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...