Unexpected results for the mean of a DataFrame of ufloat from the uncertainties package.
Related to #6898.

I find it very convenient to use a DataFrame of `ufloat` from the `uncertainties` package. Each entry consists of (value, error) and could represent the result of Monte Carlo simulations or an experiment.

At present taking sums along both axes gives the expected result, but taking the mean does not. 

``` python
import pandas as pd
import numpy as np
from uncertainties import unumpy

value = np.arange(12).reshape(3,4)
err = 0.01 * np.arange(12).reshape(3,4) + 0.005

data = unumpy.uarray(value, err)

df = pd.DataFrame(data, index=['r1', 'r2', 'r3'], columns=['c1', 'c2', 'c3', 'c4'])
```

Examples:

``` python
print (df)
               c1             c2             c3             c4
r1  0.000+/-0.005  1.000+/-0.015  2.000+/-0.025  3.000+/-0.035
r2    4.00+/-0.04    5.00+/-0.06    6.00+/-0.07    7.00+/-0.08
r3    8.00+/-0.09    9.00+/-0.10   10.00+/-0.11   11.00+/-0.12

df.sum(axis=0) # This works

c1    12.00+/-0.10
c2    15.00+/-0.11
c3    18.00+/-0.13
c4    21.00+/-0.14
dtype: object

df.sum(axis=1) # This works

r1     6.00+/-0.05
r2    22.00+/-0.12
r3    38.00+/-0.20
dtype: object

df.mean(axis=0) # This does not work

Series([], dtype: float64)

Expected (`df.apply(lambda x: x.sum() / x.size)`)

c1    4.000+/-0.032
c2      5.00+/-0.04
c3      6.00+/-0.04
c4      7.00+/-0.05
dtype: object

df.mean(axis=1) # This does not work

r1   NaN
r2   NaN
r3   NaN
dtype: float64

Expected (`df.T.apply(lambda x: x.sum() / x.size)`)

r1    1.500+/-0.011
r2    5.500+/-0.031
r3      9.50+/-0.05
dtype: object
```
