No matter whether you have chosen to install a standalone Python or instead used a scientific distribution, you may have noticed that you are actually bound on your system to the Python version you have installed. The only exception, for Windows users, is to use a WinPython distribution, since it is a portable installation and you can have as many different installations as you need.

A simple solution to breaking free of such a limitation is to use virtualenv, which is a tool for creating isolated Python environments. That means, by using different Python environments, you can easily achieve the following things:

• Testing any new package installation or doing experimentation on your Python environment without any fear of breaking anything in an irreparable way. In this case, you need a version of Python that acts as a sandbox.
• Having at hand multiple Python versions (both Python 2 and Python 3), geared with different versions of installed packages. This can help you in dealing with different versions of Python for different purposes (for instance, some of the packages we are going to present on Windows OS only work when using Python 3.4, which is not the latest release).
• Taking a replicable snapshot of your Python environment easily and having your data science prototypes work smoothly on any other computer or in production. In this case, your main concern is the immutability and replicability of your working environment.

You can find documentation about virtualenv at http://virtualenv.readthedocs.io/en/stable/, though we are going to provide you with all the directions you need to start using it immediately. In order to take advantage of virtualenv, you first have to install it on your system:

$> pip install virtualenv

After the installation completes, you can start building your virtual environments. Before proceeding, you have to make a few decisions:

• If you have more versions of Python installed on your system, you have to decide which version to pick up. Otherwise, virtualenv will take the Python version that was used when virtualenv was installed on your system. In order to set a different Python version, you have to digit the argument -p followed by the version of Python you want, or insert the path of the Python executable to be used (for instance, by using -p python2.7, or by just pointing to a Python executable such as -p c:Anaconda2python.exe).

• With virtualenv, when required to install a certain package, it will install it from scratch, even if it is already available at a system level (on the python directory you created the virtual environment from). This default behavior makes sense because it allows you to create a completely separated empty environment. In order to save disk space and limit the time of installation of all the packages, you may instead decide to take advantage of already available packages on your system by using the argument --system-site-packages.
• You may want to be able to later move around your virtual environment across Python installations, even among different machines. Therefore, you may want to make the functionality of all of the environment's scripts relative to the path it is placed in by using the argument --relocatable.

After deciding on the Python version you wish to use, linking to existing global packages, and the virtual environment being relocatable or not, in order to start, you just need to launch the command from a shell. Declare the name you would like to assign to your new environment:

$> virtualenv clone

virtualenv will just create a new directory using the name you provided, in the path from which you actually launched the command. To start using it, you can just enter the directory and digit activate:

$> cd clone
$> activate

At this point, you can start working on your separated Python environment, installing packages, and working with code.

If you need to install multiple packages at once, you may need some special function from pip, pip freeze, which will enlist all the packages (and their versions) you have installed on your system. You can record the entire list in a text file by using the following command:

$> pip freeze > requirements.txt

After saving the list in a text file, just take it into your virtual environment and install all the packages in a breeze with a single command:

$> pip install -r requirements.txt

Each package will be installed according to the order in the list (packages are listed in a case-insensitive sorted order). If a package requires other packages that are later in the list, that's not a big deal because pip automatically manages such situations. So, if your package requires NumPy and NumPy is not yet installed, pip will install it first.

When you've finished installing packages and using your environment for scripting and experimenting, in order to return to your system defaults, just issue the following command:

$> deactivate

If you want to remove the virtual environment completely, after deactivating and getting out of the environment's directory, you just have to get rid of the environment's directory itself by performing a recursive deletion. For instance, on Windows, you just do the following:

$> rd /s /q clone

On Linux and macOS, the command will be as follows:

$> rm -r -f clone