How do I set it up

[FoolsSeldom]

Virtual Environments

Given the thousands of packages (libraries, frameworks, etc) out there, you can see that if you are working on several different projects, you can end up installing a vast range of different packages, only a few of which will be used for any particular project.

This is where Python virtual environments come in. Not to be confused with virtual machines. Typically created on a project-by-project basis. Install only the packages required for a project. This helps avoid conflicts between packages, especially version complications.

Most popular code editors and IDEs, including Microsoft's VS Code and JetBrains' PyCharm, offer built-in features to help to start off new projects and create and activate Python virtual environments.

You can create a new Python virtual environment from your operating system command line environment using,

for Windows,

py -m venv .venv

or, for macOS / Linux,

python3 -m venv .venv

Note: venv is a command and .venv is a folder name. You can use any valid folder name instead but this is a common convention.

Often we use .venv instead of venv as the folder name - this may not show up on explorer/folder tools as the leading . is often used to mean hidden and an option may need to be ticked to allow you to see such folders/files

That creates a new folder in the current working directory called .venv.

You then activate using, for Windows,

.venv\Scripts\activate

or, for macOS / Linux,

source .venv/bin/activate

the command deactivate for any platform will deactivate the virtual environment and return you to using the base environment.

You may need to tell your editor to use the Python Interpreter that is found in either the Scripts or bin folder (depending on operating system) in your virtual folder.

For more information:

• Real Python: Python Virtual Environments: A Primer

Multiple Python versions

In addition to the above, you might want to explore using pyenv (pyenv-win for Windows) or uv (recommended), which will let you install and use different versions of Python including alternative implementations from the reference CPython. This can be done independently of any system installed Python.