BUG: `concat` gives incorrect results for series with the same name
As shown in the example below, when passing in two series with the same name to `concat` you end up with duplicates of the last series passed in. 

``` python
In [42]: pd.__version__
Out[42]: '0.10.1'

In [43]: dates = pd.date_range('01-Jan-2013','01-Dec-2013', freq='MS')
    ...: L = pd.TimeSeries('L', dates, name='LeftRight')
    ...: R = pd.TimeSeries('R', dates, name='LeftRight')
    ...: 

In [44]: L
Out[44]: 
2013-01-01    L
2013-02-01    L
2013-03-01    L
2013-04-01    L
2013-05-01    L
2013-06-01    L
2013-07-01    L
2013-08-01    L
2013-09-01    L
2013-10-01    L
2013-11-01    L
2013-12-01    L
Freq: MS, Name: LeftRight

In [45]: R
Out[45]: 
2013-01-01    R
2013-02-01    R
2013-03-01    R
2013-04-01    R
2013-05-01    R
2013-06-01    R
2013-07-01    R
2013-08-01    R
2013-09-01    R
2013-10-01    R
2013-11-01    R
2013-12-01    R
Freq: MS, Name: LeftRight

In [46]: pd.concat([L,R], axis=1)
Out[46]: 
           LeftRight LeftRight
2013-01-01         R         R
2013-02-01         R         R
2013-03-01         R         R
2013-04-01         R         R
2013-05-01         R         R
2013-06-01         R         R
2013-07-01         R         R
2013-08-01         R         R
2013-09-01         R         R
2013-10-01         R         R
2013-11-01         R         R
2013-12-01         R         R

In [47]: pd.concat([L,R], axis=1, keys=['Left','Right'])
Out[47]: 
           Left Right
2013-01-01    L     R
2013-02-01    L     R
2013-03-01    L     R
2013-04-01    L     R
2013-05-01    L     R
2013-06-01    L     R
2013-07-01    L     R
2013-08-01    L     R
2013-09-01    L     R
2013-10-01    L     R
2013-11-01    L     R
2013-12-01    L     R

```
