Inconsistent behaviour when dividing by zero
#### Code Sample, a copy-pastable example if possible

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

def some_func(row):
    try:
        print('type of clicks:', type(row.clicks))
        return row.impressions / row.clicks
    except:
        return np.nan

df = [('a', 1, 1), ('b', 0, 1)]
df = pd.DataFrame(df, columns=['src', 'clicks', 'impressions'])
res = df.apply(lambda row: some_func(row), axis=1)
print(res)

df = [(1, 1), (0, 1)]
df = pd.DataFrame(df, columns=['clicks', 'impressions'])
res = df.apply(lambda row: some_func(row), axis=1)
print(res)
```

#### Problem description

The behavior when dividing by zero is inconsistent in these two examples above. The difference is that in the first case there is a column with strings, while in the second example there are only numerical columns. 
Additionally, in the first case, `type` returns `int`, and in the second - `numpy.int64`
When the type is python `int`, an exception is raised, but numpy only issues a warning and returns `inf`. 

#### Expected Output

These two examples should produce the same output - either `inf` or `NaN`

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

<details>

[paste the output of ``pd.show_versions()`` here below this line]

In [2]: pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-27-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8

pandas: 0.18.1
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.2
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None


</details>
