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

Case in point:

>>> 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.

[wesm]

The Series is given an implicit 0, …, N-1 index when you don’t supply one-- so this is exactly the behavior I would expect. If data were a raw ndarray or a list, then this would not occur. So the fact that when you do:

df[col] = series

and it conforms the series exactly to the index of df, that’s a feature and not a bug 😃 so

df['test'] = ids.values

would work fine in your example

[wesm]

Well, I think the basic idea is that DataFrame is a “fixed length dict-like container of Series”. When you construct a DataFrame with a dict of Series without an explicit index, there is no obvious index other than the union of them all.

I can see the argument for implicitly extending the index, but there are tradeoffs either way