Removing duplicated rows of 2-level datetime index dataframe
Using pandas 0.13.1

I have a dataframe with three columns. The first two contain datetimes and the third floats (the entire data frame originates from df = pandas.io.sql.frame_query(...,...) ). 

I need to remove rows for which dates in the first two columns are duplicated. So something like the following would seem to be appropriate:

```
df = df.groupby(['col1','col2']).first() 
df.reset_index(inplace = True)    
```

However, this doesn't work: it executes without error, but returns df unmodified.

I can get the functionality I need by first converting the datetimes to strings:

```
df['col1'] = [date.strftime('%Y-%m-%d') for date in df['col1']]
df['col2'] = [date.strftime('%Y-%m-%d') for date in df['col2']]
```

Only after this, it seems, do the original commands work:

```
df = df.groupby(['col1','col2']).first() 
df.reset_index(inplace = True)   
```

Then to get back to the dataframe format I need, I have to revert back to datetimes:

```
df['col1'] = to_datetime(df['col1'])
df['col2'] = to_datetime(df['col2'])
```
