issue_comments

Table actions

1 row where issue = 608974755 and user = 2448579 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

  • dcherian · 1 ✖

issue 1

  • apply_ufunc gives wrong dtype with dask=parallelized and vectorized=True · 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
622599058 https://github.com/pydata/xarray/issues/4015#issuecomment-622599058 https://api.github.com/repos/pydata/xarray/issues/4015 MDEyOklzc3VlQ29tbWVudDYyMjU5OTA1OA== dcherian 2448579 2020-05-01T22:47:36Z 2020-05-01T22:47:36Z MEMBER

@mathuse is right.

The solution is to use dtype when we create meta for vectorized functions here: https://github.com/pydata/xarray/blob/3820fb77256682d909c1e41d962e29bec0edd62d/xarray/core/computation.py#L1008-L1011

@ulijh or @mathause , are either of you up for sending in a PR?

For now the workaround is to pass your own meta = np.ndarray((0,0), dtype=da.dtype)

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   apply_ufunc gives wrong dtype with dask=parallelized and vectorized=True 608974755

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]);