def _quantile_with_mask(
values: np.ndarray,
mask: np.ndarray,
fill_value,
qs: npt.NDArray[np.float64],
interpolation: str,
) -> np.ndarray:
"""
Compute the quantiles of the given values for each quantile in `qs`.
Parameters
----------
values : np.ndarray
For ExtensionArray, this is _values_for_factorize()[0]
mask : np.ndarray[bool]
mask = isna(values)
For ExtensionArray, this is computed before calling _value_for_factorize
fill_value : Scalar
The value to interpret fill NA entries with
For ExtensionArray, this is _values_for_factorize()[1]
qs : np.ndarray[float64]
interpolation : str
Type of interpolation
Returns
-------
np.ndarray
Notes
-----
Assumes values is already 2D. For ExtensionArray this means np.atleast_2d
has been called on _values_for_factorize()[0]
Quantile is computed along axis=1.
"""
assert values.ndim == 2
is_empty = values.shape[1] == 0
if is_empty:
# create the array of na_values
# 2d len(values) * len(qs)
flat = np.array([fill_value] * len(qs))
result = np.repeat(flat, len(values)).reshape(len(values), len(qs))
else:
# asarray needed for Sparse, see GH#24600
result = nanpercentile(
values,
np.array(qs) * 100,
na_value=fill_value,
mask=mask,
interpolation=interpolation,
)
result = np.array(result, copy=False)
result = result.T
return result
def quantile_with_mask(
values: np.ndarray,
mask: np.ndarray,
fill_value,
qs,
interpolation: str,
axis: int,
) -> np.ndarray:
"""
Compute the quantiles of the given values for each quantile in `qs`.
Parameters
----------
values : np.ndarray
For ExtensionArray, this is _values_for_factorize()[0]
mask : np.ndarray[bool]
mask = isna(values)
For ExtensionArray, this is computed before calling _value_for_factorize
fill_value : Scalar
The value to interpret fill NA entries with
For ExtensionArray, this is _values_for_factorize()[1]
qs : a scalar or list of the quantiles to be computed
interpolation : str
Type of interpolation
axis : int
Axis along which to compute quantiles.
Returns
-------
np.ndarray
Notes
-----
Assumes values is already 2D. For ExtensionArray this means np.atleast_2d
has been called on _values_for_factorize()[0]
"""
is_empty = values.shape[axis] == 0
orig_scalar = not is_list_like(qs)
if orig_scalar:
# make list-like, unpack later
qs = [qs]
if is_empty:
# create the array of na_values
# 2d len(values) * len(qs)
flat = np.array([fill_value] * len(qs))
result = np.repeat(flat, len(values)).reshape(len(values), len(qs))
else:
# asarray needed for Sparse, see GH#24600
result = nanpercentile(
values,
np.array(qs) * 100,
axis=axis,
na_value=fill_value,
mask=mask,
ndim=values.ndim,
interpolation=interpolation,
)
result = np.array(result, copy=False)
result = result.T
if orig_scalar:
assert result.shape[-1] == 1, result.shape
result = result[..., 0]
result = lib.item_from_zerodim(result)
return result