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

Author: jbrockmendel

pandas/tests/dtypes/cast/test_promote.py

@@ -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
@@ -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
@@ -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)
 
@@ -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
 
@@ -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(
@@ -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: