hardcoded expectations on endianness in the tests -- feature or a bug? ;)
#### Code Sample, a copy-pastable example if possible

looking at code of pandas/tests/series/test_datetime_values.py which was introduced in c76ca444fd42675982e81394736d941e2f2629db I believe:
```python

        period_index = period_range('20150301', periods=5)
        result = period_index.strftime("%Y/%m/%d")
        expected = np.array(['2015/03/01', '2015/03/02', '2015/03/03',
                             '2015/03/04', '2015/03/05'], dtype='<U10')
        self.assert_numpy_array_equal(result, expected)

```
#### Problem description

above (and some other tests... see e.g. https://buildd.debian.org/status/fetch.php?pkg=pandas&arch=s390x&ver=0.19.1-2&stamp=1480348101) lead to failed tests... which sometimes look as informative as 

```
numpy array values are different (0.0 %)
[left]:  [2015/03/01, 2015/03/02, 2015/03/03, 2015/03/04, 2015/03/05]
[right]: [2015/03/01, 2015/03/02, 2015/03/03, 2015/03/04, 2015/03/05]
```
;-)

but the real reason is different endianness:

```
(Pdb) l
325
326             period_index = period_range('20150301', periods=5)
327             result = period_index.strftime("%Y/%m/%d")
328             expected = np.array(['2015/03/01', '2015/03/02', '2015/03/03',
329                                  '2015/03/04', '2015/03/05'], dtype='<U10')
330  ->         self.assert_numpy_array_equal(result, expected)
331
332             s = Series([datetime(2013, 1, 1, 2, 32, 59), datetime(2013, 1, 2, 14,
333                                                                   32, 1)])
334             result = s.dt.strftime('%Y-%m-%d %H:%M:%S')
335             expected = Series(["2013-01-01 02:32:59", "2013-01-02 14:32:01"])
(Pdb) p expected
array([u'2015/03/01', u'2015/03/02', u'2015/03/03', u'2015/03/04',
       u'2015/03/05'],
      dtype='<U10')
(Pdb) p result
array([u'2015/03/01', u'2015/03/02', u'2015/03/03', u'2015/03/04',
       u'2015/03/05'],
      dtype='>U10')
```

tentative *fix* is easy -- remove '>' from type definition.  But I wondered if that is desired -- does pandas assume to enforce little-endianness  or not?  if it doesn't force -- then actually a proper fix, to maintain the same check of consistent endianness, would be to prefix with `=` and not `<`.

Please advise!