The join_axes kwarg of pd.concat is not very clearly documented (took me several tries to get it to work), and its name is not very clear either -- it's actually about restricting the axes that are not being concatenated (i.e. would be 'outer'-joined normally).

In particular, it is basically irrelevant with the deprecation of Panel, since there are no more ax_e_s (plural), only one non-concatenation ax_i_s.

Finally, with reindex and reindex_like, it is redundant as well:

one = pd.DataFrame([[0, 1], [2, 3]], columns=list('ab')) two = pd.DataFrame([[10, 11], [12, 13]], index=[1, 2], columns=list('bc')) ## simulating 'right'-join for the non-concatenation axis pd.concat([one, two], join='outer', axis=1, join_axes=two.index) # cryptic error message! # AssertionError: length of join_axes must not be equal to 1 ## only works with list-like join_axes pd.concat([one, two], join='outer', axis=1, join_axes=[two.index]) # a b b c # 1 2.0 3.0 10 11 # 2 NaN NaN 12 13 ## cleaner with reindex? pd.concat([one, two], join='outer', axis=1).reindex(two.index) # a b b c # 1 2.0 3.0 10.0 11.0 # 2 NaN NaN 12.0 13.0 

Note that the dtype changes due to the intermediate object having NaNs in the rows, but this will be fixed by #21160 anyway. Only question is if performance would be much worse, if concatenating huge Series/DFs before selecting small index-subset.