CLN: Unify Window._apply_window and Rolling._apply functions

pandas/_libs/window.pyx

@@ -1675,19 +1675,36 @@ def roll_generic(object obj,
     return output
 
 
-def roll_window(ndarray[float64_t, ndim=1, cast=True] values,
-                ndarray[float64_t, ndim=1, cast=True] weights,
-                int minp, bint avg=True):
+# ----------------------------------------------------------------------
+# Rolling sum and mean for weighted window
+
+
+def roll_weighted_sum(ndarray[float64_t, ndim=1, cast=True] values,
+                      ndarray[float64_t, ndim=1, cast=True] weights,
+                      int minp):
+    return _roll_weighted_sum_mean(values, weights, minp, avg=0)
+
+
+def roll_weighted_mean(ndarray[float64_t, ndim=1, cast=True] values,
+                       ndarray[float64_t, ndim=1, cast=True] weights,
+                       int minp):
+    return _roll_weighted_sum_mean(values, weights, minp, avg=1)
+
+
+def _roll_weighted_sum_mean(ndarray[float64_t, ndim=1, cast=True] values,
+                            ndarray[float64_t, ndim=1, cast=True] weights,
+                            int minp, bint avg=True):
     """
     Assume len(weights) << len(values)
     """
     cdef:
-        float64_t[:] output, tot_wgt, counts
+        ndarray[float64_t] output, tot_wgt, counts
         Py_ssize_t in_i, win_i, win_n, in_n
         float64_t val_in, val_win, c, w
 
     in_n = len(values)
     win_n = len(weights)
+
     output = np.zeros(in_n, dtype=float)
     counts = np.zeros(in_n, dtype=float)
     if avg:
@@ -1739,6 +1756,7 @@ def roll_window(ndarray[float64_t, ndim=1, cast=True] values,
 
     return output
 
+
 # ----------------------------------------------------------------------
 # Exponentially weighted moving average