There are now wheels (binary distributions of Python modules) of numpy, scipy, and scikit-learn for both Linux and Mac OS X, so it should just be a matter of `virtualenv /tmp/v` (or wherever), `source /tmp/v/bin/activate`, `pip install -U pip` to make sure your pip is new enough to handle wheels, and `pip install numpy scipy scikit-learn`. Nothing gets built from source. Unfortunately the same can't (yet) be said for pandas; `pip install pandas` at this point will try to compile it.
Actually acquiring virtualenv on OS X seems not to be super straightforward, especially if you want to use the system Python interpreter. The best option seems to be to `brew install python`, and then `pip install virtualenv`. Or download Python from python.org, and maybe get Python 3 while you're at it.
one way is to get a standalone python and git clone virtualenv, then use the standalone python to make virtualenv for each project.. this leaves out anything to do with the system python
Actually acquiring virtualenv on OS X seems not to be super straightforward, especially if you want to use the system Python interpreter. The best option seems to be to `brew install python`, and then `pip install virtualenv`. Or download Python from python.org, and maybe get Python 3 while you're at it.