issues

What happened?

After using set_index to create a MultiIndex, calling groupby on a MultiIndex level and then mean raises an error.

What did you expect to happen?

Apply mean to groups, no error.

Minimal Complete Verifiable Example

Python d = DataArray( data=[ [0, 1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12, 13], [14, 15, 16, 17, 18, 19, 20] ], coords={ "greek": ("a", ['alpha', 'beta', 'gamma']), "colors": ("a", ['red', 'green', 'blue']), "compass": ("b", ['north', 'south', 'east', 'west', 'northeast', 'southeast', 'southwest']), "integer": ("b", [0, 1, 2, 3, 4, 5, 6]), }, dims=("a", "b") ) d = d.set_index(a=['greek', 'colors'], b=['compass', 'integer']) g = d.groupby('greek') m = g.mean(...)

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

Python Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.10/site-packages/xarray/core/_aggregations.py", line 5698, in mean return self.reduce( File "/usr/local/lib/python3.10/site-packages/xarray/core/groupby.py", line 1201, in reduce return self.map(reduce_array, shortcut=shortcut) File "/usr/local/lib/python3.10/site-packages/xarray/core/groupby.py", line 1104, in map return self._combine(applied, shortcut=shortcut) File "/usr/local/lib/python3.10/site-packages/xarray/core/groupby.py", line 1136, in _combine index, index_vars = create_default_index_implicit(coord) File "/usr/local/lib/python3.10/site-packages/xarray/core/indexes.py", line 1045, in create_default_index_implicit index = PandasMultiIndex(array, name) File "/usr/local/lib/python3.10/site-packages/xarray/core/indexes.py", line 615, in __init__ raise ValueError( ValueError: conflicting multi-index level name 'greek' with dimension 'greek'

Anything else we need to know?

No response

Environment

http://xarray.pydata.org/en/stable/groupby.html says:

xarray supports “group by” operations with the same API as pandas

but when I supply the level keyword argument as described at https://pandas.pydata.org/pandas-docs/stable/groupby.html#groupby-with-multiindex, I get:
``` TypeError Traceback (most recent call last) <ipython-input-12-566fc67c0151> in <module>() ----> 1 hvm_it_v6_obj = hvm_it_v6.groupby(level=["category","obj"]).mean(dim="presentation") 2 hvm_it_v6_obj

TypeError: groupby() got an unexpected keyword argument 'level' ```

If I call .stack() on a DataArray, and any of the dimensions to be stacked already has a MultiIndex on it, I get an error:

TypeError: Must pass list-like as `names`.

Maybe it doesn't make sense for .stack() to work on an existing MultiIndex, but that should probably be documented, and perhaps throw a more informative error.

Here's the full trace:

``` Traceback (most recent call last) <ipython-input-6-ce81ac38d71e> in <module>() ----> 1 xr_one_flat_stacked = xr_one_flat.stack(alphanumeric=("letters", "numbers")) 2 xr_one_flat_stacked

/Users/jjpr/anaconda/envs/dldata/lib/python2.7/site-packages/xarray/core/dataarray.pyc in stack(self, dimensions) 1027 DataArray.unstack 1028 """ -> 1029 ds = self._to_temp_dataset().stack(dimensions) 1030 return self._from_temp_dataset(ds) 1031

/Users/jjpr/anaconda/envs/dldata/lib/python2.7/site-packages/xarray/core/dataset.pyc in stack(self, **dimensions) 1832 result = self 1833 for new_dim, dims in dimensions.items(): -> 1834 result = result._stack_once(dims, new_dim) 1835 return result 1836

/Users/jjpr/anaconda/envs/dldata/lib/python2.7/site-packages/xarray/core/dataset.pyc in _stack_once(self, dims, new_dim) 1801 else level 1802 for level in levels] -> 1803 idx = utils.multiindex_from_product_levels(levels, names=dims) 1804 variables[new_dim] = IndexVariable(new_dim, idx) 1805

/Users/jjpr/anaconda/envs/dldata/lib/python2.7/site-packages/xarray/core/utils.pyc in multiindex_from_product_levels(levels, names) 79 indexing='ij') 80 labels = [x.ravel() for x in labels_mesh] ---> 81 return pd.MultiIndex(levels, labels, sortorder=0, names=names) 82 83

/Users/jjpr/anaconda/envs/dldata/lib/python2.7/site-packages/pandas/indexes/multi.pyc in new(cls, levels, labels, sortorder, names, copy, verify_integrity, _set_identity, name, **kwargs) 103 if names is not None: 104 # handles name validation --> 105 result._set_names(names) 106 107 if sortorder is not None:

/Users/jjpr/anaconda/envs/dldata/lib/python2.7/site-packages/pandas/indexes/multi.pyc in _set_names(self, names, level, validate) 505 # set the name 506 for l, name in zip(level, names): --> 507 self.levels[l].rename(name, inplace=True) 508 509 names = property(fset=_set_names, fget=_get_names,

/Users/jjpr/anaconda/envs/dldata/lib/python2.7/site-packages/pandas/indexes/base.pyc in set_names(self, names, level, inplace) 997 998 if not is_list_like(names) and level is None and self.nlevels > 1: --> 999 raise TypeError("Must pass list-like as names.") 1000 1001 if not is_list_like(names):

TypeError: Must pass list-like as names. ```

Code Sample, a copy-pastable example if possible

The problem occurs for me in the midst of a data-processing pipeline that starts with some ~40MB netCDF files. I've tried to create pasteable code that reproduces the behavior from scratch, but I haven't succeeded.

Problem description

I pass two DataArrays to xr.align() with join="outer". The DataArrays are dtype float64, and contain a mix of NaNs and floats. They are 2D and have MultiIndexes with some numeric and some string levels.

The tuple of DataArrays returned by align() have the correct shape and expected indexes, but the contents of the arrays are all NaNs. The original float values are gone. np.nonzero(~np.isnan(da)) returns an empty array.

I've set breakpoints and delved into the code. On line 656 in xarray.core.variable.Variable._getitem_with_mask, self contains non-NaN values, but the data returned by as_indexable(self._data)[actual_indexer] evaluates as all NaNs. However, data.array at that point (which is xarray.backends.netCDF4_.NetCDF4ArrayWrapper) has non-NaNs. So it's some sort of masking caused by the indexing that makes it look like data is all NaNs.

Expected Output

A tuple of DataArrays which contain some non-NaN values.

Output of xr.show_versions()