kevinjqliu commented on code in PR #590:
URL: https://github.com/apache/iceberg-python/pull/590#discussion_r1563557100


##########
tests/integration/test_writes/test_writes.py:
##########
@@ -270,6 +270,48 @@ def get_current_snapshot_id(identifier: str) -> int:
     assert tbl.current_snapshot().snapshot_id == 
get_current_snapshot_id(identifier)  # type: ignore
 
 
+@pytest.mark.integration
+@pytest.mark.parametrize("format_version", [1, 2])
+def test_python_writes_special_character_column_with_spark_reads(
+    spark: SparkSession, session_catalog: Catalog, format_version: int
+) -> None:
+    identifier = 
"default.python_writes_special_character_column_with_spark_reads"
+    column_name_with_special_character = "letter/abc"
+    TEST_DATA_WITH_SPECIAL_CHARACTER_COLUMN = {
+        column_name_with_special_character: ['a', None, 'z'],
+        'id': [1, 2, 3],
+        'name': ['AB', 'CD', 'EF'],
+        'address': [
+            {'street': '123', 'city': 'SFO', 'zip': 12345, 
column_name_with_special_character: 'a'},
+            {'street': '456', 'city': 'SW', 'zip': 67890, 
column_name_with_special_character: 'b'},
+            {'street': '789', 'city': 'Random', 'zip': 10112, 
column_name_with_special_character: 'c'},
+        ],
+    }
+    pa_schema = pa.schema([
+        pa.field(column_name_with_special_character, pa.string()),
+        pa.field('id', pa.int32()),
+        pa.field('name', pa.string()),
+        pa.field(
+            'address',
+            pa.struct([
+                pa.field('street', pa.string()),
+                pa.field('city', pa.string()),
+                pa.field('zip', pa.int32()),
+                pa.field(column_name_with_special_character, pa.string()),
+            ]),
+        ),
+    ])
+    arrow_table_with_special_character_column = 
pa.Table.from_pydict(TEST_DATA_WITH_SPECIAL_CHARACTER_COLUMN, schema=pa_schema)
+    tbl = _create_table(session_catalog, identifier, {"format-version": 
format_version}, schema=pa_schema)
+
+    tbl.overwrite(arrow_table_with_special_character_column)
+    # PySpark toPandas() turns nested field into tuple by default, but returns 
the proper schema when Arrow is enabled
+    spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")

Review Comment:
   good catch! i didn't know about the fixture scope behavior. Moved to conftest



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