BUG: Fix getitem dtype preservation with multiindexes

pandas/core/frame.py

@@ -3831,18 +3831,8 @@ def _getitem_multilevel(self, key):
         if isinstance(loc, (slice, np.ndarray)):
             new_columns = self.columns[loc]
             result_columns = maybe_droplevels(new_columns, key)
-            if self._is_mixed_type:
-                result = self.reindex(columns=new_columns)
-                result.columns = result_columns
-            else:
-                new_values = self._values[:, loc]
-                result = self._constructor(
-                    new_values, index=self.index, columns=result_columns, copy=False
-                )
-                if using_copy_on_write() and isinstance(loc, slice):
-                    result._mgr.add_references(self._mgr)  # type: ignore[arg-type]
-
-                result = result.__finalize__(self)
+            result = self.iloc[:, loc]
+            result.columns = result_columns
 
             # If there is only one column being returned, and its name is
             # either an empty string, or a tuple with an empty string as its

pandas/tests/indexing/multiindex/test_multiindex.py

@@ -4,6 +4,8 @@
 import pandas._libs.index as _index
 from pandas.errors import PerformanceWarning
 
+from pandas.core.dtypes.common import is_categorical_dtype
+
 import pandas as pd
 from pandas import (
     DataFrame,
@@ -12,6 +14,7 @@
     Series,
 )
 import pandas._testing as tm
+from pandas.core.arrays.boolean import BooleanDtype
 
 
 class TestMultiIndexBasic:
@@ -207,6 +210,24 @@ def test_multiindex_with_na_missing_key(self):
         with pytest.raises(KeyError, match="missing_key"):
             df[[("missing_key",)]]
 
+    def test_multiindex_dtype_preservation(self):
+        # GH51261
+        columns = MultiIndex.from_tuples([("A", "B")], names=["lvl1", "lvl2"])
+        df = DataFrame(["value"], columns=columns).astype("category")
+        df_no_multiindex = df["A"]
+        assert is_categorical_dtype(df_no_multiindex["B"])
+
+        # geopandas 1763 analogue
+        df = DataFrame(
+            [[1, 0], [0, 1]],
+            columns=[
+                ["foo", "foo"],
+                ["location", "location"],
+                ["x", "y"],
+            ],
+        ).assign(bools=Series([True, False], dtype="boolean"))
+        assert isinstance(df["bools"].dtype, BooleanDtype)
+
     def test_multiindex_from_tuples_with_nan(self):
         # GH#23578
         result = MultiIndex.from_tuples([("a", "b", "c"), np.nan, ("d", "", "")])