REF: Compute complete result_index upfront in groupby

pandas/core/groupby/generic.py

@@ -418,7 +418,6 @@ def _wrap_applied_output(
             # GH #823 #24880
             index = self.grouper.result_index
             res_df = self.obj._constructor_expanddim(values, index=index)
-            res_df = self._reindex_output(res_df)
             # if self.observed is False,
             # keep all-NaN rows created while re-indexing
             res_ser = res_df.stack(future_stack=True)
@@ -444,7 +443,7 @@ def _wrap_applied_output(
             if not self.as_index:
                 result = self._insert_inaxis_grouper(result)
                 result.index = default_index(len(result))
-            return self._reindex_output(result)
+            return result
 
     def _aggregate_named(self, func, *args, **kwargs):
         # Note: this is very similar to _aggregate_series_pure_python,
@@ -674,7 +673,8 @@ def nunique(self, dropna: bool = True) -> Series | DataFrame:
         2023-02-01    1
         Freq: MS, dtype: int64
         """
-        ids, _, ngroups = self.grouper.group_info
+        ids, ngroups = self.grouper.group_info
+
         val = self.obj._values
         codes, uniques = algorithms.factorize(val, use_na_sentinel=dropna, sort=False)
 
@@ -707,7 +707,7 @@ def nunique(self, dropna: bool = True) -> Series | DataFrame:
         if not self.as_index:
             result = self._insert_inaxis_grouper(result)
             result.index = default_index(len(result))
-        return self._reindex_output(result, fill_value=0)
+        return result
 
     @doc(Series.describe)
     def describe(self, percentiles=None, include=None, exclude=None) -> Series:
@@ -735,7 +735,7 @@ def value_counts(
         from pandas.core.reshape.merge import get_join_indexers
         from pandas.core.reshape.tile import cut
 
-        ids, _, _ = self.grouper.group_info
+        ids, _ = self.grouper.group_info
         val = self.obj._values
 
         index_names = self.grouper.names + [self.obj.name]
@@ -805,9 +805,18 @@ def value_counts(
         rep = partial(np.repeat, repeats=np.add.reduceat(inc, idx))
 
         # multi-index components
-        codes = self.grouper._reconstructed_codes
+        if isinstance(self.grouper.result_index, MultiIndex):
+            codes = list(self.grouper.result_index.codes)
+        else:
+            codes = [
+                algorithms.factorize(
+                    self.grouper.result_index,
+                    sort=self.grouper._sort,
+                    use_na_sentinel=self.grouper.dropna,
+                )[0]
+            ]
         codes = [rep(level_codes) for level_codes in codes] + [llab(lab, inc)]
-        levels = [ping._group_index for ping in self.grouper.groupings] + [lev]
+        levels = self.grouper.levels + [lev]
 
         if dropna:
             mask = codes[-1] != -1
@@ -1675,7 +1684,7 @@ def _wrap_applied_output_series(
         if not self.as_index:
             result = self._insert_inaxis_grouper(result)
 
-        return self._reindex_output(result)
+        return result
 
     def _cython_transform(
         self,