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numpy use cases for where and out

      I'm writing some code for sparse arrays that is intended to pretty
much follow the numpy API. Because my arrays can have different default
values there is an issue with using the 'out' keyword argument for
functions. e.g. If I elementwise multiply 2 arrays with defaults a and
b, then the default of the product becomes a*b. Changing the default of
'out' to a*b (if that is not the existing default) requires pre-existing
values in 'out' equal to the previous default to be filled in. It seems
to be more hassle than it's worth, and basically eliminates sparsity.

So I am wondering if there are any compelling use cases for 'out'. At
the same time, I'm wondering about 'where'. It's less of an issue. But
given my dictionary of keys implementation, and returning new arrays
rather than views, I'm wondering if I could reasonably eliminate both. I
don't think I've ever used either argument when using numpy. So I'm
asking here in case there are use cases I've overlooked. TIA.