issue_comments

Table actions

1 row where author_association = "CONTRIBUTOR", issue = 1517575123 and user = 44147817 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

  • gcaria · 1 ✖

issue 1

  • Implement `DataArray.to_dask_dataframe()` · 1 ✖

author_association 1

  • CONTRIBUTOR · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1398072586 https://github.com/pydata/xarray/issues/7409#issuecomment-1398072586 https://api.github.com/repos/pydata/xarray/issues/7409 IC_kwDOAMm_X85TVOUK gcaria 44147817 2023-01-20T08:39:31Z 2023-01-20T08:39:31Z CONTRIBUTOR

Yes I did, but unfortunately I didn't think about the trick of converting to Dataset. My only thought then is that it'd be nice to also get a dask Series, so something like to_dask instead of to_dask_dataframe, which I guess entails adding a DataFrame.squeeze() somewhere in the existing to_dask_dataframe code.

Feel free to close this.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Implement `DataArray.to_dask_dataframe()` 1517575123

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]);