issue_comments

Table actions

1 row where author_association = "MEMBER", issue = 190026722 and user = 2443309 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

These facets timed out: author_association, issue

user 1

  • jhamman · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
315178012 https://github.com/pydata/xarray/pull/1131#issuecomment-315178012 https://api.github.com/repos/pydata/xarray/issues/1131 MDEyOklzc3VlQ29tbWVudDMxNTE3ODAxMg== jhamman 2443309 2017-07-13T19:27:25Z 2017-07-13T19:27:25Z MEMBER

In reviewing this PR, I'm not really a fan of special casing the 1-D array in the way described above. These sorts of decisions lead to unexpected/confusing behavior when scaling up analysis. Obviously there is a tradeoff here as we diverge from how the numpy.diff function works but I think that is something we'd prefer to live with. I think this falls under the explicit is better than implicit idea...

 
{
    "total_count": 3,
    "+1": 3,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Fix #1040: diff dim argument should be optional 190026722

Advanced export

JSON shape: default, array, newline-delimited, object

CSV options: 

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]);