vectorised setting of timestamp columns fails with python datetime and numpy datetime64
```
import pandas as pd
import numpy as np
import datetime as dt
z=dt.date(2010,11,1)
zs=[z+dt.timedelta(days=r) for r in range(5)]
df=pd.DataFrame({'obj':zs, 'b':pd.Timestamp('2010-10-01'),'c':pd.Timestamp('2010-10-01')})
df.dtypes

#df.loc[0:2,'c']=dt.date(2010,10,12) # causes error: long() argument must be a string or a number, not 'datetime.date
df.loc[0:2,'c']=np.datetime64('2010-10-12') # sets to 1970...
df.at[4,'c']=np.datetime64('2010-10-12') # works
df.loc[0:2,'obj']=np.datetime64('2010-10-12') #works

df.loc[0:2,'obj']=dt.date(2010,10,12)

df
```

| ind | b | c | obj |
| --- | --- | --- | --- |
| 0 | 2010-10-01 | 1970-01-01 | 2010-10-12 |
| 1 | 2010-10-01 | 1970-01-01 | 2010-10-12 |
| 2 | 2010-10-01 | 1970-01-01 | 2010-10-12 |
| 3 | 2010-10-01 | 2010-10-01 | 2010-11-04 |
| 4 | 2010-10-01 | 2010-10-12 | 2010-11-05 |

I am using Pandas 0.16.2
