kylebarron commented on code in PR #8790:
URL: https://github.com/apache/arrow-rs/pull/8790#discussion_r2496363534
##########
arrow-pyarrow/src/lib.rs:
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@@ -484,6 +484,102 @@ impl IntoPyArrow for ArrowArrayStreamReader {
}
}
+/// This is a convenience wrapper around `Vec<RecordBatch>` that tries to
simplify conversion from
+/// and to `pyarrow.Table`.
+///
+/// This could be used in circumstances where you either want to consume a
`pyarrow.Table` directly
+/// (although technically, since `pyarrow.Table` implements the
ArrayStreamReader PyCapsule
+/// interface, one could also consume a `PyArrowType<ArrowArrayStreamReader>`
instead) or, more
+/// importantly, where one wants to export a `pyarrow.Table` from a
`Vec<RecordBatch>` from the Rust
+/// side.
+///
+/// ```ignore
+/// #[pyfunction]
+/// fn return_table(...) -> PyResult<PyArrowType<Table>> {
+/// let batches: Vec<RecordBatch>;
+/// PyarrowType(Table::try_new(batches).map_err(|err|
err.into_py_err(py))?)
+/// }
+/// ```
+#[derive(Clone)]
+pub struct Table {
+ record_batches: Vec<RecordBatch>,
+}
+
+impl Table {
+ pub unsafe fn new_unchecked(record_batches: Vec<RecordBatch>) -> Self {
+ Self { record_batches }
+ }
+
+ pub fn try_new(record_batches: Vec<RecordBatch>) -> Result<Self,
ArrowError> {
+ let schema = record_batches[0].schema();
Review Comment:
An Arrow table can be empty with no batches. It would probably be more
reliable to store both batches and a standalone schema.
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