QST: Any support for df["x"] = y where y.columns are MultiIndexed?
We're working with DataFrames where the columns are MultiIndexed, so e.g. ones that look like this:

```
        pca          
       pca1      pca2
0  0.754675  1.868685
1 -1.861651 -0.048236
2 -0.797750  0.388400

```

which one can get through `pd.DataFrame(np.random.normal(size=(6,)).reshape((3,2)), columns=pd.MultiIndex.from_product([['pca'], ["pca1", "pca2"]]))`.

We now want to combine several of those to e.g. get this:

```
        pca                 nmf          
       pca1      pca2      nmf1      nmf2
0  1.671707  0.452155  1.671707  0.452155
1  0.861315 -0.100849  0.861315 -0.100849
2  1.056616 -0.852532  1.056616 -0.852532

```

We know that we can do this through e.g. `pd.concat([df_pca, df_nmf], axis=1)`. Is there any support for doing the same like this: `df["pca"] = df_pca` for some df? We get `ValueError: Wrong number of items passed 4, placement implies 1`.

It's really important for us to allow usage like this: `df["pca"] = df_pca`  and not just through concat. 
