ENH: implement 'reverse' for grouping
when there are lots of columns of index levels it is more convenient to specify the column or index level that should be merged rather than all of the other columns or index levels

for example a multiindex dataframe

```
df.mean(level=['year', 'month', 'day', 'hour', 'min', 'trial'])
```

can be replaced with

```
df.mean(level='rep', reverse_level=True)
```

or for a flat index dataframe (where the data is in a column named 'result')

```
df.groupby(['year', 'month', 'day', 'hour', 'min', 'trial']).mean()
```

can be replaced with

```
df.groupby(['rep', 'result'], reverse=True)
```

http://stackoverflow.com/questions/16808682/is-there-in-pandas-operation-complementary-opposite-to-groupby

aside from the fact there there are no tests, use case quoted in the SO

`df.groupby(df.columns.drop(['col6']))['vals'].mean()` seems just fine to me.

would take an example of doing this in the cookbook / docs. If you want to repurpose as that pls reopen.
 typically these things are better brought up as an issue. Like hey what do people think about this. Then after comments / design a PR can be brought.
 Sure, the main use case is really for multiindexing where the index names are a fixed list so you can't do names.drop('somename'), however this 'solution' is really a hack because it only address the case where a level name is provided.  I'll submit an issue next time.
