[Python][Dataset] Add filters parameter to orc.read_table() for predicate pushdown

[cbb330]

Summary

Part 4 of ORC predicate pushdown (#48986). Depends on #49361.

Add a filters parameter to pyarrow.orc.read_table() for API parity with Parquet's read_table(). This makes ORC predicate pushdown accessible to Python users without requiring the lower-level Dataset API.

Changes

python/pyarrow/orc.py:

Add filters parameter to read_table(). When specified, delegate to the Dataset API:

def read_table(source, columns=None, filesystem=None, filters=None):
    if filters is not None:
        import pyarrow.dataset as ds
        filter_expr = filters
        if not isinstance(filters, ds.Expression):
            filter_expr = ds.filters_to_expression(filters)
        dataset = ds.dataset(source, format='orc', filesystem=filesystem)
        return dataset.to_table(columns=columns, filter=filter_expr)
    # ... existing non-filter path unchanged

Supported filter formats:

• Expression format: ds.field('id') > 100
• DNF tuple format: [('id', '>', 100)] (Parquet-compatible)
• Supported operators: ==, !=, <, >, <=, >=, in, not in

No Cython changes. This is pure Python, reusing existing Dataset API bindings and the filters_to_expression() utility already used by Parquet.

Examples

import pyarrow.orc as orc
import pyarrow.dataset as ds

# Expression format
table = orc.read_table('data.orc', filters=ds.field('id') > 1000)

# DNF tuple format
table = orc.read_table('data.orc', filters=[('id', '>', 1000)])

# Multiple conditions (AND)
table = orc.read_table('data.orc', filters=[('id', '>', 100), ('id', '<', 200)])

# With column projection
table = orc.read_table('data.orc', columns=['id', 'value'],
                       filters=[('id', '>', 1000)])

Tests

Tests in python/pyarrow/tests/test_orc.py:

• Expression format smoke test
• DNF tuple format smoke test
• Integration with column projection
• Correctness validation: filtered result matches post-filter of full read
• filters=None preserves existing behavior

Component(s)

Python