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[SPARK-42574][CONNECT][PYTHON] Fix toPandas to handle duplicated column names #40170

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[SPARK-42574][CONNECT][PYTHON] Fix toPandas to handle duplicated column names #40170

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4377e00
Fix toPandas to handle duplicated column names.
ueshin Feb 25, 2023
1c65f60
Update test_dataframe.py
ueshin Feb 25, 2023
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3 changes: 3 additions & 0 deletions python/pyspark/sql/connect/client.py
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Original file line number Diff line number Diff line change
Expand Up @@ -545,7 +545,10 @@ def to_pandas(self, plan: pb2.Plan) -> "pd.DataFrame":
req = self._execute_plan_request_with_metadata()
req.plan.CopyFrom(plan)
table, metrics = self._execute_and_fetch(req)
column_names = table.column_names
table = table.rename_columns([f"col_{i}" for i in range(len(column_names))])
pdf = table.to_pandas()
pdf.columns = column_names
if len(metrics) > 0:
pdf.attrs["metrics"] = metrics
return pdf
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8 changes: 2 additions & 6 deletions python/pyspark/sql/tests/connect/test_parity_dataframe.py
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Expand Up @@ -112,15 +112,11 @@ def test_to_pandas_for_array_of_struct(self):
def test_to_pandas_from_null_dataframe(self):
super().test_to_pandas_from_null_dataframe()

# TODO(SPARK-41834): Implement SparkSession.conf
@unittest.skip("Fails in Spark Connect, should enable.")
def test_to_pandas_on_cross_join(self):
super().test_to_pandas_on_cross_join()
self.check_to_pandas_on_cross_join()

# TODO(SPARK-41834): Implement SparkSession.conf
@unittest.skip("Fails in Spark Connect, should enable.")
def test_to_pandas_with_duplicated_column_names(self):
super().test_to_pandas_with_duplicated_column_names()
self.check_to_pandas_with_duplicated_column_names()

# TODO(SPARK-42367): DataFrame.drop should handle duplicated columns properly
@unittest.skip("Fails in Spark Connect, should enable.")
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40 changes: 21 additions & 19 deletions python/pyspark/sql/tests/test_dataframe.py
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Expand Up @@ -1129,19 +1129,27 @@ def test_to_pandas(self):

@unittest.skipIf(not have_pandas, pandas_requirement_message) # type: ignore
def test_to_pandas_with_duplicated_column_names(self):
for arrow_enabled in [False, True]:
with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": arrow_enabled}):
self.check_to_pandas_with_duplicated_column_names()

def check_to_pandas_with_duplicated_column_names(self):
import numpy as np

sql = "select 1 v, 1 v"
for arrowEnabled in [False, True]:
with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": arrowEnabled}):
df = self.spark.sql(sql)
pdf = df.toPandas()
types = pdf.dtypes
self.assertEqual(types.iloc[0], np.int32)
self.assertEqual(types.iloc[1], np.int32)
df = self.spark.sql(sql)
pdf = df.toPandas()
types = pdf.dtypes
self.assertEqual(types.iloc[0], np.int32)
self.assertEqual(types.iloc[1], np.int32)

@unittest.skipIf(not have_pandas, pandas_requirement_message) # type: ignore
def test_to_pandas_on_cross_join(self):
for arrow_enabled in [False, True]:
with self.sql_conf({"spark.sql.execution.arrow.pyspark.enabled": arrow_enabled}):
self.check_to_pandas_on_cross_join()

def check_to_pandas_on_cross_join(self):
import numpy as np

sql = """
Expand All @@ -1151,18 +1159,12 @@ def test_to_pandas_on_cross_join(self):
select explode(sequence(1, 3)) v
) t2
"""
for arrowEnabled in [False, True]:
with self.sql_conf(
{
"spark.sql.crossJoin.enabled": True,
"spark.sql.execution.arrow.pyspark.enabled": arrowEnabled,
}
):
df = self.spark.sql(sql)
pdf = df.toPandas()
types = pdf.dtypes
self.assertEqual(types.iloc[0], np.int32)
self.assertEqual(types.iloc[1], np.int32)
with self.sql_conf({"spark.sql.crossJoin.enabled": True}):
df = self.spark.sql(sql)
pdf = df.toPandas()
types = pdf.dtypes
self.assertEqual(types.iloc[0], np.int32)
self.assertEqual(types.iloc[1], np.int32)

@unittest.skipIf(have_pandas, "Required Pandas was found.")
def test_to_pandas_required_pandas_not_found(self):
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