Merge matches np.nan and None while merging

[shenaimm]

Code Sample

pd.merge(pd.DataFrame({'a':[None,np.nan,'A','B']}),pd.DataFrame({'a':[None,np.nan,'A']}))

Problem description

The ouput I get is this
a
0 None
1 None
2 NaN
3 NaN
4 A

Expected Output

The ideal behavior should be
a
1 A

Output of pd.show_versions()

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

commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-134-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_IN
LOCALE: en_IN.ISO8859-1
pandas: 0.22.0
pytest: 3.3.2
pip: 18.0
setuptools: 38.4.0
Cython: 0.27.3
numpy: 1.13.3
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.6.6
patsy: 0.5.0
dateutil: 2.6.0
pytz: 2017.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.1.2
openpyxl: 2.4.10
xlrd: 1.0.0
xlwt: None
xlsxwriter: 1.0.2
lxml: 4.1.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.1
pymysql: None
psycopg2: 2.7.4 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

[WillAyd]

Arguably a duplicate of #22491 though this does call out None as an extra consideration.

Investigation and PRs are welcome

[mroeschke]

Closing as duplicate of #32306 with a more recent discussion on the future policy we want.