Thanks for the follow-up. Again, I understand the convenience benefit of the self-contained conda.jl Python. My concern is about where it leads practically. Sorry to bring up the ideology stuff. YMMV.
As a practical matter, if Continuum were much faster to post updates to their repo at Anaconda.com this might be less of a problem. On the conda group, someone requested a more upgraded matplotlib (this was from some time ago and the requester wanted 1.4) and the Continuum reply was that the matplotlib maintainers were free to also post their latest to what was then called Binstar.com. This is a bit of an attitude issue that got me "off". The matplotlib maintainers have no such obligation. They have a master branch on their github repo and they release to PyPi. If Continuum wants to create their own binary as a service, of course they may--but it's on them to do so. There is now a bug in 1.4.3 with Numpy 1.10 that results in this message: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison This was fixed by matplotlib 1.5.0 about 2 weeks ago (along with other things of course). Not that long ago and a little bit of a lag as mutual dependencies get their issues sorted out isn't unreasonable. But, they have and the master branch is fully released. This is hard for Julia and I was too harsh. With large dependencies there is no totally easy way out. Until a week ago I was happily using versions of Python I downloaded myself. PyCall was finding them and all was good. I ran into one annoying bug after upgrading matplotlib (first chart figure cannot be closed by code) so I was trying to sort that out. Never figured out what broke. So, I switched to the conda.jl approach and moved on. And then there was the latest... ...which lead to my post.
