groupby, apply, and set may function properly together
To see the issue, consider the following dataframe

<code>
In [66]: dat = pandas.DataFrame(['a','b','c','d','e','f','g','h'],columns=['letters'])
</code>
<code>
In [67]: dat['numbers'] = pandas.Series([1,2,3,4,5,6,7,8])
</code>
<code>
In [68]: dat['names'] = pandas.Series(['jim','jan','jerry','george','mary','mary','sue','sue'])
</code>
<code>
In [69]: dat
</code>
<code>
Out[69]:
  letters  numbers   names
0       a        1     jim
1       b        2     jan
2       c        3   jerry
3       d        4  george
4       e        5    mary
5       f        6    mary
6       g        7     sue
7       h        8     sue
[8 rows x 3 columns]
</code>

Group by names 

<code>
In [78]: dat = dat.groupby(['names'])[['letters']]
</code>

Now my attempt to concat letters produces a funny result:

<code>
In [80]: dat.apply(lambda x: '|'.join(set(x)))
</code>
<code>
Out[80]:
names
george    letters|numbers|names
jan       letters|numbers|names
jerry     letters|numbers|names
jim       letters|numbers|names
mary      letters|numbers|names
sue       letters|numbers|names
dtype: object
</code>

Also strange is this behavior:

<code>
In [79]: dat.apply(lambda x: '|'.join([str(elm) for elm in set(x)]))
</code>
<code>
Out[79]:
names
george    letters|numbers|names
jan       letters|numbers|names
jerry     letters|numbers|names
jim       letters|numbers|names
mary      letters|numbers|names
sue       letters|numbers|names
dtype: object
</code>

The following hack seems to work

<code>
In [81]: In [72]: dat.apply(lambda x: '|'.join([str(elm) for elm in set(x['letters'])]))
Out[81]:
names
george      d
jan         b
jerry       c
jim         a
mary      e|f
sue       h|g
dtype: object
</code>
## INSTALLED VERSIONS

commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Darwin
OS-release: 13.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.13.1
Cython: 0.20.1
numpy: 1.6.2
scipy: 0.11.0
statsmodels: 0.5.0
IPython: 2.0.0
sphinx: 1.2.2
patsy: 0.2.1
scikits.timeseries: None
dateutil: 1.5
pytz: 2012d
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.1.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
sqlalchemy: None
lxml: 3.3.5
bs4: 4.3.2
html5lib: None
bq: None
apiclient: None
`
