REGR: fix consolidation/cache issue with take operation

doc/source/whatsnew/v1.1.2.rst

@@ -17,6 +17,7 @@ Fixed regressions
 - Regression in :meth:`DatetimeIndex.intersection` incorrectly raising ``AssertionError`` when intersecting against a list (:issue:`35876`)
 - Fix regression in updating a column inplace (e.g. using ``df['col'].fillna(.., inplace=True)``) (:issue:`35731`)
 - Performance regression for :meth:`RangeIndex.format` (:issue:`35712`)
+- Fix regression in invalid cache after an indexing operation; this can manifest when setting which does not update the data (:issue:`35521`)
 - Regression in :meth:`DataFrame.replace` where a ``TypeError`` would be raised when attempting to replace elements of type :class:`Interval` (:issue:`35931`)
 - Fixed regression in :meth:`DataFrameGroupBy.agg` where a ``ValueError: buffer source array is read-only`` would be raised when the underlying array is read-only (:issue:`36014`)
 

pandas/core/generic.py

@@ -3534,6 +3534,8 @@ class  max_speed
 
         nv.validate_take(tuple(), kwargs)
 
+        self._consolidate_inplace()
+
         new_data = self._mgr.take(
             indices, axis=self._get_block_manager_axis(axis), verify=True
         )

pandas/tests/frame/test_block_internals.py

@@ -658,3 +658,26 @@ def test_update_inplace_sets_valid_block_values():
 
     # smoketest for OP bug from GH#35731
     assert df.isnull().sum().sum() == 0
+
+
+def test_nonconsolidated_item_cache_take():
+    # https://github.com/pandas-dev/pandas/issues/35521
+
+    # create non-consolidated dataframe with object dtype columns
+    df = pd.DataFrame()
+    df["col1"] = pd.Series(["a"], dtype=object)
+    df["col2"] = pd.Series([0], dtype=object)
+
+    # access column (item cache)
+    df["col1"] == "A"
+    # take operation
+    # (regression was that this consolidated but didn't reset item cache,
+    # resulting in an invalid cache and the .at operation not working properly)
+    df[df["col2"] == 0]
+
+    # now setting value should update actual dataframe
+    df.at[0, "col1"] = "A"
+
+    expected = pd.DataFrame({"col1": ["A"], "col2": [0]}, dtype=object)
+    tm.assert_frame_equal(df, expected)
+    assert df.at[0, "col1"] == "A"