Grouped timeseries operations fail on multi-indexed DataFrames
This might be a potential bug: doing grouped timeseries operations fails silently on a multi-indexed DataFrame.

``` python
    import pandas as pd
    import pandas.io.data as web

    # Get some market data
    df = web.DataReader(['AAPL', 'GOOG'], 'yahoo', pd.Timestamp('2013'), pd.Timestamp('2014')).to_frame()
    df.index.names = ('dt', 'symbol')

    In [21]: df.head()
    Out[21]: 
                            Open       High        Low      Close     Volume  \
    dt         symbol                                                          
    2013-01-02 AAPL    553.82001  555.00000  541.62994  549.03003  140129500   
    2013-01-03 AAPL    547.88000  549.67004  541.00000  542.10004   88241300   
    2013-01-04 AAPL    536.96997  538.63000  525.82996  527.00000  148583400   
    2013-01-07 AAPL    522.00000  529.30005  515.20001  523.90002  121039100   
    2013-01-08 AAPL    529.21002  531.89001  521.25000  525.31000  114676800   

                       Adj Close  
    dt         symbol             
    2013-01-02 AAPL     74.63931  
    2013-01-03 AAPL     73.69719  
    2013-01-04 AAPL     71.64438  
    2013-01-07 AAPL     71.22294  
    2013-01-08 AAPL     71.41463 
```

Let's say we want to resample this to monthly data. This fails and returns an empty DataFrame:

``` python
    df_M = df.groupby(level='symbol').resample('M', how='mean')
    In [23]: df_M
    Out[23]: 
    Empty DataFrame
    Columns: []
    Index: []
```

This, however, works, but requires a seemingly-unnecessary re-indexing:

``` python
    df_M = df.reset_index().set_index('dt').groupby('symbol').resample('M', how='mean')
    In [26]: df_M.head()
    Out[26]: 
                       Adj Close       Close        High         Low        Open  \
    symbol dt                                                                      
    AAPL   2013-01-31  67.677750  497.822382  504.407623  492.969997  500.083329   
           2013-02-28  62.388477  456.808942  463.231056  452.106325  458.503692   
           2013-03-31  60.417287  441.841000  446.803495  437.337996  442.011512   
           2013-04-30  57.398619  419.765001  425.553183  414.722271  419.766820   
           2013-05-31  61.340151  446.452734  451.658190  441.495455  446.400919   

                             Volume  
    symbol dt                        
    AAPL   2013-01-31  1.562312e+08  
           2013-02-28  1.229478e+08  
           2013-03-31  1.147110e+08  
           2013-04-30  1.245851e+08  
           2013-05-31  1.073583e+08  
```

The fact that you need to do the `reset_index().set_index('dt')` and then `groupby('symbol')` instead of `groupby(level='symbol')` seems to defeat the purpose of a multi-index! What gives?

I also realize that data like this is perhaps more well-suited to a Panel than a DataFrame, but when dealing with very large amounts of (often sparse) data, the 3D Panel structure presents performance and memory issues compared to the flat DataFrame.
