pandas.isnull() (also pd.isna(), in newer versions) checks for missing values in both numeric and string/object arrays. From the documentation, it checks for:

NaN in numeric arrays, None/NaN in object arrays

Quick example:

import pandas as pd
import numpy as np
s = pd.Series(['apple', np.nan, 'banana'])
0    False
1     True
2    False
dtype: bool

The idea of using numpy.nan to represent missing values is something that pandas introduced, which is why pandas has the tools to deal with it.

Datetimes too (if you use pd.NaT you won't need to specify the dtype)

In [24]: s = Series([Timestamp('20130101'),np.nan,Timestamp('20130102 9:30')],dtype='M8[ns]')

In [25]: s
0   2013-01-01 00:00:00
1                   NaT
2   2013-01-02 09:30:00
dtype: datetime64[ns]``

In [26]: pd.isnull(s)
0    False
1     True
2    False
dtype: bool