I am, however, self-aware enough to recognize that what I care about is a subset of what everyone in computing cares about. Scientific computing may be important in multiple places, but it is still a single domain. An important domain, sure. But Python is still used in many places were such concerns are not important. Arguments about the utility of number crunching on dense vectors and matrices are great. But the attitude I've seen in this thread is "the domain I care about is so important that my concerns should be elevated above the concerns of other domains." That is not going to fly when it comes to changing a general purpose language used in many domains.
In the 1960s, there were those who claimed that the benefit of recursion was not worth the complexity it added to programming languages, except in certain special domains. In the 1970s and 1980s, we had the same argument about depth-first search — SNOBOL's and Prolog's backtracking feature. It turned out that the proponents of recursion were right, and the proponents of backtracking were probably wrong. (Although the backtracking feature of SNOBOL's child Icon directly inspired Python's generators, although implementationally they're maybe more similar to CLU iterators.)
Now, when it comes to matrix and vector manipulation, should we imagine it as a single domain, or as a feature that's useful across a wide range of domains?
I'm arguing for the latter.
I think this is shortsighted to the point of ignorance. "Scientific computing" here really means "performance-critical numerical computation on regular arrays". Basically all of the new things that people are doing with computers in the last five years and the next five years — machine learning and other statistics, software-defined radio, audio synthesis, real-time video processing, cool visual effects, speech recognition, machine vision, and 3-D rendering, and arguably Bitcoin — consist largely of performance-critical numerical computation on regular arrays. It's what GPUs are for. Five years ago, Numeric or NumPy was probably the best way to do that for a wide range of things, although a lot of people still use Matlab instead, and R deserves at least a mention. Today it's not clear. Five years from now there will be something much better than current NumPy, and it could be a better version of NumPy or it could be R or Matlab or Octave or something.
In short, "scientific computing" is not a single domain, but a set of capabilities increasingly important in many different domains.