applymap fails in presence of nan values and function returning None
Is this the proper behavior?

```python
df = pd.DataFrame([{'a': np.nan}, {'a': 3.5}])
def convert_cell(x): 
    if isinstance(x, float): 
        if math.isnan(x): 
            return None 
        else: 
            return round(x) 
    else: 
        return x

df.applymap(convert_cell)
# gives
#     a
#0  NaN
#1  4.0

[ convert_cell(x) for x in [np.nan, 3.5] ]
# gives
# [None, 4]
```
If we replace `None` with say, -1, in `convert_cell` it works as expected. If all values in the column are nan, it works. If all values are non-nan floats it works. If `convert_cell = lambda x: None`, that works.

```
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.9.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.0.0-37-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_CA.UTF-8
LOCALE           : en_CA.UTF-8

pandas           : 0.25.3
numpy            : 1.18.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 9.0.1
setuptools       : 39.0.1
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.3.2
html5lib         : 0.999999999
pymysql          : 0.8.0
psycopg2         : None
jinja2           : 2.10
IPython          : 7.3.0
pandas_datareader: None
bs4              : 4.6.0
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.3.2
matplotlib       : 3.0.3
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.2.1
sqlalchemy       : 1.1.11
tables           : None
xarray           : 0.14.1
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : None
```

