def _hash_ndarray( vals: np.ndarray, encoding: str = "utf8", hash_key: str = _default_hash_key, categorize: bool = True, ) -> np.ndarray: """ See hash_array.__doc__. """ dtype = vals.dtype # we'll be working with everything as 64-bit values, so handle this # 128-bit value early if np.issubdtype(dtype, np.complex128): return hash_array(np.real(vals)) + 23 * hash_array(np.imag(vals)) # First, turn whatever array this is into unsigned 64-bit ints, if we can # manage it. elif isinstance(dtype, bool): vals = vals.astype("u8") elif issubclass(dtype.type, (np.datetime64, np.timedelta64)): vals = vals.view("i8").astype("u8", copy=False) elif issubclass(dtype.type, np.number) and dtype.itemsize <= 8: vals = vals.view(f"u{vals.dtype.itemsize}").astype("u8") else: # With repeated values, its MUCH faster to categorize object dtypes, # then hash and rename categories. We allow skipping the categorization # when the values are known/likely to be unique. if categorize: from pandas import ( Categorical, Index, factorize, ) codes, categories = factorize(vals, sort=False) cat = Categorical(codes, Index(categories), ordered=False, fastpath=True) return _hash_categorical(cat, encoding, hash_key) try: vals = hash_object_array(vals, hash_key, encoding) except TypeError: # we have mixed types vals = hash_object_array( vals.astype(str).astype(object), hash_key, encoding) # Then, redistribute these 64-bit ints within the space of 64-bit ints vals ^= vals >> 30 vals *= np.uint64(0xBF58476D1CE4E5B9) vals ^= vals >> 27 vals *= np.uint64(0x94D049BB133111EB) vals ^= vals >> 31 return vals
def hash_array(vals, encoding='utf8', hash_key=None, categorize=True): """ Given a 1d array, return an array of deterministic integers. .. versionadded:: 0.19.2 Parameters ---------- vals : ndarray, Categorical encoding : string, default 'utf8' encoding for data & key when strings hash_key : string key to encode, default to _default_hash_key categorize : bool, default True Whether to first categorize object arrays before hashing. This is more efficient when the array contains duplicate values. .. versionadded:: 0.20.0 Returns ------- 1d uint64 numpy array of hash values, same length as the vals """ if not hasattr(vals, 'dtype'): raise TypeError("must pass a ndarray-like") dtype = vals.dtype if hash_key is None: hash_key = _default_hash_key # For categoricals, we hash the categories, then remap the codes to the # hash values. (This check is above the complex check so that we don't ask # numpy if categorical is a subdtype of complex, as it will choke). if is_categorical_dtype(dtype): return _hash_categorical(vals, encoding, hash_key) elif is_extension_array_dtype(dtype): vals, _ = vals._values_for_factorize() dtype = vals.dtype # we'll be working with everything as 64-bit values, so handle this # 128-bit value early if np.issubdtype(dtype, np.complex128): return hash_array(vals.real) + 23 * hash_array(vals.imag) # First, turn whatever array this is into unsigned 64-bit ints, if we can # manage it. elif isinstance(dtype, np.bool): vals = vals.astype('u8') elif issubclass(dtype.type, (np.datetime64, np.timedelta64)): vals = vals.view('i8').astype('u8', copy=False) elif issubclass(dtype.type, np.number) and dtype.itemsize <= 8: vals = vals.view('u{}'.format(vals.dtype.itemsize)).astype('u8') else: # With repeated values, its MUCH faster to categorize object dtypes, # then hash and rename categories. We allow skipping the categorization # when the values are known/likely to be unique. if categorize: from pandas import factorize, Categorical, Index codes, categories = factorize(vals, sort=False) cat = Categorical(codes, Index(categories), ordered=False, fastpath=True) return _hash_categorical(cat, encoding, hash_key) try: vals = hashing.hash_object_array(vals, hash_key, encoding) except TypeError: # we have mixed types vals = hashing.hash_object_array(vals.astype(str).astype(object), hash_key, encoding) # Then, redistribute these 64-bit ints within the space of 64-bit ints vals ^= vals >> 30 vals *= np.uint64(0xbf58476d1ce4e5b9) vals ^= vals >> 27 vals *= np.uint64(0x94d049bb133111eb) vals ^= vals >> 31 return vals
def hash_array( vals, encoding: str = "utf8", hash_key: str = _default_hash_key, categorize: bool = True, ): """ Given a 1d array, return an array of deterministic integers. Parameters ---------- vals : ndarray, Categorical encoding : str, default 'utf8' Encoding for area_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 ------- 1d uint64 numpy array of hash values, same length as the vals """ if not hasattr(vals, "dtype"): raise TypeError("must pass a ndarray-like") dtype = vals.dtype # For categoricals, we hash the categories, then remap the codes to the # hash values. (This check is above the complex check so that we don't ask # numpy if categorical is a subdtype of complex, as it will choke). if is_categorical_dtype(dtype): return _hash_categorical(vals, encoding, hash_key) elif is_extension_array_dtype(dtype): vals, _ = vals._values_for_factorize() dtype = vals.dtype # we'll be working with everything as 64-bit values, so handle this # 128-bit value early if np.issubdtype(dtype, np.complex128): return hash_array(np.real(vals)) + 23 * hash_array(np.imag(vals)) # First, turn whatever array this is into unsigned 64-bit ints, if we can # manage it. elif isinstance(dtype, bool): vals = vals.astype("u8") elif issubclass(dtype.type, (np.datetime64, np.timedelta64)): vals = vals.view("i8").astype("u8", copy=False) elif issubclass(dtype.type, np.number) and dtype.itemsize <= 8: vals = vals.view(f"u{vals.dtype.itemsize}").astype("u8") else: # With repeated values, its MUCH faster to categorize object dtypes, # then hash and rename categories. We allow skipping the categorization # when the values are known/likely to be unique. if categorize: from pandas import Categorical, Index, factorize codes, categories = factorize(vals, sort=False) cat = Categorical(codes, Index(categories), ordered=False, fastpath=True) return _hash_categorical(cat, encoding, hash_key) try: vals = hashing.hash_object_array(vals, hash_key, encoding) except TypeError: # we have mixed types vals = hashing.hash_object_array( vals.astype(str).astype(object), hash_key, encoding) # Then, redistribute these 64-bit ints within the space of 64-bit ints vals ^= vals >> 30 vals *= np.uint64(0xBF58476D1CE4E5B9) vals ^= vals >> 27 vals *= np.uint64(0x94D049BB133111EB) vals ^= vals >> 31 return vals