BUG: Wrong groupby indices

[albertvillanova]

Code Sample, a copy-pastable example if possible

# Your code here
df = pd.DataFrame({'a':['1', '2'], 'b':[None, '20']})
df.groupby(['a', 'b']).indices.keys()

Output is:

dict_keys([('1', '20'), ('2', '20')])

Problem description

Current behavior creates a group index item that does not exist in the DataFrame, because of the presence of a NaN.

Expected Output

The expected output I guess it should be the same as the one given by .groups

In [12]: df.groupby(['a', 'b']).groups.keys()
Out[12]: dict_keys([('1', nan), ('2', '20')])

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.29.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C
LOCALE: None.None

pandas: 0.20.3
pytest: 3.0.7
pip: 9.0.1
setuptools: 36.5.0
Cython: 0.25.2
numpy: 1.13.3
scipy: 0.19.0
xarray: None
IPython: 5.3.0
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.3.0
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.6.0
html5lib: 0.999
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

[jreback]

.indicies is internal

In [5]: df.groupby(['a', 'b']).groups
Out[5]: 
{('1', nan): Int64Index([0], dtype='int64'),
 ('2', '20'): Int64Index([1], dtype='int64')}

is this what you are looking for? what you are trying to do?

[jreback]

in any event, this is a duplicate: #9304

if you'd like to have a look there would be great.