redo using private methods

Author: johnzangwill

pandas/core/frame.py

@@ -5776,6 +5776,20 @@ class    max    type
         lion           mammal   80.5     run
         monkey         mammal    NaN    jump
         """
+        return self._reset_index(level, drop, inplace, col_level, col_fill)
+
+    def _reset_index(
+        self,
+        level: Hashable | Sequence[Hashable] | None = None,
+        drop: bool = False,
+        inplace: bool = False,
+        col_level: Hashable = 0,
+        col_fill: Hashable = "",
+        allow_duplicates: bool = False,
+    ) -> DataFrame | None:
+        """
+        Private version of reset_index with additional allow_duplicates parameter
+        """
         inplace = validate_bool_kwarg(inplace, "inplace")
         self._check_inplace_and_allows_duplicate_labels(inplace)
         if inplace:
@@ -5833,7 +5847,7 @@ class    max    type
                         level_values, lab, allow_fill=True, fill_value=lev._na_value
                     )
 
-                new_obj.insert(0, name, level_values)
+                new_obj.insert(0, name, level_values, allow_duplicates=allow_duplicates)
 
         new_obj.index = new_index
         if not inplace:

pandas/core/groupby/generic.py

@@ -26,10 +26,7 @@
 
 import numpy as np
 
-from pandas._libs import (
-    lib,
-    reduction as libreduction,
-)
+from pandas._libs import reduction as libreduction
 from pandas._typing import (
     ArrayLike,
     Manager,
@@ -1734,7 +1731,7 @@ def value_counts(
                 observed=self.observed,
                 dropna=self.dropna,
             )
-            result = gb.size()
+            result = cast(Series, gb.size())
 
             if normalize:
                 # Normalize the results by dividing by the original group sizes.
@@ -1753,33 +1750,24 @@ def value_counts(
             if sort:
                 # Sort the values and then resort by the main grouping
                 index_level = range(len(self.grouper.groupings))
-                result = (
-                    cast(Series, result)
-                    .sort_values(ascending=ascending)
-                    .sort_index(level=index_level, sort_remaining=False)
+                result = result.sort_values(ascending=ascending).sort_index(
+                    level=index_level, sort_remaining=False
                 )
 
-            if not self.as_index:
+            if self.as_index:
+                return result.__finalize__(self.obj, method="value_counts")
+            else:
                 # Convert to frame
                 name = "proportion" if normalize else "count"
                 columns = result.index.names
                 if name in columns:
                     raise ValueError(
                         f"Column label '{name}' is duplicate of result column"
                     )
-                columns = com.fill_missing_names(columns)
-                values = result.values
-                result_frame = DataFrame()
-                for i, column in enumerate(columns):
-                    level_values = result.index.get_level_values(i)._values
-                    if level_values.dtype == np.object_:
-                        level_values = lib.maybe_convert_objects(
-                            cast(np.ndarray, level_values)
-                        )
-                    result_frame.insert(i, column, level_values, allow_duplicates=True)
-                result = result_frame.assign(**{name: values})
-
-            return result.__finalize__(self.obj, method="value_counts")
+                result_frame = cast(
+                    DataFrame, result._reset_index(name=name, allow_duplicates=True)
+                )
+                return result_frame.__finalize__(self.obj, method="value_counts")
 
 
 def _wrap_transform_general_frame(

pandas/core/series.py

@@ -1462,6 +1462,19 @@ def reset_index(self, level=None, drop=False, name=lib.no_default, inplace=False
         2  baz  one    2
         3  baz  two    3
         """
+        return self._reset_index(level, drop, name, inplace)
+
+    def _reset_index(
+        self,
+        level: Hashable | Sequence[Hashable] | None = None,
+        drop: bool = False,
+        name: Hashable | lib.NoDefault = lib.no_default,
+        inplace: bool = False,
+        allow_duplicates: bool = False,
+    ) -> Series | DataFrame | None:
+        """
+        Private version of reset_index with additional allow_duplicates parameter
+        """
         inplace = validate_bool_kwarg(inplace, "inplace")
         if drop:
             new_index = default_index(len(self))
@@ -1492,7 +1505,11 @@ def reset_index(self, level=None, drop=False, name=lib.no_default, inplace=False
                     name = self.name
 
             df = self.to_frame(name)
-            return df.reset_index(level=level, drop=drop)
+            return df._reset_index(
+                level=level, drop=drop, allow_duplicates=allow_duplicates
+            )
+
+        return None
 
     # ----------------------------------------------------------------------
     # Rendering Methods