issues: 1589771368

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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
1589771368 I_kwDOAMm_X85ewfxo 7541 standard deviation over one dimension of a chunked DataArray leads to NaN 11750960 closed 0     2 2023-02-17T18:07:06Z 2023-02-17T18:12:10Z 2023-02-17T18:12:10Z CONTRIBUTOR      

What happened?

When computing the standard deviation over one dimension of a chunked DataArray, one may get NaNs.

What did you expect to happen?

We should not have any NaNs

Minimal Complete Verifiable Example

```Python x = (np.random.randn(10,10) + 1j*np.random.randn(10,10)) da = xr.DataArray(x).chunk(dict(dim_0=3))

da.std("dim_0").compute() # NaN da.compute().std("dim_0") # no NaNs

MVCE confirmation

  • [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
  • [X] Complete example — the example is self-contained, including all data and the text of any traceback.
  • [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
  • [X] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

No response

Anything else we need to know?

No response

Environment

INSTALLED VERSIONS ------------------ commit: None python: 3.10.8 (main, Nov 24 2022, 08:09:04) [Clang 14.0.6 ] python-bits: 64 OS: Darwin OS-release: 22.3.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: fr_FR.UTF-8 LOCALE: ('fr_FR', 'UTF-8') libhdf5: 1.12.2 libnetcdf: 4.8.1 xarray: 2022.12.0 pandas: 1.5.2 numpy: 1.23.5 scipy: 1.9.3 netCDF4: 1.6.2 pydap: None h5netcdf: None h5py: 3.7.0 Nio: None zarr: 2.13.3 cftime: 1.5.1.1 nc_time_axis: None PseudoNetCDF: None rasterio: 1.3.4 cfgrib: None iris: None bottleneck: 1.3.5 dask: 2022.02.1 distributed: 2022.2.1 matplotlib: 3.6.2 cartopy: 0.21.1 seaborn: 0.12.1 numbagg: None fsspec: 2022.11.0 cupy: None pint: None sparse: None flox: None numpy_groupies: None setuptools: 65.5.0 pip: 22.3.1 conda: None pytest: None mypy: None IPython: 8.7.0 sphinx: None 
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   completed 13221727 issue

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