mean of series of dataframes gives TypeError
I just posted this, slightly modified, on [SO](http://stackoverflow.com/questions/27709433/why-does-a-pandas-series-of-dataframe-mean-fail-but-sum-does-not-and-how-to-ma). I'm getting a `TypeError` when I try to take the mean of a series of pandas DataFrames, as shown below:

``` python
import pandas as pd
import numpy as np

df1 = pd.DataFrame([[1, 0], [1, 2], [2, 0]], columns=['a', 'b'])
df2 = df1.copy()
df3 = df1.copy()

idx = pd.date_range("2010-01-01", freq='H', periods=3)
s = pd.Series([df1, df2, df3], index=idx)
# This causes an error
s.mean()
```

I won't post the whole traceback, but the main error message is interesting:

```
TypeError: Could not convert    melt  T_s
0     6   12
1     0    6
2     6   10 to numeric
```

It looks like the dataframe was successfully summed, but not divided by the length of the series.

However, we _can_ take the sum of the dataframes in the series:

``` python
s.sum()
```

Returns

```
      a     b
0     6   12
1     0    6
2     6   10
```

Why wouldn't mean work when sum does? Is this a bug or a missing feature? This does work:

``` python
(df1 + df2 + df3)/3.0
```

But this of course is not ideal. Is this a missing feature or a bug? 

Here's my version/dependencies info:

```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 14.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.14.1
nose: 1.3.4
Cython: 0.21
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.3
patsy: 0.3.0
scikits.timeseries: None
dateutil: 1.5
pytz: 2014.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.0
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
