Some related issues:

• first nonzero element (Trac #1673) #2269 first nonzero element
• gufunc to test for all elements equal along axis #8513 gufunc to test for all elements equal along axis (PR ENH: Adding logical gufuncs, ie all_equal #8528)
• ENH: minmax function #9836 ENH: minmax function
• Make np.argmax, np.argmin to return indexes and values at the same time #15623 Make np.argmax, np.argmin to return indexes and values at the same time
• ENH: Create the ability for fused operations (fused ufuncs or map_reduce) style #11622 Fast, memory efficient absolute value max

One thing about which to put into NumPy proper and which not: I think a good starting point would be to argue that anything could go into an np.lib.performance if it is also useful for NumPy itself. For examples some functions will be useful for np.linalg.norm.

I wonder if we could grow a "fused loop" ability, where you would say np.fuse(max, min)(args, ...) which would

• create an output array for each operation
• internally loop once over the values and do both operations on each value

This is complicated for SIMD loops.

Also am curious how other array libraries would extend or build on top of these. Though that is probably less important than the points already raised.

@mattip, I agree. I was using gh-11622 as an "issue" gather those, but maybe we should make a second one.

Fused ops, such as minmax make sense even without fused-loops. And some of them, might be OK to just have anyway as optimized-loops now, but replace later with a generic machinery. Things like all_greater are a bit tricky though, and I might prefer them outside of NumPy for now personally.

minmax might be a good "trial balloon", because we can use it internally in NumPy for histogram and ptp (and maybe more)