id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
199188476,MDU6SXNzdWUxOTkxODg0NzY=,1194,Use masked arrays while preserving int,500246,open,0,,,9,2017-01-06T12:40:22Z,2020-03-29T20:37:29Z,,CONTRIBUTOR,,,,"A great beauty of numpys masked arrays is that it works with any dtype, since it does not use `nan`.  Unfortunately, when I try to put my data into an `xarray.Dataset`, it converts ints to float, as shown below:

```
In [137]: x = arange(30, dtype=""i1"").reshape(3, 10)

In [138]: xr.Dataset({""count"": ([""x"", ""y""], ma.masked_where(x%5>3, x))}, coords={""x"": range(3), ""y"":
     ...: range(10)})
Out[138]:
<xarray.Dataset>
Dimensions:  (x: 3, y: 10)
Coordinates:
  * y        (y) int64 0 1 2 3 4 5 6 7 8 9
  * x        (x) int64 0 1 2
Data variables:
    count    (x, y) float64 0.0 1.0 2.0 3.0 nan 5.0 6.0 7.0 8.0 nan 10.0 ...
```

This happens in the function [`_maybe_promote`](https://github.com/pydata/xarray/blob/master/xarray/core/common.py#L693).

Such type “promotion” is unaffordable for me; the memory consumption of my multi-gigabyte arrays would explode by a factor 4.  Secondly, many of my integer-dtype fields are bit arrays, for which floating point representation is not desirable.

It would greatly benefit `xarray` if it could use masking while preserving the dtype of input data.

(See also: [Stackoverflow question](http://stackoverflow.com/q/41505699/974555))","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1194/reactions"", ""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue