jorisvandenbossche commented on pull request #10410:
URL: https://github.com/apache/arrow/pull/10410#issuecomment-852904327
I did a quick comparison with `np.where` (using plain float64 arrays without
nulls to have it comparable), and it seems this implementation is already doing
quite good compared to numpy (using a release build):
```
In [19]: N = 10_000_000
...: arr1 = np.random.randn(N)
...: arr2 = np.random.randn(N)
...: mask = np.random.randint(0, 2, N).astype(bool)
...:
...: pa_arr1 = pa.array(arr1)
...: pa_arr2 = pa.array(arr2)
...: pa_mask = pa.array(mask)
...:
In [20]: %timeit np.where(mask, arr1, arr2)
82.3 ms ± 8.76 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [21]: %timeit pc.if_else(pa_mask, pa_arr1, pa_arr2)
50.4 ms ± 6.08 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
```
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