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id ▼ | node_id | number | title | user | state | locked | assignee | milestone | comments | created_at | updated_at | closed_at | author_association | pull_request | body | repo | type | active_lock_reason | performed_via_github_app | reactions | draft | state_reason |
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377155320 | MDU6SXNzdWUzNzcxNTUzMjA= | 370 | Integration with JupyterLab | psychemedia 82988 | open | 0 | 4 | 2018-11-04T13:57:13Z | 2022-09-29T08:17:47Z | CONTRIBUTOR | I just watched a demo video for the [JupyterLab Chart Editor](https://www.crowdcast.io/e/introducing-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](https://jupyterlab.readthedocs.io/en/stable/user/extensions.html) - a [cookiecutter for wrting JupyterLab extensions using Javascript](https://github.com/jupyterlab/extension-cookiecutter-js) - a [cookiecutter for writing JupyterLab extensions using Typescript](https://github.com/jupyterlab/extension-cookiecutter-ts) - tutorial: [Let’s Make an xkcd JupyterLab Extension](https://jupyterlab.readthedocs.io/en/stable/developer/xkcd_extension_tutorial.html) | datasette 107914493 | issue | {"url": "https://api.github.com/repos/simonw/datasette/issues/370/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} | ||||||||
944846776 | MDU6SXNzdWU5NDQ4NDY3NzY= | 297 | Option for importing CSV data using the SQLite .import mechanism | simonw 9599 | open | 0 | 22 | 2021-07-14T22:36:41Z | 2022-09-29T23:06:44Z | OWNER | As seen in https://til.simonwillison.net/sqlite/import-csv - `.mode csv` and then `.import school.csv schools` is hugely faster than importing via `sqlite-utils insert` and doing the work in Python - but it can only be implemented by shelling out to the `sqlite3` CLI tool, it's not functionality that is exposed to the Python `sqlite3` module. An option to use this would be useful - maybe something like this: sqlite-utils insert blah.db blah blah.csv --fast | sqlite-utils 140912432 | issue | {"url": "https://api.github.com/repos/simonw/sqlite-utils/issues/297/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} |
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