pitrou commented on issue #46063:
URL: https://github.com/apache/arrow/issues/46063#issuecomment-3055807630
As a data point, NumPy has a function `nanmin` that ignores NaN when
computing an array's minimum, but it still returns NaN if no other values are
encountered (it also emits a warning):
```python
>>> np.nanmin(np.array([1.0, float('nan')]))
np.float64(1.0)
>>> np.nanmin(np.array([float('nan')]))
<ipython-input-17-324f73f42199>:1: RuntimeWarning: All-NaN slice encountered
np.nanmin(np.array([float('nan')]))
np.float64(nan)
```
However, it also doesn't support an empty input array unless you pass an
`initial` argument, and then...
```python
>>> np.nanmin(np.array([]))
Traceback (most recent call last):
...
ValueError: zero-size array to reduction operation fmin which has no identity
>>> np.nanmin(np.array([]), initial=5.0)
np.float64(5.0)
>>> np.nanmin(np.array([float('nan')]), initial=5.0)
np.float64(5.0)
```
Arrow on the other hand returns Null if an empty array is given:
```python
>>> pc.min(pa.array([], type=pa.float64()))
<pyarrow.DoubleScalar: None>
```
Thought @jorisvandenbossche @bkietz @westonpace @WillAyd ?
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