issues
3 rows where "created_at" is on date 2019-06-24 and state = "open"
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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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459689615 | MDExOlB1bGxSZXF1ZXN0MjkwOTcxMjk1 | 524 | Sort commits using isort, refs #516 | simonw 9599 | open | 0 | 0 | 2019-06-24T05:04:48Z | 2019-06-24T05:45:00Z | OWNER | simonw/datasette/pulls/524 | Also added a lint unit test to ensure they stay sorted. #516 | datasette 107914493 | pull | {"url": "https://api.github.com/repos/simonw/datasette/issues/524/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} | 0 | ||||||
459882902 | MDU6SXNzdWU0NTk4ODI5MDI= | 526 | Stream all results for arbitrary SQL and canned queries | matej-fr 50578294 | open | 0 | 23 | 2019-06-24T13:09:45Z | 2022-09-28T04:01:25Z | NONE | I think that there is a difficulty with canned queries. When I want to stream all results of a canned query TwoDays I get only first 1.000 records. Example: `http://myserver/history_sample/two_days.csv?_stream=on` returns only first 1.000 records. If I do the same with the whole database i.e. `http://myserver/history_sample/database.csv?_stream=on` I get correctly all records. Any ideas? | datasette 107914493 | issue | {"url": "https://api.github.com/repos/simonw/datasette/issues/526/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} | ||||||||
460095928 | MDU6SXNzdWU0NjAwOTU5Mjg= | 528 | Establish a pattern for Datasette plugins built on top of Pandas | simonw 9599 | open | 0 | 0 | 2019-06-24T21:05:52Z | 2019-06-24T21:05:52Z | OWNER | The Pandas ecosystem is huge, varied and full of tools that are really good at doing interesting analysis on top of tabular data. Pandas should not be a dependency of Datasette core, but I think there is a lot of potential in having plugins which use Pandas to apply interesting analysis to data sucked out of Datasette's SQLite tables. One example ([thanks, Tony](https://twitter.com/psychemedia/status/1143259809715752962)): https://github.com/ResidentMario/missingno could form the basis of a fantastic plugin for getting a high-level overview of how complete each column in a table is. Some thought is needed here about what shape these kind of plugins might take, and what plugin hooks they would use. | datasette 107914493 | issue | {"url": "https://api.github.com/repos/simonw/datasette/issues/528/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0} |
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