def putmask_smart(values: np.ndarray, mask: np.ndarray, new) -> np.ndarray:
"""
Return a new ndarray, try to preserve dtype if possible.
Parameters
----------
values : np.ndarray
`values`, updated in-place.
mask : np.ndarray[bool]
Applies to both sides (array like).
new : `new values` either scalar or an array like aligned with `values`
Returns
-------
values : ndarray with updated values
this *may* be a copy of the original
See Also
--------
ndarray.putmask
"""
# we cannot use np.asarray() here as we cannot have conversions
# that numpy does when numeric are mixed with strings
# n should be the length of the mask or a scalar here
if not is_list_like(new):
new = np.repeat(new, len(mask))
# see if we are only masking values that if putted
# will work in the current dtype
try:
nn = new[mask]
except TypeError:
# TypeError: only integer scalar arrays can be converted to a scalar index
pass
else:
# make sure that we have a nullable type if we have nulls
if not isna_compat(values, nn[0]):
pass
elif not (is_float_dtype(nn.dtype) or is_integer_dtype(nn.dtype)):
# only compare integers/floats
pass
elif not (is_float_dtype(values.dtype)
or is_integer_dtype(values.dtype)):
# only compare integers/floats
pass
else:
# we ignore ComplexWarning here
with warnings.catch_warnings(record=True):
warnings.simplefilter("ignore", np.ComplexWarning)
nn_at = nn.astype(values.dtype)
comp = nn == nn_at
if is_list_like(comp) and comp.all():
nv = values.copy()
nv[mask] = nn_at
return nv
new = np.asarray(new)
if values.dtype.kind == new.dtype.kind:
# preserves dtype if possible
return _putmask_preserve(values, new, mask)
# change the dtype if needed
dtype, _ = maybe_promote(new.dtype)
values = values.astype(dtype)
return _putmask_preserve(values, new, mask)
def putmask_smart(values: np.ndarray, mask: np.ndarray, new) -> np.ndarray:
"""
Return a new ndarray, try to preserve dtype if possible.
Parameters
----------
values : np.ndarray
`values`, updated in-place.
mask : np.ndarray[bool]
Applies to both sides (array like).
new : `new values` either scalar or an array like aligned with `values`
Returns
-------
values : ndarray with updated values
this *may* be a copy of the original
See Also
--------
ndarray.putmask
"""
# we cannot use np.asarray() here as we cannot have conversions
# that numpy does when numeric are mixed with strings
# n should be the length of the mask or a scalar here
if not is_list_like(new):
new = np.broadcast_to(new, mask.shape)
# see if we are only masking values that if putted
# will work in the current dtype
try:
nn = new[mask]
except TypeError:
# TypeError: only integer scalar arrays can be converted to a scalar index
pass
else:
# make sure that we have a nullable type if we have nulls
if not isna_compat(values, nn[0]):
pass
elif not (is_float_dtype(nn.dtype) or is_integer_dtype(nn.dtype)):
# only compare integers/floats
pass
elif not (is_float_dtype(values.dtype) or is_integer_dtype(values.dtype)):
# only compare integers/floats
pass
else:
# we ignore ComplexWarning here
with warnings.catch_warnings(record=True):
warnings.simplefilter("ignore", np.ComplexWarning)
nn_at = nn.astype(values.dtype)
comp = nn == nn_at
if is_list_like(comp) and comp.all():
nv = values.copy()
nv[mask] = nn_at
return nv
new = np.asarray(new)
if values.dtype.kind == new.dtype.kind:
# preserves dtype if possible
return _putmask_preserve(values, new, mask)
dtype = find_common_type([values.dtype, new.dtype])
# error: Argument 1 to "astype" of "_ArrayOrScalarCommon" has incompatible type
# "Union[dtype[Any], ExtensionDtype]"; expected "Union[dtype[Any], None, type,
# _SupportsDType, str, Union[Tuple[Any, int], Tuple[Any, Union[int, Sequence[int]]],
# List[Any], _DTypeDict, Tuple[Any, Any]]]"
values = values.astype(dtype) # type: ignore[arg-type]
return _putmask_preserve(values, new, mask)
