rok commented on issue #48003:
URL: https://github.com/apache/arrow/issues/48003#issuecomment-3473306119
Thanks for compiling the list in #39233, it is useful to see!
I suppose the issue you're describing occurs because multiplying double with
`duration[xs]` would need to return a `duration[xs]`, which would requires an
implicit rounding step (`duration[xs]` is physically an `int64`). We can
discuss adding this as implicit logic, but we try to avoid implicit steps like
this. You can work around this by adding a round step:
```python
import pyarrow as pa
deltas = pa.array([1, 2, 3], type=pa.duration("s"))
unit = pa.scalar(1, type=pa.duration("s"))
# forward transform to float:
x = pa.compute.divide(deltas, unit) # gives double
# added rounding step
x = pc.compute.round(x, unit.value).cast("int64") # This won't work if unit
is not an integer
# backward transform to duration
y = pa.compute.multiply(x, unit)
```
But are you doing temporal rounding?
```math
\frac{timestamp - reference}{unit} * unit + reference
```
If so you can try:
```python
import pyarrow as pa
timestamps = pa.array([1, 2, 3], type=pa.timestamp("s"))
durations = pa.array([1, 2, 3], type=pa.duration("s"))
y = pa.compute.round_temporal(timestamps, unit="second", multiple=1) # We
should have this work on durations too
```
Or go unitless:
```python
import pyarrow as pa
durations = pa.array([1, 2, 3], type=pa.duration("s"))
y = pa.compute.round_to_multiple(durations.cast("int64"), 1)
```
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