issue_comments

Table actions

where issue = 712189206 and user = 1217238 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

These facets timed out: author_association, issue

user 1

  • shoyer · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
701694586 https://github.com/pydata/xarray/issues/4475#issuecomment-701694586 https://api.github.com/repos/pydata/xarray/issues/4475 MDEyOklzc3VlQ29tbWVudDcwMTY5NDU4Ng== shoyer 1217238 2020-09-30T23:13:33Z 2020-09-30T23:13:33Z MEMBER

I think we could support delayed objects in save_mfdataset, at least in principle. But if you're OK using delayed objects, you might as well write each netCDF file separately using dask.delayed, e.g., ``` def write_dataset(dataset, path): your_function(ds).to_netcdf(path)

result = [dask.delayed(write_dataset)(ds, path) for ds, path in zip(datasets, path)] dask.compute(result) ```

 
{
    "total_count": 3,
    "+1": 3,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Preprocess function for save_mfdataset 712189206

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]);