]j(lSrSSKJr SSKrSSKJr SSKrSSKJ r SSK J r SSK J r SSKJrJrJrJrJr \(aSS KJrJrJr SS KJrJr SS KJrJrJrJr S r SS jr!SS\ S4SSjjr"S\ 4SSjjr#S\ S4SSjjr$S\ S4SSjjr%g)z" data hash pandas / numpy objects ) annotationsN) TYPE_CHECKING)hash_object_array) is_list_like)CategoricalDtype) ABCDataFrameABCExtensionArrayABCIndex ABCMultiIndex ABCSeries)HashableIterableIterator) ArrayLikenpt) DataFrameIndex MultiIndexSeries0123456789123456c[U5n[ R "U/U5n[R"S5n[R"U5[R"S5-nSn[U5H2upgX- nXG-nXC-nU[R"SU-U-5- nUnM4 US-U:XdS5eU[R"S5- nU$![a& [R"/[RS9s$f=f) z Parameters ---------- arrays : Iterator[np.ndarray] num_items : int Returns ------- np.ndarray[uint64] Should be the same as CPython's tupleobject.c )dtypeiCBixV4riXBzFed in wrong num_itemsi|) next StopIterationnparrayuint64 itertoolschain zeros_like enumerate) arrays num_itemsfirstmultoutlast_iia inverse_is U/home/mande/repo/quber/.venv/lib/python3.13/site-packages/pandas/core/util/hashing.pycombine_hash_arraysr-0s-V __eWf -F 99W D -- 8!4 4C F&!M     %)+i788 " A: "<$<< "299U C J! -xx")),,-s C-DDTutf8c^^^^SSKJn Tc[m[T[5(aU"[ TTT5SSS9$[T[ 5(a1[TRTTT5RSSS9nU"UTSSS9nU$[T[5(au[TRTTT5RSSS9nU(a3UUUU4SjS 5n[R"U/U5n [U S 5nU"UTRSSS9nU$[T[5(aUUU4S jTR!55n [#TR$5n U(a3UUUU4S jS 5n U S - n [R"X5n SU 5n [X5nU"UTRSSS9nU$['S[)T535e)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 r)rrF)rcopyr0)indexrr0c 3f># UH&n[TRSTTTS9Rv M( g7fF)r2encodinghash_key categorizeNhash_pandas_objectr2_values.0_r7r5r6objs r, %hash_pandas_object..s@  A#II%%)  '  .1Nc3X># UHup[URTTT5v M! g7frB) hash_arrayr:)r<r=seriesr7r5r6s r,r?r@s, (  v~~x: F F(s'*c 3f># UH&n[TRSTTTS9Rv M( g7fr4r8r;s r,r?r@s@ $ A#II%%)  '  rArc3$# UHov M g7frB)r<xs r,r?r@s)AaszUnexpected type for hashing )pandasr_default_hash_key isinstancer hash_tuplesr rEr:astyper rr r-r2ritemslencolumns TypeErrortype)r>r2r5r6r7rhser index_iterr#hashesr$index_hash_generator_hashess` ``` r,r9r9TsZ$#}%%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 J6tCykBCCc ^^[U5(d [S5eSSKJnJn [ U[ 5(dUR"U5nOUn[UR5Vs/sH7nURURU[URUSS95PM9 nnUU4SjU5n[U[U55n U $s snf)z Hash a MultiIndex / listlike-of-tuples efficiently. Parameters ---------- vals : MultiIndex or listlike-of-tuples encoding : str, default 'utf8' hash_key : str, default _default_hash_key Returns ------- ndarray[np.uint64] of hashed values z'must be convertible to a list-of-tuplesr) CategoricalrF categoriesorderedc3F># UHnURTTSS9v M g7f)Fr5r6r7N)_hash_pandas_object)r<catr5r6s r,r?hash_tuples..s+C HQVWs!)rrSrKr]rrMr from_tuplesrangenlevels _simple_newcodesrlevelsr-rQ) valsr5r6r]rmilevelcat_valsrXrUs `` r,rNrNs$   ABB dM * *  # #D ) 2::&  'E  HHUO  %(8% H '  F FCM2A Hs'>C c[US5(d [S5e[U[5(aUR XUS9$[U[ R 5(d"[S[U5RS35e[XX#5$)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) rzmust pass an ndarray-likerbz6hash_array requires np.ndarray or ExtensionArray, not z!. Use hash_pandas_object instead.) hasattrrSrMr rcrndarrayrT__name__ _hash_ndarray)rlr5r6r7s r,rErEsJ 4 ! !344$)**''Z(   dBJJ ' ' DDz""##D F   >>r[clURn[R"U[R5(a6[ UR XU5n[ UR XU5nUSU--$U[:XaURS5nGO![UR[R[R45(a URS5RSSS9nO[UR[R5(aHURS::a8URSURR35RS5nOWU(aCSS KJnJnJn U "USS 9up[)U"U SS9SS 9n UR+X5n U R-XSS 9$[/XU5nXS - -nU[R6"S5-nXS- -nU[R6"S5-nXS- -nU$![0a5 [/UR[25R[45X!5nNf=f)z See hash_array.__doc__. u8i8Fr1ur)r]r factorize)sortr^rble9z lb&&& )rr issubdtype complex128rtrealimagboolrO issubclassrT datetime64 timedelta64viewnumberitemsizerKr]rr{rrircrrSstrobjectr)rlr5r6r7r hash_real hash_imagr]rr{rjr_tdtyperds r,rtrt+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 G44zIndex | DataFrame | Seriesr2rr5rr6z str | Noner7rrr)rlz+MultiIndex | Iterable[tuple[Hashable, ...]]r5rr6rrr) rlrr5rr6rr7rrr) rlz np.ndarrayr5rr6rr7rrr)&__doc__ __future__rrtypingrnumpyrpandas._libs.hashingrpandas.core.dtypes.commonrpandas.core.dtypes.dtypesrpandas.core.dtypes.genericrr r r r collections.abcr rrpandas._typingrrrKrrrrrLr-r9rNrErtrIr[r,rsq# 226  '! !-0!!L, k #k kk k  k  k`%/ 5/ / /  / h% 4? 4?4?4? 4?  4?r% > >>> >  >r[