adopt nits

Author: sungwy

pyiceberg/transforms.py

@@ -198,7 +198,10 @@ def pyarrow_transform(self, source: IcebergType) -> "Callable[[pa.Array], pa.Arr
     def _pyiceberg_transform_wrapper(
         self, transform_func: Callable[["ArrayLike", Any], "ArrayLike"], *args: Any
     ) -> Callable[["ArrayLike"], "ArrayLike"]:
-        import pyarrow as pa
+        try:
+            import pyarrow as pa
+        except ModuleNotFoundError as e:
+            raise ModuleNotFoundError("For bucket/truncate transforms, PyArrow needs to be installed") from e
 
         def _transform(array: "ArrayLike") -> "ArrayLike":
             if isinstance(array, pa.Array):

tests/integration/test_writes/test_partitioned_writes.py

@@ -412,6 +412,12 @@ def test_dynamic_partition_overwrite_unpartitioned_evolve_to_identity_transform(
     spark: SparkSession, session_catalog: Catalog, arrow_table_with_null: pa.Table, part_col: str, format_version: int
 ) -> None:
     identifier = f"default.unpartitioned_table_v{format_version}_evolve_into_identity_transformed_partition_field_{part_col}"
+
+    try:
+        session_catalog.drop_table(identifier=identifier)
+    except NoSuchTableError:
+        pass
+
     tbl = session_catalog.create_table(
         identifier=identifier,
         schema=TABLE_SCHEMA,
@@ -805,6 +811,90 @@ def test_truncate_transform(
     assert files_df.count() == 3
 
 
+@pytest.mark.integration
+@pytest.mark.parametrize(
+    "spec",
+    [
+        # mixed with non-identity is not supported
+        (
+            PartitionSpec(
+                PartitionField(source_id=4, field_id=1001, transform=BucketTransform(2), name="int_bucket"),
+                PartitionField(source_id=1, field_id=1002, transform=IdentityTransform(), name="bool"),
+            )
+        ),
+    ],
+)
+@pytest.mark.parametrize("format_version", [1, 2])
+def test_identity_and_bucket_transform_spec(
+    spec: PartitionSpec,
+    spark: SparkSession,
+    session_catalog: Catalog,
+    arrow_table_with_null: pa.Table,
+    format_version: int,
+) -> None:
+    identifier = "default.identity_and_bucket_transform"
+
+    try:
+        session_catalog.drop_table(identifier=identifier)
+    except NoSuchTableError:
+        pass
+
+    tbl = _create_table(
+        session_catalog=session_catalog,
+        identifier=identifier,
+        properties={"format-version": str(format_version)},
+        data=[arrow_table_with_null],
+        partition_spec=spec,
+    )
+
+    assert tbl.format_version == format_version, f"Expected v{format_version}, got: v{tbl.format_version}"
+    df = spark.table(identifier)
+    assert df.count() == 3, f"Expected 3 total rows for {identifier}"
+    for col in arrow_table_with_null.column_names:
+        assert df.where(f"{col} is not null").count() == 2, f"Expected 2 non-null rows for {col}"
+        assert df.where(f"{col} is null").count() == 1, f"Expected 1 null row for {col} is null"
+
+    assert tbl.inspect.partitions().num_rows == 3
+    files_df = spark.sql(
+        f"""
+            SELECT *
+            FROM {identifier}.files
+        """
+    )
+    assert files_df.count() == 3
+
+
+@pytest.mark.integration
+@pytest.mark.parametrize(
+    "spec",
+    [
+        (PartitionSpec(PartitionField(source_id=11, field_id=1001, transform=TruncateTransform(2), name="binary_trunc"))),
+    ],
+)
+def test_unsupported_transform(
+    spec: PartitionSpec, spark: SparkSession, session_catalog: Catalog, arrow_table_with_null: pa.Table
+) -> None:
+    identifier = "default.unsupported_transform"
+
+    try:
+        session_catalog.drop_table(identifier=identifier)
+    except NoSuchTableError:
+        pass
+
+    tbl = session_catalog.create_table(
+        identifier=identifier,
+        schema=TABLE_SCHEMA,
+        partition_spec=spec,
+        properties={"format-version": "1"},
+    )
+
+    with pytest.raises(
+        ValueError,
+        match="FeatureUnsupported => Unsupported data type for truncate transform: LargeBinary",
+    ):
+        tbl.append(arrow_table_with_null)
+
+
 @pytest.mark.integration
 @pytest.mark.parametrize(
     "spec, expected_rows",