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TST: Remove maybe_promote tests for iNaT as an NA value #28775

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merged 3 commits into from
Oct 5, 2019
Merged

TST: Remove maybe_promote tests for iNaT as an NA value #28775

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44f5365
TST: Remove tests for iNaT as an NA value
jbrockmendel Oct 3, 2019
8619007
Remove unused import
jbrockmendel Oct 3, 2019
e0e8c7a
dummy
jbrockmendel Oct 3, 2019
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40 changes: 6 additions & 34 deletions pandas/tests/dtypes/cast/test_promote.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@
import numpy as np
import pytest

from pandas._libs.tslibs import NaT, iNaT
from pandas._libs.tslibs import NaT
from pandas.compat import is_platform_windows

from pandas.core.dtypes.cast import maybe_promote
Expand All @@ -19,7 +19,6 @@
is_integer_dtype,
is_object_dtype,
is_scalar,
is_string_dtype,
is_timedelta64_dtype,
)
from pandas.core.dtypes.dtypes import DatetimeTZDtype
Expand Down Expand Up @@ -156,7 +155,7 @@ def _assert_match(result_fill_value, expected_fill_value):
match_value = result_fill_value == expected_fill_value

# Note: type check above ensures that we have the _same_ NA value
# for missing values, None == None and iNaT == iNaT (which is checked
# for missing values, None == None (which is checked
# through match_value above), but np.nan != np.nan and pd.NaT != pd.NaT
match_missing = isna(result_fill_value) and isna(expected_fill_value)

Expand Down Expand Up @@ -536,22 +535,15 @@ def test_maybe_promote_datetimetz_with_datetimetz(
)


@pytest.mark.parametrize("fill_value", [None, np.nan, NaT, iNaT])
@pytest.mark.parametrize("fill_value", [None, np.nan, NaT])
# override parametrization due to to many xfails; see GH 23982 / 25425
@pytest.mark.parametrize("box", [(False, None)])
def test_maybe_promote_datetimetz_with_na(tz_aware_fixture, fill_value, box):

dtype = DatetimeTZDtype(tz=tz_aware_fixture)
boxed, box_dtype = box # read from parametrized fixture

# takes the opinion that DatetimeTZ should have single na-marker
# using iNaT would lead to errors elsewhere -> NaT
if not boxed and fill_value == iNaT:
# TODO: are we sure iNaT _should_ be cast to NaT?
pytest.xfail("wrong missing value marker")

expected_dtype = dtype
# DatetimeTZDtype does not use iNaT as missing value marker
exp_val_for_scalar = NaT
exp_val_for_array = NaT

Expand Down Expand Up @@ -820,7 +812,7 @@ def test_maybe_promote_any_with_object(any_numpy_dtype_reduced, object_dtype, bo
)


@pytest.mark.parametrize("fill_value", [None, np.nan, NaT, iNaT])
@pytest.mark.parametrize("fill_value", [None, np.nan, NaT])
# override parametrization due to to many xfails; see GH 23982 / 25425
@pytest.mark.parametrize("box", [(False, None)])
def test_maybe_promote_any_numpy_dtype_with_na(
Expand All @@ -836,37 +828,17 @@ def test_maybe_promote_any_numpy_dtype_with_na(
and fill_value is not NaT
):
pytest.xfail("does not upcast to object")
elif dtype == "uint64" and not boxed and fill_value == iNaT:
pytest.xfail("does not upcast correctly")
# below: opinionated that iNaT should be interpreted as missing value
elif (
not boxed
and (is_float_dtype(dtype) or is_complex_dtype(dtype))
and fill_value == iNaT
):
pytest.xfail("does not cast to missing value marker correctly")
elif (is_string_dtype(dtype) or dtype == bool) and not boxed and fill_value == iNaT:
pytest.xfail("does not cast to missing value marker correctly")

if is_integer_dtype(dtype) and dtype == "uint64" and fill_value == iNaT:
# uint64 + negative int casts to object; iNaT is considered as missing
expected_dtype = np.dtype(object)
exp_val_for_scalar = np.nan
elif is_integer_dtype(dtype) and fill_value == iNaT:
# other integer + iNaT casts to int64
expected_dtype = np.int64
exp_val_for_scalar = iNaT
elif is_integer_dtype(dtype) and fill_value is not NaT:
# integer + other missing value (np.nan / None) casts to float
expected_dtype = np.float64
exp_val_for_scalar = np.nan
elif is_object_dtype(dtype) and (fill_value == iNaT or fill_value is NaT):
elif is_object_dtype(dtype) and fill_value is NaT:
# inserting into object does not cast the value
# but *does* cast None to np.nan
expected_dtype = np.dtype(object)
exp_val_for_scalar = fill_value
elif is_datetime_or_timedelta_dtype(dtype):
# datetime / timedelta cast all missing values to iNaT
# datetime / timedelta cast all missing values to dtyped-NaT
expected_dtype = dtype
exp_val_for_scalar = dtype.type("NaT", "ns")
elif fill_value is NaT:
Expand Down