issue_comments

Table actions

1 row where author_association = "NONE" and user = 4998171 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

  • lbybee · 1 ✖

issue 1

author_association 1

  • NONE · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
355383374 https://github.com/pydata/xarray/issues/1375#issuecomment-355383374 https://api.github.com/repos/pydata/xarray/issues/1375 MDEyOklzc3VlQ29tbWVudDM1NTM4MzM3NA== lbybee 4998171 2018-01-04T19:59:28Z 2018-01-04T19:59:28Z NONE

I'm interested to see if there have been any developments on this. I currently have an application where I'm working with multiple dask arrays, some of which are sparse (text data). It'd be worth my time to move my project to xarray, so I'm be interested in contributing something here if there is a need.

 
{
    "total_count": 3,
    "+1": 3,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Sparse arrays 221858543

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]);