Authors: Mathabo Malange, Phangoxolo Sishuba

3.2. Python Working Environments#

A Python working environment is a tool that helps keep dependencies required by different projects separate by creating virtual environments for them. They are useful for instances where you are working on two different projects on the same machine, where both projects require the same package but different versions of it.

For example, you are working on project A which requires packageX version 1.0 which you have installed to your global Python library. You then switch to work on project B on the same machine and install packageX but version 2.0 which has several changes from version 1.0. If you had to go back and run project A after installing version 2.0, you would get all sorts of errors. To avoid this, we use virtual environments, as the name suggests, which are created as isolated environments for specific projects as discussed in this article.

3.2.1. How to Set up a Python Working Environment#

To create a Python working environment, we will use the Anaconda Prompt terminal and the command conda. With conda, you can create, export, list, remove, and update environments that have different versions of Python and/or packages installed in them. Switching or moving between environments is called activating the environment. You can also share an environment file. Here, we only provide the steps necessary to create a working Anaconda environment. For more in depth discussion, please refer to Anaconda user’s guide. As mentioned in Section 3.1.2 above, before proceeding, please make sure that you have already installed Anaconda by following the installation instructions here.

3.2.2. Creating an environment in Anaconda#

Open a terminal or command prompt and verify that Anaconda has been properly installed and active. In Windows, there is a dedicated Anaconda Prompt that can be found from the Search bar by typing Anaconda Prompt. In the Unix-based systems, after the installation is complete and Anaconda activated, the terminal should have the word “(base)” appear in the terminal. Use the terminal or an Anaconda Prompt with the following steps:

  1. To create an environment:

$conda create --name myenv

Note

Replace “myenv” with the environment name of your choice.

When asked to proceed, type y:

proceed ([y]/n)?

This creates the “myenv” environment in the envs directory of Anaconda. The command $ conda env list print a list of available environments. No packages will be installed in this environment.

  1. To create an environment with a specific version of Python:

$conda create -n myenv python=3.9

The option “-n” is a short form of the long “–name”.

  1. To create an environment with a specific package:

$conda create -n myenv python=3.9 scipy

or

$conda create -n myenv python
$conda install -n myenv scipy

  1. To create an environment with a specific version of a package:

$conda create -n myenv scipy=0.17.3

  1. To create an environment with a specific version of Python and multiple packages:

$conda create -n myenv python=3.9 scipy=0.17.3 matplotlib

  1. Once the environment is create, activate it environment with the command

$conda activate myenv

You can add much more to the conda create command. For details, run

$conda create –-help

3.2.3. Creating an environment from an environment file#

We can create an Anaconda environment using an environment file. This file can be created from an existing environment by typing conda env export. To limit the export to only packages that were explicitly installed by the user, use conda env export --from-history. Additionally, environment files can be created manually. For the latter, follow the guide here. Use the terminal or an Anaconda Prompt for the following steps:

  1. Create the environment from a file named “environment.yml”

$conda env create -f environment.yml

Note that the environment name “myenv” is specified inside the “environment.yml” file. Verify the new environment was installed correctly:

$conda env list
$conda info --envs

3.2.4. Updating an environment#

You may need to update your environment for a variety of reasons. For example, it may be the case that:

  • One of your core dependencies just released a new version (dependency version number update).

  • You may need an additional package for data analysis (add a new dependency).

  • You may have found a better package and no longer need the older package (add new dependency and remove old dependency).

If any of these occur, all you need to do is update the contents of your environment.yml file accordingly and then run the following command:

$conda env update --prefix ./env --file environment.yml --prune

The --prune option causes conda to remove any dependencies that are no longer required from the environment.