REF: always use grouper=self.grouper in resample

pandas/core/resample.py

@@ -338,8 +338,7 @@ def aggregate(self, func=None, *args, **kwargs):
         result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
         if result is None:
             how = func
-            grouper = None
-            result = self._groupby_and_aggregate(how, grouper, *args, **kwargs)
+            result = self._groupby_and_aggregate(how, *args, **kwargs)
 
         result = self._apply_loffset(result)
         return result
@@ -409,12 +408,11 @@ def _gotitem(self, key, ndim: int, subset=None):
         except KeyError:
             return grouped
 
-    def _groupby_and_aggregate(self, how, grouper=None, *args, **kwargs):
+    def _groupby_and_aggregate(self, how, *args, **kwargs):
         """
         Re-evaluate the obj with a groupby aggregation.
         """
-        if grouper is None:
-            grouper = self.grouper
+        grouper = self.grouper
 
         obj = self._selected_obj
 
@@ -1055,7 +1053,7 @@ def __init__(self, obj, parent=None, groupby=None, **kwargs):
         self.groupby = copy.copy(parent.groupby)
 
     @no_type_check
-    def _apply(self, f, grouper=None, *args, **kwargs):
+    def _apply(self, f, *args, **kwargs):
         """
         Dispatch to _upsample; we are stripping all of the _upsample kwargs and
         performing the original function call on the grouped object.
@@ -1296,15 +1294,15 @@ def _downsample(self, how, **kwargs):
 
         if is_subperiod(ax.freq, self.freq):
             # Downsampling
-            return self._groupby_and_aggregate(how, grouper=self.grouper, **kwargs)
+            return self._groupby_and_aggregate(how, **kwargs)
         elif is_superperiod(ax.freq, self.freq):
             if how == "ohlc":
                 # GH #13083
                 # upsampling to subperiods is handled as an asfreq, which works
                 # for pure aggregating/reducing methods
                 # OHLC reduces along the time dimension, but creates multiple
                 # values for each period -> handle by _groupby_and_aggregate()
-                return self._groupby_and_aggregate(how, grouper=self.grouper)
+                return self._groupby_and_aggregate(how)
             return self.asfreq()
         elif ax.freq == self.freq:
             return self.asfreq()