issue_comments
12 rows where issue = 58117200 sorted by updated_at descending
This data as json, CSV (advanced)
Suggested facets: reactions, created_at (date), updated_at (date)
issue 1
- Support multi-dimensional grouped operations and group_over · 12 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
---|---|---|---|---|---|---|---|---|---|---|---|
1054569287 | https://github.com/pydata/xarray/issues/324#issuecomment-1054569287 | https://api.github.com/repos/pydata/xarray/issues/324 | IC_kwDOAMm_X84-23NH | dcherian 2448579 | 2022-02-28T19:03:17Z | 2022-02-28T19:03:17Z | MEMBER | I have this almost ready in flox (needs more tests). So we should be able to do this soon. In the mean time note that we can view grouping over multiple variables as a "factorization" (group identification) problem for aggregations. That means you can
1. use |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Support multi-dimensional grouped operations and group_over 58117200 | |
1054526670 | https://github.com/pydata/xarray/issues/324#issuecomment-1054526670 | https://api.github.com/repos/pydata/xarray/issues/324 | IC_kwDOAMm_X84-2szO | alimanfoo 703554 | 2022-02-28T18:10:02Z | 2022-02-28T18:10:02Z | CONTRIBUTOR | Still relevant, would like to be able to group by multiple variables along a single dimension. |
{ "total_count": 6, "+1": 6, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Support multi-dimensional grouped operations and group_over 58117200 | |
781427391 | https://github.com/pydata/xarray/issues/324#issuecomment-781427391 | https://api.github.com/repos/pydata/xarray/issues/324 | MDEyOklzc3VlQ29tbWVudDc4MTQyNzM5MQ== | matthiasdemuzere 6926916 | 2021-02-18T15:33:06Z | 2021-02-18T15:33:06Z | NONE | still relevant, also for me ... I just wanted to group by half hours, for which I'd need access to |
{ "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Support multi-dimensional grouped operations and group_over 58117200 | |
531964854 | https://github.com/pydata/xarray/issues/324#issuecomment-531964854 | https://api.github.com/repos/pydata/xarray/issues/324 | MDEyOklzc3VlQ29tbWVudDUzMTk2NDg1NA== | shoyer 1217238 | 2019-09-16T21:26:21Z | 2019-09-16T21:26:21Z | MEMBER | Still relevant. |
{ "total_count": 3, "+1": 3, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Support multi-dimensional grouped operations and group_over 58117200 | |
531937119 | https://github.com/pydata/xarray/issues/324#issuecomment-531937119 | https://api.github.com/repos/pydata/xarray/issues/324 | MDEyOklzc3VlQ29tbWVudDUzMTkzNzExOQ== | stale[bot] 26384082 | 2019-09-16T20:08:04Z | 2019-09-16T20:08:04Z | NONE | In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity If this issue remains relevant, please comment here or remove the |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Support multi-dimensional grouped operations and group_over 58117200 | |
336983333 | https://github.com/pydata/xarray/issues/324#issuecomment-336983333 | https://api.github.com/repos/pydata/xarray/issues/324 | MDEyOklzc3VlQ29tbWVudDMzNjk4MzMzMw== | shoyer 1217238 | 2017-10-16T18:24:33Z | 2017-10-16T18:24:33Z | MEMBER |
No, I think the biggest issue is that grouping variables into a |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Support multi-dimensional grouped operations and group_over 58117200 | |
336925565 | https://github.com/pydata/xarray/issues/324#issuecomment-336925565 | https://api.github.com/repos/pydata/xarray/issues/324 | MDEyOklzc3VlQ29tbWVudDMzNjkyNTU2NQ== | jjpr-mit 25231875 | 2017-10-16T15:35:06Z | 2017-10-16T15:35:06Z | NONE | Is use case 1 (Multiple groupby arguments along a single dimension) being held back for use case 2 (Multiple groupby arguments along different dimensions)? Use case 1 would be very useful by itself. |
{ "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Support multi-dimensional grouped operations and group_over 58117200 | |
265462343 | https://github.com/pydata/xarray/issues/324#issuecomment-265462343 | https://api.github.com/repos/pydata/xarray/issues/324 | MDEyOklzc3VlQ29tbWVudDI2NTQ2MjM0Mw== | hottwaj 5629061 | 2016-12-07T14:35:01Z | 2016-12-07T14:35:01Z | NONE | In case it is of interest to anyone, the snippet below is a temporary and quite dirty solution I've used to do a multi-dimensional groupby... It runs nested groupby-apply operations over each given dimension until no further grouping needs to be done, then applies the given function "apply_fn"
Obviously performance can potentially be quite poor. Passing the dimensions to group over in order of increasing length will reduce your cost a little. |
{ "total_count": 3, "+1": 3, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Support multi-dimensional grouped operations and group_over 58117200 | |
131891348 | https://github.com/pydata/xarray/issues/324#issuecomment-131891348 | https://api.github.com/repos/pydata/xarray/issues/324 | MDEyOklzc3VlQ29tbWVudDEzMTg5MTM0OA== | clarkfitzg 5356122 | 2015-08-17T17:04:44Z | 2015-08-17T17:04:44Z | MEMBER | For (2) I think it makes sense to extend the existing groupby to deal with multiple dimensions. Ie, let it take an iterable of dimension names. ```
Then we'd have something similar to the SQL groupby, which is a good thing. By the way, in #527 we were considering using this approach to make the faceted plots on both rows and columns. |
{ "total_count": 4, "+1": 4, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Support multi-dimensional grouped operations and group_over 58117200 | |
131878081 | https://github.com/pydata/xarray/issues/324#issuecomment-131878081 | https://api.github.com/repos/pydata/xarray/issues/324 | MDEyOklzc3VlQ29tbWVudDEzMTg3ODA4MQ== | jhamman 2443309 | 2015-08-17T16:20:14Z | 2015-08-17T16:20:14Z | MEMBER | Agreed, we have two use cases here. For (1), can we just use the pandas grouping infrastructure. We just need to allow For (2), I'll need to think a bit more about how this would work. Do we add a groupby method to |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Support multi-dimensional grouped operations and group_over 58117200 | |
131599877 | https://github.com/pydata/xarray/issues/324#issuecomment-131599877 | https://api.github.com/repos/pydata/xarray/issues/324 | MDEyOklzc3VlQ29tbWVudDEzMTU5OTg3Nw== | jhamman 2443309 | 2015-08-16T18:51:05Z | 2015-08-17T16:07:41Z | MEMBER | @shoyer - I want to look into putting a PR together for this. I'm looking for the same functionality that you get with a pandas Series or DataFrame:
The motivation comes in making a Hovmoller diagram. What we need is this functionality:
If you can point me in the right direction, I'll see if I can put something together. |
{ "total_count": 7, "+1": 7, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Support multi-dimensional grouped operations and group_over 58117200 | |
131644079 | https://github.com/pydata/xarray/issues/324#issuecomment-131644079 | https://api.github.com/repos/pydata/xarray/issues/324 | MDEyOklzc3VlQ29tbWVudDEzMTY0NDA3OQ== | shoyer 1217238 | 2015-08-17T00:13:47Z | 2015-08-17T00:13:47Z | MEMBER | @jhamman For your use case, both hour and dayofyear are along the time dimension, so arguably the result should be 1D with a MultiIndex instead of 2D. So it might make more sense to start with that, and then layer on stack/unstack or pivot functionality. I guess there are two related use cases here: 1. Multiple groupby arguments along a single dimension (pandas does this one already) 2. Multiple groupby arguments along different dimensions (pandas doesn't do this one). |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
Support multi-dimensional grouped operations and group_over 58117200 |
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 9