BUG: Series.rename() ignores level argument on MultiIndex

[TomAugspurger]

At least I think this is a bug. #4627 was meant to unify the Series and DataFrame.rename APIs right?

In [35]: idx = pd.MultiIndex.from_tuples([(1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3)])

In [36]: s = pd.Series(np.arange(6), index=idx)

In [37]: s
Out[37]: 
1  1    0
   2    1
   3    2
2  1    3
   2    4
   3    5
dtype: int64

In [38]: s.index.names = ['outer', 'inner']

In [39]: s.rena
s.rename       s.rename_axis  

In [39]: s.rename({1: 'L1', 2: 'L2', 3: 'L3'}, level='inner')
Out[39]: 
outer  inner
L1     L1       0
       L2       1
       L3       2
L2     L1       3
       L2       4
       L3       5
dtype: int64

Edit: I guess I'm a bit confused because the docstring seems to indicate that Series.rename() does something a bit different:

Conform Series to new index with optional filling logic, placing
NA/NaN in locations having no value in the previous index. A new object
is produced unless the new index is equivalent to the current one and
copy=False

[jreback]

closed via 5dde943

was picking up the wrong docstring

[TomAugspurger]

That was fast :) I thought my issue hadn't been created, but it was already closed.

But what about the levels argument? For

In [39]: s.rename({1: 'L1', 2: 'L2', 3: 'L3'}, level='inner')
Out[39]: 
outer  inner
L1     L1       0
       L2       1
       L3       2
L2     L1       3
       L2       4
       L3       5
dtype: int64

I think only the inner level should be renamed, not both.

[jreback]

doesn't take a level argument. IIRC their is an issue about this....

[TomAugspurger]

OK, thanks. I'll see if I can track it down.

[jreback]

see #4160 and #4023 (may be somewhat duplicated issues)