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Python Virtual Environments with virtualenv and Anaconda

This article will explain and demonstrate virtual environments in Python using virtualenv and in Anaconda.

What is a ‘Virtual Environment’?

In Python, a virtual environment is an isolated directory that contains all of the dependencies of a Python project.

By default, when you install a package using the pip package manager, it is installed globally – that version of the package is available to all Python scripts running on your system.

This can cause problems. It clutters up your global Python installation, and all projects are stuck using the same version, which may not be appropriate if you start a new project that relies on a newer version of a package that is incompatible with the version used in your previous projects.

It also makes portability and collaboration difficult. You may not have the same dependencies installed on another machine you work on, or those you collaborate with on code may not wish to install the package globally for fear of conflicting with their own configurations.

Virtual environments solve this – when activated, all packages are installed in the project directory (locally rather than globally). Everything is isolated, removing the potential for conflicts.

Virtual environments in Python with virtualenv

virtualenv provides virtual environments for the standard release of Python. Once installed globally, it can be used to create and activate virtual environments for you to work in:

To create a virtual environment and install NumPy, run the following commands:

# Install virtualenv globally (the default Python environment)
pip install virtualenv

# Create a new virtualenv environment, which will act as the root of your project
virtualenv -p python3 MyProject

# Change directory to the project
cd MyProject

# Activate the virtual environment

# Install a package to test the virtual environment
pip install numpy

Above, the NumPy package is installed to test the virtual environment.

If you want to return to your global (default) Python environment, run:


Recording your Project’s Requirements/Dependencies

Once you are working in a virtual environment, you can output all of the installed packages to a text file so that you can easily reproduce it by running:

python -m pip freeze > dependencies.txt

To install the packages listed in the file (for example on a different computer), run:

python -m pip install -r dependencies.txt

Virtual Environments in Anaconda

Anaconda, a popular data science platform that includes Python, has it’s own virtual environments implementation as well, which works much the same way:

# Create and activate a new environment for your project
conda create -n MyProject
conda activate MyProject

# Install a package to test the virtual environment
conda install numpy
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I'm Brad, and I'm nearing 20 years of experience with Linux. I've worked in just about every IT role there is before taking the leap into software development. Currently, I'm building desktop and web-based solutions with NodeJS and PHP hosted on Linux infrastructure. Visit my blog or find me on Twitter to see what I'm up to.

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