issue_comments

Table actions

1 row where user = 17321532 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

  • bladwig1 · 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
240541467 https://github.com/pydata/xarray/issues/946#issuecomment-240541467 https://api.github.com/repos/pydata/xarray/issues/946 MDEyOklzc3VlQ29tbWVudDI0MDU0MTQ2Nw== bladwig1 17321532 2016-08-17T20:42:55Z 2016-08-17T20:42:55Z NONE

@shoyer I work in the NCL team at NCAR and am developing the soon-to-be released wrf-python package, which gives Python users the same functionality found in WRF-NCL. The metadata is managed using xarray.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   List of projects using xarray 169547144

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]);