issue_comments

Table actions

1 row where issue = 110726841 and user = 291576 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

  • WeatherGod · 1 ✖

issue 1

  • operations with pd.to_timedelta() now fails · 1 ✖

author_association 1

id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
146976549 https://github.com/pydata/xarray/issues/615#issuecomment-146976549 https://api.github.com/repos/pydata/xarray/issues/615 MDEyOklzc3VlQ29tbWVudDE0Njk3NjU0OQ== WeatherGod 291576 2015-10-09T20:15:49Z 2015-10-09T20:15:49Z CONTRIBUTOR

hmm, good point. I wish I knew why I ended up using pd.to_timedelta() in the first place. Did numpy not support converting timedelta objects at one point?

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   operations with pd.to_timedelta() now fails 110726841

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]);