issue_comments

Table actions

4 rows where author_association = "MEMBER" and issue = 294182366 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

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

user 3

issue 1

  • Whether should we follow pandas or numpy if they have different API? · 4 ✖

author_association 1

  • MEMBER · 4 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
363612599 https://github.com/pydata/xarray/issues/1886#issuecomment-363612599 https://api.github.com/repos/pydata/xarray/issues/1886 MDEyOklzc3VlQ29tbWVudDM2MzYxMjU5OQ== fujiisoup 6815844 2018-02-07T00:23:09Z 2018-02-07T00:23:09Z MEMBER

Thanks. I did not notice pandas issue. OK. I will follow numpy's API for argmin/argmax.

The difference between ddof=0 and ddof=1 has never mattered to me

Me neither.

but I don't know if it's worth the trouble of changing it.

OK. Agreed. I keep the current API.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Whether should we follow pandas or numpy if they have different API? 294182366
362943502 https://github.com/pydata/xarray/issues/1886#issuecomment-362943502 https://api.github.com/repos/pydata/xarray/issues/1886 MDEyOklzc3VlQ29tbWVudDM2Mjk0MzUwMg== shoyer 1217238 2018-02-04T22:00:05Z 2018-02-04T22:00:05Z MEMBER

I think a distinction between argmin/idxmin is useful -- and it seems that pandas is likely to restore this in the future as well.

The difference between ddof=0 and ddof=1 has never mattered to me. I suppose it probably does make more sense for us to follow pandas here (it's what we usually do), but I don't know if it's worth the trouble of changing it.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Whether should we follow pandas or numpy if they have different API? 294182366
362923699 https://github.com/pydata/xarray/issues/1886#issuecomment-362923699 https://api.github.com/repos/pydata/xarray/issues/1886 MDEyOklzc3VlQ29tbWVudDM2MjkyMzY5OQ== max-sixty 5635139 2018-02-04T17:25:19Z 2018-02-04T17:25:19Z MEMBER

Re the argmin / argmax - I think pandas is changing: https://github.com/pandas-dev/pandas/issues/16830

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Whether should we follow pandas or numpy if they have different API? 294182366
362909203 https://github.com/pydata/xarray/issues/1886#issuecomment-362909203 https://api.github.com/repos/pydata/xarray/issues/1886 MDEyOklzc3VlQ29tbWVudDM2MjkwOTIwMw== fujiisoup 6815844 2018-02-04T14:06:15Z 2018-02-04T14:11:10Z MEMBER

I also noticed that pandas' argmin and argmax behave differently from those of numpy. I think we should follow pandas because the extension of pandas is our core concept. Currently, our argmin and argmax behave as those of numpy.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Whether should we follow pandas or numpy if they have different API? 294182366

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]);