TST: fix xfails/skips (#44706)

Author: jbrockmendel

pandas/core/computation/expressions.py

@@ -144,9 +144,9 @@ def _evaluate_numexpr(op, op_str, a, b):
     roperator.rfloordiv: None,
     # we require Python semantics for mod of negative for backwards compatibility
     # see https://github.com/pydata/numexpr/issues/365
-    # so sticking with unaccelerated for now
+    # so sticking with unaccelerated for now GH#36552
     operator.mod: None,
-    roperator.rmod: "%",
+    roperator.rmod: None,
     operator.pow: "**",
     roperator.rpow: "**",
     operator.eq: "==",

pandas/tests/scalar/timedelta/test_arithmetic.py

@@ -981,7 +981,6 @@ def test_compare_td64_ndarray(self):
         result = arr != td
         tm.assert_numpy_array_equal(result, ~expected)
 
-    @pytest.mark.skip(reason="GH#20829 is reverted until after 0.24.0")
     def test_compare_custom_object(self):
         """
         Make sure non supported operations on Timedelta returns NonImplemented

pandas/tests/test_expressions.py

@@ -372,7 +372,7 @@ def test_frame_series_axis(self, axis, arith):
         "op",
         [
             "__mod__",
-            pytest.param("__rmod__", marks=pytest.mark.xfail(reason="GH-36552")),
+            "__rmod__",
             "__floordiv__",
             "__rfloordiv__",
         ],

pandas/tests/test_nanops.py

@@ -156,6 +156,15 @@ def check_fun_data(
             else:
                 targ = targfunc(targartempval, axis=axis, **kwargs)
 
+            if targartempval.dtype == object and (
+                targfunc is np.any or targfunc is np.all
+            ):
+                # GH#12863 the numpy functions will retain e.g. floatiness
+                if isinstance(targ, np.ndarray):
+                    targ = targ.astype(bool)
+                else:
+                    targ = bool(targ)
+
             res = testfunc(testarval, axis=axis, skipna=skipna, **kwargs)
             self.check_results(targ, res, axis, check_dtype=check_dtype)
             if skipna:
@@ -270,7 +279,6 @@ def _badobj_wrap(self, value, func, allow_complex=True, **kwargs):
                 value = value.astype("f8")
         return func(value, **kwargs)
 
-    @pytest.mark.xfail(reason="GH12863: numpy result won't match for object type")
     @pytest.mark.parametrize(
         "nan_op,np_op", [(nanops.nanany, np.any), (nanops.nanall, np.all)]
     )