groupby nunique makes inplace replacement of NaN values to -9.223372036854776e18
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
import numpy as np

df = pd.DataFrame({'x': [1, 1, 1, 2, 2, 2], 'y': [1, 2, np.nan, np.nan, np.nan, 6]})
print(df)

# Doesn't replace NaN's
df.groupby('x').sum()
print(df)

# Replaces NaN's
df.groupby('x').nunique()
print(df)

```
#### Problem description

Using DataFrameGroupBy.nunique on a dataframe makes an inplace replacement of NaN values. Simply applying the function (and not assigning it to anything) will replace existing NaN's with -9223372036854775808.

There are two distinct issues:
1. There is some connection to issue #16674 in comparing NaN with -9223372036854775808 (thanks @mroeschke)
2. The inplace replacement, which seems to be more problematic.


#### Expected Output

   x    y
0  1  1.0
1  1  2.0
2  1  NaN
3  2  NaN
4  2  NaN
5  2  6.0
   x    y
0  1  1.0
1  1  2.0
2  1  NaN
3  2  NaN
4  2  NaN
5  2  6.0
   x    y
0  1  1.0
1  1  2.0
2  1  NaN
3  2  NaN
4  2  NaN
5  2  6.0

#### My Output

   x    y
0  1  1.0
1  1  2.0
2  1  NaN
3  2  NaN
4  2  NaN
5  2  6.0
   x    y
0  1  1.0
1  1  2.0
2  1  NaN
3  2  NaN
4  2  NaN
5  2  6.0
   x             y
0  1  1.000000e+00
1  1  2.000000e+00
2  1 -9.223372e+18
3  2 -9.223372e+18
4  2 -9.223372e+18
5  2  6.000000e+00

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.6.final.0
python-bits      : 64
OS               : Linux
OS-release       : 2.6.32-754.27.1.el6.x86_64
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.1
numpy            : 1.18.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 45.2.0.post20200210
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : 2.4.0
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.1
IPython          : 7.12.0
pandas_datareader: None
bs4              : 4.8.0
bottleneck       : None
fastparquet      : 0.3.2
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.3
numexpr          : 2.7.0
odfpy            : None
openpyxl         : 3.0.3
pandas_gbq       : None
pyarrow          : 0.15.1
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : None
tables           : 3.6.1
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None
numba            : 0.48.0

</details>
