home / github

Menu
  • GraphQL API
  • Search all tables

issue_comments

Table actions
  • GraphQL API for issue_comments

4 rows where author_association = "NONE" and user = 449558 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

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

issue 3

  • How should xarray use/support sparse arrays? 2
  • Question: Guaranteed zero-copy round-trip from numpy? 1
  • [Proposal] Expose Variable without Pandas dependency 1

user 1

  • amueller · 4 ✖

author_association 1

  • NONE · 4 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
615500990 https://github.com/pydata/xarray/issues/3213#issuecomment-615500990 https://api.github.com/repos/pydata/xarray/issues/3213 MDEyOklzc3VlQ29tbWVudDYxNTUwMDk5MA== amueller 449558 2020-04-17T23:07:57Z 2020-04-17T23:07:57Z NONE

@shoyer thanks! Mostly spitballing here, but it's interesting to know that 2) would be the bigger problem in your opinion, I had assumed 1) would be the main issue. That raises the question whether it's easier to wrap scipy.sparse in a duck array, or to make pydata/sparse a viable solution for sklearn.

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  How should xarray use/support sparse arrays? 479942077
615497160 https://github.com/pydata/xarray/issues/3213#issuecomment-615497160 https://api.github.com/repos/pydata/xarray/issues/3213 MDEyOklzc3VlQ29tbWVudDYxNTQ5NzE2MA== amueller 449558 2020-04-17T22:51:09Z 2020-04-17T22:51:09Z NONE

Small comment from #3981: sklearn has just started running benchmarks, but it looks like pydata/sparse is not feature complete enough for us to use. We might be interested in having scipy.sparse support in xarray.

There are two problems with scipy.sparse for us as far as I can see (this is very preliminary): it only has COO, which is not good for us, and ideally we'd want to avoid memory copies whenever we want to use xarray, and I think going from scipy.sparse to pydata/sparse will involve memory copies, even if pydata/sparse adds other formats.

{
    "total_count": 1,
    "+1": 1,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  How should xarray use/support sparse arrays? 479942077
615490533 https://github.com/pydata/xarray/issues/3981#issuecomment-615490533 https://api.github.com/repos/pydata/xarray/issues/3981 MDEyOklzc3VlQ29tbWVudDYxNTQ5MDUzMw== amueller 449558 2020-04-17T22:24:36Z 2020-04-17T22:24:36Z NONE

FYI the conversation on sklearn is far from resolved, and at this point I think the added pandas dependency is not what will keep us from using xarray. I think right now we're most concerned about sparse data representations (and I was considering asking you folks if you'd support scipy.sparse ;)

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  [Proposal] Expose Variable without Pandas dependency 602256880
508785516 https://github.com/pydata/xarray/issues/3077#issuecomment-508785516 https://api.github.com/repos/pydata/xarray/issues/3077 MDEyOklzc3VlQ29tbWVudDUwODc4NTUxNg== amueller 449558 2019-07-05T14:58:12Z 2019-07-05T14:58:12Z NONE

Thank you @shoyer, that's very useful input. It seems that xarray would fulfill our requirements and so at least is a reasonable candidate for us.

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  Question: Guaranteed zero-copy round-trip from numpy? 463841931

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]);
Powered by Datasette · Queries took 17.368ms · About: xarray-datasette