groupby w/ levels on MultiIndex and then interpolate
I would expect something like this to work:

```
In [111]:

df_1 = pd.DataFrame({'v':[1,np.nan,3]},index=pd.to_datetime([1,2,3], unit="d"))
pd.concat({'a':df_1, 'b':df_1}).groupby(level=0).apply(lambda x:x.interpolate(method="time"))

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-111-a2f8b8d05920> in <module>()
      1 df_1 = pd.DataFrame({'v':[1,np.nan,3]},index=pd.to_datetime([1,2,3], unit="d"))
----> 2 pd.concat({'a':df_1, 'b':df_1}).groupby(level=0).apply(lambda x:x.interpolate(method="time"))

/usr/lib64/python2.7/site-packages/pandas/core/groupby.py in apply(self, func, *args, **kwargs)
    422             return func(g, *args, **kwargs)
    423 
--> 424         return self._python_apply_general(f)
    425 
    426     def _python_apply_general(self, f):

/usr/lib64/python2.7/site-packages/pandas/core/groupby.py in _python_apply_general(self, f)
    425 
    426     def _python_apply_general(self, f):
--> 427         keys, values, mutated = self.grouper.apply(f, self.obj, self.axis)
    428 
    429         return self._wrap_applied_output(keys, values,

/usr/lib64/python2.7/site-packages/pandas/core/groupby.py in apply(self, f, data, axis)
    881             # group might be modified
    882             group_axes = _get_axes(group)
--> 883             res = f(group)
    884             if not _is_indexed_like(res, group_axes):
    885                 mutated = True

/usr/lib64/python2.7/site-packages/pandas/core/groupby.py in f(g)
    420         @wraps(func)
    421         def f(g):
--> 422             return func(g, *args, **kwargs)
    423 
    424         return self._python_apply_general(f)

<ipython-input-111-a2f8b8d05920> in <lambda>(x)
      1 df_1 = pd.DataFrame({'v':[1,np.nan,3]},index=pd.to_datetime([1,2,3], unit="d"))
----> 2 pd.concat({'a':df_1, 'b':df_1}).groupby(level=0).apply(lambda x:x.interpolate(method="time"))

/usr/lib64/python2.7/site-packages/pandas/core/generic.py in interpolate(self, method, axis, limit, inplace, downcast, **kwargs)
   2507 
   2508         if isinstance(self.index, MultiIndex) and method != 'linear':
-> 2509             raise ValueError("Only `method=linear` interpolation is supported "
   2510                              "on MultiIndexes.")
   2511 

ValueError: Only `method=linear` interpolation is supported on MultiIndexes.
```

This operation does work on the underlying data:

```
In [114]:

print df_1.interpolate(method="time")

            v
1970-01-02  1
1970-01-03  2
1970-01-04  3

[3 rows x 1 columns]
```
