issue_comments

Table actions

1 row where issue = 589850951 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

  • running numpy functions on xarray objects · 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
611323478 https://github.com/pydata/xarray/issues/3917#issuecomment-611323478 https://api.github.com/repos/pydata/xarray/issues/3917 MDEyOklzc3VlQ29tbWVudDYxMTMyMzQ3OA== shoyer 1217238 2020-04-09T04:37:28Z 2020-04-09T04:37:40Z MEMBER

I think this is probably a good idea, assuming we figure out the type casting hierarchy challenge. Right now people often check for the existence of __array_function__ to indicate NumPy duck arrays, which clearly isn't quite right.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   running numpy functions on xarray objects 589850951

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]);