How do you update the levels of a pandas MultiIndex after slicing its DataFrame?

Question

I have a Dataframe with a pandas MultiIndex:

In [1]: import pandas as pd In [2]: multi_index = pd.MultiIndex.from_product([['CAN','USA'],['total']],names=['country','sex']) In [3]: df = pd.DataFrame({'pop':[35,318]},index=multi_index) In [4]: df Out[4]:                pop country sex CAN     total   35 USA     total  318 

Then I remove some rows from that DataFrame:

In [5]: df = df.query('pop > 100')  In [6]: df Out[6]:                pop country sex USA     total  318 

But when I consult the MutliIndex, it still has both countries in its levels.

In [7]: df.index.levels[0] Out[7]: Index([u'CAN', u'USA'], dtype='object') 

I can fix this myself in a rather strange way:

In [8]: idx_names = df.index.names  In [9]: df = df.reset_index(drop=False)  In [10]: df = df.set_index(idx_names)  In [11]: df Out[11]:                pop country sex USA     total  318  In [12]: df.index.levels[0] Out[12]: Index([u'USA'], dtype='object') 

But this seems rather messy. Is there a better way I'm missing?

Answer 1

From version pandas 0.20.0+ use MultiIndex.remove_unused_levels:

print (df.index) MultiIndex(levels=[['CAN', 'USA'], ['total']],            labels=[[1], [0]],            names=['country', 'sex'])  df.index = df.index.remove_unused_levels()  print (df.index) MultiIndex(levels=[['USA'], ['total']],            labels=[[0], [0]],            names=['country', 'sex'])