+ Ŝi(&Rt^RIHt^RIt^RIHt^RIt^RIH t ^RI H t ^RI H t ^RIHtHtHtHtHt]'d^RIHtHtHt^R IHtHt^R IHtHtHtHtR t R R lt!RR] R3RRllt"R] 3RRllt#R] R3RRllt$R] R3RRllt%R#)z" data hash pandas / numpy objects ) annotationsN) TYPE_CHECKING)hash_object_array) is_list_like)CategoricalDtype) ABCDataFrameABCExtensionArrayABCIndex ABCMultiIndex ABCSeries)HashableIterableIterator) ArrayLikenpt) DataFrameIndex MultiIndexSeries0123456789123456c$V^8dQhRRRRRR/#)arrayszIterator[np.ndarray] num_itemsintreturnnpt.NDArray[np.uint64])formats"a/Users/mibo/.openclaw/workspace/.venv-ak/lib/python3.14/site-packages/pandas/core/util/hashing.py __annotate__r 0s$!! !-0!!cd\V4p\ P !T.T4p\P!R4p\P!T4\P!R4,p^p\T4FJwrgY, pYG,pYC,pT\P!RT,T,4, pTpKL T^,T8XgQR4hT\P!R4, pT# \d*\P!.\PR7u#i;i)z Parameters ---------- arrays : Iterator[np.ndarray] num_items : int Returns ------- np.ndarray[uint64] Should be the same as CPython's tupleobject.c )dtypeiCBixV4iXBzFed in wrong num_itemsi|) next StopIterationnparrayuint64 itertoolschain zeros_like enumerate) rrfirstmultoutlast_iia inverse_is && rcombine_hash_arraysr40s-V __eWf -F 99W D -- 8!4 4C F&!M     %)+i788 " A: "<$<< "299U C J! -xx")),,-s C;;1D/.D/Tutf8c 0V^8dQhRRRRRRRRR RR R /#) robjzIndex | DataFrame | Seriesindexboolencodingstrhash_keyz str | None categorizerrr)rs"rr r TsLkk #k kk k  k  kr!caaaa^RIHpSf\o\S\4'dV!\ SSS4RRR7#\S\ 4'd8\SPSSS4PRRR7pV!VSRRR7pV#\S\4'd}\SPSSS4PRRR7pV'd4VVVV3RlR 4p\P!V.V4p \V ^4pV!VSPRRR7pV#\S\4'dVVV3RlSP!44p \#SP$4p V'd8VVVV3R lR 4p V ^, p \P!W4p R V 4p \W4pV!VSPRRR7pV#\'R \)S4 24h) a Return a data hash of the Index/Series/DataFrame. The hash is computed element-wise using the underlying data values, and optionally includes the index when hashing a Series or DataFrame. Parameters ---------- obj : Index, Series, or DataFrame The pandas object to hash. index : bool, default True Include the index in the hash (if Series/DataFrame). encoding : str, default 'utf8' Encoding for data & key when strings. hash_key : str, default _default_hash_key Hash_key for string key to encode. categorize : bool, default True Whether to first categorize object arrays before hashing. This is more efficient when the array contains duplicate values. Returns ------- Series of uint64 Same length as the object. See Also -------- util.hash_array : Return a hash of the given array. util.hash_tuples : Hash a MultiIndex or listlike-of-tuples efficiently. Examples -------- >>> pd.util.hash_pandas_object(pd.Series([1, 2, 3])) 0 14639053686158035780 1 3869563279212530728 2 393322362522515241 dtype: uint64 )rr(F)r#copyr?)r8r#r?c 3l<"TF)p\SPRSSSR7PxK+ R#5iF)r8r:r<r=Nhash_pandas_objectr8_values.0_r=r:r<r7s& r %hash_pandas_object..s@  A#II%%)  '  14c3X<"TFwr\VPSSS4xK! R#5iN) hash_arrayrE)rGrHseriesr=r:r<s& rrIrJs, (  v~~x: F F(s'*c 3l<"TF)p\SPRSSSR7PxK+ R#5irBrCrFs& rrIrJs@ $ A#II%%)  '  rKc3$"TFqxK R#5irMr)rGxs& rrIrJs)AaszUnexpected type for hashing rM)pandasr_default_hash_key isinstancer hash_tuplesr rNrEastyper r)r*r4r8ritemslencolumns TypeErrortype)r7r8r:r<r=rhser index_iterrhashesrindex_hash_generator_hashessf&fff rrDrDTsZ$#}%%k#x:(QVWW C " " s{{Hh C J J 5 K Qc>d Ja C # # s{{Hh C J J 5 K     J__aS*5F#FA.AQciixeD< J9 C & &  YY[  $  $  $  NI oofCG))F  2QciixeD J6tCykBCCr!c(V^8dQhRRRRRRRR/#)rvalsz+MultiIndex | Iterable[tuple[Hashable, ...]]r:r;r<rrr)rs"rr r s0/ / 5/ / /  / r!c aa\V4'g \R4h^RIHpHp\ V\ 4'gVP!V4pMTp\VP4Uu.uFBpVPVPV,\VPV,RR74NKD ppVV3RlV4p\V\V44p V #uupi)z Hash a MultiIndex / listlike-of-tuples efficiently. Parameters ---------- vals : MultiIndex or listlike-of-tuples encoding : str, default 'utf8' hash_key : str, default _default_hash_key Returns ------- ndarray[np.uint64] of hashed values z'must be convertible to a list-of-tuples) CategoricalrF categoriesorderedc3L<"TFpVPSSRR7xK R#5i)Fr:r<r=N)_hash_pandas_object)rGcatr:r<s& rrIhash_tuples..s.C HQVWWs!$)rr[rSrfrrUr from_tuplesrangenlevels _simple_newcodesrlevelsr4rY) rdr:r<rfrmilevelcat_valsr`r]s &ff rrVrVs$   ABB dM * *  # #D ) 2::&  'E  HHUO  %(8% H '  F FCM2A Hs)ACc ,V^8dQhRRRRRRRRRR /#) rrdrr:r;r<r=r9rrr)rs"rr r s:4?4? 4?4?4? 4?  4?r!c*\VR4'g \R4h\V\4'dVP WVR7#\V\ P 4'g#\R\V4P R24h\WW#4#)ag Given a 1d array, return an array of deterministic integers. Parameters ---------- vals : ndarray or ExtensionArray The input array to hash. encoding : str, default 'utf8' Encoding for data & key when strings. hash_key : str, default _default_hash_key Hash_key for string key to encode. categorize : bool, default True Whether to first categorize object arrays before hashing. This is more efficient when the array contains duplicate values. Returns ------- ndarray[np.uint64, ndim=1] Hashed values, same length as the vals. See Also -------- util.hash_pandas_object : Return a data hash of the Index/Series/DataFrame. util.hash_tuples : Hash a MultiIndex / listlike-of-tuples efficiently. Examples -------- >>> pd.util.hash_array(np.array([1, 2, 3])) array([ 6238072747940578789, 15839785061582574730, 2185194620014831856], dtype=uint64) r#zmust pass an ndarray-likerkz6hash_array requires np.ndarray or ExtensionArray, not z!. Use hash_pandas_object instead.) hasattrr[rUrrlr&ndarrayr\__name__ _hash_ndarray)rdr:r<r=s&&&&rrNrNsJ 4 ! !344$)**''Z(   dBJJ ' ' DDz""##D F   >>r!c ,V^8dQhRRRRRRRRRR /#) rrdz np.ndarrayr:r;r<r=r9rrr)rs"rr r +s:>> >>> >  >r!cVPp\P!V\P4'd?\ VP WV4p\ VP WV4pV^V,,#V\8XdVPR4pEM4\VP\P\P34'd$VPR4PRRR7pM\VP\P4'dJVP^8:d9VPRVPP 24PR4pMdV'dP^RIHpHpHp V !VRR7wr\)V!V RR7RR7p VP+W4p V P-WRR 7#\/WV4pW^, ,pV\P6!R 4,pW^, ,pV\P6!R 4,pW^, ,pV# \0d6\/TP\24P\44Y!4pLi;i) z See hash_array.__doc__. u8i8Fr@u)rfr factorize)sortrgrkle9z lb&&& )r#r& issubdtype complex128r}realimagr9rW issubclassr\ datetime64 timedelta64viewnumberitemsizerSrfrrrrrrlrr[r;objectr()rdr:r<r=r# hash_real hash_imagrfrrrsrhtdtyperms&&&& rr}r}+s JJE }}UBMM**!$))XL !$))XL 2 >)) }{{4  EJJ ? @ @yy%%d%7 EJJ * *u~~/Byy1TZZ00123::4@    !*$U ; E% %8%F))%8C**!+  $TX>D BJDBII( ))DBJDBII( ))DBJD K $ C ''/D s< H11=I10I1)&__doc__ __future__rr)typingrnumpyr&pandas._libs.hashingrpandas.core.dtypes.commonrpandas.core.dtypes.dtypesrpandas.core.dtypes.genericrrr r r collections.abcr r rpandas._typingrrrSrrrrrTr4rDrVrNr}rr!rrs# 226  '!L, k`%/ h% 4?r% >>r!