issue_comments

Table actions

3 rows where issue = 594594646 and user = 5821660 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: reactions, created_at (date), updated_at (date)

user 1

  • kmuehlbauer · 3 ✖

issue 1

  • Support multiple dimensions in DataArray.argmin() and DataArray.argmax() methods · 3 ✖

author_association 1

id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
609607223 https://github.com/pydata/xarray/pull/3936#issuecomment-609607223 https://api.github.com/repos/pydata/xarray/issues/3936 MDEyOklzc3VlQ29tbWVudDYwOTYwNzIyMw== kmuehlbauer 5821660 2020-04-06T07:13:00Z 2020-04-06T07:13:00Z MEMBER

@johnomotani FYI: For #3871 (merged) there is #3922 (yet unmerged) to fix dask-handling. 

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Support multiple dimensions in DataArray.argmin() and DataArray.argmax() methods 594594646
609605888 https://github.com/pydata/xarray/pull/3936#issuecomment-609605888 https://api.github.com/repos/pydata/xarray/issues/3936 MDEyOklzc3VlQ29tbWVudDYwOTYwNTg4OA== kmuehlbauer 5821660 2020-04-06T07:09:45Z 2020-04-06T07:09:45Z MEMBER

reduce over all given dimensions?

By this do you mean find the minimum as if the array were first (partially or totally) flattened along the given dims somehow? I'm not sure we provide that kind of behaviour anywhere in the current API.

@TomNicholas Probably I was bit confused by the current docstring. I think I understand now and there should be no problem at all.

 
{
    "total_count": 1,
    "+1": 1,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Support multiple dimensions in DataArray.argmin() and DataArray.argmax() methods 594594646
609591692 https://github.com/pydata/xarray/pull/3936#issuecomment-609591692 https://api.github.com/repos/pydata/xarray/issues/3936 MDEyOklzc3VlQ29tbWVudDYwOTU5MTY5Mg== kmuehlbauer 5821660 2020-04-06T06:31:12Z 2020-04-06T06:31:22Z MEMBER

+1 for making argmin/argmax (and idxmin/idxmax) work, when given multiple dimensions.

According to the current docstring it should already work that way for argmin/'argmax`:

Reduce this DataArray’s data by applying argmin along some dimension(s).

Returns: New DataArray/Dataset object with argmin applied to it's data and the indicated dimension(s) removed

But this behaviour is broken currently (works only for one given dim).

My main concern for changing the API as suggested above is, how should we discern (at least for argmin/argmax), if the user want's to:

  1. reduce over all given dimensions? (current behaviour according to docstring)
  2. reduce along all given dimensions? (suggested behaviour by @johnomotani [please correct if I'm wrong])
 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Support multiple dimensions in DataArray.argmin() and DataArray.argmax() methods 594594646

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]);