What is the difference between pyenv, virtualenv, and Anaconda?

I am a Ruby programmer trying to learn Python. I am pretty familiar with pyenv since it is like a copy and paste from RBEnv. Pyenv helps in allowing to have more than one version of Python in a system and also to isolate the Python instance without touching sensitive parts of system.

I suppose every Python installation comes with a pip package. What I still don't understand is, there are many good Python libraries out there that suggest to use this virtualenv and Anaconda. I can even find a virtualenv plugin for pyenv.

Now I am getting confused with the purpose of these two, pyenv and virtualenv. Worse, inside pyenv, there is a virtualenv plugin.

• What is the difference between pyenv and virtualenv?
• Is there any difference in using a pip command inside both pyenv and virtualenv?
• What does this pyenv virtualenv do?

It's worth mentioning pip here as well, as conda and pip have similarities and differences that are relevant to this topic.

pip: the Python Package Manager.

• You might think of pip as the Python equivalent of the Ruby gem command
• pip is not included with Python by default.
• You may install Python using Homebrew, which will install pip automatically: brew install python
• The final version of OS X did not include pip by default. To add pip to your Mac system's version of Python, you can sudo easy_install pip
• You can find and publish Python packages using PyPI: The Python Package Index
• The requirements.txt file is comparable to the ruby gemfile
• To create a requirements text file, pip freeze > requirements.txt
• Note, at this point, we have Python installed on our system, and we have created a requirements.txt file that outlines all of the Python packages that have been installed on your system.

pyenv: Python Version Manager

• From the documentation: pyenv lets you easily switch between multiple versions of Python. It's simple, unobtrusive, and follows the Unix tradition of single-purpose tools that do one thing well. This project was forked from RBEnv and ruby-build, and modified for Python.
• Many folks hesitate to use Python 3.
• If you need to use different versions of Python, pyenv lets you manage this easily.

virtualenv: Python Environment Manager.

• From the documentation: The basic problem being addressed is one of dependencies and versions, and indirectly permissions. Imagine you have an application that needs version 1 of LibFoo, but another application requires version 2. How can you use both these applications? If you install everything into /usr/lib/python2.7/site-packages (or whatever your platform’s standard location is), it’s easy to end up in a situation where you unintentionally upgrade an application that shouldn’t be upgraded.
• To create a virtualenv, simply invoke virtualenv ENV, where ENV is is a directory to place the new virtual environment.
• To initialize the virtualenv, you need to source ENV/bin/activate. To stop using, simply call deactivate.
• Once you activate the virtualenv, you might install all of a workspace's package requirements by running pip install -r against the project's requirements.txt file.

Anaconda: Package Manager + Python Version Manager + Environment Manager + Additional Scientific Libraries.

• Anaconda is a commercial distribution of Python with the most popular Python libraries. You are not permitted to use Anaconda in an organisation with more than 200 employees.
• From the documentation: Anaconda 4.2.0 includes an easy installation of Python (2.7.12, 3.4.5, and/or 3.5.2) and updates of over 100 pre-built and tested scientific and analytic Python packages that include NumPy, Pandas, SciPy, Matplotlib, and IPython, with over 620 more packages available via a simple conda install <packagename>
• As a web developer, I haven't used Anaconda. It's approximately 3 GB, including all the packages.
• There is a slimmed down Miniconda version, which seems like it could be a more simple option than using pip + virtualenv, although I don't have experience using it personally.
• While Conda allows you to install packages, these packages are separate than PyPI packages, so you may still need to use pip additionally depending on the types of packages you need to install.

See also:

• Conda vs pip vs virtualenv (section in documentation from anaconda)
• The difference between pip and Conda (Stack Overflow)
• The relationship between virtualenv and pyenv (Stack Overflow)