Earlier quoted context omitted.
> I wish all the "big data" tooling was written in Ruby. On a practical note, Ruby's heavy use of reflection, inheritance and method_missing style dispatch currently makes performance for big data tasks less than ideal. Python is less expressive but its "one true way" makes things like vectorization and type specialization easier for data tasks. Sometimes you really just need a better Fortran.
I believe you’re confusing Ruby with Rails. While these can eventually come in handy sometimes, they are by and large seldom used, but Rails heavily leverages such patterns for flashy yet ultimately questionable magic. As a long time Rubyist, I believe Rails, for all its innovation, has been warping the view of what Ruby is, and when and how to use its features responsibly.
The dynamic inheritance part of Ruby is still core even in a non-Rails ecosystem. That does make optimizing performance more difficult.
But on a much simpler level, a lot of Pythonic data code is just functions + values, without them being complected in an object. That makes a lot of stuff like wrapping numerical libraries in C very easy in Python. I think you'd still have a bit of a culture shock writing procedural, return-by-value code in Ruby. That's pretty normal in Python and I think one of the (many) reasons it got picked up by STEM disciplines in academia. Looks over shoulder at awful code in Numerical Recipes in Fortran book.
Different strokes for different folks and all that, I just think there are probably constraints (and cultural values) that Python has that make it more suited to data problems than Ruby.