GH-48344: [Python] Fix Table.from_struct_array for empty ChunkedArray

[1fanwang]

Rationale for this change

Round-tripping an empty table through to_struct_array() and
Table.from_struct_array() raises
ValueError: Must pass schema, or at least one RecordBatch.
to_struct_array() returns a zero-chunk struct ChunkedArray, so
from_struct_array() has no batch from which to recover the schema.

For a zero-chunk input, from_struct_array() now calls combine_chunks() and
converts the resulting empty struct array into a RecordBatch. Nonempty inputs
keep the existing path. An empty non-struct ChunkedArray still raises the
expected TypeError.

What changes are included in this PR?

The empty ChunkedArray path creates one empty batch before calling
Table.from_batches. The regression coverage checks the round-trip result,
schema preservation, and the invalid non-struct input.

Are these changes tested?

I ran this script against PyArrow 25.0.1 and the compiled branch:

python - <<'PY'
import pyarrow as pa

value = pa.chunked_array(
    [], type=pa.struct([("ints", pa.int32()), ("floats", pa.float32())])
)
print(f"pyarrow={pa.__version__}, chunks={value.num_chunks}")
try:
    result = pa.Table.from_struct_array(value)
    print(f"rows={result.num_rows}, schema={result.schema}")
except Exception as error:
    print(f"{type(error).__name__}: {error}")
PY

Before:

pyarrow=25.0.1, chunks=0
ValueError: Must pass schema, or at least one RecordBatch

After:

pyarrow=26.0.0.dev175+ge4ad179ae, chunks=0
rows=0, schema=ints: int32
floats: float

The focused regression suite also runs against the compiled branch:

$ python -m pytest python/pyarrow/tests/test_table.py -k from_struct_array -q
.......                                                                  [100%]
7 passed, 208 deselected in 0.51s

Are there any user-facing changes?

Table.from_struct_array() now returns the expected empty table for a
zero-chunk struct ChunkedArray instead of raising ValueError.

• GitHub Issue: pa.Table.from_struct_array fails for an empty array #48344

• GitHub Issue: pa.Table.from_struct_array fails for an empty array #48344

[github-actions]

⚠️ GitHub issue #48344 has been automatically assigned in GitHub to PR creator.

[github-actions]

⚠️ GitHub issue #48344 has been automatically assigned in GitHub to PR creator.

[github-actions]

⚠️ GitHub issue #48344 has been automatically assigned in GitHub to PR creator.

[AlenkaF]

Thanks for the contribution! Looks good to me, just added a nit to the test comment.

[Copilot]

Pull request overview

Fixes pyarrow.Table.from_struct_array to correctly round-trip empty tables when the input is a zero-chunk struct ChunkedArray by ensuring Table.from_batches receives an explicit schema.

Changes:

• Pass schema=schema(struct_array.type.fields) when constructing a Table from a struct ChunkedArray (including the zero-chunk case).
• Add a regression test covering Table.from_struct_array with an empty struct ChunkedArray.

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 1 comment.

File	Description
python/pyarrow/table.pxi	Ensures from_struct_array supplies a schema to from_batches for chunked struct inputs, fixing the zero-chunk path.
python/pyarrow/tests/test_table.py	Adds a regression test for constructing a table from a zero-chunk struct ChunkedArray.

[github-actions]

⚠️ GitHub issue #48344 has been automatically assigned in GitHub to PR creator.

[AlenkaF]

@1fanwang could you take a look at the Copilot review comment?

[Copilot]

Pull request overview

Copilot reviewed 2 out of 2 changed files in this pull request and generated no new comments.

[AlenkaF]

Thanks for the update, looks good now!
One minor thing: could you update the description of the PR so it reflects the current change: schema=schema(struct_array.type.fields) is not used anymore but the combine_chunks() is in case of empty arrays.

The linter error should be fixed with a rebase.

[Copilot]

Pull request overview

Copilot reviewed 2 out of 2 changed files in this pull request and generated no new comments.

[AlenkaF]

Thank you again @1fanwang!