# Python: 3.9.2 (tags/v3.9.2:1a79785, Feb 19 2021, 13:44:55) [MSC v.1928 64 bit (AMD64)] # Library: pandas, version: 1.2.4 # Module: pandas._libs.hashtable, version: unspecified import typing import builtins as _mod_builtins class Factorizer(_mod_builtins.object): def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... @property def count(self) -> typing.Any: ... def factorize(self) -> typing.Any: "\n Examples\n --------\n Factorize values with nans replaced by na_sentinel\n\n >>> factorize(np.array([1,2,np.nan], dtype='O'), na_sentinel=20)\n array([ 0, 1, 20])\n " ... def get_count(self) -> typing.Any: ... @property def table(self) -> typing.Any: ... def unique(self) -> typing.Any: ... @property def uniques(self) -> typing.Any: ... def __getattr__(self, name) -> typing.Any: ... class Float32HashTable(HashTable): def __contains__(self, key) -> bool: 'Return key in self.' ... def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def _unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[float32]\n Array of values of which unique will be calculated\n uniques : Float32Vector\n Vector into which uniques will be written\n count_prior : Py_ssize_t, default 0\n Number of existing entries in uniques\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n ignore_na : boolean, default False\n Whether NA-values should be ignored for calculating the uniques. If\n True, the labels corresponding to missing values will be set to\n na_sentinel.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[float32]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse=True)\n The labels from values to uniques\n ' ... def factorize(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Missing values are not included in the "uniques" for this method.\n The labels for any missing values will be set to "na_sentinel"\n\n Parameters\n ----------\n values : ndarray[float32]\n Array of values of which unique will be calculated\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n\n Returns\n -------\n uniques : ndarray[float32]\n Unique values of input, not sorted\n labels : ndarray[int64]\n The labels from values to uniques\n ' ... def get_item(self) -> typing.Any: ... def get_labels(self) -> typing.Any: ... def get_labels_groupby(self) -> typing.Any: ... def lookup(self) -> typing.Any: ... def map(self) -> typing.Any: ... def map_locations(self) -> typing.Any: ... def set_item(self) -> typing.Any: ... def sizeof(self) -> typing.Any: ' return the size of my table in bytes ' ... def unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[float32]\n Array of values of which unique will be calculated\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[float32]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse)\n The labels from values to uniques\n ' ... def __getattr__(self, name) -> typing.Any: ... class Float32Vector(_mod_builtins.object): def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def to_array(self) -> typing.Any: ... def __getattr__(self, name) -> typing.Any: ... class Float64HashTable(HashTable): def __contains__(self, key) -> bool: 'Return key in self.' ... def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def _unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[float64]\n Array of values of which unique will be calculated\n uniques : Float64Vector\n Vector into which uniques will be written\n count_prior : Py_ssize_t, default 0\n Number of existing entries in uniques\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n ignore_na : boolean, default False\n Whether NA-values should be ignored for calculating the uniques. If\n True, the labels corresponding to missing values will be set to\n na_sentinel.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[float64]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse=True)\n The labels from values to uniques\n ' ... def factorize(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Missing values are not included in the "uniques" for this method.\n The labels for any missing values will be set to "na_sentinel"\n\n Parameters\n ----------\n values : ndarray[float64]\n Array of values of which unique will be calculated\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n\n Returns\n -------\n uniques : ndarray[float64]\n Unique values of input, not sorted\n labels : ndarray[int64]\n The labels from values to uniques\n ' ... def get_item(self) -> typing.Any: ... def get_labels(self) -> typing.Any: ... def get_labels_groupby(self) -> typing.Any: ... def lookup(self) -> typing.Any: ... def map(self) -> typing.Any: ... def map_locations(self) -> typing.Any: ... def set_item(self) -> typing.Any: ... def sizeof(self) -> typing.Any: ' return the size of my table in bytes ' ... def unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[float64]\n Array of values of which unique will be calculated\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[float64]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse)\n The labels from values to uniques\n ' ... def __getattr__(self, name) -> typing.Any: ... class Float64Vector(_mod_builtins.object): def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def to_array(self) -> typing.Any: ... def __getattr__(self, name) -> typing.Any: ... class HashTable(_mod_builtins.object): def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def __getattr__(self, name) -> typing.Any: ... class Int16HashTable(HashTable): def __contains__(self, key) -> bool: 'Return key in self.' ... def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def _unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[int16]\n Array of values of which unique will be calculated\n uniques : Int16Vector\n Vector into which uniques will be written\n count_prior : Py_ssize_t, default 0\n Number of existing entries in uniques\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n ignore_na : boolean, default False\n Whether NA-values should be ignored for calculating the uniques. If\n True, the labels corresponding to missing values will be set to\n na_sentinel.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[int16]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse=True)\n The labels from values to uniques\n ' ... def factorize(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Missing values are not included in the "uniques" for this method.\n The labels for any missing values will be set to "na_sentinel"\n\n Parameters\n ----------\n values : ndarray[int16]\n Array of values of which unique will be calculated\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n\n Returns\n -------\n uniques : ndarray[int16]\n Unique values of input, not sorted\n labels : ndarray[int64]\n The labels from values to uniques\n ' ... def get_item(self) -> typing.Any: ... def get_labels(self) -> typing.Any: ... def get_labels_groupby(self) -> typing.Any: ... def lookup(self) -> typing.Any: ... def map(self) -> typing.Any: ... def map_locations(self) -> typing.Any: ... def set_item(self) -> typing.Any: ... def sizeof(self) -> typing.Any: ' return the size of my table in bytes ' ... def unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[int16]\n Array of values of which unique will be calculated\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[int16]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse)\n The labels from values to uniques\n ' ... def __getattr__(self, name) -> typing.Any: ... class Int16Vector(_mod_builtins.object): def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def to_array(self) -> typing.Any: ... def __getattr__(self, name) -> typing.Any: ... class Int32HashTable(HashTable): def __contains__(self, key) -> bool: 'Return key in self.' ... def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def _unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[int32]\n Array of values of which unique will be calculated\n uniques : Int32Vector\n Vector into which uniques will be written\n count_prior : Py_ssize_t, default 0\n Number of existing entries in uniques\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n ignore_na : boolean, default False\n Whether NA-values should be ignored for calculating the uniques. If\n True, the labels corresponding to missing values will be set to\n na_sentinel.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[int32]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse=True)\n The labels from values to uniques\n ' ... def factorize(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Missing values are not included in the "uniques" for this method.\n The labels for any missing values will be set to "na_sentinel"\n\n Parameters\n ----------\n values : ndarray[int32]\n Array of values of which unique will be calculated\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n\n Returns\n -------\n uniques : ndarray[int32]\n Unique values of input, not sorted\n labels : ndarray[int64]\n The labels from values to uniques\n ' ... def get_item(self) -> typing.Any: ... def get_labels(self) -> typing.Any: ... def get_labels_groupby(self) -> typing.Any: ... def lookup(self) -> typing.Any: ... def map(self) -> typing.Any: ... def map_locations(self) -> typing.Any: ... def set_item(self) -> typing.Any: ... def sizeof(self) -> typing.Any: ' return the size of my table in bytes ' ... def unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[int32]\n Array of values of which unique will be calculated\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[int32]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse)\n The labels from values to uniques\n ' ... def __getattr__(self, name) -> typing.Any: ... class Int32Vector(_mod_builtins.object): def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def to_array(self) -> typing.Any: ... def __getattr__(self, name) -> typing.Any: ... class Int64Factorizer(_mod_builtins.object): def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... @property def count(self) -> typing.Any: ... def factorize(self) -> typing.Any: "\n Examples\n --------\n Factorize values with nans replaced by na_sentinel\n\n >>> factorize(np.array([1,2,np.nan], dtype='O'), na_sentinel=20)\n array([ 0, 1, 20])\n " ... def get_count(self) -> typing.Any: ... @property def table(self) -> typing.Any: ... @property def uniques(self) -> typing.Any: ... def __getattr__(self, name) -> typing.Any: ... class Int64HashTable(HashTable): def __contains__(self, key) -> bool: 'Return key in self.' ... def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def _unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[int64]\n Array of values of which unique will be calculated\n uniques : Int64Vector\n Vector into which uniques will be written\n count_prior : Py_ssize_t, default 0\n Number of existing entries in uniques\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n ignore_na : boolean, default False\n Whether NA-values should be ignored for calculating the uniques. If\n True, the labels corresponding to missing values will be set to\n na_sentinel.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[int64]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse=True)\n The labels from values to uniques\n ' ... def factorize(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Missing values are not included in the "uniques" for this method.\n The labels for any missing values will be set to "na_sentinel"\n\n Parameters\n ----------\n values : ndarray[int64]\n Array of values of which unique will be calculated\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n\n Returns\n -------\n uniques : ndarray[int64]\n Unique values of input, not sorted\n labels : ndarray[int64]\n The labels from values to uniques\n ' ... def get_item(self) -> typing.Any: ... def get_labels(self) -> typing.Any: ... def get_labels_groupby(self) -> typing.Any: ... def lookup(self) -> typing.Any: ... def map(self) -> typing.Any: ... def map_locations(self) -> typing.Any: ... def set_item(self) -> typing.Any: ... def sizeof(self) -> typing.Any: ' return the size of my table in bytes ' ... def unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[int64]\n Array of values of which unique will be calculated\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[int64]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse)\n The labels from values to uniques\n ' ... def __getattr__(self, name) -> typing.Any: ... class Int64Vector(_mod_builtins.object): def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def to_array(self) -> typing.Any: ... def __getattr__(self, name) -> typing.Any: ... class Int8HashTable(HashTable): def __contains__(self, key) -> bool: 'Return key in self.' ... def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def _unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[int8]\n Array of values of which unique will be calculated\n uniques : Int8Vector\n Vector into which uniques will be written\n count_prior : Py_ssize_t, default 0\n Number of existing entries in uniques\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n ignore_na : boolean, default False\n Whether NA-values should be ignored for calculating the uniques. If\n True, the labels corresponding to missing values will be set to\n na_sentinel.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[int8]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse=True)\n The labels from values to uniques\n ' ... def factorize(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Missing values are not included in the "uniques" for this method.\n The labels for any missing values will be set to "na_sentinel"\n\n Parameters\n ----------\n values : ndarray[int8]\n Array of values of which unique will be calculated\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n\n Returns\n -------\n uniques : ndarray[int8]\n Unique values of input, not sorted\n labels : ndarray[int64]\n The labels from values to uniques\n ' ... def get_item(self) -> typing.Any: ... def get_labels(self) -> typing.Any: ... def get_labels_groupby(self) -> typing.Any: ... def lookup(self) -> typing.Any: ... def map(self) -> typing.Any: ... def map_locations(self) -> typing.Any: ... def set_item(self) -> typing.Any: ... def sizeof(self) -> typing.Any: ' return the size of my table in bytes ' ... def unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[int8]\n Array of values of which unique will be calculated\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[int8]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse)\n The labels from values to uniques\n ' ... def __getattr__(self, name) -> typing.Any: ... class Int8Vector(_mod_builtins.object): def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def to_array(self) -> typing.Any: ... def __getattr__(self, name) -> typing.Any: ... class ObjectVector(_mod_builtins.object): def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def to_array(self) -> typing.Any: ... def __getattr__(self, name) -> typing.Any: ... class PyObjectHashTable(HashTable): def __contains__(self, key) -> bool: 'Return key in self.' ... def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def _unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[object]\n Array of values of which unique will be calculated\n uniques : ObjectVector\n Vector into which uniques will be written\n count_prior : Py_ssize_t, default 0\n Number of existing entries in uniques\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then None _plus_\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n ignore_na : boolean, default False\n Whether NA-values should be ignored for calculating the uniques. If\n True, the labels corresponding to missing values will be set to\n na_sentinel.\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[object]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse=True)\n The labels from values to uniques\n ' ... def factorize(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Missing values are not included in the "uniques" for this method.\n The labels for any missing values will be set to "na_sentinel"\n\n Parameters\n ----------\n values : ndarray[object]\n Array of values of which unique will be calculated\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then None _plus_\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n mask : ndarray[bool], optional\n Not yet implemented for PyObjectHashTable.\n\n Returns\n -------\n uniques : ndarray[object]\n Unique values of input, not sorted\n labels : ndarray[int64]\n The labels from values to uniques\n ' ... def get_item(self) -> typing.Any: ... def get_labels(self) -> typing.Any: ... def lookup(self) -> typing.Any: ... def map_locations(self) -> typing.Any: ... def set_item(self) -> typing.Any: ... def sizeof(self) -> typing.Any: ' return the size of my table in bytes ' ... def unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[object]\n Array of values of which unique will be calculated\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[object]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse)\n The labels from values to uniques\n ' ... def __getattr__(self, name) -> typing.Any: ... SIZE_HINT_LIMIT: int class StringHashTable(HashTable): def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def _unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[object]\n Array of values of which unique will be calculated\n uniques : ObjectVector\n Vector into which uniques will be written\n count_prior : Py_ssize_t, default 0\n Number of existing entries in uniques\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then any value\n that is not a string is considered missing. If na_value is\n not None, then _additionally_ any value "val" satisfying\n val == na_value is considered missing.\n ignore_na : boolean, default False\n Whether NA-values should be ignored for calculating the uniques. If\n True, the labels corresponding to missing values will be set to\n na_sentinel.\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[object]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse=True)\n The labels from values to uniques\n ' ... def factorize(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Missing values are not included in the "uniques" for this method.\n The labels for any missing values will be set to "na_sentinel"\n\n Parameters\n ----------\n values : ndarray[object]\n Array of values of which unique will be calculated\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then any value\n that is not a string is considered missing. If na_value is\n not None, then _additionally_ any value "val" satisfying\n val == na_value is considered missing.\n mask : ndarray[bool], optional\n Not yet implementd for StringHashTable.\n\n Returns\n -------\n uniques : ndarray[object]\n Unique values of input, not sorted\n labels : ndarray[int64]\n The labels from values to uniques\n ' ... def get_indexer(self) -> typing.Any: ... def get_item(self) -> typing.Any: ... def get_labels(self) -> typing.Any: ... def lookup(self) -> typing.Any: ... def map_locations(self) -> typing.Any: ... na_string_sentinel: str def set_item(self) -> typing.Any: ... def sizeof(self) -> typing.Any: ... def unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[object]\n Array of values of which unique will be calculated\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[object]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse)\n The labels from values to uniques\n ' ... def __getattr__(self, name) -> typing.Any: ... class StringVector(_mod_builtins.object): def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def to_array(self) -> typing.Any: ... def __getattr__(self, name) -> typing.Any: ... class UInt16HashTable(HashTable): def __contains__(self, key) -> bool: 'Return key in self.' ... def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def _unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[uint16]\n Array of values of which unique will be calculated\n uniques : UInt16Vector\n Vector into which uniques will be written\n count_prior : Py_ssize_t, default 0\n Number of existing entries in uniques\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n ignore_na : boolean, default False\n Whether NA-values should be ignored for calculating the uniques. If\n True, the labels corresponding to missing values will be set to\n na_sentinel.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[uint16]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse=True)\n The labels from values to uniques\n ' ... def factorize(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Missing values are not included in the "uniques" for this method.\n The labels for any missing values will be set to "na_sentinel"\n\n Parameters\n ----------\n values : ndarray[uint16]\n Array of values of which unique will be calculated\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n\n Returns\n -------\n uniques : ndarray[uint16]\n Unique values of input, not sorted\n labels : ndarray[int64]\n The labels from values to uniques\n ' ... def get_item(self) -> typing.Any: ... def get_labels(self) -> typing.Any: ... def get_labels_groupby(self) -> typing.Any: ... def lookup(self) -> typing.Any: ... def map(self) -> typing.Any: ... def map_locations(self) -> typing.Any: ... def set_item(self) -> typing.Any: ... def sizeof(self) -> typing.Any: ' return the size of my table in bytes ' ... def unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[uint16]\n Array of values of which unique will be calculated\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[uint16]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse)\n The labels from values to uniques\n ' ... def __getattr__(self, name) -> typing.Any: ... class UInt16Vector(_mod_builtins.object): def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def to_array(self) -> typing.Any: ... def __getattr__(self, name) -> typing.Any: ... class UInt32HashTable(HashTable): def __contains__(self, key) -> bool: 'Return key in self.' ... def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def _unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[uint32]\n Array of values of which unique will be calculated\n uniques : UInt32Vector\n Vector into which uniques will be written\n count_prior : Py_ssize_t, default 0\n Number of existing entries in uniques\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n ignore_na : boolean, default False\n Whether NA-values should be ignored for calculating the uniques. If\n True, the labels corresponding to missing values will be set to\n na_sentinel.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[uint32]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse=True)\n The labels from values to uniques\n ' ... def factorize(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Missing values are not included in the "uniques" for this method.\n The labels for any missing values will be set to "na_sentinel"\n\n Parameters\n ----------\n values : ndarray[uint32]\n Array of values of which unique will be calculated\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n\n Returns\n -------\n uniques : ndarray[uint32]\n Unique values of input, not sorted\n labels : ndarray[int64]\n The labels from values to uniques\n ' ... def get_item(self) -> typing.Any: ... def get_labels(self) -> typing.Any: ... def get_labels_groupby(self) -> typing.Any: ... def lookup(self) -> typing.Any: ... def map(self) -> typing.Any: ... def map_locations(self) -> typing.Any: ... def set_item(self) -> typing.Any: ... def sizeof(self) -> typing.Any: ' return the size of my table in bytes ' ... def unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[uint32]\n Array of values of which unique will be calculated\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[uint32]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse)\n The labels from values to uniques\n ' ... def __getattr__(self, name) -> typing.Any: ... class UInt32Vector(_mod_builtins.object): def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def to_array(self) -> typing.Any: ... def __getattr__(self, name) -> typing.Any: ... class UInt64HashTable(HashTable): def __contains__(self, key) -> bool: 'Return key in self.' ... def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def _unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[uint64]\n Array of values of which unique will be calculated\n uniques : UInt64Vector\n Vector into which uniques will be written\n count_prior : Py_ssize_t, default 0\n Number of existing entries in uniques\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n ignore_na : boolean, default False\n Whether NA-values should be ignored for calculating the uniques. If\n True, the labels corresponding to missing values will be set to\n na_sentinel.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[uint64]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse=True)\n The labels from values to uniques\n ' ... def factorize(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Missing values are not included in the "uniques" for this method.\n The labels for any missing values will be set to "na_sentinel"\n\n Parameters\n ----------\n values : ndarray[uint64]\n Array of values of which unique will be calculated\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n\n Returns\n -------\n uniques : ndarray[uint64]\n Unique values of input, not sorted\n labels : ndarray[int64]\n The labels from values to uniques\n ' ... def get_item(self) -> typing.Any: ... def get_labels(self) -> typing.Any: ... def get_labels_groupby(self) -> typing.Any: ... def lookup(self) -> typing.Any: ... def map(self) -> typing.Any: ... def map_locations(self) -> typing.Any: ... def set_item(self) -> typing.Any: ... def sizeof(self) -> typing.Any: ' return the size of my table in bytes ' ... def unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[uint64]\n Array of values of which unique will be calculated\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[uint64]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse)\n The labels from values to uniques\n ' ... def __getattr__(self, name) -> typing.Any: ... class UInt64Vector(_mod_builtins.object): def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def to_array(self) -> typing.Any: ... def __getattr__(self, name) -> typing.Any: ... class UInt8HashTable(HashTable): def __contains__(self, key) -> bool: 'Return key in self.' ... def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def _unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[uint8]\n Array of values of which unique will be calculated\n uniques : UInt8Vector\n Vector into which uniques will be written\n count_prior : Py_ssize_t, default 0\n Number of existing entries in uniques\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n ignore_na : boolean, default False\n Whether NA-values should be ignored for calculating the uniques. If\n True, the labels corresponding to missing values will be set to\n na_sentinel.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[uint8]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse=True)\n The labels from values to uniques\n ' ... def factorize(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Missing values are not included in the "uniques" for this method.\n The labels for any missing values will be set to "na_sentinel"\n\n Parameters\n ----------\n values : ndarray[uint8]\n Array of values of which unique will be calculated\n na_sentinel : Py_ssize_t, default -1\n Sentinel value used for all NA-values in inverse\n na_value : object, default None\n Value to identify as missing. If na_value is None, then\n any value "val" satisfying val != val is considered missing.\n If na_value is not None, then _additionally_, any value "val"\n satisfying val == na_value is considered missing.\n mask : ndarray[bool], optional\n If not None, the mask is used as indicator for missing values\n (True = missing, False = valid) instead of `na_value` or\n condition "val != val".\n\n Returns\n -------\n uniques : ndarray[uint8]\n Unique values of input, not sorted\n labels : ndarray[int64]\n The labels from values to uniques\n ' ... def get_item(self) -> typing.Any: ... def get_labels(self) -> typing.Any: ... def get_labels_groupby(self) -> typing.Any: ... def lookup(self) -> typing.Any: ... def map(self) -> typing.Any: ... def map_locations(self) -> typing.Any: ... def set_item(self) -> typing.Any: ... def sizeof(self) -> typing.Any: ' return the size of my table in bytes ' ... def unique(self) -> typing.Any: '\n Calculate unique values and labels (no sorting!)\n\n Parameters\n ----------\n values : ndarray[uint8]\n Array of values of which unique will be calculated\n return_inverse : boolean, default False\n Whether the mapping of the original array values to their location\n in the vector of uniques should be returned.\n\n Returns\n -------\n uniques : ndarray[uint8]\n Unique values of input, not sorted\n labels : ndarray[int64] (if return_inverse)\n The labels from values to uniques\n ' ... def __getattr__(self, name) -> typing.Any: ... class UInt8Vector(_mod_builtins.object): def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... def __len__(self) -> int: 'Return len(self).' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def to_array(self) -> typing.Any: ... def __getattr__(self, name) -> typing.Any: ... __doc__: typing.Any __file__: str __name__: str __package__: str def __pyx_unpickle_Enum() -> typing.Any: ... def __pyx_unpickle_Factorizer() -> typing.Any: ... def __pyx_unpickle_HashTable() -> typing.Any: ... def __pyx_unpickle_Int64Factorizer() -> typing.Any: ... __test__: dict def duplicated_float32() -> typing.Any: ... def duplicated_float64() -> typing.Any: ... def duplicated_int16() -> typing.Any: ... def duplicated_int32() -> typing.Any: ... def duplicated_int64() -> typing.Any: ... def duplicated_int8() -> typing.Any: ... def duplicated_object() -> typing.Any: ... def duplicated_uint16() -> typing.Any: ... def duplicated_uint32() -> typing.Any: ... def duplicated_uint64() -> typing.Any: ... def duplicated_uint8() -> typing.Any: ... def get_hashtable_trace_domain() -> typing.Any: ... def ismember_float32() -> typing.Any: '\n Return boolean of values in arr on an\n element by-element basis\n\n Parameters\n ----------\n arr : float32 ndarray\n values : float32 ndarray\n\n Returns\n -------\n boolean ndarry len of (arr)\n ' ... def ismember_float64() -> typing.Any: '\n Return boolean of values in arr on an\n element by-element basis\n\n Parameters\n ----------\n arr : float64 ndarray\n values : float64 ndarray\n\n Returns\n -------\n boolean ndarry len of (arr)\n ' ... def ismember_int16() -> typing.Any: '\n Return boolean of values in arr on an\n element by-element basis\n\n Parameters\n ----------\n arr : int16 ndarray\n values : int16 ndarray\n\n Returns\n -------\n boolean ndarry len of (arr)\n ' ... def ismember_int32() -> typing.Any: '\n Return boolean of values in arr on an\n element by-element basis\n\n Parameters\n ----------\n arr : int32 ndarray\n values : int32 ndarray\n\n Returns\n -------\n boolean ndarry len of (arr)\n ' ... def ismember_int64() -> typing.Any: '\n Return boolean of values in arr on an\n element by-element basis\n\n Parameters\n ----------\n arr : int64 ndarray\n values : int64 ndarray\n\n Returns\n -------\n boolean ndarry len of (arr)\n ' ... def ismember_int8() -> typing.Any: '\n Return boolean of values in arr on an\n element by-element basis\n\n Parameters\n ----------\n arr : int8 ndarray\n values : int8 ndarray\n\n Returns\n -------\n boolean ndarry len of (arr)\n ' ... def ismember_object() -> typing.Any: '\n Return boolean of values in arr on an\n element by-element basis\n\n Parameters\n ----------\n arr : object ndarray\n values : object ndarray\n\n Returns\n -------\n boolean ndarry len of (arr)\n ' ... def ismember_uint16() -> typing.Any: '\n Return boolean of values in arr on an\n element by-element basis\n\n Parameters\n ----------\n arr : uint16 ndarray\n values : uint16 ndarray\n\n Returns\n -------\n boolean ndarry len of (arr)\n ' ... def ismember_uint32() -> typing.Any: '\n Return boolean of values in arr on an\n element by-element basis\n\n Parameters\n ----------\n arr : uint32 ndarray\n values : uint32 ndarray\n\n Returns\n -------\n boolean ndarry len of (arr)\n ' ... def ismember_uint64() -> typing.Any: '\n Return boolean of values in arr on an\n element by-element basis\n\n Parameters\n ----------\n arr : uint64 ndarray\n values : uint64 ndarray\n\n Returns\n -------\n boolean ndarry len of (arr)\n ' ... def ismember_uint8() -> typing.Any: '\n Return boolean of values in arr on an\n element by-element basis\n\n Parameters\n ----------\n arr : uint8 ndarray\n values : uint8 ndarray\n\n Returns\n -------\n boolean ndarry len of (arr)\n ' ... def mode_float32() -> typing.Any: ... def mode_float64() -> typing.Any: ... def mode_int16() -> typing.Any: ... def mode_int32() -> typing.Any: ... def mode_int64() -> typing.Any: ... def mode_int8() -> typing.Any: ... def mode_object() -> typing.Any: ... def mode_uint16() -> typing.Any: ... def mode_uint32() -> typing.Any: ... def mode_uint64() -> typing.Any: ... def mode_uint8() -> typing.Any: ... def unique_label_indices() -> typing.Any: '\n Indices of the first occurrences of the unique labels\n *excluding* -1. equivalent to:\n np.unique(labels, return_index=True)[1]\n ' ... def value_count_float32() -> typing.Any: ... def value_count_float64() -> typing.Any: ... def value_count_int16() -> typing.Any: ... def value_count_int32() -> typing.Any: ... def value_count_int64() -> typing.Any: ... def value_count_int8() -> typing.Any: ... def value_count_object() -> typing.Any: ... def value_count_uint16() -> typing.Any: ... def value_count_uint32() -> typing.Any: ... def value_count_uint64() -> typing.Any: ... def value_count_uint8() -> typing.Any: ... def __getattr__(name) -> typing.Any: ...