def test_can_hold_element_int_values_float_ndarray():
arr = np.array([], dtype=np.int64)
element = np.array([1.0, 2.0])
assert can_hold_element(arr, element)
assert not can_hold_element(arr, element + 0.5)
# integer but not losslessly castable to int64
element = np.array([3, 2**65], dtype=np.float64)
assert not can_hold_element(arr, element)
def test_can_hold_element_int8_int():
arr = np.array([], dtype=np.int8)
element = 2
assert can_hold_element(arr, element)
assert can_hold_element(arr, np.int8(element))
assert can_hold_element(arr, np.uint8(element))
assert can_hold_element(arr, np.int16(element))
assert can_hold_element(arr, np.uint16(element))
assert can_hold_element(arr, np.int32(element))
assert can_hold_element(arr, np.uint32(element))
assert can_hold_element(arr, np.int64(element))
assert can_hold_element(arr, np.uint64(element))
def putmask_smart(values: np.ndarray, mask: npt.NDArray[np.bool_],
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 : listlike `new values` aligned with `values`
Returns
-------
values : ndarray with updated values
this *may* be a copy of the original
See Also
--------
np.putmask
"""
# we cannot use np.asarray() here as we cannot have conversions
# that numpy does when numeric are mixed with strings
# 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:
# We only get to putmask_smart when we cannot hold 'new' in values.
# The "smart" part of putmask_smart is checking if we can hold new[mask]
# in values, in which case we can still avoid the need to cast.
if can_hold_element(values, nn):
values[mask] = nn
return values
new = np.asarray(new)
if values.dtype.kind == new.dtype.kind:
# preserves dtype if possible
np.putmask(values, mask, new)
return values
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]
np.putmask(values, mask, new)
return values
def putmask_smart(values: np.ndarray, mask: npt.NDArray[np.bool_],
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 : listlike `new values` aligned with `values`
Returns
-------
values : ndarray with updated values
this *may* be a copy of the original
See Also
--------
np.putmask
"""
# we cannot use np.asarray() here as we cannot have conversions
# that numpy does when numeric are mixed with strings
# 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:
# We only get to putmask_smart when we cannot hold 'new' in values.
# The "smart" part of putmask_smart is checking if we can hold new[mask]
# in values, in which case we can still avoid the need to cast.
if can_hold_element(values, nn):
values[mask] = nn
return values
new = np.asarray(new)
if values.dtype.kind == new.dtype.kind:
# preserves dtype if possible
np.putmask(values, mask, new)
return values
dtype = find_common_type([values.dtype, new.dtype])
values = values.astype(dtype)
np.putmask(values, mask, new)
return values
def test_can_hold_element_range(any_int_numpy_dtype):
# GH#44261
dtype = np.dtype(any_int_numpy_dtype)
arr = np.array([], dtype=dtype)
rng = range(2, 127)
assert can_hold_element(arr, rng)
# negatives -> can't be held by uint dtypes
rng = range(-2, 127)
if dtype.kind == "i":
assert can_hold_element(arr, rng)
else:
assert not can_hold_element(arr, rng)
rng = range(2, 255)
if dtype == "int8":
assert not can_hold_element(arr, rng)
else:
assert can_hold_element(arr, rng)
rng = range(-255, 65537)
if dtype.kind == "u":
assert not can_hold_element(arr, rng)
elif dtype.itemsize < 4:
assert not can_hold_element(arr, rng)
else:
assert can_hold_element(arr, rng)
# empty
rng = range(-(10**10), -(10**10))
assert len(rng) == 0
# assert can_hold_element(arr, rng)
rng = range(10**10, 10**10)
assert len(rng) == 0
assert can_hold_element(arr, rng)
def _can_hold_element_patched(obj, element) -> bool:
if isinstance(element, PandasArray):
element = element.to_numpy()
return can_hold_element(obj, element)
