issue_comments
where issue = 258500654 and user = 1217238 sorted by updated_at descending
This data as json, CSV (advanced)
These facets timed out: author_association, issue
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
---|---|---|---|---|---|---|---|---|---|---|---|
330271312 | https://github.com/pydata/xarray/issues/1576#issuecomment-330271312 | https://api.github.com/repos/pydata/xarray/issues/1576 | MDEyOklzc3VlQ29tbWVudDMzMDI3MTMxMg== | shoyer 1217238 | 2017-09-18T16:04:47Z | 2017-09-18T16:04:47Z | MEMBER | We currently decode anything with a However, this isn't really a useful thing to do for a dataset like this where the values really represent enums/categories. It seems like the CF compliant way to indicate this is with the various flag_* attributes. So we could look for those to indicate that we shouldn't fill-in fill values. Eventually, we could possibly also use this for decoding into a true "categorical" dtype, but numpy doesn't have anything like that yet. |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Variable of dtype int8 casted to float64 258500654 |
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