issues
3 rows where comments = 14, repo = 13221727 and user = 1217238 sorted by updated_at descending
This data as json, CSV (advanced)
Suggested facets: created_at (date), updated_at (date), closed_at (date)
id | node_id | number | title | user | state | locked | assignee | milestone | comments | created_at | updated_at ▲ | closed_at | author_association | active_lock_reason | draft | pull_request | body | reactions | performed_via_github_app | state_reason | repo | type |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
874331538 | MDExOlB1bGxSZXF1ZXN0NjI4OTE0NDQz | 5252 | Add mode="r+" for to_zarr and use consolidated writes/reads by default | shoyer 1217238 | closed | 0 | 14 | 2021-05-03T07:57:16Z | 2021-06-22T06:51:35Z | 2021-06-17T17:19:26Z | MEMBER | 0 | pydata/xarray/pulls/5252 |
This PR includes several related changes to
These changes gave me a ~5x boost in write performance in a large
parallel job making use of
|
{ "url": "https://api.github.com/repos/pydata/xarray/issues/5252/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
xarray 13221727 | pull | |||||
29136905 | MDU6SXNzdWUyOTEzNjkwNQ== | 60 | Implement DataArray.idxmax() | shoyer 1217238 | closed | 0 | 1.0 741199 | 14 | 2014-03-10T22:03:06Z | 2020-03-29T01:54:25Z | 2020-03-29T01:54:25Z | MEMBER | Should match the pandas function: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.idxmax.html |
{ "url": "https://api.github.com/repos/pydata/xarray/issues/60/reactions", "total_count": 2, "+1": 2, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
completed | xarray 13221727 | issue | |||||
188113943 | MDU6SXNzdWUxODgxMTM5NDM= | 1097 | Better support for subclasses: tests, docs and API | shoyer 1217238 | open | 0 | 14 | 2016-11-08T21:54:00Z | 2019-08-22T13:07:44Z | MEMBER | Given that people do currently subclass xarray objects, it's worth considering making a subclass API like pandas: http://pandas.pydata.org/pandas-docs/stable/internals.html#subclassing-pandas-data-structures At the very least, it would be nice to have docs that describe how/when it's safe to subclass, and tests that verify our support for such subclasses. |
{ "url": "https://api.github.com/repos/pydata/xarray/issues/1097/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
xarray 13221727 | issue |
Advanced export
JSON shape: default, array, newline-delimited, object
CREATE TABLE [issues] ( [id] INTEGER PRIMARY KEY, [node_id] TEXT, [number] INTEGER, [title] TEXT, [user] INTEGER REFERENCES [users]([id]), [state] TEXT, [locked] INTEGER, [assignee] INTEGER REFERENCES [users]([id]), [milestone] INTEGER REFERENCES [milestones]([id]), [comments] INTEGER, [created_at] TEXT, [updated_at] TEXT, [closed_at] TEXT, [author_association] TEXT, [active_lock_reason] TEXT, [draft] INTEGER, [pull_request] TEXT, [body] TEXT, [reactions] TEXT, [performed_via_github_app] TEXT, [state_reason] TEXT, [repo] INTEGER REFERENCES [repos]([id]), [type] TEXT ); CREATE INDEX [idx_issues_repo] ON [issues] ([repo]); CREATE INDEX [idx_issues_milestone] ON [issues] ([milestone]); CREATE INDEX [idx_issues_assignee] ON [issues] ([assignee]); CREATE INDEX [idx_issues_user] ON [issues] ([user]);