issue_comments

Table actions

1 row where issue = 188996339 and user = 19403647 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

  • serazing · 1 ✖

issue 1

  • Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data · 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
260379241 https://github.com/pydata/xarray/issues/1115#issuecomment-260379241 https://api.github.com/repos/pydata/xarray/issues/1115 MDEyOklzc3VlQ29tbWVudDI2MDM3OTI0MQ== serazing 19403647 2016-11-14T16:10:55Z 2016-11-14T16:10:55Z NONE

I agree with @rabernat in the sense that it could be part of another package (e.g., signal processing). This would also allow the computation of statistical test to assess the significance of the correlation (which is useful since correlation may often be misinterpreted without statistical tests).

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data 188996339

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]);