If we convert a pandas dataframe with a multiindex, slice it to remove some entries from the index, a converted DataArray still contains the removed items in the coordinates (although the values are NaN).

```python

We create an example dataframe

idx = pd.MultiIndex.from_product([list('abc'), list('xyz')]) df = pd.DataFrame(data={'col': np.random.randn(len(idx))}, index=idx) df.columns.name = 'cols' df.index.names = ['idx1', 'idx2'] df2 = df.loc[['a', 'b']] python

df2 does not contain c in the first level

df2 cols col idx1 idx2
a x -0.844476 y -0.845998 z 1.965143 b x -0.159293 y 0.188163 z -1.076204

It still shows up in the converted xarray though:

xr.DataArray(df2).unstack('dim_0') <xarray.DataArray (cols: 1, idx1: 3, idx2: 3)> array([[[-0.844476, -0.845998, 1.965143], [-0.159293, 0.188163, -1.076204], [ nan, nan, nan]]]) Coordinates: * cols (cols) object 'col' * idx1 (idx1) object 'a' 'b' 'c' * idx2 (idx2) object 'x' 'y' 'z' ```

If the original dataframe is very sparse, this can lead to gigantic unnecessary memory usage.