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Oct 5, 2019
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TST: port more maybe_promote tests, adds 260 xfails #28777

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157 changes: 154 additions & 3 deletions pandas/tests/dtypes/cast/test_promote.py
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Original file line number Diff line number Diff line change
Expand Up @@ -163,9 +163,160 @@ def _assert_match(result_fill_value, expected_fill_value):
assert match_value or match_missing


def test_maybe_promote_int_with_int():
# placeholder due to too many xfails; see GH 23982 / 25425
pass
@pytest.mark.parametrize(
"dtype, fill_value, expected_dtype",
[
# size 8
("int8", 1, "int8"),
("int8", np.iinfo("int8").max + 1, "int16"),
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I guess for comprehensiveness should we also add unsigned fill_value values for a signed dtype? Looks like we have the other way around

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Makes sense, but for the moment this is porting tests from one of @h-vetinari’s PRs with minimal changes.

("int8", np.iinfo("int16").max + 1, "int32"),
("int8", np.iinfo("int32").max + 1, "int64"),
("int8", np.iinfo("int64").max + 1, "object"),
("int8", -1, "int8"),
("int8", np.iinfo("int8").min - 1, "int16"),
("int8", np.iinfo("int16").min - 1, "int32"),
("int8", np.iinfo("int32").min - 1, "int64"),
("int8", np.iinfo("int64").min - 1, "object"),
# keep signed-ness as long as possible
("uint8", 1, "uint8"),
("uint8", np.iinfo("int8").max + 1, "uint8"),
("uint8", np.iinfo("uint8").max + 1, "uint16"),
("uint8", np.iinfo("int16").max + 1, "uint16"),
("uint8", np.iinfo("uint16").max + 1, "uint32"),
("uint8", np.iinfo("int32").max + 1, "uint32"),
("uint8", np.iinfo("uint32").max + 1, "uint64"),
("uint8", np.iinfo("int64").max + 1, "uint64"),
("uint8", np.iinfo("uint64").max + 1, "object"),
# max of uint8 cannot be contained in int8
("uint8", -1, "int16"),
("uint8", np.iinfo("int8").min - 1, "int16"),
("uint8", np.iinfo("int16").min - 1, "int32"),
("uint8", np.iinfo("int32").min - 1, "int64"),
("uint8", np.iinfo("int64").min - 1, "object"),
# size 16
("int16", 1, "int16"),
("int16", np.iinfo("int8").max + 1, "int16"),
("int16", np.iinfo("int16").max + 1, "int32"),
("int16", np.iinfo("int32").max + 1, "int64"),
("int16", np.iinfo("int64").max + 1, "object"),
("int16", -1, "int16"),
("int16", np.iinfo("int8").min - 1, "int16"),
("int16", np.iinfo("int16").min - 1, "int32"),
("int16", np.iinfo("int32").min - 1, "int64"),
("int16", np.iinfo("int64").min - 1, "object"),
("uint16", 1, "uint16"),
("uint16", np.iinfo("int8").max + 1, "uint16"),
("uint16", np.iinfo("uint8").max + 1, "uint16"),
("uint16", np.iinfo("int16").max + 1, "uint16"),
("uint16", np.iinfo("uint16").max + 1, "uint32"),
("uint16", np.iinfo("int32").max + 1, "uint32"),
("uint16", np.iinfo("uint32").max + 1, "uint64"),
("uint16", np.iinfo("int64").max + 1, "uint64"),
("uint16", np.iinfo("uint64").max + 1, "object"),
("uint16", -1, "int32"),
("uint16", np.iinfo("int8").min - 1, "int32"),
("uint16", np.iinfo("int16").min - 1, "int32"),
("uint16", np.iinfo("int32").min - 1, "int64"),
("uint16", np.iinfo("int64").min - 1, "object"),
# size 32
("int32", 1, "int32"),
("int32", np.iinfo("int8").max + 1, "int32"),
("int32", np.iinfo("int16").max + 1, "int32"),
("int32", np.iinfo("int32").max + 1, "int64"),
("int32", np.iinfo("int64").max + 1, "object"),
("int32", -1, "int32"),
("int32", np.iinfo("int8").min - 1, "int32"),
("int32", np.iinfo("int16").min - 1, "int32"),
("int32", np.iinfo("int32").min - 1, "int64"),
("int32", np.iinfo("int64").min - 1, "object"),
("uint32", 1, "uint32"),
("uint32", np.iinfo("int8").max + 1, "uint32"),
("uint32", np.iinfo("uint8").max + 1, "uint32"),
("uint32", np.iinfo("int16").max + 1, "uint32"),
("uint32", np.iinfo("uint16").max + 1, "uint32"),
("uint32", np.iinfo("int32").max + 1, "uint32"),
("uint32", np.iinfo("uint32").max + 1, "uint64"),
("uint32", np.iinfo("int64").max + 1, "uint64"),
("uint32", np.iinfo("uint64").max + 1, "object"),
("uint32", -1, "int64"),
("uint32", np.iinfo("int8").min - 1, "int64"),
("uint32", np.iinfo("int16").min - 1, "int64"),
("uint32", np.iinfo("int32").min - 1, "int64"),
("uint32", np.iinfo("int64").min - 1, "object"),
# size 64
("int64", 1, "int64"),
("int64", np.iinfo("int8").max + 1, "int64"),
("int64", np.iinfo("int16").max + 1, "int64"),
("int64", np.iinfo("int32").max + 1, "int64"),
("int64", np.iinfo("int64").max + 1, "object"),
("int64", -1, "int64"),
("int64", np.iinfo("int8").min - 1, "int64"),
("int64", np.iinfo("int16").min - 1, "int64"),
("int64", np.iinfo("int32").min - 1, "int64"),
("int64", np.iinfo("int64").min - 1, "object"),
("uint64", 1, "uint64"),
("uint64", np.iinfo("int8").max + 1, "uint64"),
("uint64", np.iinfo("uint8").max + 1, "uint64"),
("uint64", np.iinfo("int16").max + 1, "uint64"),
("uint64", np.iinfo("uint16").max + 1, "uint64"),
("uint64", np.iinfo("int32").max + 1, "uint64"),
("uint64", np.iinfo("uint32").max + 1, "uint64"),
("uint64", np.iinfo("int64").max + 1, "uint64"),
("uint64", np.iinfo("uint64").max + 1, "object"),
("uint64", -1, "object"),
("uint64", np.iinfo("int8").min - 1, "object"),
("uint64", np.iinfo("int16").min - 1, "object"),
("uint64", np.iinfo("int32").min - 1, "object"),
("uint64", np.iinfo("int64").min - 1, "object"),
],
)
def test_maybe_promote_int_with_int(dtype, fill_value, expected_dtype, box):
dtype = np.dtype(dtype)
expected_dtype = np.dtype(expected_dtype)
boxed, box_dtype = box # read from parametrized fixture

if not boxed:
if expected_dtype == object:
pytest.xfail("overflow error")
if expected_dtype == "int32":
pytest.xfail("always upcasts to platform int")
if dtype == "int8" and expected_dtype == "int16":
pytest.xfail("casts to int32 instead of int16")
if (
issubclass(dtype.type, np.unsignedinteger)
and np.iinfo(dtype).max < fill_value <= np.iinfo("int64").max
):
pytest.xfail("falsely casts to signed")
if (dtype, expected_dtype) in [
("uint8", "int16"),
("uint32", "int64"),
] and fill_value != np.iinfo("int32").min - 1:
pytest.xfail("casts to int32 instead of int8/int16")
# this following xfail is "only" a consequence of the - now strictly
# enforced - principle that maybe_promote_with_scalar always casts
pytest.xfail("wrong return type of fill_value")
if boxed:
if expected_dtype != object:
pytest.xfail("falsely casts to object")
if box_dtype is None and (
fill_value > np.iinfo("int64").max or np.iinfo("int64").min < fill_value < 0
):
pytest.xfail("falsely casts to float instead of object")

# output is not a generic int, but corresponds to expected_dtype
exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
# no missing value marker for integers
exp_val_for_array = None if expected_dtype != "object" else np.nan

_check_promote(
dtype,
fill_value,
boxed,
box_dtype,
expected_dtype,
exp_val_for_scalar,
exp_val_for_array,
)


# override parametrization due to to many xfails; see GH 23982 / 25425
Expand Down