Pivot_table drop rows whose entries are all NaN
```
>>> dataframe = pd.DataFrame([['a', 1, 10], ['b', 10, 100], ['c', None, None], ['a', 2, 4]], columns=['lit', 'num1', 'num2'])
>>> dataframe.pivot_table(index='lit', columns='num1', values='num2', aggfunc='max')
num1  1.0   2.0    10.0
lit
a     10.0   4.0    NaN
b      NaN   NaN  100.0
```

Pivot_table is silently dropping row whose entries fully consisting with NaN. (according to the documentation - dropna : boolean, default True; Do not include **columns** whose entries are all NaN)

It works fine at version 0.21.1 and 0.22.0.

[I found only old bug from 2013](https://github.com/pandas-dev/pandas/issues/3729).

#### Expected Output
```
>>> dataframe.pivot_table(index='lit', columns='num1', values='num2', aggfunc='max')
num1    1    2      10
lit
a     10.0  4.0    NaN
b      NaN  NaN  100.0
c      NaN  NaN    NaN
```

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.4.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.23.1
pytest: None
pip: 9.0.1
setuptools: 28.8.0
Cython: 0.27
numpy: 1.14.5
scipy: None
pyarrow: 0.9.0
xarray: None
IPython: 6.2.1
sphinx: None
patsy: 0.4.1
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: 0.4.0
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
