BUG: groupby reorders categorical categories

doc/source/whatsnew/v2.0.0.rst

@@ -243,7 +243,7 @@ Bug fixes
 Categorical
 ^^^^^^^^^^^
 - Bug in :meth:`Categorical.set_categories` losing dtype information (:issue:`48812`)
--
+- Bug in :meth:`DataFrame.groupby` and :meth:`Series.groupby` would reorder categories when used as a grouper (:issue:`48749`)
 
 Datetimelike
 ^^^^^^^^^^^^

pandas/core/groupby/categorical.py

@@ -1,7 +1,5 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
-
 import numpy as np
 
 from pandas.core.algorithms import unique1d
@@ -11,9 +9,6 @@
     recode_for_categories,
 )
 
-if TYPE_CHECKING:
-    from pandas.core.indexes.api import CategoricalIndex
-
 
 def recode_for_groupby(
     c: Categorical, sort: bool, observed: bool
@@ -77,7 +72,7 @@ def recode_for_groupby(
     # sort=False should order groups in as-encountered order (GH-8868)
 
     # xref GH:46909: Re-ordering codes faster than using (set|add|reorder)_categories
-    all_codes = np.arange(c.categories.nunique(), dtype=np.int8)
+    all_codes = np.arange(c.categories.nunique())
     # GH 38140: exclude nan from indexer for categories
     unique_notnan_codes = unique1d(c.codes[c.codes != -1])
     if c.ordered:
@@ -90,32 +85,3 @@ def recode_for_groupby(
         take_codes = unique_notnan_codes
 
     return Categorical(c, c.unique().categories.take(take_codes)), None
-
-
-def recode_from_groupby(
-    c: Categorical, sort: bool, ci: CategoricalIndex
-) -> CategoricalIndex:
-    """
-    Reverse the codes_to_groupby to account for sort / observed.
-
-    Parameters
-    ----------
-    c : Categorical
-    sort : bool
-        The value of the sort parameter groupby was called with.
-    ci : CategoricalIndex
-        The codes / categories to recode
-
-    Returns
-    -------
-    CategoricalIndex
-    """
-    # we re-order to the original category orderings
-    if sort:
-        # error: "CategoricalIndex" has no attribute "set_categories"
-        return ci.set_categories(c.categories)  # type: ignore[attr-defined]
-
-    # we are not sorting, so add unobserved to the end
-    new_cats = c.categories[~c.categories.isin(ci.categories)]
-    # error: "CategoricalIndex" has no attribute "add_categories"
-    return ci.add_categories(new_cats)  # type: ignore[attr-defined]

pandas/core/groupby/grouper.py

@@ -39,10 +39,7 @@
 import pandas.core.common as com
 from pandas.core.frame import DataFrame
 from pandas.core.groupby import ops
-from pandas.core.groupby.categorical import (
-    recode_for_groupby,
-    recode_from_groupby,
-)
+from pandas.core.groupby.categorical import recode_for_groupby
 from pandas.core.indexes.api import (
     CategoricalIndex,
     Index,
@@ -462,6 +459,7 @@ class Grouping:
     _group_index: Index | None = None
     _passed_categorical: bool
     _all_grouper: Categorical | None
+    _orig_cats: Index | None
     _index: Index
 
     def __init__(
@@ -479,6 +477,7 @@ def __init__(
         self._orig_grouper = grouper
         self.grouping_vector = _convert_grouper(index, grouper)
         self._all_grouper = None
+        self._orig_cats = None
         self._index = index
         self._sort = sort
         self.obj = obj
@@ -529,6 +528,7 @@ def __init__(
             # a passed Categorical
             self._passed_categorical = True
 
+            self._orig_cats = self.grouping_vector.categories
             self.grouping_vector, self._all_grouper = recode_for_groupby(
                 self.grouping_vector, sort, observed
             )
@@ -646,7 +646,9 @@ def result_index(self) -> Index:
         if self._all_grouper is not None:
             group_idx = self.group_index
             assert isinstance(group_idx, CategoricalIndex)
-            return recode_from_groupby(self._all_grouper, self._sort, group_idx)
+            categories = self._all_grouper.categories
+            # set_categories is dynamically added
+            return group_idx.set_categories(categories)  # type: ignore[attr-defined]
         return self.group_index
 
     @cache_readonly
@@ -678,6 +680,8 @@ def _codes_and_uniques(self) -> tuple[npt.NDArray[np.signedinteger], ArrayLike]:
             uniques = Categorical.from_codes(
                 codes=ucodes, categories=categories, ordered=cat.ordered
             )
+            if not self._observed:
+                uniques = uniques.reorder_categories(self._orig_cats)
             return cat.codes, uniques
 
         elif isinstance(self.grouping_vector, ops.BaseGrouper):