if4@ddlmZddlZddlmZmZmZmZddlZ ddl m Z m Z ddlmZddlmZddlmZddlmZdd lmZdd lmZdd lmZmZerddlZdd lmZm Z m!Z!dd lm"Z"eedGddeZ# d ddZ$edGddeZ%y)) annotationsN) TYPE_CHECKINGClassVarSelfcast)libmissing) set_module) is_list_like)register_extension_dtype)isna)ops)masked_accumulations)BaseMaskedArrayBaseMaskedDtype)DtypeObjnpttype_t)ExtensionDtypepandasceZdZUdZdZded<dZeddZeddZ eddZ dd Z dd Z edd Z edd Z dd Zy) BooleanDtypeaP Extension dtype for boolean data. This is a pandas Extension dtype for boolean data with support for missing values. BooleanDtype is the dtype companion to :class:`.BooleanArray`, which implements Kleene logic (sometimes called three-value logic) for logical operations. See :ref:`boolean.kleene` for more. .. warning:: BooleanDtype is considered experimental. The implementation and parts of the API may change without warning. Attributes ---------- None Methods ------- None See Also -------- arrays.BooleanArray : Array of boolean (True/False) data with missing values. Int64Dtype : Extension dtype for int64 integer data. StringDtype : Extension dtype for string data. Examples -------- >>> pd.BooleanDtype() BooleanDtype >>> pd.array([True, False, None], dtype=pd.BooleanDtype()) [True, False, ] Length: 3, dtype: boolean >>> pd.array([True, False, None], dtype="boolean") [True, False, ] Length: 3, dtype: boolean booleanz ClassVar[str]nameFc"tjSN)npbool_selfs W/app/cer_product_mecsu/.venv/lib/python3.12/site-packages/pandas/core/arrays/boolean.pytypezBooleanDtype.type_s xxcy)Nbrs r!kindzBooleanDtype.kindcsr#c,tjdS)Nbool)rdtypers r! numpy_dtypezBooleanDtype.numpy_dtypegsxxr#ctS)zq Return the array type associated with this dtype. Returns ------- type ) BooleanArrayrs r!construct_array_typez!BooleanDtype.construct_array_typeks r#cy)Nrr&rs r!__repr__zBooleanDtype.__repr__usr#cyNTr&rs r! _is_booleanzBooleanDtype._is_booleanxr#cyr2r&rs r! _is_numericzBooleanDtype._is_numeric|r4r#cddl}|j|jk7r>|jj |jst d|jdt ||jr|g}t|}n|j}|j}|jj |jrBtj|t}tj|t}t||Sg}|D] }|j!} |jj#|jt|d| dg|j$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$Expected array of boolean type, got z insteadr*)offsetF)zero_copy_only)pyarrowr"rtypesis_null TypeError isinstanceArraylenchunkslengthronesr)emptyr-buffers from_buffersr:to_numpy null_countzerosappendarray_concat_same_type) r rMr<rCrDmaskdataresultsarrbuflistbool_arrs r!__from_arrow__zBooleanDtype.__from_arrow__s  :: (1F1Fuzz1RB5::,hWX X eW]] +WFZF\\F\\^F ==  ,776.D88F$/Dd+ + %CkkmG''44#c(T71:$6szz5heh, ~~"++88HHc#hwqz(:3::9(%(0uxxC5#D$/H NN8 $ % 288,bhhr.J  11': :r#N)returnr")rVstr)rVznp.dtype)rVztype_t[BooleanArray])rVr))rMz$pyarrow.Array | pyarrow.ChunkedArrayrVr-)__name__ __module__ __qualname____doc__r__annotations___internal_fill_valuepropertyr"r'r+r.r0r3r6rUr&r#r!rr*s)V$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 inputiufcbr8zNeed to pass bool-like valuesTskipna)floatingintegerzmixed-integer-floatrrFnpt.NDArray[np.bool_]z&values.shape and mask.shape must match)r@r- ValueError_data_maskcopyrndarrayr*rr'r rKrBr)astypeallr?asarrayobjectr infer_dtyperfloatshaperM)valuesrOri mask_values values_bool values_objectinferred_dtype integer_likes r!coerce_to_arrayrxs"&,'  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"** %$***@  "+%D 99;DxxD)  "+%D ||tzz!ABB 4<r#z pandas.arraysc.eZdZdZhdZhdZedfd Z d dfd Ze ddZ eddddd  dd Z e jejee j"fZedd  dd Zd Zdd ddZxZS)r-as Array of boolean (True/False) data with missing values. This is a pandas Extension array for boolean data, under the hood represented by 2 numpy arrays: a boolean array with the data and a boolean array with the mask (True indicating missing). BooleanArray implements Kleene logic (sometimes called three-value logic) for logical operations. See :ref:`boolean.kleene` for more. To construct a BooleanArray from generic array-like input, use :func:`pandas.array` specifying ``dtype="boolean"`` (see examples below). .. warning:: BooleanArray is considered experimental. The implementation and parts of the API may change without warning. Parameters ---------- values : numpy.ndarray A 1-d boolean-dtype array with the data. mask : numpy.ndarray A 1-d boolean-dtype array indicating missing values (True indicates missing). copy : bool, default False Whether to copy the `values` and `mask` arrays. Attributes ---------- None Methods ------- None Returns ------- BooleanArray See Also -------- array : Create an array from data with the appropriate dtype. BooleanDtype : Extension dtype for boolean data. Series : One-dimensional ndarray with axis labels (including time series). DataFrame : Two-dimensional, size-mutable, potentially heterogeneous tabular data. Examples -------- Create a BooleanArray with :func:`pandas.array`: >>> pd.array([True, False, None], dtype="boolean") [True, False, ] Length: 3, dtype: boolean >1.01TRUETruetrue>0.00FALSEFalsefalsecFt|||}t|_|Sr)super _simple_newr_dtype)clsrrrOresult __class__s r!rzBooleanArray._simple_newFs"$VT2$  r#Fct|tjr|jtjk(s t dt |_t|%|||y)NzIvalues should be boolean numpy array. Use the 'pd.array' function insteadri) r@rrjr*rr?rrr__init__)r rrrOrirs r!rzBooleanArray.__init__LsS62::.6<<2883K2 #n  D1r#c|jSr)rrs r!r*zBooleanArray.dtypeWs {{r#N)ri 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)rf)sfalse_values_unionrtrue_values_unions r! map_stringz:BooleanArray._from_sequence_of_strings..map_stringls9%%((k! A3&=!>??r#r8)r*ri)rVz bool | None) _TRUE_VALUESunion _FALSE_VALUESrrMrnr listmap_from_sequence) rstringsr*rirrrrscalarsrOrrs ` @@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)rx)rvaluer*ris r!_coerce_to_arrayzBooleanArray._coerce_to_array}s" I% %%u400r#c|jdvsJtj|}d}t|tr|j |j }}nrt|r=tj|d}|jdkDrtSt|d\}}n*t|tjr|j}|rI|tj ur7tj"|s"t%dt'|jd|s"t)|t)|k7r t+d |jd vr0t-j.|j ||j |\}}nm|jd vr0t-j0|j ||j |\}}n/t-j2|j ||j |\}}|j5||S) N>or_xorand_ror_rxorrand_r)r8r9Frz+'other' should be pandas.NA or a bool. Got z instead.zLengths must match>rr>rr)rXr is_scalarr@r-rgrhr rrmndimNotImplementedrxritem libmissingNAis_boolr?r"rBrfr kleene_or kleene_and kleene_xor_maybe_mask_result)r otheropother_is_scalarrOrs r!_logical_methodzBooleanArray._logical_methods{{MMMM--. e\ *++u{{4E % JJuF3EzzA~%%)%e>$**eTZZNLFD>>$**eTZZNLFD&&vt44r#Trac |j}|j}|dvr1tt|}|||fd|i|\}}|j ||Sddlm}||jt|j|fd|i|S)N)cummincummaxrbr) IntegerArray) rgrhgetattrrrpandas.core.arraysrrkint _accumulate)r rrbkwargsrPrOrrs r!rzBooleanArray._accumulateszzzz ' '-t4BD$@v@@JD$##D$/ / 7C< C 0$7CC#'- r#)rr np.ndarrayrOrerVr)F)rrrrOrrir)rVNone)rVr)rz list[str]r*rrir)rlist[str] | NonerrrrrVr-)r*rrir)rVtuple[np.ndarray, np.ndarray])rrWrbr)rVr)rXrYrZr[rr classmethodrrr^r*rrrjnumbersNumberr)r_HANDLED_TYPESrrr __classcell__)rs@r!r-r-sG8t8L;M BG 2  2(2 2:> 2  2 (,)-(,CC C  C & C'C&C CC>jj'..$AN5:1&1.21 &11!5H,0$( r#r-)NF)rir)rVr)& __future__rrtypingrrrrnumpyr pandas._libsrr rpandas.util._decoratorsr pandas.core.dtypes.commonr pandas.core.dtypes.dtypesr pandas.core.dtypes.missingr pandas.corerpandas.core.array_algosrpandas.core.arrays.maskedrrr<pandas._typingrrrrrrxr-r&r#r!rs"/2>+8  9 HC;?C;C;N%*R!R"Rj On?nnr#