In 2015, I have almost exclusively used Python installations provided through Continuum Analytics's Conda/Anaconda platform. You have to install each version yourself and then tell virtualenv to use it. virtualenv also provides no helping in actually managing Python versions. In my experience, getting started with virtualenv is cumbersome and confusing to this day, I have to look up the command to create a Python 3 virtualenv. If you use virtualenv, each project you work on can have its own version of Python with its own 3rd party packages (hopefully specified in an requirements.txt file). While this may sound convenient, it causes problems if you (1) install different packages with the same name (2) need to use different versions of the same package (3) upgrade your operating system (OS X will delete all the packages you have installed).įor many years, best practice for Python developers was to use virtualenv to create a sandbox-ed environment for each project. Secondly, if you install 3rd party packages with pip, every package is installed into the same globally accessible directory. First, it's probably an old version of Python. There are several good reasons to stop using the system Python. Many new Python programmers rely on their system install of Python to run their scripts.
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