issue_comments: 436037692
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| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | issue | performed_via_github_app |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/simonw/datasette/issues/370#issuecomment-436037692 | https://api.github.com/repos/simonw/datasette/issues/370 | 436037692 | MDEyOklzc3VlQ29tbWVudDQzNjAzNzY5Mg== | 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} | 377155320 |