Fix the fix for the failing test

python/pyarrow/tests/interchange/test_conversion.py

@@ -171,11 +171,6 @@ def test_pandas_roundtrip(uint, int, float, np_float):
             "c": pa.array(np.array(arr, dtype=np_float), type=float),
         }
     )
-    if Version(pd.__version__) >= Version("2.0"):
-        # See https://github.com/pandas-dev/pandas/issues/50554
-        table["d"] = ["a", "", "c"]
-        # large string is not supported by pandas implementation
-
     from pandas.api.interchange import (
         from_dataframe as pandas_from_dataframe
     )
@@ -192,6 +187,34 @@ def test_pandas_roundtrip(uint, int, float, np_float):
     assert table_protocol.column_names() == result_protocol.column_names()
 
 
+@pytest.mark.pandas
+def test_roundtrip_pandas_string():
+    # See https://github.com/pandas-dev/pandas/issues/50554
+    if Version(pd.__version__) < Version("1.6"):
+        pytest.skip(" Column.size() called as a method in pandas 2.0.0")
+
+    # large string is not supported by pandas implementation
+    table = pa.table({"a": pa.array(["a", "", "c"])})
+
+    from pandas.api.interchange import (
+        from_dataframe as pandas_from_dataframe
+    )
+    pandas_df = pandas_from_dataframe(table)
+    result = pi.from_dataframe(pandas_df)
+
+    assert result[0].to_pylist() == table[0].to_pylist()
+    assert pa.types.is_string(table[0].type)
+    assert pa.types.is_large_string(result[0].type)
+
+    table_protocol = table.__dataframe__()
+    result_protocol = result.__dataframe__()
+
+    assert table_protocol.num_columns() == result_protocol.num_columns()
+    assert table_protocol.num_rows() == result_protocol.num_rows()
+    assert table_protocol.num_chunks() == result_protocol.num_chunks()
+    assert table_protocol.column_names() == result_protocol.column_names()
+
+
 @pytest.mark.pandas
 def test_roundtrip_pandas_boolean():
     if Version(pd.__version__) < Version("1.5.0"):
@@ -228,12 +251,12 @@ def test_roundtrip_pandas_datetime(unit):
     dt_arr = [dt(2007, 7, 13), dt(2007, 7, 14), dt(2007, 7, 15)]
     table = pa.table({"a": pa.array(dt_arr, type=pa.timestamp(unit))})
 
-    if Version(pd.__version__) >= Version("2.0"):
-        expected = table
-    else:
+    if Version(pd.__version__) < Version("1.6"):
         # pandas < 2.0 always creates datetime64 in "ns"
         # resolution
         expected = pa.table({"a": pa.array(dt_arr, type=pa.timestamp('ns'))})
+    else:
+        expected = table
 
     from pandas.api.interchange import (
         from_dataframe as pandas_from_dataframe