issue_comments

Table actions

1 row where author_association = "MEMBER", issue = 420584430 and user = 1217238 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: created_at (date), updated_at (date)

user 1

  • shoyer · 1 ✖

issue 1

  • Improving documentation on `apply_ufunc` · 1 ✖

author_association 1

  • MEMBER · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
472553694 https://github.com/pydata/xarray/issues/2808#issuecomment-472553694 https://api.github.com/repos/pydata/xarray/issues/2808 MDEyOklzc3VlQ29tbWVudDQ3MjU1MzY5NA== shoyer 1217238 2019-03-13T18:40:47Z 2019-03-13T18:40:47Z MEMBER

I agree, this is a powerful but complex function. Probably the best approach is a longer tutorial (e.g., on a dedicated docs page), including even more examples.

Contributions would be very welcome here!

 
{
    "total_count": 9,
    "+1": 9,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Improving documentation on `apply_ufunc` 420584430

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]);