issue_comments

Table actions

1 row where issue = 243964948 and user = 10050469 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

  • fmaussion · 1 ✖

issue 1

  • Support for jagged array · 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
316317971 https://github.com/pydata/xarray/issues/1482#issuecomment-316317971 https://api.github.com/repos/pydata/xarray/issues/1482 MDEyOklzc3VlQ29tbWVudDMxNjMxNzk3MQ== fmaussion 10050469 2017-07-19T08:52:20Z 2017-07-19T08:52:20Z MEMBER

"Supported", yes, in the sense that you can create a DataArray for each of your differently sized arrays without any problem. If you want to store them all in a Dataset, you'll have to give a different dimension name for each new dimension, which can be clumsy.

However, it is true that xarray shines at handling more structured data and that most examples in the docs are those of dataset variables sharing similar dimensions. What kind of "support" exactly were you thinking of?

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Support for jagged array 243964948

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]);