difference in overflows handling while doing pow on large integers
#### Output from running tests and troubleshooting

probably related to having numpy beta release or freshish numexpr...

```
$> nosetests -s -v --pdb pandas/tests/test_expressions.py
...
test_integer_arithmetic (pandas.tests.test_expressions.TestExpressions) ... > /home/yoh/deb/gits/pkg-exppsy/pandas/pandas/util/testing.py(1018)raise_assert_detail()
-> raise AssertionError(msg)
*(Pdb) print msg
DataFrame.iloc[:, 0] are different

DataFrame.iloc[:, 0] values are different (85.16148 %)
[left]:  [2565992168703393792, 2110287360296998863, 0, 0, 2644539488379390509, -5278486589563110205, -4689350456247753643, 8450172506621111015, -2101438300051996672, 8028209804145959321, 302875106592253, -5278486589563110205, -4053175462519618527, -4071272324936764365, 0, 0, 0, 256, 8450172506621111015, 0, -145357957422043229, 3030756035345000135, 256, 0, -4071272324936764365, -5278486589563110205, -2863221430593058543, 0, 0, 0, 4951460147845608313, 0, -1595931050845505211, -5821812116355782473, -6284992600390200471, 2435553365352800019, -1595931050845505211, -4053175462519618527, 0, 3792227444683241861, -6776596920136667815, -5821812116355782473, 0, -8795647696940200745, 4593512421086651489, 256, 0, 0, 2644539488379390509, -5821812116355782473, -7924192819420782515, 1826852818786091687, 0, 2110287360296998863, -2559158286230232783, -4689350456247753643, -6646187150092009472, 8028209804145959321, 0, 437893890380859375, 0, -5278486589563110205, 0, 2467972595799031808, 2604998672350111773, -4689350456247753643, 7437354637604294721, 0, 285311670611, 5529354540715494413, 2435553365352800019, -1595931050845505211, 256, 823543, -2559158286230232783, 0, -3660930864499998817, 0, -4642015662142636065, -4071272324936764365, 4611686018427387904, 285311670611, 1, 0, 2565992168703393792, 8028209804145959321, -145357957422043229, 8450172506621111015, -5821812116355782473, 0, -4675932718989443137, 8037740809528595931, -4071272324936764365, 0, 6353754964178307979, 0, -1595931050845505211, -1595931050845505211, -3264474685660004015, 2823291043684304491, ...]
[right]: [-9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, 302875106592253, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, 256, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, 256, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, 256, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, 437893890380859375, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, 285311670611, -9223372036854775808, -9223372036854775808, -9223372036854775808, 256, 823543, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, 285311670611, 1, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808, ...]
(Pdb) up
> /home/yoh/deb/gits/pkg-exppsy/pandas/pandas/src/testing.pyx(173)pandas._testing.assert_almost_equal (pandas/src/testing.c:3274)()
-> raise_assert_detail(obj, msg, lobj, robj)
(Pdb) 
> /home/yoh/deb/gits/pkg-exppsy/pandas/pandas/src/testing.pyx(59)pandas._testing.assert_almost_equal (pandas/src/testing.c:4156)()
-> cpdef assert_almost_equal(a, b,
(Pdb) 
> /home/yoh/deb/gits/pkg-exppsy/pandas/pandas/util/testing.py(1181)assert_series_equal()
-> obj='{0}'.format(obj))
(Pdb) 
> /home/yoh/deb/gits/pkg-exppsy/pandas/pandas/util/testing.py(1313)assert_frame_equal()
-> obj='DataFrame.iloc[:, {0}]'.format(i))
(Pdb) 
> /home/yoh/deb/gits/pkg-exppsy/pandas/pandas/tests/test_expressions.py(100)run_arithmetic_test()
-> assert_func(expected, result)
(Pdb) p df
        A   B   C   D
0      30  89  97  21
1      47  22  16  80
2      78  23  84  33
3      88  25  61  59
4      61   1  41  96
5      27  67  76  98
6      37  13  13  82
7      23  60  93   1
8      20   6  18  90
9      89  97  27  73
10     13  95  37  38
11     27  53  70  77
12     33  51  44  39
13     43  89  95   5
14     36  84  51  97
15     44   8  82  31
16     84  18  50  35
17      4  52  74  37
18     23  52  73  42
19     76  99  11  67
20     59  95  87  68
21     55  18  89  73
22      4  26  64  19
23     94  59  47  36
24     43  79  37  26
25     27  42  41  84
26     17  30   4   3
27     24  37  63  88
28     66  14  92  86
29     52  38  27  97
...    ..  ..  ..  ..
9971   25  13  53  68
9972    9  46  89  93
9973   70  53  47  33
9974   10  30  47   3
9975   98  15  98  43
9976   34   5  53  76
9977   21  29  65  95
9978   30  83  67  82
9979   52   1  58  17
9980    4  84  38  79
9981   83  96  57  12
9982   13  38  93  72
9983   58  64  24  13
9984   18  46   5  43
9985   70  84  88  45
9986   79  94  57  29
9987   18  31  63  57
9988   58  90  99   5
9989    8  88  77  55
9990   46  12  61  61
9991   69  70  96  45
9992   71  67  48  25
9993   77  68  46  16
9994   88  55  80  37
9995    6  93  76  73
9996   11  68  45  52
9997   57  79   3  69
9998   63  48  79   8
9999   11   5  70  16
10000  10  32  32  88

[10001 rows x 4 columns]
(Pdb) p op
<function pow at 0x7efedaaad758>

```
#### Problem description

Not sure what to blame here -- numexpr or numpy or ...  What would you expect while trying to get e.g. `30**30`? max for int of original pow base type?

#### Output of ``pd.show_versions()``

<details>
# Paste the output here pd.show_versions() here
$> ./ci/print_versions.py

INSTALLED VERSIONS
------------------
commit: ad13f08e305951204e1beee0281725c3e0538024
python: 2.7.13.candidate.1
python-bits: 64
OS: Linux
OS-release: 4.8.0-1-amd64
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.19.0+git14-ga40e185
nose: 1.3.7
pip: 8.1.2
setuptools: 28.7.1
Cython: 0.25.2b0
numpy: 1.12.0b1
scipy: 0.18.1
statsmodels: 0.8.0rc1
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1+dev
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
feather: None
matplotlib: 1.5.3
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.6.4
bs4: 4.5.1
html5lib: 0.999
httplib2: 0.9.2
apiclient: 1.5.2
sqlalchemy: 1.0.15
pymysql: None
psycopg2: 2.6.2 (dt dec mx pq3 ext lo64)
jinja2: 2.8
s3fs: None
pandas_datareader: None
</details>
