Diff on datetime64 column evoking "ValueError: Could not convert object to NumPy datetime"
With pandas 0.10.1 and numpy 1.7.0 (Anaconda 1.4.0), I get a ValueError when trying to use the `diff` method on a datetime64 column or series.

Eg:

``` python
In [3]: t = np.arange(start='2012-01-01 00:00:01', stop='2012-01-01 00:00:10', d
type='datetime64')

In [4]: df = pd.DataFrame(np.arange(len(t)))

In [5]: df['time'] = t
```

Using the `diff` method gives an error:

``` python
In [6]: df['time'].diff()
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-6-83fe0aa0c1ed> in <module>()
----> 1 df['time'].diff()

C:\Anaconda\lib\site-packages\pandas\core\series.pyc in diff(self, periods)
   1736         diffed : Series
   1737         """
-> 1738         result = com.diff(self.values, periods)
   1739         return Series(result, self.index, name=self.name)
   1740

C:\Anaconda\lib\site-packages\pandas\core\common.pyc in diff(arr, n, axis)
    475     na_indexer = [slice(None)] * arr.ndim
    476     na_indexer[axis] = slice(None, n) if n >= 0 else slice(n, None)
--> 477     out_arr[tuple(na_indexer)] = np.nan
    478
    479     if arr.ndim == 2 and arr.dtype.name in _diff_special:

ValueError: Could not convert object to NumPy datetime
```

while the numpy diff works as expected:

``` python
In [7]: np.diff(df['time'].values)
Out[7]:
array([1000000000, 1000000000, 1000000000, 1000000000, 1000000000,
       1000000000, 1000000000, 1000000000], dtype='timedelta64[ns]')
```

The problems seems to be that `np.nan` cannot be converted to a numpy datetime64.

My code also did work with a previously version of numpy and/or pandas (but am not certain wich version. Before I updated my scientific stack with anaconda).
