issue_comments

Table actions

1 row where issue = 28262599 and user = 514053 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

  • akleeman · 1 ✖

issue 1

  • ENH: NETCDF4 in pandas · 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
36039528 https://github.com/pydata/xarray/issues/18#issuecomment-36039528 https://api.github.com/repos/pydata/xarray/issues/18 MDEyOklzc3VlQ29tbWVudDM2MDM5NTI4 akleeman 514053 2014-02-25T18:16:38Z 2014-02-25T18:16:38Z CONTRIBUTOR

@jreback I'll spend some time getting a better feel for how/if we could push some of the backend into pandas' HDFStore. Certainly, we'd like to leverage other more powerful packages (pandas, numpy) as much as possible. Thanks for the suggestion.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   ENH: NETCDF4 in pandas 28262599

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]);