allisonwang-db commented on code in PR #42161:
URL: https://github.com/apache/spark/pull/42161#discussion_r1274354197
##########
python/pyspark/sql/pandas/serializers.py:
##########
@@ -385,37 +385,28 @@ def _create_struct_array(self, df, arrow_struct_type):
"""
import pyarrow as pa
- # Input partition and result pandas.DataFrame empty, make empty Arrays
with struct
- if len(df) == 0 and len(df.columns) == 0:
- arrs_names = [
- (pa.array([], type=field.type), field.name) for field in
arrow_struct_type
- ]
+ if len(df.columns) == 0:
+ return pa.array([{}] * len(df), arrow_struct_type)
Review Comment:
Under what scenario would it be possible for a DataFrame to have rows but no
columns? i.e len(df.columns) == 0 but len(df) > 0?
##########
python/pyspark/sql/tests/test_udtf.py:
##########
@@ -487,6 +487,14 @@ def eval(self, x: int):
self.assertEqual(TestUDTF(lit(1)).collect(), [Row(x={1: "1"})])
+ def test_udtf_with_empty_output_types(self):
+ @udtf(returnType=StructType())
+ class TestUDTF:
+ def eval(self):
+ yield tuple()
+
+ self.assertEqual(TestUDTF().collect(), [Row()])
Review Comment:
Let's use assertDataFrameEqual :)
##########
python/pyspark/sql/pandas/serializers.py:
##########
@@ -385,37 +385,28 @@ def _create_struct_array(self, df, arrow_struct_type):
"""
import pyarrow as pa
- # Input partition and result pandas.DataFrame empty, make empty Arrays
with struct
- if len(df) == 0 and len(df.columns) == 0:
- arrs_names = [
- (pa.array([], type=field.type), field.name) for field in
arrow_struct_type
- ]
+ if len(df.columns) == 0:
+ return pa.array([{}] * len(df), arrow_struct_type)
# Assign result columns by schema name if user labeled with strings
elif self._assign_cols_by_name and any(isinstance(name, str) for name
in df.columns):
Review Comment:
```suggestion
if self._assign_cols_by_name and any(isinstance(name, str) for name
in df.columns):
```
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