Possible BUG - assignment of Series to DataFrame slice only assigns NaNs 
Consider these two datasets: [df.csv](https://www.dropbox.com/meta_dl/eyJzdWJfcGF0aCI6ICIiLCAidGVzdF9saW5rIjogZmFsc2UsICJzZXJ2ZXIiOiAiZGwuZHJvcGJveHVzZXJjb250ZW50LmNvbSIsICJpdGVtX2lkIjogbnVsbCwgImlzX2RpciI6IGZhbHNlLCAidGtleSI6ICJyNzdmajNqOGp6cnNnb2wifQ/AAMY9NezWTa4v1rGZH6U2dENP0_xo8S73JRUY3wLfIX96Q?dl=1) (~110MB) & [common.csv](https://www.dropbox.com/meta_dl/eyJzdWJfcGF0aCI6ICIiLCAidGVzdF9saW5rIjogZmFsc2UsICJzZXJ2ZXIiOiAiZGwuZHJvcGJveHVzZXJjb250ZW50LmNvbSIsICJpdGVtX2lkIjogbnVsbCwgImlzX2RpciI6IGZhbHNlLCAidGtleSI6ICJxaTRyYnBmYnBseGVtdm4ifQ/AAP29aTjNHhpt1LLPyS9-rUV6qoVWn-gXm97cMqI3yVYFw?dl=1) (small)

common.head():

```
          key             value
0  3559045188  0584e46e2ccefa0c
1  3559045189  df15f61e8d2dbbd4
2  3559045191  bc75d05dae4a1aaf
3  3559045192  41db9e3f3c9996e0
4  3559045398  4933d72213672819
```

df.head():

```
         key        a             datetime
0  968366584   -29.00  2008-01-01 10:10:01
1  968366585   915.83  2008-01-01 10:28:01
2  968366586  -700.00  2008-01-01 22:46:01
3  968366587  2585.85  2008-01-01 22:57:01
4  968366588   500.00  2007-12-31 23:13:01
```

Both are pre-sorted by key (int64).  All keys in `common` are also in df.

The following code:

``` python
import pandas as pd
df = pd.read_csv('df.csv')
common = pd.read_csv('common.csv')

df['value']=np.nan
df.loc[df.key.isin(common.key),'value']=common.value
all(pd.isnull(df.value))  # True
```

yields `True` as the `df.value` column contains only `NaN` entries, none of the string entries from `common`.

However, the code:

``` python
df['value']=np.nan
df.loc[df.key.isin(common.key),'value']=common.loc[:,'value'].values
all(pd.isnull(df.value))  # False
```

yields `False` as the common.value entries have been assign to the corresponding keys in df, as expected.

Unsure if this is a bug or my lack of understanding of the mechanics of the syntax (- though it does seem counter-intuitive that the results are not the same in both instances - something to do with indexes perhaps?)

Occurs in Pandas 0.13 and 0.14 (numpy 1.8.0, Win7 64bit)

(for reference, see [stackoverflow question](http://stackoverflow.com/questions/24060890/elegant-and-efficient-way-to-repeatedly-merge-dataframes-into-single-column-of-a) )
