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  • Fix behaviour of min_count in reducing functions · 6 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
781910976 https://github.com/pydata/xarray/pull/4911#issuecomment-781910976 https://api.github.com/repos/pydata/xarray/issues/4911 MDEyOklzc3VlQ29tbWVudDc4MTkxMDk3Ng== mathause 10194086 2021-02-19T08:12:38Z 2021-02-19T08:12:38Z MEMBER

Thanks @bcbnz. I see this is your first PR here - welcome to xarray!

 
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   Fix behaviour of min_count in reducing functions 808558647
780878085 https://github.com/pydata/xarray/pull/4911#issuecomment-780878085 https://api.github.com/repos/pydata/xarray/issues/4911 MDEyOklzc3VlQ29tbWVudDc4MDg3ODA4NQ== mathause 10194086 2021-02-17T21:51:19Z 2021-02-17T21:51:19Z MEMBER

I think we can merge this before the weekend, unless someone objects?

 
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   Fix behaviour of min_count in reducing functions 808558647
779750462 https://github.com/pydata/xarray/pull/4911#issuecomment-779750462 https://api.github.com/repos/pydata/xarray/issues/4911 MDEyOklzc3VlQ29tbWVudDc3OTc1MDQ2Mg== bcbnz 367900 2021-02-16T10:40:28Z 2021-02-16T10:40:28Z CONTRIBUTOR

Not for this PR but I wonder if xr.DataArray([1]).sum(min_count=2) should actually return NA.

It would make it more consistent, the current behaviour is

  • xr.DataArray([1]).sum(min_count=2) -> 1
  • xr.DataArray([1.0]).sum(min_count=2) -> nan

due to the different dtype defaults for skipna. I guess this would require rework of the dispatch logic in xarray.core.duck_array_ops._create_nan_agg_method to make it call nansum/nanprod when appropriate (e.g., should setting min_count imply skipna=True?).

 
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   Fix behaviour of min_count in reducing functions 808558647
779709464 https://github.com/pydata/xarray/pull/4911#issuecomment-779709464 https://api.github.com/repos/pydata/xarray/issues/4911 MDEyOklzc3VlQ29tbWVudDc3OTcwOTQ2NA== bcbnz 367900 2021-02-16T09:36:32Z 2021-02-16T09:36:32Z CONTRIBUTOR
  • Changed to use duck_array_ops.where
  • Docstring updated and entry added to breaking changes of whats new (I assumed this was appropriate). Note its a bit more nuanced than @mathause stated: for an integer array, skipna defaults to False and so xarray.core.duck_array_ops._create_nan_agg_method does not call nansum. This means you have to force skipna=True to see the difference:
    • master: xr.DataArray([1]).sum(min_count=1).dtype -> int64
    • this PR: xr.DataArray([1]).sum(min_count=1).dtype -> int64
    • master: xr.DataArray([1]).sum(skipna=True, min_count=1).dtype -> int64
    • this PR: xr.DataArray([1]).sum(skipna=True, min_count=1).dtype -> float64

Hopefully this is clear in the docstring changes, suggestions welcome.

 
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   Fix behaviour of min_count in reducing functions 808558647
779507029 https://github.com/pydata/xarray/pull/4911#issuecomment-779507029 https://api.github.com/repos/pydata/xarray/issues/4911 MDEyOklzc3VlQ29tbWVudDc3OTUwNzAyOQ== keewis 14808389 2021-02-16T00:23:09Z 2021-02-16T00:23:41Z MEMBER

We can use the where_method from duck_array_ops instead of np.where

even better would be duck_array_ops.where (it is closer to np.where)

 
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   Fix behaviour of min_count in reducing functions 808558647
779505394 https://github.com/pydata/xarray/pull/4911#issuecomment-779505394 https://api.github.com/repos/pydata/xarray/issues/4911 MDEyOklzc3VlQ29tbWVudDc3OTUwNTM5NA== dcherian 2448579 2021-02-16T00:18:01Z 2021-02-16T00:18:01Z MEMBER

looks like np.where is not yet lazy in dask 2.9.2

We can use the where_method from duck_array_ops instead of np.where

 
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   Fix behaviour of min_count in reducing functions 808558647

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