Integration with JupyterLab

I just watched a demo video for the JupyterLab Chart Editor which wraps the plotly chart editor app in a JupyterLab panel and lets you open a plotly chart JSON file in that editor. Essentially, it pops an HTML app into a panel in JupyterLab, and I think registers the app as a file viewer for a particular file type. (I'm not completely taken by it, tbh, because it means you can do irreproducible things to the chart definition file, but that's another issue).

JupyterLab extensions can also open files from a dialogue as the iframe/html previewer shows: https://github.com/timkpaine/jupyterlab_iframe.

This made me wonder about what datasette integration with JupyterLab might do.

For example, by right-clicking on a CSV file (for which there is already a CSV table view) in the file browser, offer a View / Run as datasette file viewer option that will:

• run the CSV file through csvs-to-sqlite;
• launch the datasette server and display the datasette view in a JupyterLab panel.

(? Create a new SQLite db for each CSV file and launch each datasette view on a new port? Or have a JupyterLab (session?) SQLite db that stores all datasette viewed CSVs and runs on a single port?)

As a freebie, the datasette API would allow you to run efficient SQL queries against the file eg using using pandas.read_sql() queries in a notebook in the same space.

Related:

• JupyterLab extensions docs
• a cookiecutter for wrting JupyterLab extensions using Javascript
• a cookiecutter for writing JupyterLab extensions using Typescript
• tutorial: Let’s Make an xkcd JupyterLab Extension

Start with the linked JupyterLab extension documentation and iframe extension, then investigate how csvs-to-sqlite and Datasette could be invoked for a CSV file. Done means a JupyterLab file-browser action opens a CSV in a Datasette panel and exposes the resulting API for notebook SQL queries, with the database, port, and session model decided.