issue_comments

Table actions

1 row where author_association = "MEMBER", issue = 416962458 and user = 2443309 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

  • jhamman · 1 ✖

issue 1

  • Performance: numpy indexes small amounts of data 1000 faster than xarray · 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
786816356 https://github.com/pydata/xarray/issues/2799#issuecomment-786816356 https://api.github.com/repos/pydata/xarray/issues/2799 MDEyOklzc3VlQ29tbWVudDc4NjgxNjM1Ng== jhamman 2443309 2021-02-26T18:25:13Z 2021-02-26T18:25:13Z MEMBER

I agree, I think a "xarray lite" package with only named dimensions could indeed be a valuable contribution.

FWIW, I think the xarray-lite concept would be a great chunk of work to write a small-ish proposal around. I think we could target the next round of CZI EOSS with such a concept. 

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Performance: numpy indexes small amounts of data 1000 faster than xarray 416962458

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]);