CLN: Remove MultiIndex._get_grouper_for_level

pandas/core/groupby/grouper.py

@@ -472,19 +472,19 @@ def __init__(
 
         ilevel = self._ilevel
         if ilevel is not None:
-            mapper = self.grouping_vector
             # In extant tests, the new self.grouping_vector matches
             #  `index.get_level_values(ilevel)` whenever
             #  mapper is None and isinstance(index, MultiIndex)
             if isinstance(index, MultiIndex):
                 index_level = index.get_level_values(ilevel)
             else:
                 index_level = index
-            (
-                self.grouping_vector,  # Index
-                self._codes,
-                self._group_index,
-            ) = index_level._get_grouper_for_level(mapper, dropna=dropna)
+
+            if self.grouping_vector is None:
+                self.grouping_vector = index_level
+            else:
+                mapper = self.grouping_vector
+                self.grouping_vector = index_level.map(mapper)
 
         # a passed Grouper like, directly get the grouper in the same way
         # as single grouper groupby, use the group_info to get codes
@@ -600,10 +600,6 @@ def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
 
     @property
     def codes(self) -> npt.NDArray[np.signedinteger]:
-        if self._codes is not None:
-            # _codes is set in __init__ for MultiIndex cases
-            return self._codes
-
         return self._codes_and_uniques[0]
 
     @cache_readonly
@@ -612,11 +608,7 @@ def group_arraylike(self) -> ArrayLike:
         Analogous to result_index, but holding an ArrayLike to ensure
         we can retain ExtensionDtypes.
         """
-        if self._group_index is not None:
-            # _group_index is set in __init__ for MultiIndex cases
-            return self._group_index._values
-
-        elif self._all_grouper is not None:
+        if self._all_grouper is not None:
             # retain dtype for categories, including unobserved ones
             return self.result_index._values
 
@@ -636,10 +628,6 @@ def result_index(self) -> Index:
 
     @cache_readonly
     def group_index(self) -> Index:
-        if self._group_index is not None:
-            # _group_index is set in __init__ for MultiIndex cases
-            return self._group_index
-
         uniques = self._codes_and_uniques[1]
         return Index._with_infer(uniques, name=self.name)
 

pandas/core/indexes/base.py

@@ -2062,42 +2062,6 @@ def _drop_level_numbers(self, levnums: list[int]):
                 verify_integrity=False,
             )
 
-    def _get_grouper_for_level(
-        self,
-        mapper,
-        *,
-        level=None,
-        dropna: bool = True,
-    ) -> tuple[Index, npt.NDArray[np.signedinteger] | None, Index | None]:
-        """
-        Get index grouper corresponding to an index level
-
-        Parameters
-        ----------
-        mapper: Group mapping function or None
-            Function mapping index values to groups
-        level : int or None
-            Index level, positional
-        dropna : bool
-            dropna from groupby
-
-        Returns
-        -------
-        grouper : Index
-            Index of values to group on.
-        labels : ndarray of int or None
-            Array of locations in level_index.
-        uniques : Index or None
-            Index of unique values for level.
-        """
-        assert level is None or level == 0
-        if mapper is None:
-            grouper = self
-        else:
-            grouper = self.map(mapper)
-
-        return grouper, None, None
-
     # --------------------------------------------------------------------
     # Introspection Methods
 

pandas/core/indexes/multi.py

@@ -1458,32 +1458,6 @@ def _set_names(self, names, *, level=None, validate: bool = True):
 
     # --------------------------------------------------------------------
 
-    @doc(Index._get_grouper_for_level)
-    def _get_grouper_for_level(
-        self,
-        mapper,
-        *,
-        level=None,
-        dropna: bool = True,
-    ) -> tuple[Index, npt.NDArray[np.signedinteger] | None, Index | None]:
-        if mapper is not None:
-            indexer = self.codes[level]
-            # Handle group mapping function and return
-            level_values = self.levels[level].take(indexer)
-            grouper = level_values.map(mapper)
-            return grouper, None, None
-
-        values = self.get_level_values(level)
-        codes, uniques = algos.factorize(values, sort=True, use_na_sentinel=dropna)
-        assert isinstance(uniques, Index)
-
-        if self.levels[level]._can_hold_na:
-            grouper = uniques.take(codes, fill_value=True)
-        else:
-            grouper = uniques.take(codes)
-
-        return grouper, codes, uniques
-
     @cache_readonly
     def inferred_type(self) -> str:
         return "mixed"