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[SPARK-54182][SQL][PYTHON] Optimize non-arrow conversion of df.toPandas #52897

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[SPARK-54182][SQL][PYTHON] Optimize non-arrow conversion of df.toPandas #52897

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cd50afd
refactor: remove intermedia data frame
Yicong-Huang Nov 5, 2025
26ad2cc
fix: format
Yicong-Huang Nov 5, 2025
dd32ab3
fix: empty df code path
Yicong-Huang Nov 6, 2025
55c2634
refactor: simplify arrow to pandas conversion
Yicong-Huang Nov 10, 2025
dc43392
chore: clean up
Yicong-Huang Nov 10, 2025
5d7a578
feat: use iterator to replace list
Yicong-Huang Nov 11, 2025
6a7b7a3
fix: mypy issue
Yicong-Huang Nov 12, 2025
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29 changes: 17 additions & 12 deletions python/pyspark/sql/pandas/conversion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
from typing import (
Any,
Callable,
Iterator,
List,
Optional,
Union,
Expand Down Expand Up @@ -208,18 +209,20 @@ def toPandas(self) -> "PandasDataFrameLike":

# Below is toPandas without Arrow optimization.
rows = self.collect()
if len(rows) > 0:
pdf = pd.DataFrame.from_records(
rows, index=range(len(rows)), columns=self.columns # type: ignore[arg-type]
)
else:
pdf = pd.DataFrame(columns=self.columns)

if len(pdf.columns) > 0:
if len(self.columns) > 0:
timezone = sessionLocalTimeZone
struct_in_pandas = pandasStructHandlingMode

return pd.concat(
# Extract columns from rows and apply converters
if len(rows) > 0:
# Use iterator to avoid materializing intermediate data structure
columns_data: Iterator[Any] = iter(zip(*rows))
else:
columns_data = iter([] for _ in self.schema.fields)

# Build DataFrame from columns
pdf = pd.concat(
[
_create_converter_to_pandas(
field.dataType,
Expand All @@ -230,13 +233,15 @@ def toPandas(self) -> "PandasDataFrameLike":
),
error_on_duplicated_field_names=False,
timestamp_utc_localized=False,
)(pser)
for (_, pser), field in zip(pdf.items(), self.schema.fields)
)(pd.Series(col_data, dtype=object))
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@zhengruifeng zhengruifeng Nov 11, 2025

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why is dtype=object necessary?

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@Yicong-Huang Yicong-Huang Nov 11, 2025

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here is building a series to pass to _create_converter_to_pandas, which will convert the series to the declared type in field.dataType. So I think the dtype here is optional and unnecessary. If we do not supply object, then when creating series it will start to infer the type, which will be quite slow.

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@Yicong-Huang Yicong-Huang Nov 11, 2025

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I also tried to use field.dataType explicitly but it would need some conversion to pandas data type, which is the purpose of _create_converter_to_pandas. So I suggest we keep use object for disabling type inference.

for col_data, field in zip(columns_data, self.schema.fields)
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@zhengruifeng zhengruifeng Nov 11, 2025

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can we avoid creating columns_data: list[list] ?

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@Yicong-Huang Yicong-Huang Nov 11, 2025

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good point, although I think it is a list of list references so memory won't be too large of a difference. I changed it to iterator.

],
axis="columns",
axis=1,
keys=self.columns,
)
else:
return pdf
else:
return pd.DataFrame(columns=[], index=range(len(rows)))

def toArrow(self) -> "pa.Table":
from pyspark.sql.dataframe import DataFrame
Expand Down