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[WIP][SPARK-53967][PYTHON] Avoid intermediate pandas dataframe creation in df.toPandas #52680

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[WIP][SPARK-53967][PYTHON] Avoid intermediate pandas dataframe creation in df.toPandas #52680

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zhengruifeng Oct 21, 2025
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zhengruifeng Oct 21, 2025
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zhengruifeng Oct 21, 2025
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80 changes: 43 additions & 37 deletions python/pyspark/sql/pandas/conversion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,7 +80,9 @@ def toPandas(self) -> "PandasDataFrameLike":
from pyspark.sql.pandas.utils import require_minimum_pyarrow_version

require_minimum_pyarrow_version()
to_arrow_schema(self.schema, prefers_large_types=jconf.arrowUseLargeVarTypes())
arrow_schema = to_arrow_schema(
self.schema, prefers_large_types=jconf.arrowUseLargeVarTypes()
)
except Exception as e:
if jconf.arrowPySparkFallbackEnabled():
msg = (
Expand Down Expand Up @@ -112,41 +114,40 @@ def toPandas(self) -> "PandasDataFrameLike":

self_destruct = jconf.arrowPySparkSelfDestructEnabled()
batches = self._collect_as_arrow(split_batches=self_destruct)
if len(batches) > 0:
table = pa.Table.from_batches(batches)
# Ensure only the table has a reference to the batches, so that
# self_destruct (if enabled) is effective
del batches
# Pandas DataFrame created from PyArrow uses datetime64[ns] for date type
# values, but we should use datetime.date to match the behavior with when
# Arrow optimization is disabled.
pandas_options = {
"date_as_object": True,
"coerce_temporal_nanoseconds": True,
}
if self_destruct:
# Configure PyArrow to use as little memory as possible:
# self_destruct - free columns as they are converted
# split_blocks - create a separate Pandas block for each column
# use_threads - convert one column at a time
pandas_options.update(
{
"self_destruct": True,
"split_blocks": True,
"use_threads": False,
}
)
# Rename columns to avoid duplicated column names.
pdf = table.rename_columns(
[f"col_{i}" for i in range(table.num_columns)]
).to_pandas(**pandas_options)

# Rename back to the original column names.
pdf.columns = self.columns
# Rename columns to avoid duplicated column names.
temp_col_names = [f"col_{i}" for i in range(len(self.columns))]
if len(batches) > 0:
table = pa.Table.from_batches(batches).rename_columns(temp_col_names)
else:
pdf = pd.DataFrame(columns=self.columns)
# empty dataset
table = arrow_schema.empty_table().rename_columns(temp_col_names)

# Ensure only the table has a reference to the batches, so that
# self_destruct (if enabled) is effective
del batches

# Pandas DataFrame created from PyArrow uses datetime64[ns] for date type
# values, but we should use datetime.date to match the behavior with when
# Arrow optimization is disabled.
pandas_options = {
"date_as_object": True,
"coerce_temporal_nanoseconds": True,
}
if self_destruct:
# Configure PyArrow to use as little memory as possible:
# self_destruct - free columns as they are converted
# split_blocks - create a separate Pandas block for each column
# use_threads - convert one column at a time
pandas_options.update(
{
"self_destruct": True,
"split_blocks": True,
"use_threads": False,
}
)

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

Expand All @@ -155,21 +156,26 @@ def toPandas(self) -> "PandasDataFrameLike":
error_on_duplicated_field_names = True
struct_in_pandas = "dict"

return pd.concat(
pdf = pd.concat(
[
_create_converter_to_pandas(
field.dataType,
field.nullable,
timezone=timezone,
struct_in_pandas=struct_in_pandas,
error_on_duplicated_field_names=error_on_duplicated_field_names,
)(pser)
for (_, pser), field in zip(pdf.items(), self.schema.fields)
)(arrow_col.to_pandas(**pandas_options))
for arrow_col, field in zip(table.columns, self.schema.fields)
],
axis="columns",
)
else:
return pdf
# empty columns
pdf = table.to_pandas(**pandas_options)

pdf.columns = self.columns
return pdf

except Exception as e:
# We might have to allow fallback here as well but multiple Spark jobs can
# be executed. So, simply fail in this case for now.
Expand Down