The scientists he's talking about are smart people, but aren't really into computers and (what's more) have no patience at all for the amount of pain it takes to compile the dependencies Python packages need.
It's not the Python side of things that's the problem. Numpy and Scipy (which nearly all Python scientific software depend on) is based on both bindings to C and to Fortran, and getting those library ducks in a row - especially on Windows or MacOS X, on Linux your package manager does it for you - is a pain for someone who knows what they're doing and almost impossible for someone who doesn't.
Plus, well, most scientists outside physics use Windows. If you need a command-line tool you've already lost in that respect. What you're competing against is often Excel.
The battle over Numpy being in core Python has been fought and lost, but short of that level of integration, I don't see that there's going to be much effective to do about this.
The problem is more complicated than just C code being more difficult to build.
The whole distutils infrastructure is messy and badly designed. It takes care of everything from build up to installation and packaging, and all those parts are tighly coupled. It is also incredibly inflexible, and the way to extend it through subclassing leads to incompatible code (if package A subclass distutils, and package B subclass the same thing, how can you use A and B ?). Almost every design decision of distutils is wrong, and badly implemented.
Numpy and scipy binaries are built for every release: actually, that's the platform we support the best in some sense since we can reliably build binaries, and that saddens me quite a bit.
You guys do an amazing and thankless job, and I'm sorry if I oversimplified that aspect of it. It's just difficult to see how to solve the problems you describe, let alone to explain those problems to a researcher you're trying to wean off Fortran. :)
... and even so, I've seen people struggle to install them - Scipy in particular - countless times. I'm not sure how easy is easy enough, but it's going to have to be basically impossible to screw up.
And I'm not sure that's possible short of bundling everything with Python, and of course that's a bad idea.
It's not the Python side of things that's the problem. Numpy and Scipy (which nearly all Python scientific software depend on) is based on both bindings to C and to Fortran, and getting those library ducks in a row - especially on Windows or MacOS X, on Linux your package manager does it for you - is a pain for someone who knows what they're doing and almost impossible for someone who doesn't.
Plus, well, most scientists outside physics use Windows. If you need a command-line tool you've already lost in that respect. What you're competing against is often Excel.
The battle over Numpy being in core Python has been fought and lost, but short of that level of integration, I don't see that there's going to be much effective to do about this.