AttributeError: 'DataFrame' object has no attribute 'droplevel' in pandas

Question

I am getting a strange (to my understanding) message when I try to drop a level from a multi-indexed pandas dataframe.

For a reproducible example:

toy.to_json()
'{"["ISRG","EPS_diluted"]":{"2004-12-31":0.33,"2005-01-28":0.33,"2005-03-31":0.25,"2005-04-01":0.25,"2005-04-29":0.25},"["DHR","EPS_diluted"]":{"2004-12-31":0.67,"2005-01-28":0.67,"2005-03-31":0.67,"2005-04-01":0.58,"2005-04-29":0.58},"["BDX","EPS_diluted"]":{"2004-12-31":0.75,"2005-01-28":0.75,"2005-03-31":0.72,"2005-04-01":0.72,"2005-04-29":0.72},"["SYK","EPS_diluted"]":{"2004-12-31":0.4,"2005-01-28":0.4,"2005-03-31":0.42,"2005-04-01":0.42,"2005-04-29":0.42},"["BSX","EPS_diluted"]":{"2004-12-31":0.35,"2005-01-28":0.35,"2005-03-31":0.42,"2005-04-01":0.42,"2005-04-29":0.42},"["BAX","EPS_diluted"]":{"2004-12-31":0.18,"2005-01-28":0.18,"2005-03-31":0.36,"2005-04-01":0.36,"2005-04-29":0.36},"["EW","EPS_diluted"]":{"2004-12-31":0.4,"2005-01-28":0.4,"2005-03-31":0.5,"2005-04-01":0.5,"2005-04-29":0.5},"["MDT","EPS_diluted"]":{"2004-12-31":0.44,"2005-01-28":0.45,"2005-03-31":0.45,"2005-04-01":0.45,"2005-04-29":0.16},"["ABT","EPS_diluted"]":{"2004-12-31":0.63,"2005-01-28":0.63,"2005-03-31":0.53,"2005-04-01":0.53,"2005-04-29":0.53}}'

toy.droplevel(level = 1, axis = 1)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-33-982eee5ba162> in <module>()
----> 1 toy.droplevel(level = 1, axis = 1)

C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\site-packages\pandas\core\generic.py in __getattr__(self, name)
   4370             if self._info_axis._can_hold_identifiers_and_holds_name(name):
   4371                 return self[name]
-> 4372             return object.__getattribute__(self, name)
   4373 
   4374     def __setattr__(self, name, value):

AttributeError: 'DataFrame' object has no attribute 'droplevel'

Answer 1

Problem is the use of an older pandas version, because if you check DataFrame.droplevel:

New in version 0.24.0.

The solution is to use MultiIndex.droplevel:

toy.columns = toy.columns.droplevel(level = 1)