I'm struggling with hierarchical indexes in the Python `pandas` package. Specifically I don't understand how to **filter** and **compare** data in rows after it has been pivoted. 

Here is the example table from the documentation: 

```
import pandas as pd
import numpy as np

In [1027]: df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 6,
                              'B' : ['A', 'B', 'C'] * 8,
                              'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 4,
                              'D' : np.random.randn(24),
                              'E' : np.random.randn(24)})

In [1029]: pd.pivot_table(df, values='D', rows=['A', 'B'], cols=['C'])
Out[1029]: 
    C             bar       foo
    A     B                    
    one   A -1.154627 -0.243234
          B -1.320253 -0.633158
          C  1.188862  0.377300
    three A -1.327977       NaN
          B       NaN -0.079051
          C -0.832506       NaN
    two   A       NaN -0.128534
          B  0.835120       NaN
          C       NaN  0.838040

```

I would like to analyze as follows:

1) Filter this table on column attributes, for example selecting rows with negative `foo`:

```
    C             bar       foo
    A     B                    
    one   A -1.154627 -0.243234
          B -1.320253 -0.633158
    three B       NaN -0.079051
    two   A       NaN -0.128534

```

2) Compare the remaining `B` series values between the distinct `A` series groups? I am not sure how to access this information: `{'one':['A','B'], 'two':['A'], 'three':['B']}` and determine which series `B` values are unique to each key, or seen in multiple key groups, etc

Is there a way to do this directly within the pivot table structure, or do I need to convert this back in to a `pandas` `dataframe`?

**Update:** I think this code is a step in the right direction. It at least lets me access individual values within this table, but I am still hard-coding the series vales:

```
table = pivot_table(df, values='D', rows=['A', 'B'], cols=['C'])
table.ix['one', 'A']

```