[goatlover]

But numpy is the underlying library used by the rest of the SciPy stack, right? I use Pandas, so taking that as an example, it can be slow when you're doing stuff that isn't mostly leveraging Numpy. If I have to loop over a relatively large dataset to do some complicated row checking/filtering stuff, then it can be very slow, and I might as well take a coffee break. You can rewrite that into using just the numpy values array and it will be performant, but you lose all the nice Pandas features when you do that.

Also, if I'm just restricted to using Pandas on a laptop or small server instance, then loading in a several gigabyte csv file can really tax the memory.