issue_comments
2 rows where issue = 597475005 and user = 35968931 sorted by updated_at descending
This data as json, CSV (advanced)
Suggested facets: created_at (date), updated_at (date)
issue 1
- Extending Xarray for domain-specific toolkits · 2 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
---|---|---|---|---|---|---|---|---|---|---|---|
611967822 | https://github.com/pydata/xarray/issues/3959#issuecomment-611967822 | https://api.github.com/repos/pydata/xarray/issues/3959 | MDEyOklzc3VlQ29tbWVudDYxMTk2NzgyMg== | TomNicholas 35968931 | 2020-04-10T10:02:39Z | 2020-04-10T10:02:39Z | MEMBER |
There surely must be some way to do that, but I'm afraid I'm not a docs wizard. However the accessor is still just a class, whose methods you want to document - would it be too unclear for them to hang off each
There is some caching, but you shouldn't rely on it. In #3268 @crusaderky said "The more high level discussion is that the statefulness of the accessor is something that is OK to use for caching and performance improvements, and not OK for storing functional information like yours."
Checking dtype and dimensions shouldn't be expensive though, or is it more than that?
If you have other questions about dtypes in xarray then please feel free to raise another issue about that. |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Extending Xarray for domain-specific toolkits 597475005 | |
611719548 | https://github.com/pydata/xarray/issues/3959#issuecomment-611719548 | https://api.github.com/repos/pydata/xarray/issues/3959 | MDEyOklzc3VlQ29tbWVudDYxMTcxOTU0OA== | TomNicholas 35968931 | 2020-04-09T19:46:50Z | 2020-04-09T19:47:45Z | MEMBER |
One of the more immediate problems you'll find if you subclass is that xarray internally uses methods like You could make custom accessors which perform checks on the input arrays when they get used? ```python @xr.register_dataset_accessor('haplo') def HaploDatasetAccessor: def init(self, ds) check_conforms_to_haplo_requirements(ds) self.data = ds
ds.haplo.analyse() ``` I'm also wondering whether given that the only real difference (not just by convention) of your desired data structures from xarray's is the dtype, then (if xarray actually offered it) would something akin to pandas' |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Extending Xarray for domain-specific toolkits 597475005 |
Advanced export
JSON shape: default, array, newline-delimited, object
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]);
user 1