o $j%@sdZddlmZddlZddlmZddlZddlm Z ddl m Z ddl m Z ddlmZmZmZmZmZerVdd lmZmZmZdd lmZmZdd lmZmZmZmZd Z d-ddZ!dde dfd.d!d"Z"de fd/d%d&Z#de dfd0d(d)Z$de dfd1d+d,Z%dS)2z" data hash pandas / numpy objects ) annotationsN) TYPE_CHECKING)hash_object_array) is_list_like)CategoricalDtype) ABCDataFrameABCExtensionArrayABCIndex ABCMultiIndex ABCSeries)HashableIterableIterator) ArrayLikenpt) DataFrameIndex MultiIndexSeriesZ0123456789123456arraysIterator[np.ndarray] num_itemsintreturnnpt.NDArray[np.uint64]c Cszt|}WntytjgtjdYSwt|g|}td}t|td}d}t|D]\}}||}||N}||9}|td||7}|}q4|d|ks\Jd|td7}|S) 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 itertoolschainZ zeros_like enumerate) rrfirstZmultoutZlast_iiaZ inverse_ir)Y/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/pandas/core/util/hashing.pycombine_hash_arrays/s$   r+Tutf8objIndex | DataFrame | Seriesindexboolencodingstrhash_key str | None categorizercs|ddlm}dur tttr|tdddSttr8tjj ddd}||ddd}|Stt rotjj ddd}|rdfd d d D}t |g|} t | d }||jddd}|Sttrfd d D} tj} |rfdd d D} | d7} t | | } dd | D} t | | }||jddd}|Stdt)a> Return a data hash of the Index/Series/DataFrame. Parameters ---------- obj : Index, Series, or DataFrame 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 Examples -------- >>> pd.util.hash_pandas_object(pd.Series([1, 2, 3])) 0 14639053686158035780 1 3869563279212530728 2 393322362522515241 dtype: uint64 r)rNr!F)rcopyr6)r/rr6c3&|]}tjddjVqdSF)r/r1r3r5Nhash_pandas_objectr/_values.0_r5r1r3r-r)r*  z%hash_pandas_object..Nc3s$|] \}}t|jVqdSrC) hash_arrayr<)r>r?Zseries)r5r1r3r)r*rAs  c3r8r9r:r=r@r)r*rArBrcss|]}|VqdSrCr))r>xr)r)r*rAszUnexpected type for hashing )pandasr_default_hash_key isinstancer hash_tuplesr rEr<astyper r"r#r+r/ritemslencolumns TypeErrortype)r-r/r1r3r5rhZserZ index_iterrhashesrZindex_hash_generator_hashesr)r@r*r;SsN #  2      r;vals+MultiIndex | Iterable[tuple[Hashable, ...]]cszt|stdddlmm}t|ts||n|fddtj D}fdd|D}t |t |}|S)a Hash an 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) Categoricalrc s,g|]}j|tj|ddqS)F categoriesordered) _simple_newcodesrZlevels)r>level)rVmir)r* szhash_tuples..c3s |] }|jddVqdS)Fr1r3r5N)_hash_pandas_object)r>cat)r1r3r)r*rAs  zhash_tuples..) rrOrGrVrrIr from_tuplesrangeZnlevelsr+rM)rTr1r3rZcat_valsrRrQr))rVr1r3r]r*rJs    rJrcCs\t|ds tdt|tr|j|||dSt|tjs'tdt|jdt ||||S)a Given a 1d array, return an array of deterministic integers. Parameters ---------- vals : ndarray or ExtensionArray 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. Examples -------- >>> pd.util.hash_array(np.array([1, 2, 3])) array([ 6238072747940578789, 15839785061582574730, 2185194620014831856], dtype=uint64) rzmust pass a ndarray-liker_z6hash_array requires np.ndarray or ExtensionArray, not z!. Use hash_pandas_object instead.) hasattrrOrIrr`rZndarrayrP__name__ _hash_ndarray)rTr1r3r5r)r)r*rEs   rE np.ndarrayc Cs|j}t|tjr t|j|||}t|j|||}|d|S|tkr*|d}nwt |j tj tj fr?| djddd}nbt |j tjrY|jdkrY| d|jjd}nH|rdd lm}m}m} | |dd \} } t|| dd }|| |} | j||dd Szt|||}Wntyt|tt||}Ynw||d ?N}|td9}||d?N}|td9}||d?N}|S)z! See hash_array.__doc__. u8i8Fr7ur)rVr factorize)sortrWr_le9z lb&&& )rrZ issubdtypeZ complex128rfrealimagr0rK issubclassrPZ datetime64Z timedelta64viewnumberitemsizerGrVrrmrrZr`rrOr2objectr!) rTr1r3r5rZ hash_realZ hash_imagrVrrmr[rXrar)r)r*rfs@        rf)rrrrrr) r-r.r/r0r1r2r3r4r5r0rr)rTrUr1r2r3r2rr) rTrr1r2r3r2r5r0rr) rTrgr1r2r3r2r5r0rr)&__doc__ __future__rr"typingrnumpyrZpandas._libs.hashingrZpandas.core.dtypes.commonrZpandas.core.dtypes.dtypesrZpandas.core.dtypes.genericrrr r r collections.abcr r rZpandas._typingrrrGrrrrrHr+r;rJrErfr)r)r)r*s>      & f 4 3