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  • kmuehlbauer · 1 ✖

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  • Masking and preserving int type · 1 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
610799451 https://github.com/pydata/xarray/issues/3955#issuecomment-610799451 https://api.github.com/repos/pydata/xarray/issues/3955 MDEyOklzc3VlQ29tbWVudDYxMDc5OTQ1MQ== kmuehlbauer 5821660 2020-04-08T07:31:26Z 2020-04-08T07:42:03Z MEMBER

There has been a lot of discussion about the int vs nan problem in the past, here one issue #1194. My question for xarray-devs would be too, if there is some idea on adapting to the pandas scheme?

In the time being, you might just go the other way round (isel before where) and this little hack:

```python

overwrite fill_values with 0

sub = xr.where(z_indices == fill_value, 0, z_indices)

isel with sub and mask with where

indexed_array = val_arr.isel(z=sub).where(z_indices != fill_value) `` Update: Nevermind, this will make theindexed_arraya float. You might use the samewhere-machinery and overwrite with afill_value ` of your liking:

```python

overwrite fill_values with 0

sub = xr.where(z_indices == fill_value, 0, z_indices)

isel with sub and mask with where

indexed_array = val_arr.isel(z=sub) indexed_array = xr.where(z_indices == fill_value, fill_value, indexed_array) ``` I can't immediately see, but there might be a cleaner way to achieve this.

 
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   Masking and preserving int type 596352097

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