REF: remove no-op casting (pandas-dev#41317)

Author: jbrockmendel

pandas/core/groupby/groupby.py

@@ -60,9 +60,7 @@ class providing the base-class of operations.
     doc,
 )
 
-from pandas.core.dtypes.cast import maybe_downcast_numeric
 from pandas.core.dtypes.common import (
-    ensure_float,
     is_bool_dtype,
     is_datetime64_dtype,
     is_integer_dtype,
@@ -1280,19 +1278,7 @@ def _python_agg_general(self, func, *args, **kwargs):
             except TypeError:
                 continue
 
-            assert result is not None
             key = base.OutputKey(label=name, position=idx)
-
-            if self.grouper._filter_empty_groups:
-                mask = counts.ravel() > 0
-
-                # since we are masking, make sure that we have a float object
-                values = result
-                if is_numeric_dtype(values.dtype):
-                    values = ensure_float(values)
-
-                    result = maybe_downcast_numeric(values[mask], result.dtype)
-
             output[key] = result
 
         if not output:
@@ -3091,9 +3077,7 @@ def _reindex_output(
             Object (potentially) re-indexed to include all possible groups.
         """
         groupings = self.grouper.groupings
-        if groupings is None:
-            return output
-        elif len(groupings) == 1:
+        if len(groupings) == 1:
             return output
 
         # if we only care about the observed values

pandas/core/groupby/ops.py

@@ -56,7 +56,6 @@
     needs_i8_conversion,
 )
 from pandas.core.dtypes.dtypes import ExtensionDtype
-from pandas.core.dtypes.generic import ABCCategoricalIndex
 from pandas.core.dtypes.missing import (
     isna,
     maybe_fill,
@@ -89,6 +88,7 @@
     grouper,
 )
 from pandas.core.indexes.api import (
+    CategoricalIndex,
     Index,
     MultiIndex,
     ensure_index,
@@ -676,7 +676,6 @@ def __init__(
     ):
         assert isinstance(axis, Index), axis
 
-        self._filter_empty_groups = self.compressed = len(groupings) != 1
         self.axis = axis
         self._groupings: list[grouper.Grouping] = list(groupings)
         self.sort = sort
@@ -822,9 +821,7 @@ def apply(self, f: F, data: FrameOrSeries, axis: int = 0):
     @cache_readonly
     def indices(self):
         """ dict {group name -> group indices} """
-        if len(self.groupings) == 1 and isinstance(
-            self.result_index, ABCCategoricalIndex
-        ):
+        if len(self.groupings) == 1 and isinstance(self.result_index, CategoricalIndex):
             # This shows unused categories in indices GH#38642
             return self.groupings[0].indices
         codes_list = [ping.codes for ping in self.groupings]
@@ -913,7 +910,7 @@ def reconstructed_codes(self) -> list[np.ndarray]:
 
     @cache_readonly
     def result_index(self) -> Index:
-        if not self.compressed and len(self.groupings) == 1:
+        if len(self.groupings) == 1:
             return self.groupings[0].result_index.rename(self.names[0])
 
         codes = self.reconstructed_codes
@@ -924,7 +921,9 @@ def result_index(self) -> Index:
 
     @final
     def get_group_levels(self) -> list[Index]:
-        if not self.compressed and len(self.groupings) == 1:
+        # Note: only called from _insert_inaxis_grouper_inplace, which
+        #  is only called for BaseGrouper, never for BinGrouper
+        if len(self.groupings) == 1:
             return [self.groupings[0].result_index]
 
         name_list = []
@@ -1091,7 +1090,6 @@ def __init__(
     ):
         self.bins = ensure_int64(bins)
         self.binlabels = ensure_index(binlabels)
-        self._filter_empty_groups = False
         self.mutated = mutated
         self.indexer = indexer
 
@@ -1201,10 +1199,9 @@ def names(self) -> list[Hashable]:
 
     @property
     def groupings(self) -> list[grouper.Grouping]:
-        return [
-            grouper.Grouping(lvl, lvl, in_axis=False, level=None, name=name)
-            for lvl, name in zip(self.levels, self.names)
-        ]
+        lev = self.binlabels
+        ping = grouper.Grouping(lev, lev, in_axis=False, level=None, name=lev.name)
+        return [ping]
 
     def _aggregate_series_fast(
         self, obj: Series, func: F