pyenv-virtualenv

> And finally...well, I have to admit it, but I use Python like a goddamned moron in that I still don't know how to use virutalenv/venv to do proper dev isolation. I was the same way for quite a while, until I bumped into pyenv-virtualenv[1]. Just install that plugin, and you can do, eg, pyenv virtualenv 3.5.1 my-project to get a virtual environment called `my-project` based off of Python 3.5.1 (assuming that you've installed 3.5.1 via pyenv, of course). Or, you can just do pyenv virtualenv my-project to make a virtualenv called `my-project` based off of the current version of Python that you're using. Once you do that, pyenv treats `my-project` just as another installation of Python. In fact, `my-project` will show up in the list of installed versions (`pyenv versions`), and you can switch to it: pyenv global my-project (O

While there are certainly advantages to Anaconda, I've never encountered any troubles installing Pandas, NumPy, SciPy, or scikit-learn on any OS X or linux system. In my experience, getting GCC up and running is far more of a pain in the ass (and it usually isn't even that bad). I use pyenv[1] and pyenv-virtualenv[2] to easily keep track of Python versions and virtual environments. I keep one virtual environment for each project I'm working on, and things prettymuch Just Work. [1]: https://github.com/yyuu/pyenv [2]: https://github.com/yyuu/pyenv-virtualenv