issue_comments
1 row where issue = 906023492 and user = 14371165 sorted by updated_at descending
This data as json, CSV (advanced)
Suggested facets: created_at (date), updated_at (date)
issue 1
- Multidimensional histogram · 1 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
---|---|---|---|---|---|---|---|---|---|---|---|
892180473 | https://github.com/pydata/xarray/pull/5400#issuecomment-892180473 | https://api.github.com/repos/pydata/xarray/issues/5400 | IC_kwDOAMm_X841LZf5 | Illviljan 14371165 | 2021-08-03T21:32:21Z | 2021-08-03T21:32:21Z | MEMBER | Not sure if it's any help but dask supports the numpy histogram functions now: https://github.com/dask/dask/pull/7827 |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Multidimensional histogram 906023492 |
Advanced export
JSON shape: default, array, newline-delimited, object
CREATE TABLE [issue_comments] ( [html_url] TEXT, [issue_url] TEXT, [id] INTEGER PRIMARY KEY, [node_id] TEXT, [user] INTEGER REFERENCES [users]([id]), [created_at] TEXT, [updated_at] TEXT, [author_association] TEXT, [body] TEXT, [reactions] TEXT, [performed_via_github_app] TEXT, [issue] INTEGER REFERENCES [issues]([id]) ); CREATE INDEX [idx_issue_comments_issue] ON [issue_comments] ([issue]); CREATE INDEX [idx_issue_comments_user] ON [issue_comments] ([user]);
user 1