test_pow fails due to differences at machine precision
I am testing pandas 0.13 (built against numexpr 2.2.2, numpy 1.8.0, scipy 0.13.2, all linked to MKL 10.3 using Scientific Linux 6.4's stock gcc 4.7.4), and test_pow fails with an array not equal error:
# 
## FAIL: test_pow (pandas.computation.tests.test_eval.TestEvalNumexprPandas)

Traceback (most recent call last):
  File "/software/python-2.7-2014q1-el6-x86_64/lib/python2.7/site-packages/pandas/computation/tests/test_eval.py", line 192, in test_pow
    self.check_pow(lhs, '*_', rhs)
  File "/software/python-2.7-2014q1-el6-x86_64/lib/python2.7/site-packages/pandas/computation/tests/test_eval.py", line 94, in wrapper
    f(self, lhs, arith1, rhs, *args, *_kwargs)
  File "/software/python-2.7-2014q1-el6-x86_64/lib/python2.7/site-packages/pandas/computation/tests/test_eval.py", line 408, in check_pow
    assert_array_equal(result, expected)
  File "/software/python-2.7-2014q1-el6-x86_64/lib/python2.7/site-packages/numpy/testing/utils.py", line 718, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')
  File "/software/python-2.7-2014q1-el6-x86_64/lib/python2.7/site-packages/numpy/testing/utils.py", line 644, in assert_array_compare
    raise AssertionError(msg)
AssertionError: 
Arrays are not equal

(mismatch 25.8064516129%)
 x: array([[  6.98492548e-01,   5.39900967e-01,   1.87991705e+00,
          7.67724670e-01,              nan],
       [             nan,   1.18493281e+00,   4.15163610e+01,...
 y: array([[  6.98492548e-01,   5.39900967e-01,   1.87991705e+00,
          7.67724670e-01,              nan],
       [             nan,   1.18493281e+00,   4.15163610e+01,...

---

I have modified the check_pow function in that test to identify the error and print out the difference in result and expected:
    @skip_incompatible_operand
    def check_pow(self, lhs, arith1, rhs):
        ex = 'lhs {0} rhs'.format(arith1)
        expected = self.get_expected_pow_result(lhs, rhs)
        result = pd.eval(ex, engine=self.engine, parser=self.parser)

```
    if (np.isscalar(lhs) and np.isscalar(rhs) and
            _is_py3_complex_incompat(result, expected)):
        self.assertRaises(AssertionError, assert_array_equal, result,
                          expected)
    else:
        print result, expected, result-expected
        assert_array_equal(result, expected,verbose=True)

        ex = '(lhs {0} rhs) {0} rhs'.format(arith1)
        result = pd.eval(ex, engine=self.engine, parser=self.parser)
        expected = self.get_expected_pow_result(
            self.get_expected_pow_result(lhs, rhs), rhs)
        print result, expected, result-expected
        assert_array_equal(result, expected)
```

Which yields the following (only differences reported for brevity):
[10 rows x 5 columns]               0             1             2             3             4
0 -2.775558e-17           NaN  0.000000e+00 -2.220446e-16  0.000000e+00
1  0.000000e+00  1.110223e-16           NaN           NaN           NaN
2  0.000000e+00           NaN           NaN           NaN           NaN
3  0.000000e+00           NaN           NaN  0.000000e+00  0.000000e+00
4           NaN  0.000000e+00  1.110223e-16  0.000000e+00  2.220446e-16
5           NaN -1.110223e-16 -5.551115e-17  0.000000e+00  0.000000e+00
6           NaN           NaN  0.000000e+00           NaN -4.440892e-16
7 -1.110223e-16           NaN           NaN  3.552714e-15           NaN
8           NaN  0.000000e+00  0.000000e+00           NaN  2.220446e-16
9  0.000000e+00  0.000000e+00           NaN  0.000000e+00  0.000000e+00

and

[10 rows x 5 columns]               0             1             2             3             4
0  0.000000e+00           NaN  0.000000e+00  0.000000e+00  0.000000e+00
1  0.000000e+00  1.110223e-16           NaN           NaN           NaN
2  2.220446e-16           NaN           NaN           NaN           NaN
3  0.000000e+00           NaN           NaN  5.551115e-17  0.000000e+00
4           NaN  0.000000e+00  1.110223e-16  0.000000e+00  2.220446e-16
5           NaN -1.110223e-16  0.000000e+00  0.000000e+00  0.000000e+00
6           NaN           NaN  0.000000e+00           NaN  1.110223e-16
7  2.220446e-16           NaN           NaN -4.336809e-19           NaN
8           NaN  0.000000e+00  0.000000e+00           NaN -1.110223e-16
9  0.000000e+00  1.110223e-16           NaN  0.000000e+00  0.000000e+00

The differences are only at machine precision, and may be due to the use of MKL rather than standard blas (I haven't dug any further). I have verified that changing the test from assert_array_equal to assert_allclose allows the test to succeed.
