Cumulative Ops with skipna=True Don't Ignore NA Values
```python
import numpy as np
import pandas as pd

df = pd.DataFrame({"a": ["x", "x", "x", "y", "y", "y"],
                   "b": [np.nan, 2.0, 1.0, 2.0, np.nan, 1.0]})
df
#    a    b
# 0  x  NaN
# 1  x  2.0
# 2  x  1.0
# 3  y  2.0
# 4  y  NaN
# 5  y  1.0

df.groupby("a")["b"].cummax()
# 0    NaN
# 1    2.0
# 2    2.0
# 3    2.0
# 4    NaN
# 5    2.0
# Name: b, dtype: float64
```
#### Problem description

In the example above we're taking the cumulative max of column `b` grouped by `a`, which should have the same result for rows 1 and 4 since both are taking the max of `np.nan` and `2.0` (order shouldn't matter).  It seems like the max might be `np.nan` whenever the most recent value in the window is `np.nan` since we go back to the correct value in the next row.

#### Expected Output

```
# 0    NaN
# 1    2.0
# 2    2.0
# 3    2.0
# 4    2.0
# 5    2.0
# Name: b, dtype: float64
```

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

<details>

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

INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Darwin
OS-release: 18.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.24.1
pytest: None
pip: 19.0.2
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.1
scipy: 1.2.1
pyarrow: 0.12.0
xarray: None
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.3.1
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: 0.2.0
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None

</details>
