Virtual Environments in Python Made Easy


Most Python newcomers don’t know how to set up a development environment that follows the latest standards used by professional programmers, so this tutorial will teach you how to properly create a fully working Python development environment using industry accepted best practices.

Virtual Environments

There are so many libraries I use for my own personal projects, three web application development frameworks I use and many others I would love to explore in the near future. It is very clear that a serious project in Python depends on other packages written by other developers. If you are a Django developer, I am very sure you make use of South for performing automatic database migrations, Django Debug Toolbar for gathering various debug information about the current request/response, Celery for taking care of real-time operations and scheduling as well, and so on.

I, for example, use the package requests a lot for my projects, and a Django web application I am currently working on depends on version 2.3.0. According to the official documentation, the latest version of this package is version 2.5.1. Say I go ahead and install the latest version of the library on my Ubuntu machine because I need it for another project, and everything seems to work fine until I try to make use of my older project which worked fine with 2.3.0. Suddenly, everything is broken. What happened? Maybe the API of the latest version of requests has changed since version 2.3.0? The reason does not matter at this point, as my older project is broken and no longer works.

A conflict between two projects has been created. They make use of the same library, but they require different versions of it.

The tool virtualenv is the solution to this problem.


Virtualenv helps solve project dependency conflicts by creating isolated environments which can contain all the goodies Python programmers need to develop their projects. A virtual environment created using this tool includes a fresh copy of the Python binary itself as well as a copy of the entire Python standard library.

To create a virtual environment for your Python project, you type the command virtualenv followed by the name you want to give to the virtual environment like shown below.

virtualenv virt1

It is very important to know that each time you want to use a created virtual environment, you need to activate it. The following command does this:

source virt1/bin/activate

Everything installed in the virt1 directory will not affect the global packages or the system wide installations, thus avoiding dependency conflicts.

If I now install the newest version of requests, the old one does not get uninstalled. The new one is installed in the virtual environment exclusively.

pip -I install requests 

The above command produces the following output:

Downloading/unpacking requests
  Downloading requests-2.5.1-py2.py3-none-any.whl (464kB): 464kB downloaded
Installing collected packages: requests
Successfully installed requests
Cleaning up...


A word on the pip command above – it’s a package manager that is very useful when you want to distribute your project to others, as it allows the developer to install all the required packages with a requirements.txt file.

For example, a fellow developer can activate a virtual environment and then run the following command to install the dependencies of the project:

pip install -r requirements.txt

To generate the dependencies file of your project, you run the following command.

pip freeze > requirements.txt

The pip tool can also be used to uninstall a package from your machine:

pip uninstall some-package-name

One can install pip system-wide with the default Python package manager:

sudo easy_install pip

And then use pip to install virtualenv:

sudo pip install virtualenv

After the environment is created

Once the virtual environment gets activated, your terminal prompt changes to show the user in which directory they are working:


The following command will let you deactivate the virtual environment:


You can use the which command to check the Python binary that is used in the current virtual environment.

which python

If everything is working well, you should get something similar to the following output.


If you deactivate and which again, you should get a different output.


This is because when working inside a virtual environment, the binary copy placed inside that environment is being used. The same applies to packages.

We can use the option -p while working with virtualenv in order to use a specific version of Python which is globally installed on the machine.

For example, the following command can be used to create the virtual environment virt2 with Python3 in it, if you have Python3 installed on your machine.

virtualenv -p /usr/bin/python3 virt2

And to delete a virtual environment you use the rm -r command like you do with any other directory you want to delete.

rm -r virt2


Virtualenvwrapper provides very useful commands that make working with virtual environments even easier. For example, the workon command can be used to activate and work on a virtual environment:

workon virt1

It, too, is installed easily with pip.

pip install virtualenvwrapper

I like to keep all my virtual environments in a single place. To accomplish this, run the following command to create a new directory for your virtual environments.

mkdir ~/.virtualenvs

Then set WORKON_HOME to ~/.virtualenvs like shown below.

export WORKON_HOME=~/.virtualenvs

Once this is done, we can open the .bashrc file inside the home directory using a text editor and add the following line to the end of it.

. /usr/local/bin/

Then, we reload the .bashrc file with the following command.

source ~/.bashrc

Now, the mkvirtualenv command can be used to easily make new environments placed by default inside this folder.

mkvirtualenv sitepoint

We can easily activate the virtual environment with the help of the workon command:

workon sitepoint

The command to deactivate the virtual environment is the same as the one we used before.


It’s very easy to switch between different virtual environments. For example, to workon another virtual environment:

workon another_virtualenv

To delete a virtual environment, the command rmvirtualenv should be used.

rmvirtualenv sitepoint


Another very useful tip is the one that helps automatically activate the virtual environments when the one cd’s into their project.

For this, a .env file should be placed inside the project directory.

cd sitepointproject
vim .env

Inside the .env file, we insert the following line.

workon name_of_virtualenv_here

The autoenv tool will help us automagically activate the environment when navigating into a directory containing a .env file. To install it, we clone it.

git clone git:// ~/.autoenv

Then we use echo to place another line inside the .bashrc file. You can also use a text editor to do it.

echo 'source ~/.autoenv/' >> ~/.bashrc

Now, every time you cd into your project, the virtual environment will get activated automatically.


In this tutorial, we learned some interesting hacks for efficiency when working with Python virtual environments. The tools are out there – embrace them and improve your workflow. Do you use any other interesting approaches in your Python development workflow? Let us know!

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