Joining a Pandas series with a hierarchical index back to the source DataFrame

Question

I'm trying to wrap my brain around pandas data structures and trying to use them in anger a bit. I've figured out that groupby operations result in a pandas series object. But I can't quite figure out how to use the resulting series. In particular, I want to do two thing:

1) "join" the results back to the initial DataFrame

2) select a specific value from the resulting series based on the hierarchical index.

Here's a toy example to work with:

import pandas
df = pandas.DataFrame({'group1': ['a','a','a','b','b','b'],
                       'group2': ['c','c','d','d','d','e'],
                       'value1': [1.1,2,3,4,5,6],
                       'value2': [7.1,8,9,10,11,12]
})
dfGrouped = df.groupby( ["group1", "group2"] , sort=True)

## toy function, obviously not my real function
def fun(x): return mean(x**2)

results = dfGrouped.apply(lambda x: fun(x.value1))

so the resulting series (results) looks like this:

group1  group2
a       c          2.605
        d          9.000
b       d         20.500
        e         36.000

That makes sense. But how do I:

1) join this back to the original DataFrame df

2) Select a single value where, say, group1=='b' & group2=='d'

Answer 1

So for remaining #1.

In [9]: df
Out[9]:
  group1 group2  value1  value2
0      a      c     1.1     7.1
1      a      c     2.0     8.0
2      a      d     3.0     9.0
3      b      d     4.0    10.0
4      b      d     5.0    11.0
5      b      e     6.0    12.0

In [10]: results
Out[10]:
group1  group2
a       c          2.605
        d          9.000
b       d         20.500
        e         36.000

In [11]: df.set_index(['group1', 'group2'], inplace=True)['results'] = results

In [12]: df
Out[12]:
               value1  value2  results
group1 group2
a      c          1.1     7.1    2.605
       c          2.0     8.0    2.605
       d          3.0     9.0    9.000
b      d          4.0    10.0   20.500
       d          5.0    11.0   20.500
       e          6.0    12.0   36.000

In [13]: df.reset_index()
Out[13]:
  group1 group2  value1  value2  results
0      a      c     1.1     7.1    2.605
1      a      c     2.0     8.0    2.605
2      a      d     3.0     9.0    9.000
3      b      d     4.0    10.0   20.500
4      b      d     5.0    11.0   20.500
5      b      e     6.0    12.0   36.000

Answer 2

While monkeying around I discovered the answer to #2:

results["b","d"] gives me the value where group1=='b' & group2=='d'