What happened?

When I group an xarray.DataArray in a single group and calculate the mean, then I expect the mean of this group to be the same as the mean of the input data.

When I have flox and numbagg installed next to xarray, I get inconsistant behavoir. The behaviour is consistent again when setting the option "use_flox" to False.

What did you expect to happen?

I expected xarray to give the mean of the values in the group. I expected this mean to be the same with flox as without flox. More specifically, I expected it to be (almost) equal to the numpy.mean.

Minimal Complete Verifiable Example

```Python

in a clean python.org environment:

pip install xarray, numbagg, flox

import numpy as np import xarray as xr

def grouped_mean(number): # Generate a set of random values np.random.seed(0) values = np.random.rand(number) # Use numpy to calculated the expected mean expected = np.mean(values) # Create an xarray dataset with coordinates data = xr.DataArray(values, [("dim_0", np.arange(number, dtype=float))]) # Group the coordinates to that all values fall in a single bin grouped = data.groupby_bins("dim_0", [-1.0, number + 1.0])

# Calculated the grouped mean without flox
xr.core.options.OPTIONS["use_flox"] = False
result_no_flox = grouped.mean().values[0]

# Calculate the grouped mean with flox
xr.core.options.OPTIONS["use_flox"] = True
result_flox = grouped.mean().values[0]

# Print the results
print(f"Try with number = {number}")
print(expected, "using numpy.mean")
print(result_no_flox, "grouped.mean no flox")
print(result_flox, "grouped.mean with flox")

for number in [127, 128, 255, 256, 1000]: grouped_mean(number) ```

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.
• [ ] 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.
• [x] Recent environment — the issue occurs with the latest version of xarray and its dependencies.

Relevant log output

Python Run python test.py Try with number = 127 0.5000245417623892 using numpy.mean 0.5000245417623892 grouped.mean no flox 0.5000245417623891 grouped.mean with flox Try with number = 128 0.49847415328514055 using numpy.mean 0.49847415328514055 grouped.mean no flox -0.49847415328514033 grouped.mean with flox Try with number = 255 0.4973500025365464 using numpy.mean 0.4973500025365464 grouped.mean no flox -126.82425064681932 grouped.mean with flox Try with number = 256 0.4957330979775834 using numpy.mean 0.4957330979775834 grouped.mean no flox nan grouped.mean with flox Try with number = 1000 0.49592153437178277 using numpy.mean 0.49592153437178277 grouped.mean no flox -20.663397265490953 grouped.mean with flox

Anything else we need to know?

This behaviour is only there when installing numbagg and flox next to xarray. (pip install xarray flox numbagg) The above mentioned output is from a github action, using linux and windows latest with python 3.11

Environment

What happened?

On Windows with python (3.9 and 3.10) the command import xarray results in a crash of python, if I have the packages netcdf4 and h5netcdf installed.

What did you expect to happen?

I expected that xarray would import normally, without a fatal python error.

Minimal Complete Verifiable Example

```Python

On windows:

pip install xarray pip install h5netcdf pip install netcdf4

This results in a crash

python -c "import xarray"

The crash does not occur when I first import h5netcdf and then import xarray, so the next line does not result in a crash:

python -c "import h5netcdf;import xarray"

The crash does not occur on linux.

The crash does not occur when I have only h5netcdf or netcdf4 installed.

```

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.
• [ ] 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

```Python command: python -c "import xarray" C:\hostedtoolcache\windows\Python\3.9.13\x64\lib\site-packages\h5py__init__.py:36: UserWarning: h5py is running against HDF5 1.12.1 when it was built against 1.12.2, this may cause problems _warn(("h5py is running against HDF5 {0} when it was built against {1}, " Warning! HDF5 library version mismatched error The HDF5 header files used to compile this application do not match the version used by the HDF5 library to which this application is linked. Data corruption or segmentation faults may occur if the application continues. This can happen when an application was compiled by one version of HDF5 but linked with a different version of static or shared HDF5 library. You should recompile the application or check your shared library related settings such as 'LD_LIBRARY_PATH'. You can, at your own risk, disable this warning by setting the environment variable 'HDF5_DISABLE_VERSION_CHECK' to a value of '1'. Setting it to 2 or higher will suppress the warning messages totally. Headers are 1.12.2, library is 1.12.1 SUMMARY OF THE HDF5 CONFIGURATION =================================

General Information:

               HDF5 Version: 1.12.1
              Configured on: 2022-03-04
              Configured by: Ninja
                Host system: Windows-10.0.17763
          Uname information: Windows
                   Byte sex: little-endian
         Installation point: D:/bld/hdf5_split_1646412547396/_h_env/Library

Compiling Options:

                 Build Mode: RELEASE
          Debugging Symbols: OFF
                    Asserts: OFF
                  Profiling: OFF
         Optimization Level: OFF

Linking Options:

                  Libraries:

Statically Linked Executables: OFF LDFLAGS: /machine:x64 H5_LDFLAGS: AM_LDFLAGS: Extra libraries: D:/bld/hdf5_split_1646412547396/_h_env/Library/lib/libcurl.lib;D:/bld/hdf5_split_1646412547396/_h_env/Library/lib/libssl.lib;D:/bld/hdf5_split_1646412547396/_h_env/Library/lib/libcrypto.lib Archiver: C:/Program Files (x86)/Microsoft Visual Studio/2019/Enterprise/VC/Tools/MSVC/14.16.27023/bin/HostX64/x64/lib.exe Ranlib: :

Languages:

                          C: YES
                 C Compiler: C:/Program Files (x86)/Microsoft Visual Studio/2019/Enterprise/VC/Tools/MSVC/14.16.27023/bin/HostX64/x64/cl.exe 19.16.27045.0
                   CPPFLAGS: 
                H5_CPPFLAGS: 
                AM_CPPFLAGS: 
                     CFLAGS:   /DWIN32 /D_WINDOWS 
                  H5_CFLAGS: /W3;/wd4100;/wd4706;/wd4127
                  AM_CFLAGS: 
           Shared C Library: YES
           Static C Library: YES

                    Fortran: OFF
           Fortran Compiler:  
              Fortran Flags: 
           H5 Fortran Flags: 
           AM Fortran Flags: 
     Shared Fortran Library: YES
     Static Fortran Library: YES

                        C++: ON
               C++ Compiler: C:/Program Files (x86)/Microsoft Visual Studio/2019/Enterprise/VC/Tools/MSVC/14.16.27023/bin/HostX64/x64/cl.exe 19.16.27045.0
                  C++ Flags:   
               H5 C++ Flags: /W3;/wd4100;/wd4706;/wd4127
               AM C++ Flags: 
         Shared C++ Library: YES
         Static C++ Library: YES

                        JAVA: OFF
             JAVA Compiler:

Features:

               Parallel HDF5: OFF

Parallel Filtered Dataset Writes: Large Parallel I/O: High-level library: ON Build HDF5 Tests: ON Build HDF5 Tools: ON Threadsafety: ON (recursive RW locks: ) Default API mapping: v112 With deprecated public symbols: ON I/O filters (external): DEFLATE MPE: Direct VFD: Mirror VFD: (Read-Only) S3 VFD: 1 (Read-Only) HDFS VFD: dmalloc: Packages w/ extra debug output: API Tracing: OFF Using memory checker: OFF Memory allocation sanity checks: OFF Function Stack Tracing: OFF Use file locking: best-effort Strict File Format Checks: OFF Optimization Instrumentation: Bye... Error: Process completed with exit code 1. ```

Anything else we need to know?

This bug is reproduced by the github action runner: https://github.com/daanscheltens/test-netcdf4/actions/runs/3196339371/jobs/5218135577

This action is part of a dedicated empty repository that just contains this action workflow: https://github.com/daanscheltens/test-netcdf4/blob/main/.github/workflows/action.yml

Environment

python -c "import h5netcdf; import xarray as xr;xr.show_versions()"