issue_comments

Table actions

1 row where issue = 193226503 and user = 2443309 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: updated_at (date)

user 1

  • jhamman · 1 ✖

issue 1

  • "ncdump -h" like repr? · 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
264963094 https://github.com/pydata/xarray/issues/1150#issuecomment-264963094 https://api.github.com/repos/pydata/xarray/issues/1150 MDEyOklzc3VlQ29tbWVudDI2NDk2MzA5NA== jhamman 2443309 2016-12-05T20:11:09Z 2016-12-05T20:11:09Z MEMBER

I think something like this would be quite useful. This is most applicable to the Dataset and I can see a new method for printing this info. What would we call that method?

  • Pandas has a df.head and I think this is technically information stored in the header of the netcdf file, so ds.head() could work but pandas prints out some number of rows as well.
  • Maybe just ds.info() which is simple enough and self-explanatory, also no confusion with df.head() which returns a DataFrame.
  • I don't really like the *dump* alternatives since they will be pretty close to the pickle.dump.
 
{
    "total_count": 1,
    "+1": 1,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   "ncdump -h" like repr? 193226503

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]);