issue_comments: 482865424
This data as json
| 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/427#issuecomment-482865424 | https://api.github.com/repos/simonw/datasette/issues/427 | 482865424 | MDEyOklzc3VlQ29tbWVudDQ4Mjg2NTQyNA== | 9599 | 2019-04-13T18:56:25Z | 2019-04-13T19:42:08Z | OWNER | I think there's a `Facet` base class. `class ColumnFacet(Facet):` is the default behaviour we have today `class ArrayFacet(Facet):` facet by JSON array `class ManyToManyFacet(Facet):` facet by M2M table `class DateFacet(Facet):` facet by date `class DateTimeFacet(Facet):` facet by datetime `class EmojiFacet(Facet):` super-fun demo plugin I have planned Could even have a facet against a numerical column which loads the entire set of column values into numpy or pandas and calculates complex statistics facets in memory . There’s actually a lot of potential for Datasette plugins that load several MBs of data and analyze using other Python libraries. | {"total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} | 431800286 |