uj7lddlmZddlZddlmZmZmZmZddlZddl Z ddl m Z m ZddlmZddlmZddlmZddlmZdd lmZdd lmZdd lmZdd lmZdd lm Z ddl!m"Z"m#Z#erddl$Z$ddl%m&Z&m'Z'm(Z(ddlm)Z)eedGdde#Z* d ddZ+edGdde"Z,y)) annotationsN) TYPE_CHECKINGClassVarSelfcast)libmissing)Pandas4Warning) set_module)find_stack_level) is_list_like)register_extension_dtype)isna)ops)masked_accumulations)ExtensionArray)BaseMaskedArrayBaseMaskedDtype)DtypeObjnpttype_t)ExtensionDtypepandasceZdZUdZdZded<dZeddZeddZ eddZ dd Z dd Z edd Z edd Z dd Zy) BooleanDtypeaP Extension dtype for boolean data. This is a pandas Extension dtype for boolean data with support for missing values. BooleanDtype is the dtype companion to :class:`.BooleanArray`, which implements Kleene logic (sometimes called three-value logic) for logical operations. See :ref:`boolean.kleene` for more. .. warning:: BooleanDtype is considered experimental. The implementation and parts of the API may change without warning. Attributes ---------- None Methods ------- None See Also -------- arrays.BooleanArray : Array of boolean (True/False) data with missing values. Int64Dtype : Extension dtype for int64 integer data. StringDtype : Extension dtype for string data. Examples -------- >>> pd.BooleanDtype() BooleanDtype >>> pd.array([True, False, None], dtype=pd.BooleanDtype()) [True, False, ] Length: 3, dtype: boolean >>> pd.array([True, False, None], dtype="boolean") [True, False, ] Length: 3, dtype: boolean booleanz ClassVar[str]nameFc"tjSN)npbool_selfs e/var/www/html/python-recuperauto-other/env/lib/python3.12/site-packages/pandas/core/arrays/boolean.pytypezBooleanDtype.typecs xxcy)Nbr"s r$kindzBooleanDtype.kindgsr&c,tjdS)Nbool)r dtyper"s r$ numpy_dtypezBooleanDtype.numpy_dtypeksxxr&ctS)zq Return the array type associated with this dtype. Returns ------- type ) BooleanArrayr"s r$construct_array_typez!BooleanDtype.construct_array_typeos r&cy)Nrr)r"s r$__repr__zBooleanDtype.__repr__ysr&cyNTr)r"s r$ _is_booleanzBooleanDtype._is_boolean|r&cyr5r)r"s r$ _is_numericzBooleanDtype._is_numericr7r&cddl}|j|jk7r>|jj |jst d|jdt ||jr|g}t|}n|j}|j}|jj |jrBtj|t}tj|t}t||Sg}|D] }|j!} |jj#|jt|d| dg|j$j'd }|j(dk7rU|jj#|jt|d| dg|j$j'd }|}n$tj*t|t}t||} |j-|  |sRttj.gtjtj.gtjStj1|S) zI Construct BooleanArray from pyarrow Array/ChunkedArray. rNz$Expected array of boolean type, got z insteadr-)offsetF)zero_copy_only)pyarrowr%r!typesis_null TypeError isinstanceArraylenchunkslengthr onesr,emptyr0buffers from_buffersr=to_numpy null_countzerosappendarray_concat_same_type) r#rPr?rFrGmaskdataresultsarrbuflistbool_arrs r$__from_arrow__zBooleanDtype.__from_arrow__s  :: (1F1Fuzz1RB5::,hWX X eW]] +WFZF\\F\\^F ==  ,776.D88F$/Dd+ + %CkkmG''44#c(T71:$6szz5heh, ~~"++88HHc#hwqz(:3::9(%(0uxxC5#D$/H NN8 $ % 288,bhhr.J  11': :r&N)returnr%)rYstr)rYznp.dtype)rYztype_t[BooleanArray])rYr,)rPz$pyarrow.Array | pyarrow.ChunkedArrayrYr0)__name__ __module__ __qualname____doc__r__annotations___internal_fill_valuepropertyr%r*r.r1r3r6r9rXr)r&r$rr.s)V$D-#!  /;9/; /;r&rcZt|trK| td|j|j}}|r |j }|j }||fSd}t|t jr2|jt jk(r|r|j }nt|t jr|jjdvrt|}t jt|t}||jt||<t j ||j|j||k(s t#d|}nt j$|t&}t)j*|d}d}|d d g|vr t#dt-d t|}t jt|t}||jt||<||vrQt j ||jt.||jt.k(s t#d|(|&t j|j0t}ny||}ntt|t jr8|jt jk(r|||z}n5|r3|j }n"t j2|t}|||z}|j0|j0k7r td ||fS) a Coerce the input values array to numpy arrays with a mask. Parameters ---------- values : 1D list-like mask : bool 1D array, optional copy : bool, default False if True, copy the input Returns ------- tuple of (values, mask) Nz'cannot pass mask for BooleanArray inputiufcbr;zNeed to pass bool-like valuesTskipna)floatingintegerzmixed-integer-floatrrInpt.NDArray[np.bool_]z&values.shape and mask.shape must match)rCr0 ValueError_data_maskcopyr ndarrayr-r!r*rrNrEr,astypeallrBasarrayobjectr infer_dtyperfloatshaperP)valuesrRrl mask_values values_bool values_objectinferred_dtype integer_likes r$coerce_to_arrayr{s"&,'  FG G||V\\ [[]F99;Dt|K&"**%&,,"((*B [[]F FBJJ 'FLL,=,=,H6l hhs6{$7 $*K<$8$?$?$E [L!vv  % , ,V\\ :fk\>R R ;< < 68 tDE )W!D|!D D;< <2D4GH #f+T2,k\:AA$G | l * FF |$++E2 +.55e<=  ;< < | +xx D1  D"** %$***@  "+%D 99;DxxD)  "+%D ||tzz!ABB 4<r&z pandas.arraysc.eZdZdZhdZhdZedfd Z d dfd Ze ddZ eddddd  dd Z e jejee j"fZedd  dd Zd Zdd ddZxZS)r0as Array of boolean (True/False) data with missing values. This is a pandas Extension array for boolean data, under the hood represented by 2 numpy arrays: a boolean array with the data and a boolean array with the mask (True indicating missing). BooleanArray implements Kleene logic (sometimes called three-value logic) for logical operations. See :ref:`boolean.kleene` for more. To construct a BooleanArray from generic array-like input, use :func:`pandas.array` specifying ``dtype="boolean"`` (see examples below). .. warning:: BooleanArray is considered experimental. The implementation and parts of the API may change without warning. Parameters ---------- values : numpy.ndarray A 1-d boolean-dtype array with the data. mask : numpy.ndarray A 1-d boolean-dtype array indicating missing values (True indicates missing). copy : bool, default False Whether to copy the `values` and `mask` arrays. Attributes ---------- None Methods ------- None Returns ------- BooleanArray See Also -------- array : Create an array from data with the appropriate dtype. BooleanDtype : Extension dtype for boolean data. Series : One-dimensional ndarray with axis labels (including time series). DataFrame : Two-dimensional, size-mutable, potentially heterogeneous tabular data. Examples -------- Create a BooleanArray with :func:`pandas.array`: >>> pd.array([True, False, None], dtype="boolean") [True, False, ] Length: 3, dtype: boolean >1.01TRUETruetrue>0.00FALSEFalsefalsecFt|||}t|_|Sr)super _simple_newr_dtype)clsrurRresult __class__s r$rzBooleanArray._simple_newJs"$VT2$  r&Fct|tjr|jtjk(s t dt |_t|%|||y)NzIvalues should be boolean numpy array. Use the 'pd.array' function insteadrl) rCr rmr-r!rBrrr__init__)r#rurRrlrs r$rzBooleanArray.__init__PsS62::.6<<2883K2 #n  D1r&c|jSr)rr"s r$r-zBooleanArray.dtype[s {{r&N)rl true_values false_values none_valuescJ |jj|xsg |jj|xsg gd  fd }tj|t }t |} tt||| || <|j|||S)Nc>|vry|vry|vryt|d)NTFz cannot be cast to bool)ri)sfalse_values_unionrtrue_values_unions r$ map_stringz:BooleanArray._from_sequence_of_strings..map_stringps9%%((k! A3&=!>??r&r;)r-rl)rYz bool | None) _TRUE_VALUESunion _FALSE_VALUESr rPrqrlistmap_from_sequence) rstringsr-rlrrrrscalarsrRrrs ` @@r$_from_sequence_of_stringsz&BooleanArray._from_sequence_of_strings_s ,,22;3D"E ..44\5GRH  K @((7&1G}c*gten=>!!'T!BBr&rc.|r|dk(sJt||S)Nrr)r{)rvaluer-rls r$_coerce_to_arrayzBooleanArray._coerce_to_arrays" I% %%u400r&c|jdvsJtj|||tj|}d}t |t r|j|j}}nt|rt |tttjfsPtj|s;tj dt#|jdt$t'tj(|d}|j*dkDrt,St/|d \}}n*t |tj0r|j3}|rI|t4j6ur7tj8|s"t;d t#|jd |s"t=|t=|k7r t?d |jd vr0tj@|j||j|\}}nm|jdvr0tjB|j||j|\}}n/tjD|j||j|\}}|jG||S)N>or_xorand_ror_rxorrand_zOperation with z is deprecated. In a future version these will be treated as scalar-like. To retain the old behavior, explicitly wrap in a Series instead.) stacklevelr,r;r<Frz+'other' should be pandas.NA or a bool. Got z instead.zLengths must match>rr>rr)$r[r disallow_datetimelike_logical_opr is_scalarrCr0rjrkr rrr rmhas_castable_attrwarningswarnr%r r rpndimNotImplementedr{r!item libmissingNAis_boolrBrEri kleene_or kleene_and kleene_xor_maybe_mask_result)r#otheropother_is_scalarrRrs r$_logical_methodzBooleanArray._logical_methods{{MMMM ,,T5"=--. e\ *++u{{4E % nbjj9++E2 %d5k&:&:%;<#/1 JJuF3EzzA~%%)%e>$**eTZZNLFD>>$**eTZZNLFD&&vt44r&Trdc |j}|j}|dvr1tt|}|||fd|i|\}}|j ||Sddlm}||jt|j|fd|i|S)N)cummincummaxrer) IntegerArray) rjrkgetattrrrpandas.core.arraysrrnint _accumulate)r#rrekwargsrSrRrrs r$rzBooleanArray._accumulateszzzz ' '-t4BD$@v@@JD$##D$/ / 7C< C 0$7CC#'- r&)ru np.ndarrayrRrhrYr)F)rurrRrrlr,rYNone)rYr)rz list[str]r-rrlr,rlist[str] | NonerrrrrYr0)r-rrlr,rYtuple[np.ndarray, np.ndarray])rrZrer,rYr)r[r\r]r^rr classmethodrrrar-rr rmnumbersNumberr,r!_HANDLED_TYPESrrr __classcell__)rs@r$r0r0 sG8t8L;M BG 2  2(2 2:> 2  2 (,)-(,CC C  C & C'C&C CC>jj'..$AN5:1&1.21 &11.5b,0$( r&r0)NF)rlr,rYr)- __future__rrtypingrrrrrnumpyr pandas._libsrr r pandas.errorsr pandas.util._decoratorsr pandas.util._exceptionsr pandas.core.dtypes.commonr pandas.core.dtypes.dtypesrpandas.core.dtypes.missingr pandas.corerpandas.core.array_algosrrrpandas.core.arrays.maskedrrr?pandas._typingrrrrrr{r0r)r&r$rs" ).42>+8-  9 HC;?C;C;N%*R!R"Rj O{?{{r&