Implement Series.__rdivmod__, un-xfail tests

pandas/core/ops.py

@@ -518,7 +518,7 @@ def _get_op_name(op, special):
                  'df_examples': None},
     'divmod': {'op': 'divmod',
                'desc': 'Integer division and modulo',
-               'reverse': None,
+               'reverse': 'rdivmod',
                'df_examples': None},
 
     # Comparison Operators
@@ -1039,6 +1039,7 @@ def _create_methods(cls, arith_method, comp_method, bool_method, special):
     if have_divmod:
         # divmod doesn't have an op that is supported by numexpr
         new_methods['divmod'] = arith_method(cls, divmod, special)
+        new_methods['rdivmod'] = arith_method(cls, rdivmod, special)
 
     new_methods.update(dict(
         eq=comp_method(cls, operator.eq, special),
@@ -1224,7 +1225,7 @@ def dispatch_to_extension_op(op, left, right):
     res_values = op(new_left, new_right)
     res_name = get_op_result_name(left, right)
 
-    if op.__name__ == 'divmod':
+    if op.__name__ in ['divmod', 'rdivmod']:
         return _construct_divmod_result(
             left, res_values, left.index, res_name)
 
@@ -1241,7 +1242,7 @@ def _arith_method_SERIES(cls, op, special):
     eval_kwargs = _gen_eval_kwargs(op_name)
     fill_zeros = _gen_fill_zeros(op_name)
     construct_result = (_construct_divmod_result
-                        if op is divmod else _construct_result)
+                        if op in [divmod, rdivmod] else _construct_result)
 
     def na_op(x, y):
         import pandas.core.computation.expressions as expressions
@@ -1871,8 +1872,8 @@ def f(self, other, axis=default_axis, level=None, fill_value=None):
             if fill_value is not None:
                 self = self.fillna(fill_value)
 
-            pass_op = op if lib.is_scalar(other) else na_op
-            return self._combine_const(other, pass_op, try_cast=True)
+            assert np.ndim(other) == 0
+            return self._combine_const(other, op, try_cast=True)
 
     f.__name__ = op_name
 

pandas/tests/arithmetic/test_numeric.py

@@ -516,33 +516,38 @@ def test_modulo(self, numeric_idx, box):
         result = idx % 2
         tm.assert_equal(result, expected)
 
-    def test_divmod(self, numeric_idx):
+    def test_divmod_scalar(self, numeric_idx):
         idx = numeric_idx
+
         result = divmod(idx, 2)
         with np.errstate(all='ignore'):
             div, mod = divmod(idx.values, 2)
-            expected = Index(div), Index(mod)
+
+        expected = Index(div), Index(mod)
         for r, e in zip(result, expected):
             tm.assert_index_equal(r, e)
 
+    def test_divmod_ndarray(self, numeric_idx):
+        idx = numeric_idx
         other = np.ones(idx.values.shape, dtype=idx.values.dtype) * 2
+
         result = divmod(idx, other)
         with np.errstate(all='ignore'):
             div, mod = divmod(idx.values, other)
-            expected = Index(div), Index(mod)
+
+        expected = Index(div), Index(mod)
         for r, e in zip(result, expected):
             tm.assert_index_equal(r, e)
 
-    @pytest.mark.xfail(reason='GH#19252 Series has no __rdivmod__',
-                       strict=True)
     def test_divmod_series(self, numeric_idx):
         idx = numeric_idx
         other = np.ones(idx.values.shape, dtype=idx.values.dtype) * 2
+
         result = divmod(idx, Series(other))
         with np.errstate(all='ignore'):
             div, mod = divmod(idx.values, other)
-            expected = Series(div), Series(mod)
 
+        expected = Series(div), Series(mod)
         for r, e in zip(result, expected):
             tm.assert_series_equal(r, e)
 

pandas/tests/arithmetic/test_timedelta64.py

@@ -553,12 +553,6 @@ def test_td64arr_add_intlike(self, box_df_broadcast_failure):
 
     @pytest.mark.parametrize('scalar', [1, 1.5, np.array(2)])
     def test_td64arr_add_sub_numeric_scalar_invalid(self, box, scalar, tdser):
-
-        if box is pd.DataFrame and isinstance(scalar, np.ndarray):
-            # raises ValueError
-            pytest.xfail(reason="reversed ops return incorrect answers "
-                                "instead of raising.")
-
         tdser = tm.box_expected(tdser, box)
         err = TypeError
         if box is pd.Index and not isinstance(scalar, float):