issue_comments

Table actions

1 row where issue = 117002929 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

  • Plotting on map projection much slower on v0.6.1 than 0.6.0 · 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
157776055 https://github.com/pydata/xarray/issues/657#issuecomment-157776055 https://api.github.com/repos/pydata/xarray/issues/657 MDEyOklzc3VlQ29tbWVudDE1Nzc3NjA1NQ== jhamman 2443309 2015-11-18T16:51:31Z 2015-11-18T16:51:31Z MEMBER

I can't verify right now but it may have something to do with using masked arrays under the hood. There are no nan's in your example but xray still is converting the array to a masked_array before plotting. I bet plotting with pcolormesh is slower with masked arrays than with numpy arrays.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Plotting on map projection much slower on v0.6.1 than 0.6.0 117002929

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]);