issue_comments

Table actions

4 rows where issue = 1383037028 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

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

author_association 2

issue 1

  • Should Xarray have a read_csv method? · 4 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1264569258 https://github.com/pydata/xarray/issues/7071#issuecomment-1264569258 https://api.github.com/repos/pydata/xarray/issues/7071 IC_kwDOAMm_X85LX8uq headtr1ck 43316012 2022-10-02T07:01:25Z 2022-10-02T07:01:25Z COLLABORATOR

Seeing that in another issue (can't find the number) it was discussed to move away from specialized read/write methods and only use open_dataset(maybe a Dataset.read?) and ds.write with engine arguments. Therefore option 2 would make sense.

However, new users that might be familiar with pandas are maybe more comfortable with an xr.read_csv.

On a side note: there is also dask.dataframe.read_csv.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Should Xarray have a read_csv method? 1383037028
1261236242 https://github.com/pydata/xarray/issues/7071#issuecomment-1261236242 https://api.github.com/repos/pydata/xarray/issues/7071 IC_kwDOAMm_X85LLPAS max-sixty 5635139 2022-09-28T17:32:38Z 2022-09-28T17:32:38Z MEMBER

Agree with (1) or (3). I do (1) a lot, no harm in adding it to xarray.

I could also imagine (2) with options for backends (e.g. pandas as one option). But I would vote against developing our own.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Should Xarray have a read_csv method? 1383037028
1261165644 https://github.com/pydata/xarray/issues/7071#issuecomment-1261165644 https://api.github.com/repos/pydata/xarray/issues/7071 IC_kwDOAMm_X85LK9xM dcherian 2448579 2022-09-28T16:35:19Z 2022-09-28T16:35:19Z MEMBER

(1) and (3) sound good to me.

For (2), one thing that comes to mind is that all the decode_* options don't make any sense for csv files? 

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Should Xarray have a read_csv method? 1383037028
1256327072 https://github.com/pydata/xarray/issues/7071#issuecomment-1256327072 https://api.github.com/repos/pydata/xarray/issues/7071 IC_kwDOAMm_X85K4geg TomNicholas 35968931 2022-09-23T14:59:28Z 2022-09-23T14:59:56Z MEMBER

Ideally, we don't force the user to use a third party library.

Option (2) would make more sense once we hopefully eventually make pandas an optional dependency too.

I think (1) and (3) are complementary - we already have pseudocode implementations of open_mfdataset in the docs for example.

(2) seems like overkill unless there is specific functionality you are imagining this enabling?

 
{
    "total_count": 1,
    "+1": 1,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Should Xarray have a read_csv method? 1383037028

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]);