Q:

Drop multi-indexed rows of a DataFrame based on 'AND' condition between levels

I want to be able to drop rows from a multi-indexed dataframe object using multiple level criteria (with a logical AND joining the criteria). 
Consider the pandas dataframe object given by:
import pandas as pd
df = pd. DataFrame(data = [[1,'x'],[2,'x'],[1,'y'],[2,'y']],
                   index=pd. MultiIndex(levels=[['A','B'],['a','b']],
                                       labels=[[0,1,0,1],[0,1,1,0]],
                                       names=['idx0','idx1']))

print(df) outputs:
           0  1
idx0 idx1      
A    a     1  x
B    b     2  x
A    b     1  y
B    a     2  y

I wish to eliminate the row where 'idx0'=='A' and 'idx1'=='a', so the end result is:
           0  1
idx0 idx1      
B    b     2  x
     a     2  y
A    b     1  y

It seems to me as if this cannot be done with the df.drop() method.  A 'roundabout' way which gives the correct result is to do:
df = pd.concat([df.drop(labels='A',level=0),df.drop(labels='a',level=1)])
df = df.drop_duplicates()

But I figure that there has to be a better way...

A:

To address your question regarding .drop() - just pass the MultiIndex labels as tuple:
df.drop(('A', 'a'))

           0  1
idx0 idx1      
B    b     2  x
A    b     1  y
B    a     2  y

