html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/3225#issuecomment-883805046,https://api.github.com/repos/pydata/xarray/issues/3225,883805046,IC_kwDOAMm_X840rct2,26384082,2021-07-21T01:00:55Z,2021-07-21T01:00:55Z,NONE,"In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity

If this issue remains relevant, please comment here or remove the `stale` label; otherwise it will be marked as closed automatically
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,481866516
https://github.com/pydata/xarray/issues/3225#issuecomment-522546004,https://api.github.com/repos/pydata/xarray/issues/3225,522546004,MDEyOklzc3VlQ29tbWVudDUyMjU0NjAwNA==,20225454,2019-08-19T12:19:51Z,2019-08-19T12:19:51Z,NONE,Thanks for looking into this! I agree this is an unexpected or at least under-documented behaviour of dask arrays.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,481866516
https://github.com/pydata/xarray/issues/3225#issuecomment-522353487,https://api.github.com/repos/pydata/xarray/issues/3225,522353487,MDEyOklzc3VlQ29tbWVudDUyMjM1MzQ4Nw==,1217238,2019-08-18T20:38:40Z,2019-08-18T20:38:40Z,MEMBER,"There isn't really a notion of ""deep copying"" a dask array. Dask assumes that everything you apply to a  dask array is a pure function (though this isn't directly enforced), so if you map a mutating function over the blocks of a dask array you could potentially get undefined behavior (especially likely in the context of distributed computing). So when you tell xarray to deep copy a dask array, it currently just makes a normal copy.

I agree this a little counterintuitive, but it isn't obvious to me exactly what the right fix would look like.  Perhaps we could start raising an error or warning in this case?

For your specific problem, the fix is to do the copy *inside* mapped function, e.g.,
```python
def set_fraction_randomly_to_nan(data, frac_missing):
    np.random.seed(0)
    data = data.copy()
    data[np.random.rand(*data.shape) < frac_missing] = np.nan
    return data
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,481866516