issue_comments

Table actions

1 row where issue = 244016361 and user = 4992424 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

  • darothen · 1 ✖

issue 1

  • Loss of coordinate information from groupby.apply() on a stacked object · 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
316377854 https://github.com/pydata/xarray/issues/1483#issuecomment-316377854 https://api.github.com/repos/pydata/xarray/issues/1483 MDEyOklzc3VlQ29tbWVudDMxNjM3Nzg1NA== darothen 4992424 2017-07-19T12:59:04Z 2017-07-19T12:59:04Z NONE

Instead of computing the mean over your non-stacked dimension by

python dsg = dst.groupby('allpoints').mean()

why not just instead call

python dsg = dst.mean('time', keep_attrs=True)

so that you just collapse the time dimension and preserve the attributes on your data? Then you can unstack() and everything should still be there. The idiom of stacking/applying/unstacking is really useful to fit your data to the interface of a numpy or scipy function that will do all the heavy lifting with a vectorized routine for you - isn't using groupby in this way really slow? 

{
    "total_count": 1,
    "+1": 1,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Loss of coordinate information from groupby.apply() on a stacked object 244016361

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]);