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| id ▼ | html_url | issue_url | node_id | user | created_at | updated_at | author_association | body | reactions | issue | performed_via_github_app |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 435974786 | https://github.com/simonw/datasette/issues/370#issuecomment-435974786 | https://api.github.com/repos/simonw/datasette/issues/370 | MDEyOklzc3VlQ29tbWVudDQzNTk3NDc4Ng== | simonw 9599 | 2018-11-05T18:06:56Z | 2018-11-05T18:06:56Z | OWNER | I've been thinking a bit about ways of using Jupyter Notebook more effectively with Datasette (thinks like a `publish_dataframes(df1, df2, df3)` function which publishes some Pandas dataframes and returns you a URL to a new hosted Datasette instance) but you're right, Jupyter Lab is potentially a much more interesting fit. | {"total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} | Integration with JupyterLab 377155320 | |
| 436037692 | https://github.com/simonw/datasette/issues/370#issuecomment-436037692 | https://api.github.com/repos/simonw/datasette/issues/370 | MDEyOklzc3VlQ29tbWVudDQzNjAzNzY5Mg== | psychemedia 82988 | 2018-11-05T21:15:47Z | 2018-11-05T21:18:37Z | CONTRIBUTOR | In terms of integration with `pandas`, I was pondering two different ways `datasette`/`csvs_to_sqlite` integration may work: - like [`pandasql`](https://github.com/yhat/pandasql), to provide a SQL query layer either by a direct connection to the sqlite db or via `datasette` API; - as an improvement of `pandas.to_sql()`, which is a bit ropey (e.g. `pandas.to_sql_from_csvs()`, routing the dataframe to sqlite via `csvs_tosqlite` rather than the dodgy mapping that `pandas` supports). The `pandas.publish_*` idea could be quite interesting though... Would it be useful/fruitful to think about `publish_` as a complement to [`pandas.to_`](https://pandas.pydata.org/pandas-docs/stable/api.html#id12)? | {"total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} | Integration with JupyterLab 377155320 | |
| 436042445 | https://github.com/simonw/datasette/issues/370#issuecomment-436042445 | https://api.github.com/repos/simonw/datasette/issues/370 | MDEyOklzc3VlQ29tbWVudDQzNjA0MjQ0NQ== | psychemedia 82988 | 2018-11-05T21:30:42Z | 2018-11-05T21:31:48Z | CONTRIBUTOR | Another route would be something like creating a `datasette` IPython magic for notebooks to take a dataframe and easily render it as a `datasette`. You'd need to run the app in the background rather than block execution in the notebook. Related to that, or to publishing a dataframe in notebook cell for use in other cells in a non-blocking way, there may be cribs in something like https://github.com/micahscopes/nbmultitask . | {"total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} | Integration with JupyterLab 377155320 | |
| 1261930179 | https://github.com/simonw/datasette/issues/370#issuecomment-1261930179 | https://api.github.com/repos/simonw/datasette/issues/370 | IC_kwDOBm6k_c5LN4bD | MichaelTiemannOSC 72577720 | 2022-09-29T08:17:46Z | 2022-09-29T08:17:46Z | CONTRIBUTOR | Just watched this video which demonstrates the integration of *any* webapp into JupyterLab: https://youtu.be/FH1dKKmvFtc Maybe this is the answer? | {"total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} | Integration with JupyterLab 377155320 |
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