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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/
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.
Jupyter uses ‘kernels’ to accommodate working in different programming languages and environments
While it is possible to simply install and run jupyter-lab within a Python virtualenv or conda env, it is maybe desirable to have certain environments offered to you by the launcher also within your system-level Jupyter installation. 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:
pipenv shellpipenv 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_ENVNow 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
Using Virtual Environments in Jupyter Notebook and Python: https://janakiev.com/blog/jupyter-virtual-envs/