def _try_cast(arr, dtype: Optional[DtypeObj], copy: bool,
raise_cast_failure: bool):
"""
Convert input to numpy ndarray and optionally cast to a given dtype.
Parameters
----------
arr : ndarray, list, tuple, iterator (catchall)
Excludes: ExtensionArray, Series, Index.
dtype : np.dtype, ExtensionDtype or None
copy : bool
If False, don't copy the data if not needed.
raise_cast_failure : bool
If True, and if a dtype is specified, raise errors during casting.
Otherwise an object array is returned.
"""
# perf shortcut as this is the most common case
if isinstance(arr, np.ndarray):
if maybe_castable(arr) and not copy and dtype is None:
return arr
if isinstance(dtype, ExtensionDtype) and (dtype.kind != "M"
or is_sparse(dtype)):
# create an extension array from its dtype
# DatetimeTZ case needs to go through maybe_cast_to_datetime but
# SparseDtype does not
array_type = dtype.construct_array_type()._from_sequence
subarr = array_type(arr, dtype=dtype, copy=copy)
return subarr
if is_object_dtype(dtype) and not isinstance(arr, np.ndarray):
subarr = construct_1d_object_array_from_listlike(arr)
return subarr
try:
# GH#15832: Check if we are requesting a numeric dtype and
# that we can convert the data to the requested dtype.
if is_integer_dtype(dtype):
# this will raise if we have e.g. floats
maybe_cast_to_integer_array(arr, dtype)
subarr = arr
else:
subarr = maybe_cast_to_datetime(arr, dtype)
if not isinstance(subarr, (ABCExtensionArray, ABCIndex)):
subarr = construct_1d_ndarray_preserving_na(subarr,
dtype,
copy=copy)
except OutOfBoundsDatetime:
# in case of out of bound datetime64 -> always raise
raise
except (ValueError, TypeError) as err:
if dtype is not None and raise_cast_failure:
raise
elif "Cannot cast" in str(err):
# via _disallow_mismatched_datetimelike
raise
else:
subarr = np.array(arr, dtype=object, copy=copy)
return subarr
def _try_cast(
arr, dtype: Optional[DtypeObj], copy: bool, raise_cast_failure: bool,
):
"""
Convert input to numpy ndarray and optionally cast to a given dtype.
Parameters
----------
arr : ndarray, list, tuple, iterator (catchall)
Excludes: ExtensionArray, Series, Index.
dtype : np.dtype, ExtensionDtype or None
copy : bool
If False, don't copy the data if not needed.
raise_cast_failure : bool
If True, and if a dtype is specified, raise errors during casting.
Otherwise an object array is returned.
"""
# perf shortcut as this is the most common case
if isinstance(arr, np.ndarray):
if maybe_castable(arr) and not copy and dtype is None:
return arr
try:
# GH#15832: Check if we are requesting a numeric dype and
# that we can convert the data to the requested dtype.
if is_integer_dtype(dtype):
subarr = maybe_cast_to_integer_array(arr, dtype)
subarr = maybe_cast_to_datetime(arr, dtype)
# Take care in creating object arrays (but iterators are not
# supported):
if is_object_dtype(dtype) and (
is_list_like(subarr)
and not (is_iterator(subarr) or isinstance(subarr, np.ndarray))
):
subarr = construct_1d_object_array_from_listlike(subarr)
elif not is_extension_array_dtype(subarr):
subarr = construct_1d_ndarray_preserving_na(subarr, dtype, copy=copy)
except OutOfBoundsDatetime:
# in case of out of bound datetime64 -> always raise
raise
except (ValueError, TypeError):
if is_categorical_dtype(dtype):
# We *do* allow casting to categorical, since we know
# that Categorical is the only array type for 'category'.
dtype = cast(CategoricalDtype, dtype)
subarr = dtype.construct_array_type()(
arr, dtype.categories, ordered=dtype.ordered
)
elif is_extension_array_dtype(dtype):
# create an extension array from its dtype
dtype = cast(ExtensionDtype, dtype)
array_type = dtype.construct_array_type()._from_sequence
subarr = array_type(arr, dtype=dtype, copy=copy)
elif dtype is not None and raise_cast_failure:
raise
else:
subarr = np.array(arr, dtype=object, copy=copy)
return subarr
def _try_cast(arr, take_fast_path, dtype, copy, raise_cast_failure):
# perf shortcut as this is the most common case
if take_fast_path:
if maybe_castable(arr) and not copy and dtype is None:
return arr
try:
# GH#15832: Check if we are requesting a numeric dype and
# that we can convert the data to the requested dtype.
if is_integer_dtype(dtype):
subarr = maybe_cast_to_integer_array(arr, dtype)
subarr = maybe_cast_to_datetime(arr, dtype)
# Take care in creating object arrays (but iterators are not
# supported):
if is_object_dtype(dtype) and (
is_list_like(subarr) and
not (is_iterator(subarr) or isinstance(subarr, np.ndarray))):
subarr = construct_1d_object_array_from_listlike(subarr)
elif not is_extension_type(subarr):
subarr = construct_1d_ndarray_preserving_na(subarr,
dtype,
copy=copy)
except OutOfBoundsDatetime:
# in case of out of bound datetime64 -> always raise
raise
except (ValueError, TypeError):
if is_categorical_dtype(dtype):
# We *do* allow casting to categorical, since we know
# that Categorical is the only array type for 'category'.
subarr = Categorical(arr, dtype.categories, ordered=dtype.ordered)
elif is_extension_array_dtype(dtype):
# create an extension array from its dtype
array_type = dtype.construct_array_type()._from_sequence
subarr = array_type(arr, dtype=dtype, copy=copy)
elif dtype is not None and raise_cast_failure:
raise
else:
subarr = np.array(arr, dtype=object, copy=copy)
return subarr
def _try_cast(arr, take_fast_path, dtype, copy, raise_cast_failure):
# perf shortcut as this is the most common case
if take_fast_path:
if maybe_castable(arr) and not copy and dtype is None:
return arr
try:
# GH#15832: Check if we are requesting a numeric dype and
# that we can convert the data to the requested dtype.
if is_integer_dtype(dtype):
subarr = maybe_cast_to_integer_array(arr, dtype)
subarr = maybe_cast_to_datetime(arr, dtype)
# Take care in creating object arrays (but iterators are not
# supported):
if is_object_dtype(dtype) and (is_list_like(subarr) and
not (is_iterator(subarr) or
isinstance(subarr, np.ndarray))):
subarr = construct_1d_object_array_from_listlike(subarr)
elif not is_extension_type(subarr):
subarr = construct_1d_ndarray_preserving_na(subarr, dtype,
copy=copy)
except (ValueError, TypeError):
if is_categorical_dtype(dtype):
# We *do* allow casting to categorical, since we know
# that Categorical is the only array type for 'category'.
subarr = Categorical(arr, dtype.categories,
ordered=dtype.ordered)
elif is_extension_array_dtype(dtype):
# create an extension array from its dtype
array_type = dtype.construct_array_type()._from_sequence
subarr = array_type(arr, dtype=dtype, copy=copy)
elif dtype is not None and raise_cast_failure:
raise
else:
subarr = np.array(arr, dtype=object, copy=copy)
return subarr
def _try_cast(
arr: list | np.ndarray,
dtype: DtypeObj | None,
copy: bool,
raise_cast_failure: bool,
) -> ArrayLike:
"""
Convert input to numpy ndarray and optionally cast to a given dtype.
Parameters
----------
arr : ndarray or list
Excludes: ExtensionArray, Series, Index.
dtype : np.dtype, ExtensionDtype or None
copy : bool
If False, don't copy the data if not needed.
raise_cast_failure : bool
If True, and if a dtype is specified, raise errors during casting.
Otherwise an object array is returned.
Returns
-------
np.ndarray or ExtensionArray
"""
# perf shortcut as this is the most common case
if (isinstance(arr, np.ndarray) and maybe_castable(arr.dtype) and not copy
and dtype is None):
return arr
if isinstance(dtype,
ExtensionDtype) and not isinstance(dtype, DatetimeTZDtype):
# create an extension array from its dtype
# DatetimeTZ case needs to go through maybe_cast_to_datetime but
# SparseDtype does not
array_type = dtype.construct_array_type()._from_sequence
subarr = array_type(arr, dtype=dtype, copy=copy)
return subarr
if is_object_dtype(dtype) and not isinstance(arr, np.ndarray):
subarr = construct_1d_object_array_from_listlike(arr)
return subarr
try:
# GH#15832: Check if we are requesting a numeric dtype and
# that we can convert the data to the requested dtype.
if is_integer_dtype(dtype):
# this will raise if we have e.g. floats
# error: Argument 2 to "maybe_cast_to_integer_array" has incompatible type
# "Union[dtype, ExtensionDtype, None]"; expected "Union[ExtensionDtype, str,
# dtype, Type[str], Type[float], Type[int], Type[complex], Type[bool],
# Type[object]]"
maybe_cast_to_integer_array(arr, dtype) # type: ignore[arg-type]
subarr = arr
else:
subarr = maybe_cast_to_datetime(arr, dtype)
if dtype is not None and dtype.kind == "M":
return subarr
if not isinstance(subarr, ABCExtensionArray):
subarr = construct_1d_ndarray_preserving_na(subarr,
dtype,
copy=copy)
except OutOfBoundsDatetime:
# in case of out of bound datetime64 -> always raise
raise
except (ValueError, TypeError) as err:
if dtype is not None and raise_cast_failure:
raise
elif "Cannot cast" in str(err):
# via _disallow_mismatched_datetimelike
raise
else:
subarr = np.array(arr, dtype=object, copy=copy)
return subarr
def _try_cast(
arr: list | np.ndarray,
dtype: DtypeObj | None,
copy: bool,
raise_cast_failure: bool,
) -> ArrayLike:
"""
Convert input to numpy ndarray and optionally cast to a given dtype.
Parameters
----------
arr : ndarray or list
Excludes: ExtensionArray, Series, Index.
dtype : np.dtype, ExtensionDtype or None
copy : bool
If False, don't copy the data if not needed.
raise_cast_failure : bool
If True, and if a dtype is specified, raise errors during casting.
Otherwise an object array is returned.
Returns
-------
np.ndarray or ExtensionArray
"""
is_ndarray = isinstance(arr, np.ndarray)
if dtype is None:
# perf shortcut as this is the most common case
if is_ndarray:
arr = cast(np.ndarray, arr)
if arr.dtype != object:
return sanitize_to_nanoseconds(arr, copy=copy)
out = maybe_infer_to_datetimelike(arr)
if out is arr and copy:
out = out.copy()
return out
else:
# i.e. list
varr = np.array(arr, copy=False)
# filter out cases that we _dont_ want to go through
# maybe_infer_to_datetimelike
if varr.dtype != object or varr.size == 0:
return varr
return maybe_infer_to_datetimelike(varr)
elif isinstance(dtype, ExtensionDtype):
# create an extension array from its dtype
if isinstance(dtype, DatetimeTZDtype):
# We can't go through _from_sequence because it handles dt64naive
# data differently; _from_sequence treats naive as wall times,
# while maybe_cast_to_datetime treats it as UTC
# see test_maybe_promote_any_numpy_dtype_with_datetimetz
# TODO(2.0): with deprecations enforced, should be able to remove
# special case.
return maybe_cast_to_datetime(arr, dtype)
# TODO: copy?
array_type = dtype.construct_array_type()._from_sequence
subarr = array_type(arr, dtype=dtype, copy=copy)
return subarr
elif is_object_dtype(dtype):
if not is_ndarray:
subarr = construct_1d_object_array_from_listlike(arr)
return subarr
return ensure_wrapped_if_datetimelike(arr).astype(dtype, copy=copy)
elif dtype.kind == "U":
# TODO: test cases with arr.dtype.kind in ["m", "M"]
return lib.ensure_string_array(arr, convert_na_value=False, copy=copy)
elif dtype.kind in ["m", "M"]:
return maybe_cast_to_datetime(arr, dtype)
try:
# GH#15832: Check if we are requesting a numeric dtype and
# that we can convert the data to the requested dtype.
if is_integer_dtype(dtype):
# this will raise if we have e.g. floats
subarr = maybe_cast_to_integer_array(arr, dtype)
else:
# 4 tests fail if we move this to a try/except/else; see
# test_constructor_compound_dtypes, test_constructor_cast_failure
# test_constructor_dict_cast2, test_loc_setitem_dtype
subarr = np.array(arr, dtype=dtype, copy=copy)
except (ValueError, TypeError):
if raise_cast_failure:
raise
else:
# we only get here with raise_cast_failure False, which means
# called via the DataFrame constructor
# GH#24435
warnings.warn(
f"Could not cast to {dtype}, falling back to object. This "
"behavior is deprecated. In a future version, when a dtype is "
"passed to 'DataFrame', either all columns will be cast to that "
"dtype, or a TypeError will be raised.",
FutureWarning,
stacklevel=find_stack_level(),
)
subarr = np.array(arr, dtype=object, copy=copy)
return subarr
def _try_cast(
arr: list | np.ndarray,
dtype: DtypeObj | None,
copy: bool,
raise_cast_failure: bool,
) -> ArrayLike:
"""
Convert input to numpy ndarray and optionally cast to a given dtype.
Parameters
----------
arr : ndarray or list
Excludes: ExtensionArray, Series, Index.
dtype : np.dtype, ExtensionDtype or None
copy : bool
If False, don't copy the data if not needed.
raise_cast_failure : bool
If True, and if a dtype is specified, raise errors during casting.
Otherwise an object array is returned.
Returns
-------
np.ndarray or ExtensionArray
"""
is_ndarray = isinstance(arr, np.ndarray)
# perf shortcut as this is the most common case
# Item "List[Any]" of "Union[List[Any], ndarray]" has no attribute "dtype"
if (is_ndarray and arr.dtype != object # type: ignore[union-attr]
and not copy and dtype is None):
# Argument 1 to "sanitize_to_nanoseconds" has incompatible type
# "Union[List[Any], ndarray]"; expected "ndarray"
return sanitize_to_nanoseconds(arr) # type: ignore[arg-type]
if isinstance(dtype, ExtensionDtype):
# create an extension array from its dtype
# DatetimeTZ case needs to go through maybe_cast_to_datetime but
# SparseDtype does not
if isinstance(dtype, DatetimeTZDtype):
# We can't go through _from_sequence because it handles dt64naive
# data differently; _from_sequence treats naive as wall times,
# while maybe_cast_to_datetime treats it as UTC
# see test_maybe_promote_any_numpy_dtype_with_datetimetz
# error: Incompatible return value type (got "Union[ExtensionArray,
# ndarray, List[Any]]", expected "Union[ExtensionArray, ndarray]")
return maybe_cast_to_datetime(arr,
dtype) # type: ignore[return-value]
# TODO: copy?
array_type = dtype.construct_array_type()._from_sequence
subarr = array_type(arr, dtype=dtype, copy=copy)
return subarr
elif is_object_dtype(dtype):
if not is_ndarray:
subarr = construct_1d_object_array_from_listlike(arr)
return subarr
return ensure_wrapped_if_datetimelike(arr).astype(dtype, copy=copy)
elif dtype is None and not is_ndarray:
# filter out cases that we _dont_ want to go through maybe_cast_to_datetime
varr = np.array(arr, copy=False)
if varr.dtype != object or varr.size == 0:
return varr
# error: Incompatible return value type (got "Union[ExtensionArray,
# ndarray, List[Any]]", expected "Union[ExtensionArray, ndarray]")
return maybe_cast_to_datetime(varr, None) # type: ignore[return-value]
try:
# GH#15832: Check if we are requesting a numeric dtype and
# that we can convert the data to the requested dtype.
if is_integer_dtype(dtype):
# this will raise if we have e.g. floats
dtype = cast(np.dtype, dtype)
maybe_cast_to_integer_array(arr, dtype)
subarr = arr
else:
subarr = maybe_cast_to_datetime(arr, dtype)
if dtype is not None and dtype.kind == "M":
return subarr
if not isinstance(subarr, ABCExtensionArray):
subarr = construct_1d_ndarray_preserving_na(subarr,
dtype,
copy=copy)
except OutOfBoundsDatetime:
# in case of out of bound datetime64 -> always raise
raise
except (ValueError, TypeError) as err:
if dtype is not None and raise_cast_failure:
raise
elif "Cannot cast" in str(err):
# via _disallow_mismatched_datetimelike
raise
else:
subarr = np.array(arr, dtype=object, copy=copy)
return subarr
