ujx1VdZddlmZddlZddlmZddlZddlm Z ddl m Z ddl m Z ddlmZmZmZmZmZerdd lmZmZmZdd lmZmZdd lmZmZmZmZd Z d dd Z!dde df ddZ"de f ddZ#de df ddZ$de df ddZ%y)z" data hash pandas / numpy objects ) annotationsN) TYPE_CHECKING)hash_object_array) is_list_like)CategoricalDtype) ABCDataFrameABCExtensionArrayABCIndex ABCMultiIndex ABCSeries)HashableIterableIterator) ArrayLikenpt) DataFrameIndex MultiIndexSeries0123456789123456c  t|}t j |g|}tjd}tj|tjdz}d}t|D]4\}}||z } ||z}||z}|tjd| z| zz }|}6|dz|k(sJd|tjd z }|S#t$r)tj|dtjcYSwxYw) a  Parameters ---------- arrays : Iterator[np.ndarray] num_items : int length : int, default 0 Length of the result if ``arrays`` is empty. Returns ------- np.ndarray[uint64] Should be the same as CPython's tupleobject.c is5dtypeiCBixV4riXBzFed in wrong num_itemsi|) next StopIterationnpfulluint64 itertoolschain zeros_like enumerate) arrays num_itemslengthfirstmultoutlast_iia inverse_is c/var/www/html/python-recuperauto-other/env/lib/python3.12/site-packages/pandas/core/util/hashing.pycombine_hash_arraysr/0s"BV  __eWf -F 99W D -- 8!4 4C F&!1M  q t   %)+i788  A: "<$<< "299U C J# Bwwv/ryyAABs C/DDTutf8czddlm}tttr|t ddStt r7tjjdd}||dd}|Sttrttjjdd}|r1fdd D}tj|g|} t| d }||jdd}|Sttrfd j!D} t#j$} |r2fd d D} | d z } tj| | } d| D} t| | t#}||jdd}|St'dt))a2 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). When True, the hash for each row depends on both the value and the index label, so the same value at a different index position will produce a different 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 ------- 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 By default, the hash includes the index, so the same value at a different index position will produce a different hash: >>> df1 = pd.DataFrame({"a": ["a", "b", "c"]}) >>> df2 = pd.DataFrame({"a": ["b", "a", "c"]}) >>> pd.util.hash_pandas_object(df1) 0 4578374827886788867 1 17338122309987883691 2 5473791562133574857 dtype: uint64 >>> pd.util.hash_pandas_object(df2) 0 8168238220198793318 1 14044658390916132862 2 5473791562133574857 dtype: uint64 Set ``index=False`` to hash only the values. In this case, the same value always produces the same hash regardless of its position: >>> pd.util.hash_pandas_object(df1, index=False) 0 5694802365760992243 1 2797248057711234736 2 18202460376300699891 dtype: uint64 >>> pd.util.hash_pandas_object(df2, index=False) 0 2797248057711234736 1 5694802365760992243 2 18202460376300699891 dtype: uint64 r)rrF)rcopyr2)indexrr2c3fK|](}tjdj*ywF)r4encodinghash_key categorizeNhash_pandas_objectr4_values.0_r9r7r8objs r. z%hash_pandas_object..s? #II%%)  '  .1Nc3TK|]\}}t|j!ywrC) hash_arrayr<)r>r?seriesr9r7r8s r.rAz%hash_pandas_object..s, 6 v~~x: F s%(c3fK|](}tjdj*ywr6r:r=s r.rAz%hash_pandas_object..s? $#II%%)  '  $rBrc3 K|]}|ywrC)r>xs r.rAz%hash_pandas_object..s)Aa)s zUnexpected type for hashing )pandasr_default_hash_key isinstancer hash_tuplesr rFr<astyper r r!r/r4ritemslencolumns TypeErrortype)r@r4r7r8r9rhser index_iterr$hashesr%index_hash_generator_hashess` ``` r.r;r;Ws\$#}%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 oof.BCG))F  3s8 <QciixeD J6tCykBCCc t|s tdddlm}m}t |t s|j|}n|}t|jDcgc]9}|j|j|t|j|d;}}fd|D}t|t|} | Scc}w)a 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 categoriesorderedc3FK|]}|jdyw)Fr7r8r9N)_hash_pandas_object)r>catr7r8s r.rAzhash_tuples..s,  HQVWs!)rrTrLr^rrNr from_tuplesrangenlevels _simple_newcodesrlevelsr/rR) valsr7r8r^rmilevelcat_valsrYrVs `` r.rOrOs$  ABB dM * #Z # #D ) 2::&    HHUO  %(8% H HF FCM2A Hs>Cc t|ds tdt|tr|j |||St|t j s"tdt|jdt||||S)a{ Given a 1d array, return an array of deterministic integers. This function applies a hash function to each element of the input array, producing a fixed set of uint64 values suitable for use in hashing-based algorithms. 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-likercz6hash_array requires np.ndarray or ExtensionArray, not z!. Use hash_pandas_object instead.) hasattrrTrNr rdrndarrayrU__name__ _hash_ndarray)rlr7r8r9s r.rFrFsR 4 !344$)*''Z(   dBJJ ' DDz""##D F  x: >>r\c|j}|jtjtjfvr8t |j |||}t |j|||}|d|zzS|tk(r|jd}nt|jtjtjfr$|jdjdd}nat|jtjr|jdkrd|j}|j|jd}t|jtj rvtj"tj$|j||tj&|<d ||tj"d |j|k(<|}nb|rRd d lm} m} m} | |d \} } t1| | dd }| j3| |}|j5||dS t7|||}||dz z}|tj>dz}||dz z}|tj>dz}||dz z}|S#t8$r6t7|jt:jt<||}YwxYw)z! See hash_array.__doc__. u8i8Fr3urrg)r^r factorize)sortr_rcle9z lb&&& ) rrUr complex64 complex128rtrealimagboolrP issubclass datetime64 timedelta64viewnumberitemsizefloatingarraynanisnanrLr^rr{rrirdrrTstrobjectr)rlr7r8r9r hash_real hash_imag uint_dtypeuintsr^rr{rjr`tdtyperes r.rtrtSsn JJE  zzbllBMM22!$))XxL !$))XxL 2 >)) }{{4  EJJ ? @yy%%d%7 EJJ *u~~/B()  *%,,T2 ejj"++ .%'HHRVV5$A$F$Fz$RE"((4. !KLE%288D6;;JGG H    !*$U ; E:% %8%F))%8C**!H+  $T8X>D DBJDBII( ))DDBJDBII( ))DDBJD K $ C ''/8D s9 Jrs# 226  'AB$ $-0$:=$$R, L #L LL L  L  Lb%/ 5/ / /  / h% 8? 8?8?8? 8?  8?z% H HHH H  Hr\