issue_comments
2 rows where issue = 775502974 and user = 1217238 sorted by updated_at descending
This data as json, CSV (advanced)
Suggested facets: created_at (date), updated_at (date)
issue 1
- ENH: Compute hash of xarray objects · 2 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
---|---|---|---|---|---|---|---|---|---|---|---|
752226528 | https://github.com/pydata/xarray/issues/4738#issuecomment-752226528 | https://api.github.com/repos/pydata/xarray/issues/4738 | MDEyOklzc3VlQ29tbWVudDc1MjIyNjUyOA== | shoyer 1217238 | 2020-12-29T20:13:02Z | 2020-12-29T20:13:02Z | MEMBER | I asked because this isn't an operation I've used directly on pandas objects in the past. I'm not opposed, but my suggestion would be to write a separate utility function, e.g., in |
{ "total_count": 3, "+1": 3, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
ENH: Compute hash of xarray objects 775502974 | |
751963435 | https://github.com/pydata/xarray/issues/4738#issuecomment-751963435 | https://api.github.com/repos/pydata/xarray/issues/4738 | MDEyOklzc3VlQ29tbWVudDc1MTk2MzQzNQ== | shoyer 1217238 | 2020-12-29T06:24:30Z | 2020-12-29T06:24:30Z | MEMBER | Interesting! Do pandas or dask have anything like this? |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
ENH: Compute hash of xarray objects 775502974 |
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