"
REPLs are over-hyped and have become a fashion touchstone that
few dare argue against for fear of being denounced as un-hip.
REPLs have their
place, but in the main are nowhere near as useful as people
claim.
IPython Notebooks on the other hand are a balance between
editor/execution environment and REPL that really has a lot
going for
it."
Fair argument against an earlier poster but from my perspective,
all I meant is that the absence of a shell is not a good reason
to write off D for exploring data. Because there is a shell
already that could be developed, and because one can call D from
python / Julia in a notebook.
Stats folks using R, love R and hate Python. Stats folk using
Python, love Python and hate R. In the end it's all about what
you know and can use to get the job done. To be frank (as in
open rather than Jill), D hasn't got the infrastructure to
compete with either R or Python and so is a non-starter in the
data science arena.
About the future you may or may not be right. (Whether it is
commercially interesting to run workshops in D for stats people
is certainly a interesting question. However given the ways that
technology unfolds it may be that it is less relevant for the
question I am most interested today in answering).
I want to do things in D myself, and I would find a data frame
helpful. I understand you don't program much in D these days,
and that's a reasonable decision, but for those who want to use
it to do quantish things with dataframes, perhaps we could think
about how to approach the problem. And having weighed your
warnings, if you have any suggestions on how best to implement
this, I would be open to these also.
Laeeth.
