issue_comments

Table actions

where issue = 57254455 and user = 1217238 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

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

These facets timed out: author_association

user 1

  • shoyer · 3 ✖

issue 1

  • Add head(), tail() and thin() methods? · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
459941364 https://github.com/pydata/xarray/issues/319#issuecomment-459941364 https://api.github.com/repos/pydata/xarray/issues/319 MDEyOklzc3VlQ29tbWVudDQ1OTk0MTM2NA== shoyer 1217238 2019-02-02T06:52:06Z 2019-02-02T06:52:06Z MEMBER

Anyways so yes, I agree that these would definitely be more readable than the equivalent operations with isel().

 
{
    "total_count": 1,
    "+1": 1,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Add head(), tail() and thin() methods? 57254455
459941280 https://github.com/pydata/xarray/issues/319#issuecomment-459941280 https://api.github.com/repos/pydata/xarray/issues/319 MDEyOklzc3VlQ29tbWVudDQ1OTk0MTI4MA== shoyer 1217238 2019-02-02T06:50:44Z 2019-02-02T06:51:06Z MEMBER

One virtue of tail() is particular is that we can define array.tail(x=0) to consistently return an array with an x dimension of size 0. This requires special case logic with slicing, since slice(None, -0) is the same as slice(None, 0).

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Add head(), tail() and thin() methods? 57254455
73812481 https://github.com/pydata/xarray/issues/319#issuecomment-73812481 https://api.github.com/repos/pydata/xarray/issues/319 MDEyOklzc3VlQ29tbWVudDczODEyNDgx shoyer 1217238 2015-02-11T00:14:17Z 2015-02-11T00:14:17Z MEMBER

@ebrevdo yes, I think we probably need to stick to the pandas/numpy convention for the meaning of take.

My inspiration for these names was the head() and tail() methods in pandas, which are quite convenient. But it's not entirely clear how/if these generalize cleanly to N-dimensions. I suppose take_first and take_last could be an improvement over head/tail.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Add head(), tail() and thin() methods? 57254455

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]);