ENH: add nanmean - #910
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cc @OmarManzoor |
qbarthelemy
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Aug 19, 2026
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Branch must be rebased, because code has been moved to src\array_api_extra\_agnostic\_statistical.py
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| zero = xp.asarray(0, dtype=a.dtype, device=device_a) | ||
| sum_ = xp.sum(xp.where(mask, zero, a), axis=axis) |
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We could simply use nansum:
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| zero = xp.asarray(0, dtype=a.dtype, device=device_a) | |
| sum_ = xp.sum(xp.where(mask, zero, a), axis=axis) | |
| sum_ = nansum(a, axis=axis, xp=xp) |
| sum_ = xp.sum(xp.where(mask, zero, a), axis=axis) | ||
| count = xp.count_nonzero(~mask, axis=axis) | ||
| safe_count = xp.astype( | ||
| xp.where(count == 0, xp.asarray(1, dtype=count.dtype, device=device_a), count), |
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| xp.where(count == 0, xp.asarray(1, dtype=count.dtype, device=device_a), count), | |
| xp.where(count == 0, xp.ones_like(a), count), |
| if xp.any(count == 0): | ||
| result = xp.where( | ||
| count == 0, | ||
| xp.asarray(xp.nan, dtype=result.dtype, device=device_a), |
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Suggested change
| xp.asarray(xp.nan, dtype=result.dtype, device=device_a), | |
| xp.full_like(result, xp.nan), |
With these three modifications, device_a is no longer necessary in nanmean.
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Towards #789