make lint

pyiceberg/table/__init__.py

@@ -3560,7 +3560,9 @@ def metadata_log_entry_to_row(metadata_entry: MetadataLogEntry) -> Dict[str, Any
                 "latest_sequence_number": latest_snapshot.sequence_number if latest_snapshot else None,
             }
 
-        metadata_log_entries = self.tbl.metadata.metadata_log + [MetadataLogEntry(metadata_file=self.tbl.metadata_location, timestamp_ms=self.tbl.metadata.last_updated_ms)]
+        metadata_log_entries = self.tbl.metadata.metadata_log + [
+            MetadataLogEntry(metadata_file=self.tbl.metadata_location, timestamp_ms=self.tbl.metadata.last_updated_ms)
+        ]
 
         return pa.Table.from_pylist(
             [metadata_log_entry_to_row(entry) for entry in metadata_log_entries],

pyiceberg/table/metadata.py

@@ -293,7 +293,7 @@ def snapshot_by_name(self, name: str) -> Optional[Snapshot]:
         return None
 
     def _snapshot_as_of_timestamp_ms(self, timestamp_ms: int) -> Optional[Snapshot]:
-        """ Return the snapshot that was current at the given timestamp or null if no such snapshot exists."""
+        """Return the snapshot that was current at the given timestamp or null if no such snapshot exists."""
         for entry in reversed(self.snapshot_log):
             if entry.timestamp_ms <= timestamp_ms:
                 return self.snapshot_by_id(entry.snapshot_id)

tests/integration/test_inspect_table.py

@@ -446,6 +446,7 @@ def check_pyiceberg_df_equals_spark_df(df: pa.Table, spark_df: DataFrame) -> Non
         spark_df = spark.sql(f"SELECT * FROM {identifier}.partitions VERSION AS OF {snapshot.snapshot_id}")
         check_pyiceberg_df_equals_spark_df(df, spark_df)
 
+
 @pytest.mark.integration
 @pytest.mark.parametrize("format_version", [1, 2])
 def test_inspect_metadata_log_entries(