DataFrame.apply unintuitively changes int to float because of another column
#### Code Sample, a copy-pastable example if possible

```python
>>> import pandas as pd

# This works.

>>> pd.DataFrame({'a': [1, 2, 3], 'b': ['', '', '']}).apply(lambda row: row['a'], axis=1)
0    1
1    2
2    3
dtype: int64
    
# Here we also expect 1, 2, 3, as above.

>>> pd.DataFrame({'a': [1, 2, 3], 'b': [0., 0., 0.]}).apply(lambda row: row['a'], axis=1)
0    1.0
1    2.0
2    3.0

# Why floats?!?!?!?!?!

# It's an integer column:

>>> pd.DataFrame({'a': [1, 2, 3], 'b': [0., 0., 0.]})['a'].dtype
dtype('int64')
```
#### Problem description

The column 'a' above has type integer, not float. The `apply` function should not change the type just because the dataframe has another, unrelated float column.

I understand, why it happens: it detects the most suitable type for a Series. I still consider it unintuitive that I select a group of columns to apply some function to them that only works on ints, not on floats, and suddenly I remove one unrelated column and get an exception, because now I only have numeric columns, and all ints became floats.

#### Expected Output
```
0    1
1    2
2    3
dtype: int64
```

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Darwin
OS-release: 17.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.23.4
pytest: None
pip: 10.0.1
setuptools: 40.0.0
Cython: None
numpy: 1.15.0
scipy: 1.1.0
pyarrow: 0.9.0
xarray: None
IPython: 6.5.0
sphinx: None
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>