def factorize(
self,
na_sentinel: int | lib.NoDefault = lib.no_default,
use_na_sentinel: bool | lib.NoDefault = lib.no_default,
) -> tuple[np.ndarray, ExtensionArray]:
resolved_na_sentinel = algos.resolve_na_sentinel(
na_sentinel, use_na_sentinel)
if resolved_na_sentinel is None:
raise NotImplementedError(
"Encoding NaN values is not yet implemented")
else:
na_sentinel = resolved_na_sentinel
arr = self._data
mask = self._mask
codes, uniques = factorize_array(arr,
na_sentinel=na_sentinel,
mask=mask)
# check that factorize_array correctly preserves dtype.
assert uniques.dtype == self.dtype.numpy_dtype, (uniques.dtype,
self.dtype)
uniques_ea = type(self)(uniques, np.zeros(len(uniques), dtype=bool))
return codes, uniques_ea
def factorize(self, na_sentinel: int = -1) -> tuple[np.ndarray, ExtensionArray]:
arr = self._data
mask = self._mask
codes, uniques = factorize_array(arr, na_sentinel=na_sentinel, mask=mask)
# the hashtables don't handle all different types of bits
uniques = uniques.astype(self.dtype.numpy_dtype, copy=False)
uniques_ea = type(self)(uniques, np.zeros(len(uniques), dtype=bool))
return codes, uniques_ea
def factorize(self, na_sentinel: int = -1) -> tuple[np.ndarray, ExtensionArray]:
arr = self._data
mask = self._mask
codes, uniques = factorize_array(arr, na_sentinel=na_sentinel, mask=mask)
# check that factorize_array correctly preserves dtype.
assert uniques.dtype == self.dtype.numpy_dtype, (uniques.dtype, self.dtype)
uniques_ea = type(self)(uniques, np.zeros(len(uniques), dtype=bool))
return codes, uniques_ea
def factorize(self,
na_sentinel: int = -1) -> Tuple[np.ndarray, ExtensionArray]:
"""
Encode the extension array as an enumerated type.
Parameters
----------
na_sentinel : int, default -1
Value to use in the `codes` array to indicate missing values.
Returns
-------
codes : ndarray
An integer NumPy array that's an indexer into the original
ExtensionArray.
uniques : ExtensionArray
An ExtensionArray containing the unique values of `self`.
.. note::
uniques will *not* contain an entry for the NA value of
the ExtensionArray if there are any missing values present
in `self`.
See Also
--------
factorize : Top-level factorize method that dispatches here.
Notes
-----
:meth:`pandas.factorize` offers a `sort` keyword as well.
"""
# Implementer note: There are two ways to override the behavior of
# pandas.factorize
# 1. _values_for_factorize and _from_factorize.
# Specify the values passed to pandas' internal factorization
# routines, and how to convert from those values back to the
# original ExtensionArray.
# 2. ExtensionArray.factorize.
# Complete control over factorization.
arr, na_value = self._values_for_factorize()
codes, uniques = factorize_array(arr,
na_sentinel=na_sentinel,
na_value=na_value)
uniques = self._from_factorized(uniques, self)
# error: Incompatible return value type (got "Tuple[ndarray, ndarray]",
# expected "Tuple[ndarray, ExtensionArray]")
return codes, uniques # type: ignore[return-value]
def factorize(self, na_sentinel: int = -1) -> tuple[np.ndarray, ExtensionArray]:
arr = self._data
mask = self._mask
codes, uniques = factorize_array(arr, na_sentinel=na_sentinel, mask=mask)
# the hashtables don't handle all different types of bits
uniques = uniques.astype(self.dtype.numpy_dtype, copy=False)
# error: Incompatible types in assignment (expression has type
# "BaseMaskedArray", variable has type "ndarray")
uniques = type(self)( # type: ignore[assignment]
uniques, np.zeros(len(uniques), dtype=bool)
)
# error: Incompatible return value type (got "Tuple[ndarray, ndarray]",
# expected "Tuple[ndarray, ExtensionArray]")
return codes, uniques # type: ignore[return-value]
