def _isna_ndarraylike_old(obj):
values = getattr(obj, "values", obj)
dtype = values.dtype
if is_string_dtype(dtype):
# Working around NumPy ticket 1542
shape = values.shape
if is_string_like_dtype(dtype):
result = np.zeros(values.shape, dtype=bool)
else:
result = np.empty(shape, dtype=bool)
vec = libmissing.isnaobj_old(values.ravel())
result[:] = vec.reshape(shape)
elif is_datetime64_dtype(dtype):
# this is the NaT pattern
result = values.view("i8") == iNaT
else:
result = ~np.isfinite(values)
# box
if isinstance(obj, ABCSeries):
result = obj._constructor(result, index=obj.index, name=obj.name, copy=False)
return result
def _isna_ndarraylike_old(obj):
values = getattr(obj, 'values', obj)
dtype = values.dtype
if is_string_dtype(dtype):
# Working around NumPy ticket 1542
shape = values.shape
if is_string_like_dtype(dtype):
result = np.zeros(values.shape, dtype=bool)
else:
result = np.empty(shape, dtype=bool)
vec = libmissing.isnaobj_old(values.ravel())
result[:] = vec.reshape(shape)
elif is_datetime64_dtype(dtype):
# this is the NaT pattern
result = values.view('i8') == iNaT
else:
result = ~np.isfinite(values)
# box
if isinstance(obj, ABCSeries):
result = obj._constructor(
result, index=obj.index, name=obj.name, copy=False)
return result
def _isna_array(values: ArrayLike, inf_as_na: bool = False):
"""
Return an array indicating which values of the input array are NaN / NA.
Parameters
----------
obj: ndarray or ExtensionArray
The input array whose elements are to be checked.
inf_as_na: bool
Whether or not to treat infinite values as NA.
Returns
-------
array-like
Array of boolean values denoting the NA status of each element.
"""
dtype = values.dtype
if is_extension_array_dtype(dtype):
if inf_as_na and is_categorical_dtype(dtype):
result = libmissing.isnaobj_old(values.to_numpy())
else:
result = values.isna()
elif is_string_dtype(dtype):
result = _isna_string_dtype(values, dtype, inf_as_na=inf_as_na)
elif needs_i8_conversion(dtype):
# this is the NaT pattern
result = values.view("i8") == iNaT
else:
if inf_as_na:
result = ~np.isfinite(values)
else:
result = np.isnan(values)
return result
def _isna_array(values: ArrayLike, inf_as_na: bool = False):
"""
Return an array indicating which values of the input array are NaN / NA.
Parameters
----------
obj: ndarray or ExtensionArray
The input array whose elements are to be checked.
inf_as_na: bool
Whether or not to treat infinite values as NA.
Returns
-------
array-like
Array of boolean values denoting the NA status of each element.
"""
dtype = values.dtype
if is_extension_array_dtype(dtype):
if inf_as_na and is_categorical_dtype(dtype):
# error: Item "ndarray" of "Union[ExtensionArray, ndarray]" has no attribute
# "to_numpy"
result = libmissing.isnaobj_old(
values.to_numpy() # type: ignore[union-attr]
)
else:
# error: Item "ndarray" of "Union[ExtensionArray, ndarray]" has no attribute
# "isna"
result = values.isna() # type: ignore[union-attr]
elif is_string_dtype(dtype):
# error: Argument 1 to "_isna_string_dtype" has incompatible type
# "ExtensionArray"; expected "ndarray"
# error: Argument 2 to "_isna_string_dtype" has incompatible type
# "ExtensionDtype"; expected "dtype[Any]"
result = _isna_string_dtype(
values,
dtype,
inf_as_na=inf_as_na # type: ignore[arg-type]
)
elif needs_i8_conversion(dtype):
# this is the NaT pattern
result = values.view("i8") == iNaT
else:
if inf_as_na:
# error: Argument 1 to "__call__" of "ufunc" has incompatible type
# "ExtensionArray"; expected "Union[Union[int, float, complex, str, bytes,
# generic], Sequence[Union[int, float, complex, str, bytes, generic]],
# Sequence[Sequence[Any]], _SupportsArray]"
result = ~np.isfinite(values) # type: ignore[arg-type]
else:
# error: Argument 1 to "__call__" of "ufunc" has incompatible type
# "ExtensionArray"; expected "Union[Union[int, float, complex, str, bytes,
# generic], Sequence[Union[int, float, complex, str, bytes, generic]],
# Sequence[Sequence[Any]], _SupportsArray]"
result = np.isnan(values) # type: ignore[arg-type]
return result
def _isna_string_dtype(values: np.ndarray, dtype: np.dtype, old: bool) -> np.ndarray:
# Working around NumPy ticket 1542
shape = values.shape
if is_string_like_dtype(dtype):
result = np.zeros(values.shape, dtype=bool)
else:
result = np.empty(shape, dtype=bool)
if old:
vec = libmissing.isnaobj_old(values.ravel())
else:
vec = libmissing.isnaobj(values.ravel())
result[...] = vec.reshape(shape)
return result
def _isna_string_dtype(values: np.ndarray, inf_as_na: bool) -> np.ndarray:
# Working around NumPy ticket 1542
dtype = values.dtype
shape = values.shape
if dtype.kind in ("S", "U"):
result = np.zeros(values.shape, dtype=bool)
else:
result = np.empty(shape, dtype=bool)
if inf_as_na:
vec = libmissing.isnaobj_old(values.ravel())
else:
vec = libmissing.isnaobj(values.ravel())
result[...] = vec.reshape(shape)
return result
def _isna_ndarraylike(obj, inf_as_na: bool = False):
"""
Return an array indicating which values of the input array are NaN / NA.
Parameters
----------
obj: array-like
The input array whose elements are to be checked.
inf_as_na: bool
Whether or not to treat infinite values as NA.
Returns
-------
array-like
Array of boolean values denoting the NA status of each element.
"""
values = getattr(obj, "_values", obj)
dtype = values.dtype
if is_extension_array_dtype(dtype):
if inf_as_na and is_categorical_dtype(dtype):
result = libmissing.isnaobj_old(values.to_numpy())
else:
result = values.isna()
elif is_string_dtype(dtype):
result = _isna_string_dtype(values, dtype, inf_as_na=inf_as_na)
elif needs_i8_conversion(dtype):
# this is the NaT pattern
result = values.view("i8") == iNaT
else:
if inf_as_na:
result = ~np.isfinite(values)
else:
result = np.isnan(values)
# box
if isinstance(obj, ABCSeries):
result = obj._constructor(result,
index=obj.index,
name=obj.name,
copy=False)
return result