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  • Use masked arrays while preserving int · 2 ✖

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457209272 https://github.com/pydata/xarray/issues/1194#issuecomment-457209272 https://api.github.com/repos/pydata/xarray/issues/1194 MDEyOklzc3VlQ29tbWVudDQ1NzIwOTI3Mg== max-sixty 5635139 2019-01-24T14:09:32Z 2019-01-24T14:09:32Z MEMBER

@gerritholl check out https://pandas-docs.github.io/pandas-docs-travis/whatsnew/v0.24.0.html#whatsnew-0240-enhancements-intna

I think that's the closest way of having int support; from my understanding supporting masked arrays directly would be a decent lift

 
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   Use masked arrays while preserving int 199188476
271058005 https://github.com/pydata/xarray/issues/1194#issuecomment-271058005 https://api.github.com/repos/pydata/xarray/issues/1194 MDEyOklzc3VlQ29tbWVudDI3MTA1ODAwNQ== shoyer 1217238 2017-01-07T02:54:54Z 2017-01-07T02:54:54Z MEMBER

I answered your question on StackOverflow.

I agree that this is unfortunate. The cleanest solution would be an integer dtype with missing value support in NumPy itself, but that isn't going to happen anytime soon.

I'm not entirely opposed to the idea of adding (limited) support for masked arrays in xarray (see also https://github.com/pydata/xarray/pull/1118), but this could be a lot of work for relatively limited return.

I definitely recommend trying dask for processing multi-gigabyte arrays. You might even find the performance boost compelling enough that you could forgive the limitation that it doesn't handle masked arrays, either.

 
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   Use masked arrays while preserving int 199188476

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