issue_comments

Table actions

3 rows where author_association = "NONE" and user = 585279 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

  • mitar · 3 ✖

author_association 1

  • NONE · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
320891992 https://github.com/pydata/xarray/issues/1482#issuecomment-320891992 https://api.github.com/repos/pydata/xarray/issues/1482 MDEyOklzc3VlQ29tbWVudDMyMDg5MTk5Mg== mitar 585279 2017-08-08T08:44:36Z 2017-08-08T08:44:36Z NONE

then what advantage is there (aside from convenience) of dumping them in some giant array with forced dimensions/shape per slice?

I was mostly thinking of using xarray as a basic data format for reusable code. So if I build ML pipelines using reusable components, I have to pass data around. And so initially data might be in jagged arrays and then with various preprocessing before training model, I can get it to be in a more suitable format where images are of the same size so that I can try easier. I hoped I could use the same format for all of these places where I need to pass data around.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Support for jagged array 243964948
316372189 https://github.com/pydata/xarray/issues/1482#issuecomment-316372189 https://api.github.com/repos/pydata/xarray/issues/1482 MDEyOklzc3VlQ29tbWVudDMxNjM3MjE4OQ== mitar 585279 2017-07-19T12:37:43Z 2017-07-19T12:37:43Z NONE

Hm, padding might use a lot of extra space, no?

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Support for jagged array 243964948
316323858 https://github.com/pydata/xarray/issues/1482#issuecomment-316323858 https://api.github.com/repos/pydata/xarray/issues/1482 MDEyOklzc3VlQ29tbWVudDMxNjMyMzg1OA== mitar 585279 2017-07-19T09:15:00Z 2017-07-19T09:15:00Z NONE

If you want to store them all in a Dataset, you'll have to give a different dimension name for each new dimension, which can be clumsy.

But I cannot combine multiple dimensions into same Variable, no? So if I have a dataset of multiple variables, each variable seems that it has to have uniform dimensions for all its values? Maybe I am misunderstanding dimensions concept.

What kind of "support" exactly were you thinking of?

Maybe examples how to create such jagged dataset? For example, how to have a variable which stores 2D images of different sizes.

If I understand correctly, I could batch all images of the same size into its own dimension? That might be also acceptable.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Support for jagged array 243964948

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]);