issue_comments

Table actions

1 row where author_association = "MEMBER", issue = 126205116 and user = 1217238 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: updated_at (date)

user 1

  • shoyer · 1 ✖

issue 1

  • pandas date_range as index causes TypeError on repr · 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
171004347 https://github.com/pydata/xarray/issues/716#issuecomment-171004347 https://api.github.com/repos/pydata/xarray/issues/716 MDEyOklzc3VlQ29tbWVudDE3MTAwNDM0Nw== shoyer 1217238 2016-01-12T18:32:32Z 2016-01-12T18:32:32Z MEMBER

We can't handle pandas's custom dtype (e.g., datetime64[ns] with a timezone), and right now we fail very loudly when that happens.

We've encountered a few examples of this recently -- I'll see if I can clean this up for the next release.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   pandas date_range as index causes TypeError on repr 126205116

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]);