Issue with Zero's Broadcasting down a column in Heterogeneous data columns ...
I had a recent use case which produced some unexpected output and I have isolated the problem down to a much simpler case to replicate the behavior. 

Please see simplified code example below. 
#### Code Sample, a copy-pastable example if possible

``` python
import pandas as pd
import numpy as np
data = {
    'One' : pd.Series(['A', 1.2, np.nan]),
    'Two' : pd.Series([1.4, 3.2, 4.5])
}
df = pd.DataFrame(data)
```

Doing the sum across the first column (which in this case is redundant). [**Note:** In my case I was doing groupby operations where some columns were being summed and others were single columns as indexed by a level of a MultiIndex. The issue was that some of the columns ended up being full of 0's)

``` python
df[['One']].sum(axis=1)
```

produces an **unexpected** outcome:

``` ipython
0    0.0
1    0.0
2    0.0
dtype: float64
```

I think this might be an **error** and it produces a valid column of 0's that is of `dtype=float64`.
#### Expected Output

``` ipython
0      A
1    1.2
2    NaN
Name: One, dtype: object
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS

commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-38-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
