chore(deps): lock file maintenance (#4349)

This PR contains the following updates:

| Update | Change |
|---|---|
| lockFileMaintenance | All locks refreshed |

🔧 This Pull Request updates lock files to use the latest dependency
versions.

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Signed-off-by: Daniel King <[email protected]>
Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
Co-authored-by: Daniel King <[email protected]>

Author: renovate[bot]

Cargo.lock

@@ -41,7 +41,7 @@ def __ArrowDtype_type_patched(self: pandas.ArrowDtype):
 
 
 def empty_arrow_table(schema: pyarrow.Schema) -> pyarrow.Table:
-    return pyarrow.Table.from_arrays([pyarrow.array([], type=t) for t in schema], schema=schema)  # pyright: ignore[reportUnknownMemberType, reportUnknownVariableType, reportUnknownArgumentType]
+    return pyarrow.Table.from_arrays([pyarrow.array([], type=t) for t in schema], schema=schema)  # pyright: ignore[reportUnknownVariableType, reportUnknownArgumentType]
 
 
 def arrow_table_from_struct_array(

java/testfiles/Cargo.lock

@@ -45,9 +45,8 @@ def from_path(path: str):
     def schema(self) -> pyarrow.Schema:
         return self._dataset.schema()
 
-    # regarding the ignore: https://github.com/zen-xu/pyarrow-stubs/pull/258
     @override
-    def count_rows(  # pyright: ignore[reportIncompatibleMethodOverride]
+    def count_rows(
         self,
         filter: pyarrow.dataset.Expression | Expr | None = None,
         batch_size: int | None = None,
@@ -546,6 +545,7 @@ def scanner(
     @override
     def to_batches(
         self,
+        schema: pyarrow.Schema | None = None,
         columns: list[str] | None = None,
         filter: pyarrow.dataset.Expression | Expr | None = None,
         batch_size: int | None = None,
@@ -557,6 +557,8 @@ def to_batches(
         memory_pool: pyarrow.MemoryPool | None = None,
     ) -> Iterator[pyarrow.RecordBatch]:
         """See :class:`vortex.dataset.VortexDataset.scanner`"""
+        if schema:
+            raise ValueError("schema is not supported")
         return self._dataset.to_batches(
             columns=columns,
             filter=filter,
@@ -572,6 +574,7 @@ def to_batches(
     @override
     def to_table(
         self,
+        schema: pyarrow.Schema | None = None,
         columns: list[str] | None = None,
         filter: pyarrow.dataset.Expression | Expr | None = None,
         batch_size: int | None = None,
@@ -583,6 +586,8 @@ def to_table(
         memory_pool: pyarrow.MemoryPool | None = None,
     ) -> pyarrow.Table:
         """See :class:`vortex.dataset.VortexDataset.scanner`"""
+        if schema:
+            raise ValueError("schema is not supported")
         return self._dataset.to_table(
             columns=columns,
             filter=filter,

uv.lock

@@ -36,7 +36,7 @@ def arrow_array(request):  # pyright: ignore[reportUnknownParameterType, reportM
             # Create large arrays of length 100 for each column.
             r[f"col{col}"] = [1, 2, None, 4] * 25
         rows.append(r)
-    return pa.Table.from_pylist(rows)  # pyright: ignore[reportUnknownMemberType]
+    return pa.Table.from_pylist(rows)
 
 
 def test_compress_vortex(benchmark: Callable[[Callable[[], None]], None], vortex_array: vx.Array):

vortex-python/python/vortex/arrays.py

@@ -88,7 +88,7 @@ def test_to_table(ds: pd.Dataset):
     tbl = ds.to_table(columns=["bool", "float"], filter=pc.field("float") > 100)
     # TODO(aduffy): add back once pyarrow supports casting to/from string_view
     # assert 0 == len(tbl.filter(pc.field("string") <= "10000"))
-    assert tbl.slice(0, 10) == pa.Table.from_struct_array(  # pyright: ignore[reportUnknownMemberType]
+    assert tbl.slice(0, 10) == pa.Table.from_struct_array(
         pa.array([record(x, columns={"float", "bool"}) for x in range(10001, 10011)])
     )
 
@@ -191,7 +191,7 @@ def test_fragment_to_table(ds: vx.dataset.VortexDataset):
     f = fragments[0]
 
     tbl = f.to_table(columns=["bool", "float"], filter=pc.field("float") > 100)
-    assert tbl.slice(0, 10) == pa.Table.from_struct_array(  # pyright: ignore[reportUnknownMemberType]
+    assert tbl.slice(0, 10) == pa.Table.from_struct_array(
         pa.array([record(x, columns={"float", "bool"}) for x in range(10001, 10011)])
     )
 

vortex-python/python/vortex/dataset.py

@@ -47,7 +47,7 @@ def test_vortex_datasource(tmpdir_factory):  # pyright: ignore[reportUnknownPara
     # Without an explicit sort, Ray may reorder rows *even within a single record batch*.
     ds = ds.sort("index")
 
-    tbl = pa.concat_tables(pa.Table.from_pydict(x) for x in ds.iter_batches())  # pyright: ignore[reportUnknownMemberType, reportArgumentType]
-    expected = pa.Table.from_pylist([record(x) for x in range(0, 10)], schema=tbl.schema)  # pyright: ignore[reportUnknownMemberType]
+    tbl = pa.concat_tables(pa.Table.from_pydict(x) for x in ds.iter_batches())  # pyright: ignore[reportArgumentType]
+    expected = pa.Table.from_pylist([record(x) for x in range(0, 10)], schema=tbl.schema)
 
     assert tbl == expected

vortex-python/test/bench.py

@@ -12,15 +12,15 @@ def test_struct():
     """
 
     # basic usage
-    array = pa.Table.from_arrays([pa.array(["1", "2", "3"]), pa.array([1.0, 2.0, 3.0])], names=["strings", "floats"])  # pyright: ignore[reportUnknownMemberType]
+    array = pa.Table.from_arrays([pa.array(["1", "2", "3"]), pa.array([1.0, 2.0, 3.0])], names=["strings", "floats"])
     vxarray = vortex.array(array)
     assert isinstance(vxarray, vortex.ChunkedArray)
     struct_array = vxarray.chunks()[0]
     assert isinstance(struct_array, vortex.StructArray)
     assert struct_array.names() == ["strings", "floats"]
 
     # advanced: duplicate field names
-    array = pa.Table.from_arrays(  # pyright: ignore[reportUnknownMemberType]
+    array = pa.Table.from_arrays(
         [pa.array(["1", "2", "3"]), pa.array([1.0, 2.0, 3.0]), pa.array(["one", "two", "three"])],
         names=["strings", "floats", "strings"],
     )