issue_comments

Table actions

1 row where author_association = "MEMBER", issue = 28123550 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

  • Version now contains git commit ID · 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
35839019 https://github.com/pydata/xarray/pull/15#issuecomment-35839019 https://api.github.com/repos/pydata/xarray/issues/15 MDEyOklzc3VlQ29tbWVudDM1ODM5MDE5 shoyer 1217238 2014-02-23T18:40:48Z 2014-02-23T18:40:48Z MEMBER

My preference is to stick with the presumably battle-hardened code from pandas. It would probably be slightly cleaner to use the git-python library but that adds another dependency (at least to the build) and then we'd have to debug it. I think git describe has been around for quite a while (and is unlikely to change).

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Version now contains git commit ID 28123550

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]);