issue_comments

Table actions

1 row where issue = 237710101 and user = 6181563 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

  • jmunroe · 1 ✖

issue 1

  • Dataset.to_dataframe loads dask arrays into memory · 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
317917228 https://github.com/pydata/xarray/issues/1462#issuecomment-317917228 https://api.github.com/repos/pydata/xarray/issues/1462 MDEyOklzc3VlQ29tbWVudDMxNzkxNzIyOA== jmunroe 6181563 2017-07-26T01:09:05Z 2017-07-26T01:09:05Z CONTRIBUTOR

Today, I find myself in need exact functionality. Assuming no one else is working on it, I'll give a shot at trying to fix this.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Dataset.to_dataframe loads dask arrays into memory 237710101

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]);