fix: Inconsistent schemas when converting to pyarrow #1315
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kosiew
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Dec 3, 2025
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| def test_parquet_non_null_column_to_pyarrow(ctx, tmp_path): | ||
| path = tmp_path.joinpath("t.parquet") | ||
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| ctx.sql("create table t_(a int not null)").collect() | ||
| ctx.sql("insert into t_ values (1), (2), (3)").collect() | ||
| ctx.sql(f"copy (select * from t_) to '{path}'").collect() | ||
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| ctx.register_parquet("t", path) | ||
| pyarrow_table = ctx.sql("select max(a) as m from t").to_arrow_table() | ||
| assert pyarrow_table.to_pydict() == {"m": [3]} | ||
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Contributor
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I think we should also add
- a regression test for the empty-batch case to ensure when there are zero record batches the DataFrame schema is still applied (because that’s the behavior the code has preserved).
- a test covering an edge-case where an aggregation yields a NULL (e.g., max on an empty input) and ensure to_arrow_table correctly represents that ensures we didn't hide other nullability issues
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Good call, I've added those two new tests.
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Which issue does this PR close?
Closes #1314.
Rationale for this change
Allow the conversion of batches to
pyarrowwhen there are inconsistencies with theDataFrame's schema (i.e., on the nullability of columns).What changes are included in this PR?
pyarrownow uses theRecordBatch's own schema instead of theDataFrame's (when possible): https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.from_batches.Are there any user-facing changes?
No.