]jf4NSSKJr SSKrSSKJrJrJrJr SSKr SSK J r J r SSKJr SSKJr SSKJr SSKJr SS KJr SS KJr SS KJrJr \(aSSKrSS KJrJ r J!r! SS KJ"r" \\"S5"SS\555r#SSSjjr$\"S5"SS\55r%g)) annotationsN) TYPE_CHECKINGClassVarSelfcast)libmissing) set_module) is_list_like)register_extension_dtype)isna)ops)masked_accumulations)BaseMaskedArrayBaseMaskedDtype)DtypeObjnpttype_t)ExtensionDtypepandasc\rSrSr%SrSrS\S'Sr\SSj5r \SSj5r \SS j5r SS jr SS jr \SS j5r\SS j5rSSjrSrg) BooleanDtype*a Extension dtype for boolean data. This is a pandas Extension dtype for boolean data with support for missing values. BooleanDtype is the dtype companion to :class:`.BooleanArray`, which implements Kleene logic (sometimes called three-value logic) for logical operations. See :ref:`boolean.kleene` for more. .. warning:: BooleanDtype is considered experimental. The implementation and parts of the API may change without warning. Attributes ---------- None Methods ------- None See Also -------- arrays.BooleanArray : Array of boolean (True/False) data with missing values. Int64Dtype : Extension dtype for int64 integer data. StringDtype : Extension dtype for string data. Examples -------- >>> pd.BooleanDtype() BooleanDtype >>> pd.array([True, False, None], dtype=pd.BooleanDtype()) [True, False, ] Length: 3, dtype: boolean >>> pd.array([True, False, None], dtype="boolean") [True, False, ] Length: 3, dtype: boolean booleanz ClassVar[str]nameFc"[R$N)npbool_selfs W/home/mande/repo/quber/.venv/lib/python3.13/site-packages/pandas/core/arrays/boolean.pytypeBooleanDtype.type_s xxcg)Nbr s r"kindBooleanDtype.kindcsr%c.[R"S5$)Nbool)rdtyper s r" numpy_dtypeBooleanDtype.numpy_dtypegsxxr%c[$)zI Return the array type associated with this dtype. Returns ------- type ) BooleanArrayr s r"construct_array_type!BooleanDtype.construct_array_typeks r%cg)Nrr(r s r"__repr__BooleanDtype.__repr__usr%cgNTr(r s r" _is_booleanBooleanDtype._is_booleanxr%cgr8r(r s r" _is_numericBooleanDtype._is_numeric|r;r%cSSKnURUR"5:waCURR UR5(d[ SURS35e[ XR5(aU/n[U5nOURnUR5nURR UR5(a=[R"U[S9n[R"U[S9n[Xe5$/nUHnUR!5n URR#UR[U5SU S/UR$S9R'SS 9nUR(S:waOURR#UR[U5SU S/UR$S9R'SS 9nU)nO"[R*"[U5[S9n[Xe5n UR-U 5 M U(dN[[R."/[RS9[R."/[RS95$[R1U5$) z9 Construct BooleanArray from pyarrow Array/ChunkedArray. rNz$Expected array of boolean type, got z insteadr-)offsetF)zero_copy_only)pyarrowr#rtypesis_null TypeError isinstanceArraylenchunkslengthronesr,emptyr1buffers from_buffersrBto_numpy null_countzerosappendarray_concat_same_type) r!rUrDrKrLmaskdataresultsarrbuflistbool_arrs r"__from_arrow__BooleanDtype.__from_arrow__s  :: (1F1Fuzz1R1RB5::,hWX X e]] + +WFZF\\F\\^F ==  , ,776.D88F$/D+ +CkkmG''44#c(T71:$6szz5heh, ~~"++88HHc#hwqz(:3::9(%(0uxxC5#D/H NN8 $ 288,bhhr.J  11': :r%r(N)returnr#)r_str)r_znp.dtype)r_ztype_t[BooleanArray])r_r,)rUz$pyarrow.Array | pyarrow.ChunkedArrayr_r1)__name__ __module__ __qualname____firstlineno____doc__r__annotations___internal_fill_valuepropertyr#r)r.r2r5r9r=r]__static_attributes__r(r%r"rr*s)V$D-#!  /;9/; /;r%rc[U[5(aOUb [S5eURURpU(a UR 5nUR 5nX4$Sn[U[ R5(a7UR[ R:XaU(aUR 5nGO[U[ R5(aURRS;a[U5n[ R"[U5[S9nX)R[5XC)'[ R "XC)RUR5X):H5(d [#S5eUnO[ R$"U[&S9n[(R*"USS9nSnUS S /UQ7;a [#S5e[-S [U55n[ R"[U5[S9nXS)R[5X)'Xg;aU[ R "X)R[.5XS)R[.5:H5(d [#S5eUc'Uc$[ R"UR0[S9nOUcUnO}[U[ R5(a>UR[ R:Xa UbX-nO8U(aUR 5nO [ R2"U[S9nUbX-nUR0UR0:wa [S 5eX4$) z 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-floatrrNnpt.NDArray[np.bool_]z&values.shape and mask.shape must match)rHr1 ValueError_data_maskcopyrndarrayr-rr)r rSrJr,astypeallrGasarrayobjectr infer_dtyperfloatshaperU)valuesrWrt mask_values values_bool values_objectinferred_dtype integer_likes r"coerce_to_arrayrs"&,''  FG G||V\\ [[]F99;D|K&"**%%&,,"((*B [[]F FBJJ ' 'FLL,=,=,H6l hhs6{$7 $*<$8$?$?$E L!vv  % , ,V\\ :f\>R R  ;< < 68 tDE )W!D|!D D;< <2D4GH #f+T2,\:AA$G|  * FF|$++E2 .55e<=   ;< < | +xx D1  D"** % %$***@  "%D 99;DxxD)  "%D ||tzz!ABB <r%z pandas.arrayscN^\rSrSrSr1Skr1Skr\SU4Sjj5rSSU4Sjjjr \ SSj5r \SS S S S .SS jj5r \ R\R \\ R$4r\SS .SS jj5rSrSS.SSjjrSrU=r$)r1ia 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 >1TRUETruetrue1.0>0FALSEFalsefalse0.0cD>[TU]X5n[5UlU$r)super _simple_newr_dtype)clsr}rWresult __class__s r"rBooleanArray._simple_newFs $V2$  r%Fc>[U[R5(aUR[R:Xd [ S5e[ 5Ul[TU]%XUS9 g)NzIvalues should be boolean numpy array. Use the 'pd.array' function insteadrt) rHrrur-rrGrrr__init__)r!r}rWrtrs r"rBooleanArray.__init__LsT62::..6<<2883K2 #n  D1r%cUR$rrr s r"r-BooleanArray.dtypeWs {{r%N)rt true_values false_values none_valuescT^^ ^ URRU=(d /5m URRU=(d /5m Tc/mSU UU 4Sjjn[R"U[ S9n[ U5n [[XxU )55X)'URXUS9$)NcJ>UT;agUT;agUT;ag[US35e)NTFz cannot be cast to bool)rq)sfalse_values_unionrtrue_values_unions r" map_string:BooleanArray._from_sequence_of_strings..map_stringls9%%((k! A3&=!>??r%r@)r-rt)r_z bool | None) _TRUE_VALUESunion _FALSE_VALUESrrUryr listmap_from_sequence) rstringsr-rtrrrrscalarsrWrrs ` @@r"_from_sequence_of_strings&BooleanArray._from_sequence_of_strings[s ,,22;3D"E ..44\5GRH  K @ @((7&1G}c*ten=>!!'T!BBr%rc2U(aUS:Xde[XS9$)Nrr)r)rvaluer-rts r"_coerce_to_arrayBooleanArray._coerce_to_array}s  I% %%u00r%cURS;de[R"U5nSn[U[5(aUR UR pAOw[U5(a8[R"USS9nURS:a[$[USS9upO/[U[R5(aUR5nU(aPU[R La=[R""U5(d"[%S['U5RS35eU(d#[)U5[)U5:wa [+S 5eURS ;a/[,R."UR XR U5upTOmURS ;a/[,R0"UR XR U5upTO.[,R2"UR XR U5upTUR5XT5$) N>or_xorand_ror_rxorrand_r,r@rAFrz+'other' should be pandas.NA or a bool. Got z instead.zLengths must match>rr>rr)rar is_scalarrHr1rrrsr rrxndimNotImplementedrritem libmissingNAis_boolrGr#rJrqr kleene_or kleene_and kleene_xor_maybe_mask_result)r!otheropother_is_scalarrWrs r"_logical_methodBooleanArray._logical_methods{{MMMM--. e\ * *++u{{4 % JJuF3EzzA~%%)%e>$**eZZNLFD>>$**eZZNLF&&v44r%Trlc URnURnUS;a.[[U5nU"XE4SU0UD6upEUR XE5$SSKJn U"UR[5U5R"U4SU0UD6$)N)cummincummaxrmr) IntegerArray) rrrsgetattrrrpandas.core.arraysrrvint _accumulate)r!rrmkwargsrXrWrrs r"rBooleanArray._accumulateszzzz ' '-t4BD@v@@JD##D/ / 7 C 0$7CC#'- r%r)r} np.ndarrayrWrpr_r)F)r}rrWrrtr,r_None)r_r)rz list[str]r-rrtr,rlist[str] | Nonerrrrr_r1)r-rrtr,r_tuple[np.ndarray, np.ndarray])rr`rmr,r_r)rarbrcrdrerr classmethodrrrhr-rrrunumbersNumberr,r_HANDLED_TYPESrrrri __classcell__)rs@r"r1r1sQ8t8L;M BG 2  2(2 2:> 2  2 2 (,)-(,CC C  C & C'C&C CC>jj'..$AN5:1&1.21 &11!5H,0$( r%r1)NF)rtr,r_r)& __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.maskedrrrDpandas._typingrrrrrrr1r(r%r"rs"/2>+8  9 HC;?C;C;N%*R!R"Rj On?nnr%