CLN: Unify Window._apply_window and Rolling._apply functions

pandas/_libs/window.pyx

@@ -1675,9 +1675,25 @@ 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)
     """
@@ -1688,6 +1704,7 @@ def roll_window(ndarray[float64_t, ndim=1, cast=True] values,
 
     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
 

pandas/core/window.py

@@ -354,6 +354,116 @@ def _center_window(self, result, window):
                 result = np.copy(result[tuple(lead_indexer)])
         return result
 
+    def _apply(
+        self, func, name=None, window=None, center=None, check_minp=None, **kwargs
+    ):
+        """
+        Rolling statistical measure using supplied function.
+
+        Designed to be used with passed-in Cython array-based functions.
+
+        Parameters
+        ----------
+        func : str/callable to apply
+        name : str, optional
+           name of this function
+        window : int/array, default to _get_window()
+        center : bool, default to self.center
+        check_minp : function, default to _use_window
+
+        Returns
+        -------
+        y : type of input
+        """
+        if center is None:
+            center = self.center
+
+        if check_minp is None:
+            check_minp = _use_window
+
+        blocks, obj, index = self._create_blocks()
+        block_list = list(blocks)
+
+        is_window = isinstance(self, Window)
+
+        if is_window:
+            window = self._prep_window(**kwargs)
+        else:
+            window = self._get_window()
+            index, indexi = self._get_index(index=index)
+
+        results = []
+        exclude = []
+        for i, b in enumerate(blocks):
+            try:
+                values = self._prep_values(b.values)
+
+            except (TypeError, NotImplementedError):
+                if isinstance(obj, ABCDataFrame):
+                    exclude.extend(b.columns)
+                    del block_list[i]
+                    continue
+                else:
+                    raise DataError("No numeric types to aggregate")
+
+            if values.size == 0:
+                results.append(values.copy())
+                continue
+
+            # if we have a string function name, wrap it
+            if isinstance(func, str):
+                cfunc = getattr(libwindow, func, None)
+                if cfunc is None:
+                    raise ValueError(
+                        "we do not support this function "
+                        "in libwindow.{func}".format(func=func)
+                    )
+
+                def func(arg, window, min_periods=None, closed=None):
+                    # ensure we are only rolling on floats
+                    arg = ensure_float64(arg)
+
+                    if is_window:
+                        minp = check_minp(min_periods, len(window))
+                        return cfunc(arg, window, minp)
+
+                    else:
+                        minp = check_minp(min_periods, window)
+                        return cfunc(arg, window, minp, indexi, closed, **kwargs)
+
+            # calculation function
+            if center:
+                offset = _offset(window, center)
+                additional_nans = np.array([np.NaN] * offset)
+
+                def calc(x):
+                    return func(
+                        np.concatenate((x, additional_nans)),
+                        window,
+                        min_periods=self.min_periods,
+                        closed=self.closed,
+                    )
+
+            else:
+
+                def calc(x):
+                    return func(
+                        x, window, min_periods=self.min_periods, closed=self.closed
+                    )
+
+            with np.errstate(all="ignore"):
+                if values.ndim > 1 or is_window:
+                    result = np.apply_along_axis(calc, self.axis, values)
+                else:
+                    result = calc(values)
+
+            if center:
+                result = self._center_window(result, window)
+
+            results.append(result)
+
+        return self._wrap_results(results, block_list, obj, exclude)
+
     def aggregate(self, func, *args, **kwargs):
         result, how = self._aggregate(func, *args, **kwargs)
         if result is None:
@@ -707,64 +817,6 @@ 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):
-        """
-        Applies a moving window of type ``window_type`` on the data.
-
-        Parameters
-        ----------
-        mean : bool, default True
-            If True computes weighted mean, else weighted sum
-
-        Returns
-        -------
-        y : same type as input argument
-
-        """
-        window = self._prep_window(**kwargs)
-        center = self.center
-
-        blocks, obj, index = self._create_blocks()
-        block_list = list(blocks)
-
-        results = []
-        exclude = []
-        for i, b in enumerate(blocks):
-            try:
-                values = self._prep_values(b.values)
-
-            except (TypeError, NotImplementedError):
-                if isinstance(obj, ABCDataFrame):
-                    exclude.extend(b.columns)
-                    del block_list[i]
-                    continue
-                else:
-                    raise DataError("No numeric types to aggregate")
-
-            if values.size == 0:
-                results.append(values.copy())
-                continue
-
-            offset = _offset(window, center)
-            additional_nans = np.array([np.NaN] * offset)
-
-            def f(arg, *args, **kwargs):
-                minp = _use_window(self.min_periods, len(window))
-                return libwindow.roll_window(
-                    np.concatenate((arg, additional_nans)) if center else arg,
-                    window,
-                    minp,
-                    avg=mean,
-                )
-
-            result = np.apply_along_axis(f, self.axis, values)
-
-            if center:
-                result = self._center_window(result, window)
-            results.append(result)
-
-        return self._wrap_results(results, block_list, obj, exclude)
-
     _agg_see_also_doc = dedent(
         """
     See Also
@@ -831,13 +883,13 @@ 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("roll_weighted_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("roll_weighted_mean", **kwargs)
 
 
 class _GroupByMixin(GroupByMixin):
@@ -883,105 +935,6 @@ class _Rolling(_Window):
     def _constructor(self):
         return Rolling
 
-    def _apply(
-        self, func, name=None, window=None, center=None, check_minp=None, **kwargs
-    ):
-        """
-        Rolling statistical measure using supplied function.
-
-        Designed to be used with passed-in Cython array-based functions.
-
-        Parameters
-        ----------
-        func : str/callable to apply
-        name : str, optional
-           name of this function
-        window : int/array, default to _get_window()
-        center : bool, default to self.center
-        check_minp : function, default to _use_window
-
-        Returns
-        -------
-        y : type of input
-        """
-        if center is None:
-            center = self.center
-        if window is None:
-            window = self._get_window()
-
-        if check_minp is None:
-            check_minp = _use_window
-
-        blocks, obj, index = self._create_blocks()
-        block_list = list(blocks)
-        index, indexi = self._get_index(index=index)
-
-        results = []
-        exclude = []
-        for i, b in enumerate(blocks):
-            try:
-                values = self._prep_values(b.values)
-
-            except (TypeError, NotImplementedError):
-                if isinstance(obj, ABCDataFrame):
-                    exclude.extend(b.columns)
-                    del block_list[i]
-                    continue
-                else:
-                    raise DataError("No numeric types to aggregate")
-
-            if values.size == 0:
-                results.append(values.copy())
-                continue
-
-            # if we have a string function name, wrap it
-            if isinstance(func, str):
-                cfunc = getattr(libwindow, func, None)
-                if cfunc is None:
-                    raise ValueError(
-                        "we do not support this function "
-                        "in libwindow.{func}".format(func=func)
-                    )
-
-                def func(arg, window, min_periods=None, closed=None):
-                    minp = check_minp(min_periods, window)
-                    # ensure we are only rolling on floats
-                    arg = ensure_float64(arg)
-                    return cfunc(arg, window, minp, indexi, closed, **kwargs)
-
-            # calculation function
-            if center:
-                offset = _offset(window, center)
-                additional_nans = np.array([np.NaN] * offset)
-
-                def calc(x):
-                    return func(
-                        np.concatenate((x, additional_nans)),
-                        window,
-                        min_periods=self.min_periods,
-                        closed=self.closed,
-                    )
-
-            else:
-
-                def calc(x):
-                    return func(
-                        x, window, min_periods=self.min_periods, closed=self.closed
-                    )
-
-            with np.errstate(all="ignore"):
-                if values.ndim > 1:
-                    result = np.apply_along_axis(calc, self.axis, values)
-                else:
-                    result = calc(values)
-
-            if center:
-                result = self._center_window(result, window)
-
-            results.append(result)
-
-        return self._wrap_results(results, block_list, obj, exclude)
-
 
 class _Rolling_and_Expanding(_Rolling):