BUG: Ensure dataframe preserves categorical indices with categorial series

pandas/core/indexes/base.py

@@ -112,7 +112,10 @@
     pandas_dtype,
     validate_all_hashable,
 )
-from pandas.core.dtypes.concat import concat_compat
+from pandas.core.dtypes.concat import (
+    concat_compat,
+    union_categoricals,
+)
 from pandas.core.dtypes.dtypes import (
     ArrowDtype,
     CategoricalDtype,
@@ -212,6 +215,7 @@
         PeriodArray,
     )
 
+
 __all__ = ["Index"]
 
 _unsortable_types = frozenset(("mixed", "mixed-integer"))
@@ -2922,6 +2926,27 @@ def union(self, other, sort=None):
                     "Can only union MultiIndex with MultiIndex or Index of tuples, "
                     "try mi.to_flat_index().union(other) instead."
                 )
+
+            if isinstance(self, ABCCategoricalIndex) and isinstance(
+                other, ABCCategoricalIndex
+            ):
+                both_categories = self.categories
+                # if ordered and unordered, we set categories to be unordered
+                ordered = False if self.ordered != other.ordered else None
+                if ordered is False:
+                    both_categories = union_categoricals(
+                        [self.as_unordered(), other.as_unordered()],  # type: ignore[attr-defined]
+                        sort_categories=True,
+                    ).categories
+                else:
+                    both_categories = union_categoricals(
+                        [self, other], sort_categories=True
+                    ).categories
+                # Convert both indexes to have the same categories
+                self = self.set_categories(both_categories, ordered=ordered)  # type: ignore[attr-defined]
+                other = other.set_categories(both_categories, ordered=ordered)  # type: ignore[attr-defined]
+                return self.union(other, sort=sort)
+
             self, other = self._dti_setop_align_tzs(other, "union")
 
             dtype = self._find_common_type_compat(other)
@@ -3006,7 +3031,7 @@ def _union(self, other: Index, sort: bool | None):
         else:
             missing = algos.unique1d(self.get_indexer_non_unique(other)[1])
 
-        result: Index | MultiIndex | ArrayLike
+        result: Index | MultiIndex | CategoricalIndex | ArrayLike
         if self._is_multi:
             # Preserve MultiIndex to avoid losing dtypes
             result = self.append(other.take(missing))

pandas/tests/frame/test_constructors.py

@@ -2349,7 +2349,7 @@ def test_construct_with_two_categoricalindex_series(self):
         result = DataFrame([s1, s2])
         expected = DataFrame(
             np.array([[39, 6, 4, np.nan, np.nan], [152.0, 242.0, 150.0, 2.0, 2.0]]),
-            columns=["female", "male", "unknown", "f", "m"],
+            columns=CategoricalIndex(["female", "male", "unknown", "f", "m"]),
         )
         tm.assert_frame_equal(result, expected)
 

pandas/tests/indexes/test_setops.py

@@ -573,7 +573,7 @@ def test_union_duplicate_index_subsets_of_each_other(
 
     expected = Index([1, 2, 2, 3, 3, 4], dtype=dtype)
     if isinstance(a, CategoricalIndex):
-        expected = Index([1, 2, 2, 3, 3, 4])
+        expected = CategoricalIndex([1, 2, 2, 3, 3, 4])
     result = a.union(b)
     tm.assert_index_equal(result, expected)
     result = a.union(b, sort=False)
@@ -670,7 +670,7 @@ def test_union_with_duplicate_index_not_subset_and_non_monotonic(
     b = Index([0, 0, 1], dtype=dtype)
     expected = Index([0, 0, 1, 2], dtype=dtype)
     if isinstance(a, CategoricalIndex):
-        expected = Index([0, 0, 1, 2])
+        expected = CategoricalIndex([0, 0, 1, 2])
 
     result = a.union(b)
     tm.assert_index_equal(result, expected)

pandas/tests/reshape/concat/test_append.py

@@ -234,6 +234,8 @@ def test_append_different_columns_types(self, df_columns, series_index):
         result = df._append(ser)
         idx_diff = ser.index.difference(df_columns)
         combined_columns = Index(df_columns.tolist()).append(idx_diff)
+        if isinstance(result.columns, pd.CategoricalIndex):
+            combined_columns = pd.CategoricalIndex(combined_columns)
         expected = DataFrame(
             [
                 [1.0, 2.0, 3.0, np.nan, np.nan, np.nan],