jamesdow21 commented on issue #37192:
URL: https://github.com/apache/arrow/issues/37192#issuecomment-1680954816
Setup:
```
import numpy as np
import pyarrow as pa
import pyarrow.compute as pc
arr = pa.array(np.random.randint(0, 3_600_000_000, 10000, dtype="int64"),
type=pa.duration("us"))
scalar = pa.scalar(1, type=pa.duration("s"))
```
As a more narrow example, what I'm currently doing (including what I believe
pandas is doing under the hood) essentially works out to
```
pc.divide(
pc.cast(pc.cast(arr, pa.int64()), pa.float64()),
pc.cast(pc.cast(pc.cast(scalar, arr.type), pa.int64()), pa.float64())
)
```
but it would be nice if this worked to give the same result
`pc.divide(arr, scalar)`
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