TST: fix 217 incorrect skips

pandas/core/indexes/datetimelike.py

@@ -92,8 +92,6 @@ class DatetimeIndexOpsMixin(NDArrayBackedExtensionIndex):
     freq: BaseOffset | None
     freqstr: str | None
     _resolution_obj: Resolution
-    _bool_ops: list[str] = []
-    _field_ops: list[str] = []
 
     # error: "Callable[[Any], Any]" has no attribute "fget"
     hasnans = cast(

pandas/tests/arrays/floating/test_construction.py

@@ -61,6 +61,7 @@ def test_floating_array_disallows_float16(request):
     ):
         # the locale condition may need to be refined; this fails on
         #  the CI in the ZH_CN build
+        # https://github.com/numpy/numpy/issues/20512
         mark = pytest.mark.xfail(reason="numpy does not raise on np.dtype('Float16')")
         request.node.add_marker(mark)
 

pandas/tests/arrays/test_datetimelike.py

@@ -814,7 +814,7 @@ def test_to_period_2d(self, arr1d):
             expected = arr1d.to_period("D").reshape(1, -1)
         tm.assert_period_array_equal(result, expected)
 
-    @pytest.mark.parametrize("propname", DatetimeIndex._bool_ops)
+    @pytest.mark.parametrize("propname", DatetimeArray._bool_ops)
     def test_bool_properties(self, arr1d, propname):
         # in this case _bool_ops is just `is_leap_year`
         dti = self.index_cls(arr1d)
@@ -826,16 +826,20 @@ def test_bool_properties(self, arr1d, propname):
 
         tm.assert_numpy_array_equal(result, expected)
 
-    @pytest.mark.parametrize("propname", DatetimeIndex._field_ops)
+    @pytest.mark.parametrize("propname", DatetimeArray._field_ops)
     def test_int_properties(self, arr1d, propname):
+        warn = None
+        msg = "weekofyear and week have been deprecated, please use"
         if propname in ["week", "weekofyear"]:
             # GH#33595 Deprecate week and weekofyear
-            return
+            warn = FutureWarning
+
         dti = self.index_cls(arr1d)
         arr = arr1d
 
-        result = getattr(arr, propname)
-        expected = np.array(getattr(dti, propname), dtype=result.dtype)
+        with tm.assert_produces_warning(warn, match=msg):
+            result = getattr(arr, propname)
+            expected = np.array(getattr(dti, propname), dtype=result.dtype)
 
         tm.assert_numpy_array_equal(result, expected)
 
@@ -979,7 +983,7 @@ def test_total_seconds(self, timedelta_index):
 
         tm.assert_numpy_array_equal(result, expected.values)
 
-    @pytest.mark.parametrize("propname", TimedeltaIndex._field_ops)
+    @pytest.mark.parametrize("propname", TimedeltaArray._field_ops)
     def test_int_properties(self, timedelta_index, propname):
         tdi = timedelta_index
         arr = TimedeltaArray(tdi)

pandas/tests/extension/base/dim2.py

@@ -5,12 +5,9 @@
 import pytest
 
 from pandas._libs.missing import is_matching_na
-from pandas.compat import (
-    IS64,
-    is_platform_windows,
-)
 
 import pandas as pd
+from pandas.core.arrays.integer import INT_STR_TO_DTYPE
 from pandas.tests.extension.base.base import BaseExtensionTests
 
 
@@ -203,29 +200,22 @@ def test_reductions_2d_axis0(self, data, method, request):
             else:
                 raise AssertionError("Both reductions should raise or neither")
 
+        def get_reduction_result_dtype(dtype):
+            # windows and 32bit builds will in some cases have int32/uint32
+            #  where other builds will have int64/uint64.
+            if dtype.itemsize == 8:
+                return dtype
+            elif dtype.kind in "ib":
+                return INT_STR_TO_DTYPE[np.dtype(int).name]
+            else:
+                # i.e. dtype.kind == "u"
+                return INT_STR_TO_DTYPE[np.dtype(np.uint).name]
+
         if method in ["mean", "median", "sum", "prod"]:
             # std and var are not dtype-preserving
             expected = data
             if method in ["sum", "prod"] and data.dtype.kind in "iub":
-                # FIXME: kludge
-                if data.dtype.kind in ["i", "b"]:
-                    if is_platform_windows() or not IS64:
-                        # FIXME: kludge for 32bit builds
-                        if result.dtype.itemsize == 4:
-                            dtype = pd.Int32Dtype()
-                        else:
-                            dtype = pd.Int64Dtype()
-                    else:
-                        dtype = pd.Int64Dtype()
-                elif data.dtype.kind == "u":
-                    if is_platform_windows() or not IS64:
-                        # FIXME: kludge for 32bit builds
-                        if result.dtype.itemsize == 4:
-                            dtype = pd.UInt32Dtype()
-                        else:
-                            dtype = pd.UInt64Dtype()
-                    else:
-                        dtype = pd.UInt64Dtype()
+                dtype = get_reduction_result_dtype(data.dtype)
 
                 expected = data.astype(dtype)
                 if data.dtype.kind == "b" and method in ["sum", "prod"]:

pandas/tests/scalar/test_nat.py

@@ -38,7 +38,7 @@
     "nat,idx",
     [
         (Timestamp("NaT"), DatetimeArray),
-        (Timedelta("NaT"), TimedeltaIndex),
+        (Timedelta("NaT"), TimedeltaArray),
         (Period("NaT", freq="M"), PeriodArray),
     ],
 )
@@ -68,7 +68,7 @@ def test_nat_fields(nat, idx):
 def test_nat_vector_field_access():
     idx = DatetimeIndex(["1/1/2000", None, None, "1/4/2000"])
 
-    for field in DatetimeIndex._field_ops:
+    for field in DatetimeArray._field_ops:
         # weekday is a property of DTI, but a method
         # on NaT/Timestamp for compat with datetime
         if field == "weekday":