def _quantile(
self: BaseMaskedArrayT, qs: npt.NDArray[np.float64], interpolation: str
) -> BaseMaskedArrayT:
"""
Dispatch to quantile_with_mask, needed because we do not have
_from_factorized.
Notes
-----
We assume that all impacted cases are 1D-only.
"""
mask = np.atleast_2d(np.asarray(self.isna()))
npvalues: np.ndarray = np.atleast_2d(np.asarray(self))
res = quantile_with_mask(
npvalues,
mask=mask,
fill_value=self.dtype.na_value,
qs=qs,
interpolation=interpolation,
)
assert res.ndim == 2
assert res.shape[0] == 1
res = res[0]
try:
out = type(self)._from_sequence(res, dtype=self.dtype)
except TypeError:
# GH#42626: not able to safely cast Int64
# for floating point output
out = np.asarray(res, dtype=np.float64)
return out
def _quantile(self, qs: npt.NDArray[np.float64],
interpolation: str) -> BaseMaskedArray:
"""
Dispatch to quantile_with_mask, needed because we do not have
_from_factorized.
Notes
-----
We assume that all impacted cases are 1D-only.
"""
res = quantile_with_mask(
self._data,
mask=self._mask,
# TODO(GH#40932): na_value_for_dtype(self.dtype.numpy_dtype)
# instead of np.nan
fill_value=np.nan,
qs=qs,
interpolation=interpolation,
)
if self._hasna:
# Our result mask is all-False unless we are all-NA, in which
# case it is all-True.
if self.ndim == 2:
# I think this should be out_mask=self.isna().all(axis=1)
# but am holding off until we have tests
raise NotImplementedError
elif self.isna().all():
out_mask = np.ones(res.shape, dtype=bool)
else:
out_mask = np.zeros(res.shape, dtype=bool)
else:
out_mask = np.zeros(res.shape, dtype=bool)
return self._maybe_mask_result(res, mask=out_mask)
def _quantile(
self: NDArrayBackedExtensionArrayT,
qs: npt.NDArray[np.float64],
interpolation: str,
) -> NDArrayBackedExtensionArrayT:
# TODO: disable for Categorical if not ordered?
mask = np.asarray(self.isna())
arr = self._ndarray
fill_value = self._internal_fill_value
res_values = quantile_with_mask(arr, mask, fill_value, qs, interpolation)
res_values = self._cast_quantile_result(res_values)
return self._from_backing_data(res_values)
def _quantile(
self: NDArrayBackedExtensionArrayT,
qs: npt.NDArray[np.float64],
interpolation: str,
) -> NDArrayBackedExtensionArrayT:
# TODO: disable for Categorical if not ordered?
# asarray needed for Sparse, see GH#24600
mask = np.asarray(self.isna())
mask = np.atleast_2d(mask)
arr = np.atleast_2d(self._ndarray)
# TODO: something NDArrayBacked-specific instead of _values_for_factorize[1]?
fill_value = self._values_for_factorize()[1]
res_values = quantile_with_mask(arr, mask, fill_value, qs, interpolation)
result = type(self)._from_factorized(res_values, self)
if self.ndim == 1:
assert result.shape == (1, len(qs)), result.shape
result = result[0]
return result
def _quantile(
self: NDArrayBackedExtensionArrayT,
qs: npt.NDArray[np.float64],
interpolation: str,
) -> NDArrayBackedExtensionArrayT:
# TODO: disable for Categorical if not ordered?
# asarray needed for Sparse, see GH#24600
mask = np.asarray(self.isna())
mask = np.atleast_2d(mask)
arr = np.atleast_2d(self._ndarray)
fill_value = self._internal_fill_value
res_values = quantile_with_mask(arr, mask, fill_value, qs,
interpolation)
res_values = self._cast_quantile_result(res_values)
result = self._from_backing_data(res_values)
if self.ndim == 1:
assert result.shape == (1, len(qs)), result.shape
result = result[0]
return result
