Pandas pivot table with NaN index

[jkornblum]

Code Sample, a copy-pastable example if possible

import pandas as pd
import numpy as np

df = pd.DataFrame({ 'A' : 1.,
'B' : [2, 2, 4, 4],
'C' : ['T1', 'T2', 'T1', 'T2'],
'D' : 3.,
'E' : np.nan})
#df

tablea = pd.pivot_table(df, values='D', index=['A', 'B'],
                     columns=['C'], aggfunc=np.sum)
#tablea

tableb = pd.pivot_table(df, values='D', index=['E', 'B'],
                     columns=['C'], aggfunc=np.sum)
#tableb

Problem description

One is not able to use np.nan as a pivot value.

The ouput for the first pivot table (tablea) as expected is

	C	T1	T2
A	B		
1.0	2	3.0	3.0
        4	3.0	3.0

The output for the second table (tableb) is which is completely blank.

E | B
-- | --

Expected Output

I was hoping for

	        C	T1	T2
E	        B		
np.nan	       2      	3.0	3.0
                4	3.0	3.0

This behavior is not necessarily a problem but I'm just trying to get clarity on if this is expected.
I know that groupby excludes NaN by design and I expect that this is related to that. Is that the case?

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.5.2.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel byteorder: little LC_ALL: None LANG: None LOCALE: None.None

pandas: 0.20.3
pytest: 3.2.2
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.26.1
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: 6.1.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.2
feather: 0.4.0
matplotlib: 2.0.2
openpyxl: 2.5.0a3
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.8
lxml: 3.8.0
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: 1.1.11
pymysql: 0.7.9.None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

[chris-b1]

Thanks - this is a duplicate of #3729 - same underlying issue as groupby as you noted.

There was a PR working this at #12607 - you're welcome to pick it up if interested!