Adjust tests in extension folder for new string option (#56191)

* Adjust tests in extension folder for new string option

* Fix typing

* Update setitem.py

pandas/tests/extension/base/dtype.py

@@ -59,7 +59,12 @@ def test_check_dtype(self, data):
 
         # check equivalency for using .dtypes
         df = pd.DataFrame(
-            {"A": pd.Series(data, dtype=dtype), "B": data, "C": "foo", "D": 1}
+            {
+                "A": pd.Series(data, dtype=dtype),
+                "B": data,
+                "C": pd.Series(["foo"] * len(data), dtype=object),
+                "D": 1,
+            }
         )
         result = df.dtypes == str(dtype)
         assert np.dtype("int64") != "Int64"

pandas/tests/extension/base/groupby.py

@@ -21,7 +21,12 @@ class BaseGroupbyTests:
 
     def test_grouping_grouper(self, data_for_grouping):
         df = pd.DataFrame(
-            {"A": ["B", "B", None, None, "A", "A", "B", "C"], "B": data_for_grouping}
+            {
+                "A": pd.Series(
+                    ["B", "B", None, None, "A", "A", "B", "C"], dtype=object
+                ),
+                "B": data_for_grouping,
+            }
         )
         gr1 = df.groupby("A").grouper.groupings[0]
         gr2 = df.groupby("B").grouper.groupings[0]

pandas/tests/extension/base/missing.py

@@ -44,7 +44,7 @@ def test_dropna_series(self, data_missing):
         tm.assert_series_equal(result, expected)
 
     def test_dropna_frame(self, data_missing):
-        df = pd.DataFrame({"A": data_missing})
+        df = pd.DataFrame({"A": data_missing}, columns=pd.Index(["A"], dtype=object))
 
         # defaults
         result = df.dropna()

pandas/tests/extension/base/ops.py

@@ -5,6 +5,8 @@
 import numpy as np
 import pytest
 
+from pandas._config import using_pyarrow_string_dtype
+
 from pandas.core.dtypes.common import is_string_dtype
 
 import pandas as pd
@@ -27,13 +29,23 @@ def _get_expected_exception(
         # The self.obj_bar_exc pattern isn't great in part because it can depend
         #  on op_name or dtypes, but we use it here for backward-compatibility.
         if op_name in ["__divmod__", "__rdivmod__"]:
-            return self.divmod_exc
-        if isinstance(obj, pd.Series) and isinstance(other, pd.Series):
-            return self.series_array_exc
+            result = self.divmod_exc
+        elif isinstance(obj, pd.Series) and isinstance(other, pd.Series):
+            result = self.series_array_exc
         elif isinstance(obj, pd.Series):
-            return self.series_scalar_exc
+            result = self.series_scalar_exc
         else:
-            return self.frame_scalar_exc
+            result = self.frame_scalar_exc
+
+        if using_pyarrow_string_dtype() and result is not None:
+            import pyarrow as pa
+
+            result = (  # type: ignore[assignment]
+                result,
+                pa.lib.ArrowNotImplementedError,
+                NotImplementedError,
+            )
+        return result
 
     def _cast_pointwise_result(self, op_name: str, obj, other, pointwise_result):
         # In _check_op we check that the result of a pointwise operation

pandas/tests/extension/base/setitem.py

@@ -351,11 +351,11 @@ def test_setitem_preserves_views(self, data):
 
     def test_setitem_with_expansion_dataframe_column(self, data, full_indexer):
         # https://github.com/pandas-dev/pandas/issues/32395
-        df = expected = pd.DataFrame({"data": pd.Series(data)})
+        df = expected = pd.DataFrame({0: pd.Series(data)})
         result = pd.DataFrame(index=df.index)
 
         key = full_indexer(df)
-        result.loc[key, "data"] = df["data"]
+        result.loc[key, 0] = df[0]
 
         tm.assert_frame_equal(result, expected)
 

pandas/tests/extension/test_categorical.py

@@ -18,6 +18,8 @@
 import numpy as np
 import pytest
 
+from pandas._config import using_pyarrow_string_dtype
+
 import pandas as pd
 from pandas import Categorical
 import pandas._testing as tm
@@ -100,7 +102,9 @@ def test_contains(self, data, data_missing):
             if na_value_obj is na_value:
                 continue
             assert na_value_obj not in data
-            assert na_value_obj in data_missing  # this line differs from super method
+            # this section suffers from super method
+            if not using_pyarrow_string_dtype():
+                assert na_value_obj in data_missing
 
     def test_empty(self, dtype):
         cls = dtype.construct_array_type()

pandas/tests/extension/test_numpy.py

@@ -196,7 +196,7 @@ def test_series_constructor_scalar_with_index(self, data, dtype):
 
 
 class TestDtype(BaseNumPyTests, base.BaseDtypeTests):
-    def test_check_dtype(self, data, request):
+    def test_check_dtype(self, data, request, using_infer_string):
         if data.dtype.numpy_dtype == "object":
             request.applymarker(
                 pytest.mark.xfail(
@@ -429,7 +429,7 @@ def test_setitem_with_expansion_dataframe_column(self, data, full_indexer):
         if data.dtype.numpy_dtype != object:
             if not isinstance(key, slice) or key != slice(None):
                 expected = pd.DataFrame({"data": data.to_numpy()})
-        tm.assert_frame_equal(result, expected)
+        tm.assert_frame_equal(result, expected, check_column_type=False)
 
 
 @skip_nested