timsaucer opened a new issue, #1573:
URL: https://github.com/apache/datafusion-python/issues/1573
## Background
`SessionContext::create_physical_expr` converts a logical `Expr` against a
`DFSchema` into a `PhysicalExpr` that can be evaluated directly against Arrow
record batches. The Python bindings do not expose this.
## Upstream signature
```rust
pub fn create_physical_expr(
&self,
expr: Expr,
input_dfschema: &DFSchema,
) -> Result<Arc<dyn PhysicalExpr>>
```
## User value
Useful when callers want to evaluate an Expr against an in-memory
RecordBatch without going through DataFrame execution -- for example to score /
filter individual batches in a custom processing loop, to debug optimization
rewrites, or to feed an expression into a custom physical operator. Niche but
irreplaceable for that audience.
## Why deferred
Effort is small (~180-280 LOC) but requires a new `PyPhysicalExpr` wrapper
exposing the opaque `Arc<dyn PhysicalExpr>` trait object plus an
`evaluate(batch) -> ColumnarValue` method to make it useful from Python. Zero
open user requests at the time of audit. Filed for tracking; revisit when a
user surfaces a concrete need or when adjacent FFI work pulls `PhysicalExpr`
into the binding surface.
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