REF: move misplaced tests

pandas/tests/extension/base/getitem.py

@@ -468,23 +468,3 @@ def test_item(self, data):
 
         with pytest.raises(ValueError, match=msg):
             s.item()
-
-    def test_ellipsis_index(self):
-        # GH42430 1D slices over extension types turn into N-dimensional slices over
-        #  ExtensionArrays
-        class CapturingStringArray(pd.arrays.StringArray):
-            """Extend StringArray to capture arguments to __getitem__"""
-
-            def __getitem__(self, item):
-                self.last_item_arg = item
-                return super().__getitem__(item)
-
-        df = pd.DataFrame(
-            {"col1": CapturingStringArray(np.array(["hello", "world"], dtype=object))}
-        )
-        _ = df.iloc[:1]
-
-        # String comparison because there's no native way to compare slices.
-        # Before the fix for GH42430, last_item_arg would get set to the 2D slice
-        # (Ellipsis, slice(None, 1, None))
-        tm.assert_equal(str(df["col1"].array.last_item_arg), "slice(None, 1, None)")

pandas/tests/extension/base/groupby.py

@@ -67,30 +67,6 @@ def test_groupby_agg_extension(self, data_for_grouping):
         result = df.groupby("A").first()
         tm.assert_frame_equal(result, expected)
 
-    def test_groupby_agg_extension_timedelta_cumsum_with_named_aggregation(self):
-        # GH#41720
-        expected = pd.DataFrame(
-            {
-                "td": {
-                    0: pd.Timedelta("0 days 01:00:00"),
-                    1: pd.Timedelta("0 days 01:15:00"),
-                    2: pd.Timedelta("0 days 01:15:00"),
-                }
-            }
-        )
-        df = pd.DataFrame(
-            {
-                "td": pd.Series(
-                    ["0 days 01:00:00", "0 days 00:15:00", "0 days 01:15:00"],
-                    dtype="timedelta64[ns]",
-                ),
-                "grps": ["a", "a", "b"],
-            }
-        )
-        gb = df.groupby("grps")
-        result = gb.agg(td=("td", "cumsum"))
-        tm.assert_frame_equal(result, expected)
-
     def test_groupby_extension_no_sort(self, data_for_grouping):
         df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
 

pandas/tests/extension/test_common.py

@@ -78,3 +78,26 @@ def test_astype_no_copy():
 def test_is_extension_array_dtype(dtype):
     assert isinstance(dtype, dtypes.ExtensionDtype)
     assert is_extension_array_dtype(dtype)
+
+
+class CapturingStringArray(pd.arrays.StringArray):
+    """Extend StringArray to capture arguments to __getitem__"""
+
+    def __getitem__(self, item):
+        self.last_item_arg = item
+        return super().__getitem__(item)
+
+
+def test_ellipsis_index():
+    # GH#42430 1D slices over extension types turn into N-dimensional slices
+    #  over ExtensionArrays
+    df = pd.DataFrame(
+        {"col1": CapturingStringArray(np.array(["hello", "world"], dtype=object))}
+    )
+    _ = df.iloc[:1]
+
+    # String comparison because there's no native way to compare slices.
+    # Before the fix for GH#42430, last_item_arg would get set to the 2D slice
+    # (Ellipsis, slice(None, 1, None))
+    out = df["col1"].array.last_item_arg
+    assert str(out) == "slice(None, 1, None)"

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -1606,3 +1606,28 @@ def test_agg_with_as_index_false_with_list():
         columns=MultiIndex.from_tuples([("a1", ""), ("a2", ""), ("b", "sum")]),
     )
     tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_agg_extension_timedelta_cumsum_with_named_aggregation():
+    # GH#41720
+    expected = DataFrame(
+        {
+            "td": {
+                0: pd.Timedelta("0 days 01:00:00"),
+                1: pd.Timedelta("0 days 01:15:00"),
+                2: pd.Timedelta("0 days 01:15:00"),
+            }
+        }
+    )
+    df = DataFrame(
+        {
+            "td": Series(
+                ["0 days 01:00:00", "0 days 00:15:00", "0 days 01:15:00"],
+                dtype="timedelta64[ns]",
+            ),
+            "grps": ["a", "a", "b"],
+        }
+    )
+    gb = df.groupby("grps")
+    result = gb.agg(td=("td", "cumsum"))
+    tm.assert_frame_equal(result, expected)