def _preprocess_host_value(self, value, dtype):
valid = not cudf._lib.scalar._is_null_host_scalar(value)
if isinstance(value, list):
if dtype is not None:
raise TypeError("Lists may not be cast to a different dtype")
else:
dtype = ListDtype.from_arrow(
pa.infer_type([value], from_pandas=True))
return value, dtype
elif isinstance(dtype, ListDtype):
if value not in {None, NA}:
raise ValueError(f"Can not coerce {value} to ListDtype")
else:
return NA, dtype
if isinstance(value, dict):
if dtype is None:
dtype = StructDtype.from_arrow(
pa.infer_type([value], from_pandas=True))
return value, dtype
elif isinstance(dtype, StructDtype):
if value not in {None, NA}:
raise ValueError(f"Can not coerce {value} to StructDType")
else:
return NA, dtype
if isinstance(dtype, Decimal64Dtype):
value = pa.scalar(value,
type=pa.decimal128(dtype.precision,
dtype.scale)).as_py()
if isinstance(value, decimal.Decimal) and dtype is None:
dtype = Decimal64Dtype._from_decimal(value)
value = to_cudf_compatible_scalar(value, dtype=dtype)
if dtype is None:
if not valid:
if isinstance(value, (np.datetime64, np.timedelta64)):
unit, _ = np.datetime_data(value)
if unit == "generic":
raise TypeError(
"Cant convert generic NaT to null scalar")
else:
dtype = value.dtype
else:
raise TypeError(
"dtype required when constructing a null scalar")
else:
dtype = value.dtype
if not isinstance(dtype, Decimal64Dtype):
dtype = cudf.dtype(dtype)
if not valid:
value = NA
return value, dtype
def test_list_nested_dtype():
dt = ListDtype(ListDtype("int32"))
expect = ListDtype("int32")
got = dt.element_type
assert expect == got
def test_list_dtype_eq():
lhs = ListDtype("int32")
rhs = ListDtype("int32")
assert lhs == rhs
rhs = ListDtype("int64")
assert lhs != rhs
def test_list_dtype_pyarrow_round_trip(value_type):
pa_type = pa.list_(cudf.utils.dtypes.np_to_pa_dtype(np.dtype(value_type)))
expect = pa_type
got = ListDtype.from_arrow(expect).to_arrow()
assert expect.equals(got)
