pd.DataFrame.from_dict fails when index is numpy.datetime
Suppose that we want to create a dataframe from a dictionary.

``` python
import pandas as pd,numpy as np
#1) Form dataframe
ix=pd.date_range('1/1/2015',periods=5,freq='D')
df0=pd.DataFrame(np.random.normal(size=(len(ix),5)),index=ix)
#2) Split dataframe into 2 dictionaries, based on sign
d={'+':{},'-':{}}
for loc in df0.index:
    d['+'][loc]=df0.loc[loc][df0.loc[loc]>=0]
    d['-'][loc]=df0.loc[loc][df0.loc[loc]<0]
#3) Convert dictionary to dataframe
print pd.DataFrame.from_dict(d['+'],orient='index')
```

This returns a dataframe of positive values, with NaN in place of the negative values. Now, it appears the exact same operation fails if we use "df.index.values" instead of "df.index".

``` python
d={'+':{},'-':{}}
for loc in df0.index.values: # NOTE .values!
    d['+'][loc]=df0.loc[loc][df0.loc[loc]>=0]
    d['-'][loc]=df0.loc[loc][df0.loc[loc]<0]
print pd.DataFrame.from_dict(d['+'],orient='index')
```

Now the outcome is a dataframe with all None. I think the problem is some inconsistency between pandas' datetime and numpy's datetime formats.

Pandas version 0.16.1, Numpy version 1.9.2. Thanks.
