Unexpected behavior of pd.isnull with np.nan
#### Code Sample

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

print pd.isnull(['abacaba', np.nan])  # [False, False]
print pd.isnull(pd.Series(['abacaba', np.nan])).values # [False, True]
```
#### Problem description
Inconsistent behavior. 
The call `pd.isnull(pd.Series(['abacaba', np.nan])).values` returns `[False, True]`,  
but `pd.isnull(['abacaba', np.nan])` returns `[False, False]`, which is counter-intuitive.  
`np.nan` must be detected as null in both cases. 

#### Expected Output
```
[False, True]
[False, True]
```
#### Output of ``pd.show_versions()``
<details>

INSTALLED VERSIONS
-------------------------------
commit: None  
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-96-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8

pandas: 0.20.3
pytest: None
pip: 9.0.1
setuptools: 35.0.1
Cython: 0.24
numpy: 1.13.3
scipy: 0.18.1
xarray: None
IPython: 4.2.0
sphinx: 1.2.2
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5
feather: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.3
html5lib: 0.999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: 0.1.1
pandas_gbq: None
pandas_datareader: None
</details>
