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- Resample / upsample behavior diverges from pandas · 2 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
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336592618 | https://github.com/pydata/xarray/issues/1631#issuecomment-336592618 | https://api.github.com/repos/pydata/xarray/issues/1631 | MDEyOklzc3VlQ29tbWVudDMzNjU5MjYxOA== | shoyer 1217238 | 2017-10-13T23:54:51Z | 2017-10-13T23:54:51Z | MEMBER | Let's see where the pandas discussion ends up. If xarray had a method for interpolating to fill missing values, achieving your desired result would be as a simple as chaining another interpolate call, e.g., |
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Resample / upsample behavior diverges from pandas 265056503 | |
336578729 | https://github.com/pydata/xarray/issues/1631#issuecomment-336578729 | https://api.github.com/repos/pydata/xarray/issues/1631 | MDEyOklzc3VlQ29tbWVudDMzNjU3ODcyOQ== | shoyer 1217238 | 2017-10-13T22:05:41Z | 2017-10-13T22:05:41Z | MEMBER | The key difference appears to be:
- In xarray, I think this is a bug in pandas, since the behavior is inconsistent with other resample methods like
More generally: (This does suggest that xarray could use a direct Another example: ```
It is useful that pandas's upsampling is only repeating values within the previously valid range. Otherwise it is likely to interpolate over true data gaps. As another use-case: suppose we have a temperature dataset with 3 hourly measurements, and we want to upsample it to 1 hour resolution. Occasionally, measurements are missing for day(s) at a time, which we mark with missing values (suppose the server running the model ran out of disk space). It is useful to be able to resample to a higher resolution without entirely unrealistic interpolation over data gaps. |
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Resample / upsample behavior diverges from pandas 265056503 |
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