BUG: pivot_table with nested elements and numpy 1.24 (#50682)

* Fix asarray_tuplesafe for numpy 1.24.1 deprecation

* BUG: pivot_table with nested elements and numpy 1.24

* For all numpy versions

* Undo unneeded variable

* fix for arraymanager

* use try except

* typing

* Update pandas/core/common.py

Co-authored-by: Marc Garcia <garcia.marc@gmail.com>

* line length

Co-authored-by: Marc Garcia <garcia.marc@gmail.com>

doc/source/whatsnew/v1.5.3.rst

@@ -32,8 +32,8 @@ Bug fixes
 - Bug in :meth:`Series.quantile` emitting warning from NumPy when :class:`Series` has only ``NA`` values (:issue:`50681`)
 - Bug when chaining several :meth:`.Styler.concat` calls, only the last styler was concatenated (:issue:`49207`)
 - Fixed bug when instantiating a :class:`DataFrame` subclass inheriting from ``typing.Generic`` that triggered a ``UserWarning`` on python 3.11 (:issue:`49649`)
+- Bug in :func:`pivot_table` with NumPy 1.24 or greater when the :class:`DataFrame` columns has nested elements (:issue:`50342`)
 - Bug in :func:`pandas.testing.assert_series_equal` (and equivalent ``assert_`` functions) when having nested data and using numpy >= 1.25 (:issue:`50360`)
--
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_153.other:

pandas/core/common.py

@@ -25,6 +25,7 @@
     cast,
     overload,
 )
+import warnings
 
 import numpy as np
 
@@ -235,7 +236,17 @@ def asarray_tuplesafe(values: Iterable, dtype: NpDtype | None = None) -> ArrayLi
     if isinstance(values, list) and dtype in [np.object_, object]:
         return construct_1d_object_array_from_listlike(values)
 
-    result = np.asarray(values, dtype=dtype)
+    try:
+        with warnings.catch_warnings():
+            # Can remove warning filter once NumPy 1.24 is min version
+            warnings.simplefilter("ignore", np.VisibleDeprecationWarning)
+            result = np.asarray(values, dtype=dtype)
+    except ValueError:
+        # Using try/except since it's more performant than checking is_list_like
+        # over each element
+        # error: Argument 1 to "construct_1d_object_array_from_listlike"
+        # has incompatible type "Iterable[Any]"; expected "Sized"
+        return construct_1d_object_array_from_listlike(values)  # type: ignore[arg-type]
 
     if issubclass(result.dtype.type, str):
         result = np.asarray(values, dtype=object)

pandas/tests/reshape/test_pivot.py

@@ -2312,6 +2312,75 @@ def test_pivot_table_datetime_warning(self):
         )
         tm.assert_frame_equal(result, expected)
 
+    def test_pivot_table_with_mixed_nested_tuples(self, using_array_manager):
+        # 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"]
+            ),
+        )
+        if using_array_manager:
+            # INFO(ArrayManager) column without NaNs can preserve int dtype
+            expected["small"] = expected["small"].astype("int64")
+        tm.assert_frame_equal(result, expected)
+
 
 class TestPivot:
     def test_pivot(self):