diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 28923f0fbf240..f0adc77e46e5d 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -3920,17 +3920,7 @@ def _combine_frame(self, other, func, fill_value=None, level=None):
         new_index, new_columns = this.index, this.columns
 
         def _arith_op(left, right):
-            if fill_value is not None:
-                left_mask = isna(left)
-                right_mask = isna(right)
-                left = left.copy()
-                right = right.copy()
-
-                # one but not both
-                mask = left_mask ^ right_mask
-                left[left_mask & mask] = fill_value
-                right[right_mask & mask] = fill_value
-
+            left, right = ops.fill_binop(left, right, fill_value)
             return func(left, right)
 
         if this._is_mixed_type or other._is_mixed_type:
diff --git a/pandas/core/ops.py b/pandas/core/ops.py
index effa35695fcd1..4c234ccb4dd47 100644
--- a/pandas/core/ops.py
+++ b/pandas/core/ops.py
@@ -398,6 +398,79 @@ def _make_flex_doc(op_name, typ):
     return doc
 
 
+# -----------------------------------------------------------------------------
+# Masking NA values and fallbacks for operations numpy does not support
+
+def fill_binop(left, right, fill_value):
+    """
+    If a non-None fill_value is given, replace null entries in left and right
+    with this value, but only in positions where _one_ of left/right is null,
+    not both.
+
+    Parameters
+    ----------
+    left : array-like
+    right : array-like
+    fill_value : object
+
+    Returns
+    -------
+    left : array-like
+    right : array-like
+
+    Notes
+    -----
+    Makes copies if fill_value is not None
+    """
+    # TODO: can we make a no-copy implementation?
+    if fill_value is not None:
+        left_mask = isna(left)
+        right_mask = isna(right)
+        left = left.copy()
+        right = right.copy()
+
+        # one but not both
+        mask = left_mask ^ right_mask
+        left[left_mask & mask] = fill_value
+        right[right_mask & mask] = fill_value
+    return left, right
+
+
+def mask_cmp_op(x, y, op, allowed_types):
+    """
+    Apply the function `op` to only non-null points in x and y.
+
+    Parameters
+    ----------
+    x : array-like
+    y : array-like
+    op : binary operation
+    allowed_types : class or tuple of classes
+
+    Returns
+    -------
+    result : ndarray[bool]
+    """
+    # TODO: Can we make the allowed_types arg unnecessary?
+    xrav = x.ravel()
+    result = np.empty(x.size, dtype=bool)
+    if isinstance(y, allowed_types):
+        yrav = y.ravel()
+        mask = notna(xrav) & notna(yrav)
+        result[mask] = op(np.array(list(xrav[mask])),
+                          np.array(list(yrav[mask])))
+    else:
+        mask = notna(xrav)
+        result[mask] = op(np.array(list(xrav[mask])), y)
+
+    if op == operator.ne:  # pragma: no cover
+        np.putmask(result, ~mask, True)
+    else:
+        np.putmask(result, ~mask, False)
+    result = result.reshape(x.shape)
+    return result
+
+
 # -----------------------------------------------------------------------------
 # Functions that add arithmetic methods to objects, given arithmetic factory
 # methods
@@ -1127,23 +1200,7 @@ def na_op(x, y):
             with np.errstate(invalid='ignore'):
                 result = op(x, y)
         except TypeError:
-            xrav = x.ravel()
-            result = np.empty(x.size, dtype=bool)
-            if isinstance(y, (np.ndarray, ABCSeries)):
-                yrav = y.ravel()
-                mask = notna(xrav) & notna(yrav)
-                result[mask] = op(np.array(list(xrav[mask])),
-                                  np.array(list(yrav[mask])))
-            else:
-                mask = notna(xrav)
-                result[mask] = op(np.array(list(xrav[mask])), y)
-
-            if op == operator.ne:  # pragma: no cover
-                np.putmask(result, ~mask, True)
-            else:
-                np.putmask(result, ~mask, False)
-            result = result.reshape(x.shape)
-
+            result = mask_cmp_op(x, y, op, (np.ndarray, ABCSeries))
         return result
 
     @Appender('Wrapper for flexible comparison methods {name}'
@@ -1221,23 +1278,7 @@ def na_op(x, y):
         try:
             result = expressions.evaluate(op, str_rep, x, y)
         except TypeError:
-            xrav = x.ravel()
-            result = np.empty(x.size, dtype=bool)
-            if isinstance(y, np.ndarray):
-                yrav = y.ravel()
-                mask = notna(xrav) & notna(yrav)
-                result[mask] = op(np.array(list(xrav[mask])),
-                                  np.array(list(yrav[mask])))
-            else:
-                mask = notna(xrav)
-                result[mask] = op(np.array(list(xrav[mask])), y)
-
-            if op == operator.ne:  # pragma: no cover
-                np.putmask(result, ~mask, True)
-            else:
-                np.putmask(result, ~mask, False)
-            result = result.reshape(x.shape)
-
+            result = mask_cmp_op(x, y, op, np.ndarray)
         return result
 
     @Appender('Wrapper for comparison method {name}'.format(name=name))
diff --git a/pandas/core/series.py b/pandas/core/series.py
index e4b8979d6393a..655eaa5373f5a 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -1725,19 +1725,8 @@ def _binop(self, other, func, level=None, fill_value=None):
                                      copy=False)
             new_index = this.index
 
-        this_vals = this.values
-        other_vals = other.values
-
-        if fill_value is not None:
-            this_mask = isna(this_vals)
-            other_mask = isna(other_vals)
-            this_vals = this_vals.copy()
-            other_vals = other_vals.copy()
-
-            # one but not both
-            mask = this_mask ^ other_mask
-            this_vals[this_mask & mask] = fill_value
-            other_vals[other_mask & mask] = fill_value
+        this_vals, other_vals = ops.fill_binop(this.values, other.values,
+                                               fill_value)
 
         with np.errstate(all='ignore'):
             result = func(this_vals, other_vals)