Before

```python In [2]: data = np.array([["x", 1], ["y", 2]], dtype="object")

In [3]: xr.conventions._infer_dtype(data, 'test') Out[3]: dtype('O') ```

As pointed out in #2620, this doesn't seem problematic until the user tries writing the xarray object to disk. This results in a very cryptic error message:

```python In [7]: ds.to_netcdf('test.nc', engine='netcdf4') netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Variable.setitem()

netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Variable._put()

TypeError: expected bytes, int found ```

After

```python In [2]: data = np.array([["x", 1], ["y", 2]], dtype="object")

In [3]: xr.conventions._infer_dtype(data, 'test')

ValueError Traceback (most recent call last) <ipython-input-3-addaab43c03a> in <module> ----> 1 xr.conventions._infer_dtype(data, 'test')

~/devel/pydata/xarray/xarray/conventions.py in _infer_dtype(array, name) 142 native_dtypes = set(map(lambda x: type(x), array.flatten())) 143 if len(native_dtypes) > 1: --> 144 raise ValueError( 145 "unable to infer dtype on variable {!r}; object array " 146 "contains mixed native types: {}".format(

ValueError: unable to infer dtype on variable 'test'; object array contains mixed native types: str,int ```

During I/O, the user gets:

```python ... ~/devel/pydata/xarray/xarray/conventions.py in ensure_dtype_not_object(var, name) 223 data[missing] = fill_value 224 else: --> 225 data = _copy_with_dtype(data, dtype=_infer_dtype(data, name)) 226 227 assert data.dtype.kind != "O" or data.dtype.metadata

~/devel/pydata/xarray/xarray/conventions.py in _infer_dtype(array, name) 142 native_dtypes = set(map(lambda x: type(x), array.flatten())) 143 if len(native_dtypes) > 1: --> 144 raise ValueError( 145 "unable to infer dtype on variable {!r}; object array " 146 "contains mixed native types: {}".format(

ValueError: unable to infer dtype on variable 'test'; object array contains mixed native types: str,int ```