diff --git a/pandas/_libs/window.pyx b/pandas/_libs/window.pyx
index 46e4b17b8164c..f0b6b7a5308fe 100644
--- a/pandas/_libs/window.pyx
+++ b/pandas/_libs/window.pyx
@@ -1675,9 +1675,9 @@ def roll_generic(object obj,
     return output
 
 
-def roll_window(ndarray[float64_t, ndim=1, cast=True] values,
+def roll_weighted_window(ndarray[float64_t, ndim=1, cast=True] values,
                 ndarray[float64_t, ndim=1, cast=True] weights,
-                int minp, bint avg=True):
+                int minp, name="mean"):
     """
     Assume len(weights) << len(values)
     """
@@ -1686,16 +1686,19 @@ def roll_window(ndarray[float64_t, ndim=1, cast=True] values,
         Py_ssize_t in_i, win_i, win_n, in_n
         float64_t val_in, val_win, c, w
 
+    # currently supported ops
+    assert name in ("mean", "sum")
+
     in_n = len(values)
     win_n = len(weights)
     output = np.zeros(in_n, dtype=float)
     counts = np.zeros(in_n, dtype=float)
-    if avg:
+    if name == "mean":
         tot_wgt = np.zeros(in_n, dtype=float)
 
     minp = _check_minp(len(weights), minp, in_n)
 
-    if avg:
+    if name == "mean":
         for win_i in range(win_n):
             val_win = weights[win_i]
             if val_win != val_win:
@@ -1719,7 +1722,7 @@ def roll_window(ndarray[float64_t, ndim=1, cast=True] values,
                 else:
                     output[in_i] /= tot_wgt[in_i]
 
-    else:
+    elif name == "sum":
         for win_i in range(win_n):
             val_win = weights[win_i]
             if val_win != val_win:
diff --git a/pandas/core/window.py b/pandas/core/window.py
index 0c1f6a1a6dace..510c87d1fbf70 100644
--- a/pandas/core/window.py
+++ b/pandas/core/window.py
@@ -707,7 +707,7 @@ def _pop_args(win_type, arg_names, kwargs):
             # GH #15662. `False` makes symmetric window, rather than periodic.
             return sig.get_window(win_type, window, False).astype(float)
 
-    def _apply_window(self, mean=True, **kwargs):
+    def _apply_window(self, name, **kwargs):
         """
         Applies a moving window of type ``window_type`` on the data.
 
@@ -750,11 +750,11 @@ def _apply_window(self, mean=True, **kwargs):
 
             def f(arg, *args, **kwargs):
                 minp = _use_window(self.min_periods, len(window))
-                return libwindow.roll_window(
+                return libwindow.roll_weighted_window(
                     np.concatenate((arg, additional_nans)) if center else arg,
                     window,
                     minp,
-                    avg=mean,
+                    name=name,
                 )
 
             result = np.apply_along_axis(f, self.axis, values)
@@ -831,13 +831,13 @@ def aggregate(self, arg, *args, **kwargs):
     @Appender(_shared_docs["sum"])
     def sum(self, *args, **kwargs):
         nv.validate_window_func("sum", args, kwargs)
-        return self._apply_window(mean=False, **kwargs)
+        return self._apply_window(name="sum", **kwargs)
 
     @Substitution(name="window")
     @Appender(_shared_docs["mean"])
     def mean(self, *args, **kwargs):
         nv.validate_window_func("mean", args, kwargs)
-        return self._apply_window(mean=True, **kwargs)
+        return self._apply_window(name="mean", **kwargs)
 
 
 class _GroupByMixin(GroupByMixin):