issue_comments: 618332209
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/issues/3216#issuecomment-618332209 | https://api.github.com/repos/pydata/xarray/issues/3216 | 618332209 | MDEyOklzc3VlQ29tbWVudDYxODMzMjIwOQ== | 7360639 | 2020-04-23T10:52:40Z | 2020-04-23T10:52:40Z | NONE | This would still be very useful to me in future - for the piece of work I was referring to here I came up with a workaround. I filled in the gaps roughly with NaNs, so that I could identify and remove outliers and other bad data. Only then could I use the resample functionality without smearing these artefacts across good data. However, my solution was quite clunky and slow and was based on the still-mostly-regular resolution of my dataset, rather than any neater general solution in pandas. As I was (and am) also relatively new to Python I did not think this was appropriate to add to xarray myself, but I would like to say that I would definitely use this functionality in future - as would the other colleagues in space physics/meteorology I mentioned this to. |
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