alamb commented on issue #879:
URL:
https://github.com/apache/arrow-datafusion/issues/879#issuecomment-932901002
@praveentiru I don't normally use the dataframe API (and for your case, the
SQL interface might work better).
But in any event, I tried to reproduce the problem you are having, and I was
not able to. Here is the program I used:
```rust
async fn test_cast() {
let mut ctx = ExecutionContext::new();
let month: Date32Array = vec![Some(1000)].into_iter().collect();
let total_orders: Int64Array = vec![Some(10)].into_iter().collect();
let missed_orders: Int64Array = vec![Some(3)].into_iter().collect();
let batch = RecordBatch::try_from_iter(vec![
("Month", Arc::new(month) as ArrayRef),
("Total Orders", Arc::new(total_orders) as ArrayRef),
("Missed Orders", Arc::new(missed_orders) as ArrayRef),
]).unwrap();
let m_order_schema = DFSchema::try_from_qualified_schema(
"m_orders",
batch.schema().as_ref()
).unwrap();
let t_order_schema = DFSchema::try_from_qualified_schema(
"t_orders",
batch.schema().as_ref()
).unwrap();
let table = MemTable::try_new(batch.schema(),
vec![vec![batch]]).unwrap();
let record_count = ctx.read_table(Arc::new(table)).unwrap();
let result = record_count
.select(vec![col("Month"),
col("Total Orders"),
col("Missed Orders"),
(col("Missed Orders").cast_to(&DataType::Float64,
&m_order_schema).unwrap() / col("Total Orders").cast_to(&DataType::Float64,
&t_order_schema).unwrap()).alias("Service Level")]).unwrap();
result.show().await.unwrap();
}
```
And when I ran that code, it appears to produce the output you expected:
```
Starting tests
+------------+--------------+---------------+---------------+
| Month | Total Orders | Missed Orders | Service Level |
+------------+--------------+---------------+---------------+
| 1972-09-27 | 10 | 3 | 0.3 |
+------------+--------------+---------------+---------------+
```
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