[iamlucaswolf]

I use a setup like this. Essentially, I use pyenv [1] to manage Python versions and Poetry [2] [2](https://python-poetry.org/) for virtualenvs/dependencies.

The workflow for creating a new project looks like this:

1. Create a project directory (e.g. 'myproject') and `cd` into it. 2. `git init` 3. Fixate the Python version for that project with the `pyenv local` command (e.g. `pyenv local 3.8.6`). This creates a `.python-version` file that you can put under source control. Within the `myproject` directory tree, `python` will now be automatically resolved to the specified version. Your system Python (in fact, any other Python versions you might have installed) remain untouched. 4. Create a new poetry project (`poetry init`). This creates a `pyproject.toml` which contains project metadata + dependencies and can also be checked into git. 5. Add dependencies with `poetry add`. Here, you could for instance add Jupyter Lab (`poetry add jupyterlab`).

To access installed dependencies, such as the `jupyter lab` command, you can either execute one command in the virtualenv directly (`poetry run jupyter lab`) or spawn a shell (`poetry shell`). If you open a Jupyter Notebook that way, the packages installed in the virtualenv are directly available from within Jupyter Notebooks, without having to mess around with installing IPython kernels.

I like this approach, because it gives you full flexibility, while being portable and easy to use. It gets you around having to deal with conda (which I found to be frustrating at times). Also, you're not tied to the Jupyter frontends, but could e.g. just install `ipykernel` and open notebooks in VSCode.

[1](https://github.com/pyenv/pyenv/) [2](https://python-poetry.org/)

Edit: Moved the links

[claytonjy]

I do very similarly, except I avoid installing jupyter lab for each project, instead installing `ipykernel` as a dev dependency. I install jupyter lab system-wide with pipx [1], and for each project issue a command like

    pipenv run python -m ipykernel install --user --name=this_directory

Then if I open Jupyter Lab I see "this_directory" as a listed kernel to create a notebook from.

This allows me to manage Jupyter settings and plugins in one place rather than in each env, have multiple project's notebooks open in the same Jupyter Lab instance, etc.

[1]https://pipxproject.github.io/pipx/