Weird numpy/pandas groupby behavior when using min() on a np.datetime64 field.

gistfile1.py

# OSX 10.7.5
# python 2.7.5
# pandas 0.13.0
# numpy 1.8.0

import pandas as pd
import numpy as np
from StringIO import StringIO

d = """row1,'2013-10-01'
row1,'1995-03-15'
row2,'1998-11-04'
row2,'2014-01-10'
row3,'1950-12-25'
"""

df = pd.read_csv(StringIO(d), names=['col', 'the_date'],
                    parse_dates=['the_date'])
#     col            the_date
# 0  row1 2013-10-01 00:00:00
# 1  row1 1995-03-15 00:00:00
# 2  row2 1998-11-04 00:00:00
# 3  row2 2014-01-10 00:00:00
# 4  row3 1950-12-25 00:00:00

# [5 rows x 2 columns]

print df.dtypes
# col                 object
# the_date    datetime64[ns]
# dtype: object

df.groupby('col')['the_date'].min()
# col
# row1    795225600000000000
# row2    910137600000000000
# row3   -600220800000000000
# Name: the_date, dtype: int64

df.groupby('col', as_index=False)['the_date'].min()
#     col            the_date
# 0  row1 1995-03-15 00:00:00
# 1  row2 1998-11-04 00:00:00
# 2  row3 1950-12-25 00:00:00

# [3 rows x 2 columns]