def _cast_to_common_type(arr: ArrayLike, dtype: DtypeObj) -> ArrayLike:
"""
Helper function for `arr.astype(common_dtype)` but handling all special
cases.
"""
if (is_categorical_dtype(arr.dtype) and isinstance(dtype, np.dtype)
and np.issubdtype(dtype, np.integer)):
# problem case: categorical of int -> gives int as result dtype,
# but categorical can contain NAs -> fall back to object dtype
try:
return arr.astype(dtype, copy=False)
except ValueError:
return arr.astype(object, copy=False)
if is_sparse(arr) and not is_sparse(dtype):
# problem case: SparseArray.astype(dtype) doesn't follow the specified
# dtype exactly, but converts this to Sparse[dtype] -> first manually
# convert to dense array
arr = cast(SparseArray, arr)
return arr.to_dense().astype(dtype, copy=False)
if (isinstance(arr, np.ndarray) and arr.dtype.kind in ["m", "M"]
and dtype is np.dtype("object")):
# wrap datetime-likes in EA to ensure astype(object) gives Timestamp/Timedelta
# this can happen when concat_compat is called directly on arrays (when arrays
# are not coming from Index/Series._values), eg in BlockManager.quantile
arr = array(arr)
if is_extension_array_dtype(dtype):
if isinstance(arr, np.ndarray):
# numpy's astype cannot handle ExtensionDtypes
return array(arr, dtype=dtype, copy=False)
return arr.astype(dtype, copy=False)
def cast_to_common_type(arr: ArrayLike, dtype: DtypeObj) -> ArrayLike:
"""
Helper function for `arr.astype(common_dtype)` but handling all special
cases.
"""
if is_dtype_equal(arr.dtype, dtype):
return arr
if isinstance(dtype, np.dtype) and dtype.kind in ["i", "u"]:
if is_categorical_dtype(arr.dtype) and cast("Categorical",
arr)._hasnans:
# problem case: categorical of int -> gives int as result dtype,
# but categorical can contain NAs -> float64 instead
# GH#45359
dtype = np.dtype(np.float64)
if is_sparse(arr) and not is_sparse(dtype):
# problem case: SparseArray.astype(dtype) doesn't follow the specified
# dtype exactly, but converts this to Sparse[dtype] -> first manually
# convert to dense array
# error: Argument 1 to "astype" of "_ArrayOrScalarCommon" has incompatible type
# "Union[dtype[Any], ExtensionDtype]"; expected "Union[dtype[Any], None, type, _
# SupportsDType[dtype[Any]], str, Union[Tuple[Any, int], Tuple[Any,
# Union[SupportsIndex, Sequence[SupportsIndex]]], List[Any], _DTypeDict,
# Tuple[Any, Any]]]" [arg-type]
arr = cast("SparseArray", arr)
return arr.to_dense().astype(dtype,
copy=False) # type: ignore[arg-type]
# astype_array includes ensure_wrapped_if_datetimelike
return astype_array(arr, dtype=dtype, copy=False)
def cast_to_common_type(arr: ArrayLike, dtype: DtypeObj) -> ArrayLike:
"""
Helper function for `arr.astype(common_dtype)` but handling all special
cases.
"""
if is_dtype_equal(arr.dtype, dtype):
return arr
if is_sparse(arr) and not is_sparse(dtype):
# TODO(2.0): remove special case once SparseArray.astype deprecation
# is enforced.
# problem case: SparseArray.astype(dtype) doesn't follow the specified
# dtype exactly, but converts this to Sparse[dtype] -> first manually
# convert to dense array
# error: Argument 1 to "astype" of "_ArrayOrScalarCommon" has incompatible type
# "Union[dtype[Any], ExtensionDtype]"; expected "Union[dtype[Any], None, type, _
# SupportsDType[dtype[Any]], str, Union[Tuple[Any, int], Tuple[Any,
# Union[SupportsIndex, Sequence[SupportsIndex]]], List[Any], _DTypeDict,
# Tuple[Any, Any]]]" [arg-type]
arr = cast("SparseArray", arr)
return arr.to_dense().astype(dtype,
copy=False) # type: ignore[arg-type]
# astype_array includes ensure_wrapped_if_datetimelike
return astype_array(arr, dtype=dtype, copy=False)
def cast_to_common_type(arr: ArrayLike, dtype: DtypeObj) -> ArrayLike:
"""
Helper function for `arr.astype(common_dtype)` but handling all special
cases.
"""
if is_dtype_equal(arr.dtype, dtype):
return arr
if (
is_categorical_dtype(arr.dtype)
and isinstance(dtype, np.dtype)
and np.issubdtype(dtype, np.integer)
):
# problem case: categorical of int -> gives int as result dtype,
# but categorical can contain NAs -> fall back to object dtype
try:
return arr.astype(dtype, copy=False)
except ValueError:
return arr.astype(object, copy=False)
if is_sparse(arr) and not is_sparse(dtype):
# problem case: SparseArray.astype(dtype) doesn't follow the specified
# dtype exactly, but converts this to Sparse[dtype] -> first manually
# convert to dense array
# error: Argument 1 to "astype" of "_ArrayOrScalarCommon" has incompatible type
# "Union[dtype[Any], ExtensionDtype]"; expected "Union[dtype[Any], None, type, _
# SupportsDType[dtype[Any]], str, Union[Tuple[Any, int], Tuple[Any,
# Union[SupportsIndex, Sequence[SupportsIndex]]], List[Any], _DTypeDict,
# Tuple[Any, Any]]]" [arg-type]
arr = cast(SparseArray, arr)
return arr.to_dense().astype(dtype, copy=False) # type: ignore[arg-type]
if (
isinstance(arr, np.ndarray)
and arr.dtype.kind in ["m", "M"]
and dtype is np.dtype("object")
):
# wrap datetime-likes in EA to ensure astype(object) gives Timestamp/Timedelta
# this can happen when concat_compat is called directly on arrays (when arrays
# are not coming from Index/Series._values), eg in BlockManager.quantile
arr = ensure_wrapped_if_datetimelike(arr)
if isinstance(dtype, ExtensionDtype):
if isinstance(arr, np.ndarray):
# numpy's astype cannot handle ExtensionDtypes
return pd_array(arr, dtype=dtype, copy=False)
return arr.astype(dtype, copy=False)
return arr.astype(dtype, copy=False)
