Fix Series divmod pandas-dev#26987

Author: jbrockmendel

pandas/core/missing.py

@@ -520,6 +520,26 @@ def fill_zeros(result, x, y, name, fill):
 
     Mask the nan's from x.
     """
+    if name == '__divmod__' and isinstance(result, tuple):
+        # GH#26987; sometimes we don't have a tuple when called
+        #  from dispatch_missing
+        res1 = fill_zeros(result[0], x, y, '__floordiv__', fill)
+        res2 = fill_zeros(result[1], x, y, '__mod__', fill)
+        mask = np.isinf(res1) & np.isinf(res2)
+        if mask.any():
+            res2[mask] = np.nan
+        return res1, res2
+
+    elif name == '__rdivmod__':
+        # GH#26987; sometimes we don't have a tuple when called
+        #  from dispatch_missing
+        res1 = fill_zeros(result[0], x, y, '__rfloordiv__', fill)
+        res2 = fill_zeros(result[1], x, y, '__rmod__', fill)
+        mask = np.isinf(res1) & np.isinf(res2)
+        if mask.any():
+            res2[mask] = np.nan
+        return res1, res2
+
     if fill is None or is_float_dtype(result):
         return result
 

pandas/core/ops.py

@@ -1709,6 +1709,11 @@ def wrapper(left, right):
         rvalues = right
         if isinstance(rvalues, ABCSeries):
             rvalues = rvalues.values
+        elif (isinstance(rvalues, ABCIndexClass)
+              and rvalues.dtype.kind in 'fciu'):
+            # TOOD: We should do this unpacking unconditionally, but ATM doing
+            #  so breaks on DatetimeArray because it lacks ravel()
+            rvalues = rvalues._values
 
         with np.errstate(all='ignore'):
             result = na_op(lvalues, rvalues)

pandas/tests/arithmetic/test_numeric.py

@@ -250,6 +250,42 @@ def test_ser_div_ser(self, dtype1, dtype2):
         tm.assert_series_equal(result, expected)
         assert not result.equals(second / first)
 
+    @pytest.mark.parametrize('dtype2', [
+        np.int64, np.int32, np.int16, np.int8,
+        np.float64, np.float32, np.float16,
+        np.uint64, np.uint32, np.uint16, np.uint8])
+    @pytest.mark.parametrize('dtype1', [np.int64, np.float64, np.uint64])
+    def test_ser_divmod_zero(self, dtype1, dtype2):
+        # GH#26987
+        left = pd.Series([1, 1]).astype(dtype1)
+        right = pd.Series([0, 2]).astype(dtype2)
+
+        expected = left // right, left % right
+        result = divmod(left, right)
+
+        tm.assert_series_equal(result[0], expected[0])
+        tm.assert_series_equal(result[1], expected[1])
+
+        # rdivmod case
+        result = divmod(left.values, right)
+        tm.assert_series_equal(result[0], expected[0])
+        tm.assert_series_equal(result[1], expected[1])
+
+    def test_ser_divmod_inf(self):
+        left = pd.Series([np.inf, 1.])
+        right = pd.Series([np.inf, 2.])
+
+        expected = left // right, left % right
+        result = divmod(left, right)
+
+        tm.assert_series_equal(result[0], expected[0])
+        tm.assert_series_equal(result[1], expected[1])
+
+        # rdivmod case
+        result = divmod(left.values, right)
+        tm.assert_series_equal(result[0], expected[0])
+        tm.assert_series_equal(result[1], expected[1])
+
     def test_rdiv_zero_compat(self):
         # GH#8674
         zero_array = np.array([0] * 5)