Fix StructColumn.to_pandas type handling issues (#9388)

Fixes: #9383 

This PR preserves `datetime` & `timedelta` types in `StructColumn` upon conversion to `pd.Series`, and also preserves `int`   values to be `int` rather than converting to `float`.

Authors:
  - GALI PREM SAGAR (https://github.com/galipremsagar)

Approvers:
  - Michael Wang (https://github.com/isVoid)

URL: #9388

python/cudf/cudf/core/column/struct.py

@@ -1,6 +1,7 @@
 # Copyright (c) 2020, NVIDIA CORPORATION.
 from __future__ import annotations
 
+import pandas as pd
 import pyarrow as pa
 
 import cudf
@@ -80,6 +81,16 @@ def to_arrow(self):
             pa_type, len(self), buffers, children=children
         )
 
+    def to_pandas(self, index: pd.Index = None, **kwargs) -> "pd.Series":
+        # We cannot go via Arrow's `to_pandas` because of the following issue:
+        # https://issues.apache.org/jira/browse/ARROW-12680
+
+        pd_series = pd.Series(self.to_arrow().tolist(), dtype="object")
+
+        if index is not None:
+            pd_series.index = index
+        return pd_series
+
     def __getitem__(self, args):
         result = super().__getitem__(args)
         if isinstance(result, dict):

python/cudf/cudf/tests/test_struct.py

@@ -7,7 +7,7 @@
 
 import cudf
 from cudf.core.dtypes import StructDtype
-from cudf.testing._utils import assert_eq
+from cudf.testing._utils import DATETIME_TYPES, TIMEDELTA_TYPES, assert_eq
 
 
 @pytest.mark.parametrize(
@@ -292,3 +292,35 @@ def test_struct_field_errors(data):
 
     with pytest.raises(IndexError):
         got.struct.field(100)
+
+
+@pytest.mark.parametrize("dtype", DATETIME_TYPES + TIMEDELTA_TYPES)
+def test_struct_with_datetime_and_timedelta(dtype):
+    df = cudf.DataFrame(
+        {
+            "a": [12, 232, 2334],
+            "datetime": cudf.Series([23432, 3432423, 324324], dtype=dtype),
+        }
+    )
+    series = df.to_struct()
+    a_array = np.array([12, 232, 2334])
+    datetime_array = np.array([23432, 3432423, 324324]).astype(dtype)
+
+    actual = series.to_pandas()
+    values_list = []
+    for i, val in enumerate(a_array):
+        values_list.append({"a": val, "datetime": datetime_array[i]})
+
+    expected = pd.Series(values_list)
+    assert_eq(expected, actual)
+
+
+def test_struct_int_values():
+    series = cudf.Series(
+        [{"a": 1, "b": 2}, {"a": 10, "b": None}, {"a": 5, "b": 6}]
+    )
+    actual_series = series.to_pandas()
+
+    assert isinstance(actual_series[0]["b"], int)
+    assert isinstance(actual_series[1]["b"], type(None))
+    assert isinstance(actual_series[2]["b"], int)