When adding a Series to a DataFrame with a different index, the Series gets turned into all NaNs
Case in point:

``` python

>>> df
               RP/Rsum  P.value
ID                             
A_23_P42353    17.8     0      
A_23_P369994   15.91    0      
A_33_P3262440  436.7    0.0005 
A_32_P199429   18.97    0      
A_23_P256724   22.24    0      
A_33_P3394689  24.24    0      
A_33_P3403117  27.14    0      
A_24_P252364   28.56    0      
A_23_P99515    31.82    0      
A_24_P261750   31.46    0 

>>> df.dtypes
RP/Rsum    float64
P.value    float64

>>> ids = pandas.Series(['51513', '9201', np.nan, np.nan, '8794', '6530', '7025', '4897', '84935', '11081'])
>>> df["test"] = ids
>>> df
               RP/Rsum  P.value  test
ID                                   
A_23_P42353    17.8     0        NaN 
A_23_P369994   15.91    0        NaN 
A_33_P3262440  436.7    0.0005   NaN 
A_32_P199429   18.97    0        NaN 
A_23_P256724   22.24    0        NaN 
A_33_P3394689  24.24    0        NaN 
A_33_P3403117  27.14    0        NaN 
A_24_P252364   28.56    0        NaN 
A_23_P99515    31.82    0        NaN 
A_24_P261750   31.46    0        NaN 
>>> df.dtypes
RP/Rsum    float64
P.value    float64
test       object
```

This also happens with float objects and the like. I am not sure in what the trigger is.
