BUG: DataFrame slice selection treated as hashable in Python 3.12 #57500

doc/source/whatsnew/v2.2.2.rst

@@ -25,6 +25,7 @@ Bug fixes
 - :meth:`DataFrame.__dataframe__` was producing incorrect data buffers when the column's type was nullable boolean (:issue:`55332`)
 - :meth:`DataFrame.__dataframe__` was showing bytemask instead of bitmask for ``'string[pyarrow]'`` validity buffer (:issue:`57762`)
 - :meth:`DataFrame.__dataframe__` was showing non-null validity buffer (instead of ``None``) ``'string[pyarrow]'`` without missing values (:issue:`57761`)
+- :meth:`DataFrame.__getitem__` was returning modified columns when called with ``slice`` on Python 3.12 (:issue:`57500`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_222.other:

pandas/core/frame.py

@@ -3849,6 +3849,11 @@ def __getitem__(self, key):
         key = lib.item_from_zerodim(key)
         key = com.apply_if_callable(key, self)
 
+        # Do we have a slicer (on rows)?
+        # As of Python 3.12, slice is hashable so check it first
+        if isinstance(key, slice):
+            return self._getitem_slice(key)
+
         if is_hashable(key) and not is_iterator(key):
             # is_iterator to exclude generator e.g. test_getitem_listlike
             # shortcut if the key is in columns
@@ -3865,10 +3870,6 @@ def __getitem__(self, key):
             elif is_mi and self.columns.is_unique and key in self.columns:
                 return self._getitem_multilevel(key)
 
-        # Do we have a slicer (on rows)?
-        if isinstance(key, slice):
-            return self._getitem_slice(key)
-
         # Do we have a (boolean) DataFrame?
         if isinstance(key, DataFrame):
             return self.where(key)

pandas/tests/frame/indexing/test_indexing.py

@@ -524,6 +524,16 @@ def test_loc_setitem_boolean_mask_allfalse(self):
         result.loc[result.b.isna(), "a"] = result.a.copy()
         tm.assert_frame_equal(result, df)
 
+    def test_getitem_slice_empty(self):
+        df = DataFrame([[1]], columns=MultiIndex.from_product([["A"], ["a"]]))
+        result = df[:]
+
+        expected = DataFrame([[1]], columns=MultiIndex.from_product([["A"], ["a"]]))
+
+        tm.assert_frame_equal(result, expected)
+        # Ensure df[:] returns a view of df, not the same object
+        assert result is not df
+
     def test_getitem_fancy_slice_integers_step(self):
         df = DataFrame(np.random.default_rng(2).standard_normal((10, 5)))