DataFrame.iat will create new column if .iat is used to set None on int Series
#### Code Sample, a copy-pastable example if possible

```python
>>> df = pd.DataFrame({'a':[0,1],'b':[4,5]})
>>> df
   a  b
0  0  4
1  1  5
>>> df.iat[0, 0] = None
>>> df
   a  b   0
0  0  4 NaN
1  1  5 NaN
```
#### Problem description

This is problematic for multiple reasons.
 - inconsistency between iloc and iat. 
 - creation of a brand new column is almost surely not the intended/expected behavior
 - At the very least, I would expect it to simply bail on the operation with a warning about incompatible types.

This is likely related to the non-intuitive behavior of Series which has already been documented here:
https://github.com/pandas-dev/pandas/issues/20643#issuecomment-431244590

#### Expected Output
I would expect it to do what it does when using `.iloc`:
```python
>>> df = pd.DataFrame({'a':[0,1],'b':[4,5]})
>>> df
   a  b
0  0  4
1  1  5
>>> df.iloc[0, 0] = None
>>> df
     a  b
0  NaN  4
1  1.0  5
```

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.utf8
LOCALE: en_US.UTF-8

pandas: 0.22.0
pytest: None
pip: 9.0.3
setuptools: 39.0.1
Cython: 0.27.3
numpy: 1.14.0
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: 0.1.2
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
