%+=+;?CD!#,,T!W5DD  a0 #1e,&q)49LQWW9UFFF%    %u$ OOo  !  P*E  s+0I/ J J J-A0J/J  J ct|dr|t|dj}t|||\}}t|}|3t |dt r t |}ntt|}|||fS)zA Convert a single sequence of arrays to multiple arrays. rrR) rr)_fieldsrVrDr_get_names_from_indexr(rC)rYrJrKr9rIs rOnested_data_to_arraysrszd1g7?tAw/gU;OFG7#G } d1gy ))$/E!#d),E 7E !!r^ct|dkDxrGt|dxr7t|ddddk(xr"t|txr|j dk( S)z7 Check if we should use nested_data_to_arrays. rrGr?rs)rCrr{rDrrG)rYs rOtreat_as_nestedrsc D A  F a ! F DGVQ '1 , FD.1Ddii1n E r^c>t|dk(rtjdtSt |t r t |}|dtjfSd}t|dr4tj|Dcgc] }|| c}}t|St |dtjrF|djdk(r4tj|Dcgc] }|| c}}t|S||}t|Scc}wcc}w)Nr)rrrR.c*t|rt|tr|St|d}t|tt t fr t|}t|tjr(|jtk(rtj|}|S)NTr=)rrDrr!rtupler|rrErFr9rzr r)vs rOconvertz"_prep_ndarraylike..convertsnA*Q "=H !4 0 a$u- .7:A a $F):))!,Ar^) rCrEryrzrDr|r"newaxisrr rFrGr)rrXrMrrs rOrrs 6{axxf-- FE "v&3 ?## $F1Iv6!71:67 f  F1Irzz *vay~~/Bv6!71:67 f  f 77s >D!Dc|jdk(r"|j|jddf}|S|jdk7rtd|j|S)zB Reshape 1D values, raise on anything else other than 2D. r?rrszMust pass 2-d input. shape=)rGrr}rH)rs rOrr$s\{{aa! 45 M  6v||nEFF Mr^cfd}g}g}|D]}t|ttfr~||j|}t|tr3|jj |j ur|j |}|j|j|j}nt|tr||jd}t|ttfr t|}n t|}t|ts4|jr(ddlm}||j |j}n0t%j&||jt(j*}t-|||d}t/j0|||jd|j|||fS)NOr)Series)defaultFr)rDrr%astyperK_idrxappendr~rdictr$r'rr&hasnanspandasrr fast_multigetrErr#rrequire_length_match)rYrKr9oindex homogenizedr@valrs rOrBrB/sdFKD'  cIu- . jj'#y)ciimm599.Lkk%( KK (++C#t$>"\\#.Fem^%DE%c*Cs)C!%4. +--e4<.sK640<KsrrwzUnnamed r?)rr(rCrr|rr{r%)rY has_some_namerKcountrrrs rOrrsKdKKM SY'' s4y!12E E$1 Avt $ =E!H!%)E!H QJE  <r^ct| t|}n t|}|t|}||fSt|}||fSrm)r(r))NKrKrJs rOrrsN  }a U#" '>w' '>r^c8ddlm}tt||S)a 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)rYrs rOdataclasses_to_dictsrs0# FD! ""r^c 4t|st|tjr|jj rt |jj }|Dcgc]}|| }}t|dk(r/t|D]!\}}|jdk(s|dddf||<#||fS|jdk(rJt|jdDcgc] }|dd|f }}|t|jd}||fSgt gfSt|tjrM|jj 7|t|jj }|Dcgc]}|| }}||fSt|tjrY|jdk(rJt|jdDcgc] }|dd|f }}|t|jd}||fSt|dttfr t|}nCt|dt j"rt%||\}}nt|dt&rt)||\}}n|D chc]} t+| ds| j} } t| dk(rt-d|Drt|D chc] } t| c} dk(rd| j/} t|d} t| D cgc] } t1|D cgc]} | |  c} | "}} } t3||}||fS|Dcgc] }t|}}t|}t5|||\}}||fScc}wcc}wcc}wcc}wcc} wcc} wcc} wcc} } wcc}w)a  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). Nrrsr?r9c34K|]}t|dyw)r9N)hasattr)rorows rOrqzto_arrays..Es:cGC):srR)rCrDrErFr9namesr)rrGr|r}r(r%rr_list_to_arraysrMapping_list_of_dict_to_arraysr_list_of_series_to_arraysrallrpd_array_validate_or_indexify_columns_finalize_columns_and_data)rYrJr9rwrIrrMrrr row_dtypes common_dtypencolscol_idxrLcontents rOrVrVsM$ t9 dBJJ 'zz+&tzz'7'78189$t*99t9>#,F"32388q=(+AqD F1I2w&a27 1 2FG3$q#v,GG?+DJJqM:Gw&<### D"** %$***:*:*F ?DJJ,,-G#*+a$q'++w D"** %$))q.+0 1 *>?3$q#v,?? ?#DJJqM2Gw$q'D%=)d# DGS[[ ).tW= W DGY '0w? W,0IC733HciiI I  Oq :T::.#SX./14%>>+LQLE %U|$73#g,7|LF4FGDG7? ""&'Qa''d#1#wFGW G C:H, @$J/ 8(sH M,M1* M6?M;*N<N5N=N N  N9N NcR|Dchc]%}t|r t|rdn t|'}}t|dkDr$tjdt t t|dtrtj|}|Stj|}|Scc}w)Nr?zConstructing a DataFrame from a list of sequences with mismatched lengths is deprecated and will raise in a future version. The shorter sequences are currently padded with NaN; make all sequences the same length before constructing instead.) stacklevelr) rrrCwarningswarnr r rDrr to_object_array_tuplesto_object_array)rYrlengthsrs rOrrXsKOO3IcNtCyqc#h>OGO 7|a  E ')  $q'5!,,T2 N%%d+ N!Ps*B$c|6|Dcgc]}t|ttfs|}}t|d\}}i}g}|D]}t |dd}|t t |}t||vr|t|} n|j|x} |t|<t|d} |jtj| | tj|} | |fScc}w)NFrrKTr=)rDrrr*r{r(rCidrr!rrtake_ndrEvstack) rYrJrL pass_datar indexer_cachealigned_valuesrrKrrrs rOrrrs  $Q1 1y,6O(PQQ Q+IEB +-MN  C7D) =!#a&)E e9 %#BuI.G161B1B71K KGmBuI.q5j00AB Cii'G G )Rs C-C-c\|Dcgc] }t|tur|n t|"}}|Xd|D}td|D }tj||}t |}tj ||}||fStj |t|}||fScc}w)a Convert list of dicts to numpy arrays if `columns` is not passed, column names are inferred from the records - for OrderedDict and dicts, the column names match the key insertion-order from the first record to the last. - For other kinds of dict-likes, the keys are lexically sorted. Parameters ---------- data : iterable collection of records (OrderedDict, dict) columns: iterables or None Returns ------- content : np.ndarray[object, ndim=2] columns : Index c3NK|]}t|jywrm)rr)rorLs rOrqz*_list_of_dict_to_arrays..s,!tAFFH~,s#%c3<K|]}t|tywrm)rDr)rods rOrqz*_list_of_dict_to_arrays..s9qz!T*9sr)typerrr fast_unique_multiple_list_genr)dicts_to_arrayr)rYrJrgenrpre_colsrs rOrrs28<  W  'o3&'s A A0 A++A0c|tt|}|St|t|k7r#tt|dt|d|S)a If columns is None, make numbers as column names; Otherwise, validate that columns have valid length. Parameters ---------- content : list of np.ndarrays columns : Index or None Returns ------- Index If columns is None, assign positional column index value as columns. Raises ------ AssertionError When the number of columns does not match the number of arrays in content. z! columns passed, passed data had z columns)r(rCr)rrJs rOrrs].G - N WW %7|n=c'l^8 T   Nr^cNfd}|Dcgc] }|| }}|Scc}w)aA Internal function to convert object array. Parameters ---------- content: List[np.ndarray] dtype: np.dtype or ExtensionDtype dtype_backend: Controls if nullable/pyarrow dtypes are returned. coerce_float: Cast floats that are integers to int. Returns ------- List[ArrayLike] c tjdk7rndk7xstt}t j ||}|jtjdk(rdk7}t j |dd|tjd}|r|jtjdk(rst }|j}|j||}|Sdk7r?t|tjr%|jjd vr t|d }|Sttr&j}|j|d }|Sjd vr t|}|S) Nrnumpy) try_floatrjFTrfrgrRiufbrdrmM)rEr9rDrr rrrrrFrrrr) rM to_nullablerj new_dtypearr_clscls coerce_floatr9 dtype_backends rOrz%convert_object_array..convertsr BHHSM !(72Xj6XK++&*5C}99 -0=0H-33).,02K)+'):C1SYY"((3-5O$/M "+"@"@"B%44S 4J& %#g-*S"**2Myy~~/&s7  E>2002((E(F t# -S%8 r^rn)rr9r'r&rrMrIs ``` rOrrs,,4l'. .sgcl .F . 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UP UP  UP  UP UPp" " " "  " * "4 &R11.1&1h7t */:FPS"6$ '2>$#D;?XX(7X"Xv4  >' ' ''T   # , (4 H! N *N NN N  Nr^