issue_comments

Table actions

1 row where issue = 117039129 and user = 25231875 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

  • jjpr-mit · 1 ✖

issue 1

  • groupby very slow compared to pandas · 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
334212532 https://github.com/pydata/xarray/issues/659#issuecomment-334212532 https://api.github.com/repos/pydata/xarray/issues/659 MDEyOklzc3VlQ29tbWVudDMzNDIxMjUzMg== jjpr-mit 25231875 2017-10-04T16:27:21Z 2017-10-04T16:27:21Z NONE

In case anyone gets here by Googling something like "xarray groupby slow" and you loaded data from a netCDF file, be aware that slowness you see in groupby aggregation on a Dataset or DataArray may actually be due not to this issue but to the lazy loading that's done by default. This can be fixed by calling .load() on the Dataset or DataArray. See the Tip about lazy loading at http://xarray.pydata.org/en/stable/io.html#netcdf. 

{
    "total_count": 9,
    "+1": 6,
    "-1": 0,
    "laugh": 0,
    "hooray": 1,
    "confused": 0,
    "heart": 1,
    "rocket": 1,
    "eyes": 0
}
   groupby very slow compared to pandas 117039129

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]);