How to work in Jupyter Lab

What is Jupyter Lab?

Jupyter Lab is an interface for interactive programming and data analysis that runs in your browser. If you're coming from R think about it as a browser-based R-Studio for all kinds of languages, but mostly used for Python.

Documentation and User Guide: https://jupyterlab.readthedocs.io/en/latest/

Install Jupyter Lab

Installation should be straightforward in most cases. Always follow the latest instructions by the developers:

https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html

We recommend simply using the pip installation for (most) purposes.

Install/uninstall kernels for virtual environments created with pipenv

Jupyter uses 'kernels' to accommodate working in different programming languages and environments

While it is possible to simply run jupyter-lab within a Python virtualenv, it is maybe desirable to have certain environments offered to you by the launcher. To get this, you have to specify a so-called 'kernelspec' that provides your system-level Jupyter with information about where your virtualenv lives, respectively.

To do so, assuming that Jupyter-Lab is installed on your system (outside a virtual env) already:

• enter virtual env with pipenv shell
• pipenv install ipykernel or pipenv install --dev ipykernel if end users don't need to use Jupyter.
• python -m ipykernel install --user --name=YOUR_RECOGNIZABLE_NAME_FOR_THIS_ENV

Now you should be able to start jupyter-lab on your system (outside the virtual env) and the launcher will offer you to start a notebook using this virtualenv.

If you delete the project virtualenv for whatever reason and want to keep your kernel-list in Jupyter clean, you can list all kernels Jupyter knows of

jupyter kernelspec list

and then remove the respective kernel specifications with

jupyter kernelspec uninstall YOUR_RECOGNIZABLE_NAME_FOR_THIS_ENV

Sources/More to read:

Using Virtual Environments in Jupyter Notebook and Python: https://janakiev.com/blog/jupyter-virtual-envs/