Fix asarray_tuplesafe for numpy 1.24.1 deprecation

pandas/compat/numpy/__init__.py

@@ -8,6 +8,7 @@
 _nlv = Version(_np_version)
 np_version_under1p21 = _nlv < Version("1.21")
 np_version_under1p22 = _nlv < Version("1.22")
+np_version_under1p24 = _nlv < Version("1.24")
 np_version_gte1p22 = _nlv >= Version("1.22")
 is_numpy_dev = _nlv.dev is not None
 _min_numpy_ver = "1.20.3"

pandas/core/common.py

@@ -36,13 +36,15 @@
     RandomState,
     T,
 )
+from pandas.compat.numpy import np_version_under1p24
 
 from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
 from pandas.core.dtypes.common import (
     is_array_like,
     is_bool_dtype,
     is_extension_array_dtype,
     is_integer,
+    is_list_like,
 )
 from pandas.core.dtypes.generic import (
     ABCExtensionArray,
@@ -232,7 +234,10 @@ def asarray_tuplesafe(values: Iterable, dtype: NpDtype | None = None) -> ArrayLi
     elif isinstance(values, ABCIndex):
         return values._values
 
-    if isinstance(values, list) and dtype in [np.object_, object]:
+    if isinstance(values, list) and (
+        dtype in [np.object_, object]
+        or (not np_version_under1p24 and any(is_list_like(val) for val in values))
+    ):
         return construct_1d_object_array_from_listlike(values)
 
     result = np.asarray(values, dtype=dtype)

pandas/tests/reshape/test_pivot.py

@@ -2297,6 +2297,72 @@ def test_pivot_table_datetime_warning(self):
         )
         tm.assert_frame_equal(result, expected)
 
+    def test_pivot_table_with_mixed_nested_tuples(self):
+        # GH 50342
+        df = DataFrame(
+            {
+                "A": ["foo", "foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar"],
+                "B": ["one", "one", "one", "two", "two", "one", "one", "two", "two"],
+                "C": [
+                    "small",
+                    "large",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                ],
+                "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
+                "E": [2, 4, 5, 5, 6, 6, 8, 9, 9],
+                ("col5",): [
+                    "foo",
+                    "foo",
+                    "foo",
+                    "foo",
+                    "foo",
+                    "bar",
+                    "bar",
+                    "bar",
+                    "bar",
+                ],
+                ("col6", 6): [
+                    "one",
+                    "one",
+                    "one",
+                    "two",
+                    "two",
+                    "one",
+                    "one",
+                    "two",
+                    "two",
+                ],
+                (7, "seven"): [
+                    "small",
+                    "large",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                    "small",
+                    "small",
+                    "large",
+                ],
+            }
+        )
+        result = pivot_table(
+            df, values="D", index=["A", "B"], columns=[(7, "seven")], aggfunc=np.sum
+        )
+        expected = DataFrame(
+            [[4.0, 5.0], [7.0, 6.0], [4.0, 1.0], [np.nan, 6.0]],
+            columns=Index(["large", "small"], name=(7, "seven")),
+            index=MultiIndex.from_arrays(
+                [["bar", "bar", "foo", "foo"], ["one", "two"] * 2], names=["A", "B"]
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
 
 class TestPivot:
     def test_pivot(self):