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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 r )r Nr F)r �copy)r4 )r0 r r4 c 3 s$ | ] }t �jd ��� d�jV qdS )F)r0 r1 r2 r3 N)�hash_pandas_objectr0 �_values)�.0�_)r3 r1 r2 r/ r) r* � <genexpr> s z%hash_pandas_object.<locals>.<genexpr>)N� c 3 s" | ]\}}t |j��� �V qd S )N)� hash_arrayr6 )r7 r8 Zseries)r3 r1 r2 r) r* r9 � s c 3 s$ | ] }t �jd ��� d�jV qdS )F)r0 r1 r2 r3 N)r5 r0 r6 )r7 r8 )r3 r1 r2 r/ r) r* r9 � s r c s s | ] }|V qd S )Nr) )r7 �xr) r) r* r9 � s z Unexpected type for hashing )�pandasr �_default_hash_key� isinstancer � hash_tuplesr r; r6 �astyper r! r" r+ r0 r �items�len�columns� TypeError�type)r/ r0 r1 r2 r3 r �hZserZ index_iterr �hashesr Zindex_hash_generator�_hashesr) )r3 r1 r2 r/ r* r5 P s@ r5 z+MultiIndex | Iterable[tuple[Hashable, ...]])�valsr1 r2 r c sz t | �std��ddlm� m} t| t�s6|�| ��n| �� �fdd�t�j �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 )r r c s( g | ] }� �j | �j| d dd��qS )FT)�ordered�fastpath)�codesZlevels)r7 �level)r �mir) r* � <listcomp>� s z hash_tuples.<locals>.<listcomp>c 3 s | ]}t |� �d �V qdS ))r1 r2 N)�_hash_categorical)r7 �cat)r1 r2 r) r* r9 � s z hash_tuples.<locals>.<genexpr>) r rE r= r r r? r � from_tuples�rangeZnlevelsr+ rC )rJ r1 r2 r Zcat_valsrH rG r) )r r1 r2 rO r* r@ � s r@ r )rR r1 r2 r c C sd t �| jj�}t|||dd�}| �� }t|�r<|�| j�}nt j t|�dd�}|� � r`t j ||< |S )a Hash a Categorical by hashing its categories, and then mapping the codes to the hashes Parameters ---------- cat : Categorical encoding : str hash_key : str Returns ------- ndarray[np.uint64] of hashed values, same size as len(c) F)r3 r )r ) r Zasarray� categoriesr6 r; ZisnarC ZtakerM Zzeros�anyr Zu8max)rR r1 r2 �values�hashed�mask�resultr) r) r* rQ � s rQ r )rJ r1 r2 r3 r c C s\ t | d�std��| j}t|�r6td| �} t| ||�S t| tj�sN| � � \} }t | |||�S )aK 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. r zmust pass a ndarray-liker ) �hasattrrE r r r rQ r? r ZndarrayZ_values_for_factorize� _hash_ndarray)rJ r1 r2 r3 r r8 r) r) r* r; s r; c C st | j }t�|tj�r4tt�| ��dtt�| �� S t|t�rJ| � d�} n�t |j tj tj f�rt| �d�j ddd�} n�t |j tj�r�|jdkr�| �d| j j� ��� d�} n�|r�dd lm}m}m} || dd �\}} |||| �dd d �} t| ||�S yt| ||�} W n. tk �r. t| � t�� t�||�} Y nX | | d ? N } | t�d�9 } | | d? N } | t�d�9 } | | d? N } | S )z! See hash_array.__doc__. � �u8�i8F)r4 � �ur )r r � factorize)�sortT)rK rL � l �e�9� �z � l �b&�&�& � )r r Z issubdtypeZ complex128r; �real�imagr? r- rA � issubclassrF Z datetime64Z timedelta64�view�number�itemsizer= r r rb rQ r rE r. �objectr ) rJ r1 r2 r3 r r r rb rM rU rR r) r) r* r\ * s2 r\ )'�__doc__� __future__r r! �typingr r r r r �numpyr Z pandas._libsr Zpandas._libs.hashingr Zpandas._typingr r Zpandas.core.dtypes.commonr r Zpandas.core.dtypes.genericr r r r r= r r r r r> r+ r5 r@ rQ r; r\ r) r) r) r* �<module> s4 " Y+((
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