issue_comments: 218372591
This data as json
| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/pull/818#issuecomment-218372591 | https://api.github.com/repos/pydata/xarray/issues/818 | 218372591 | MDEyOklzc3VlQ29tbWVudDIxODM3MjU5MQ== | 167164 | 2016-05-11T06:24:11Z | 2016-05-11T06:24:11Z | NONE | I want to be able to run a scikit-learn model over a bunch of variables in a 3D (lat/lon/time) dataset, and return values for each coordinate point. Is something like this multi-dimensional groupby required (I'm thinking groupby(lat, lon) => 2D matrices that can be fed straight into scikit-learn), or is there already some other mechanism that could achieve something like this? Or is the best way at the moment just to create a null dataset, and loop over lat/lon and fill in the blanks as you go? |
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