def reorder_arrays(arrays: list[ArrayLike], arr_columns: Index,
columns: Index | None,
length: int) -> tuple[list[ArrayLike], Index]:
"""
Pre-emptively (cheaply) reindex arrays with new columns.
"""
# reorder according to the columns
if columns is not None:
if not columns.equals(arr_columns):
# if they are equal, there is nothing to do
new_arrays: list[ArrayLike | None]
new_arrays = [None] * len(columns)
indexer = arr_columns.get_indexer(columns)
for i, k in enumerate(indexer):
if k == -1:
# by convention default is all-NaN object dtype
arr = np.empty(length, dtype=object)
arr.fill(np.nan)
else:
arr = arrays[k]
new_arrays[i] = arr
# Incompatible types in assignment (expression has type
# "List[Union[ExtensionArray, ndarray[Any, Any], None]]", variable
# has type "List[Union[ExtensionArray, ndarray[Any, Any]]]")
arrays = new_arrays # type: ignore[assignment]
arr_columns = columns
return arrays, arr_columns
def _cast_types(self, values, cast_type, column):
"""
Cast values to specified type
Parameters
----------
values : ndarray
cast_type : string or np.dtype
dtype to cast values to
column : string
column name - used only for error reporting
Returns
-------
converted : ndarray
"""
if is_categorical_dtype(cast_type):
known_cats = (isinstance(cast_type, CategoricalDtype)
and cast_type.categories is not None)
if not is_object_dtype(values) and not known_cats:
# TODO: this is for consistency with
# c-parser which parses all categories
# as strings
values = astype_nansafe(values, np.dtype(str))
cats = Index(values).unique().dropna()
values = Categorical._from_inferred_categories(
cats,
cats.get_indexer(values),
cast_type,
true_values=self.true_values)
# use the EA's implementation of casting
elif is_extension_array_dtype(cast_type):
# ensure cast_type is an actual dtype and not a string
cast_type = pandas_dtype(cast_type)
array_type = cast_type.construct_array_type()
try:
if is_bool_dtype(cast_type):
return array_type._from_sequence_of_strings(
values,
dtype=cast_type,
true_values=self.true_values,
false_values=self.false_values,
)
else:
return array_type._from_sequence_of_strings(
values, dtype=cast_type)
except NotImplementedError as err:
raise NotImplementedError(
f"Extension Array: {array_type} must implement "
"_from_sequence_of_strings in order to be used in parser methods"
) from err
else:
try:
values = astype_nansafe(values,
cast_type,
copy=True,
skipna=True)
except ValueError as err:
raise ValueError(
f"Unable to convert column {column} to type {cast_type}"
) from err
return values