Checking if particular value (in cell) is NaN in pandas DataFrame not working using ix or iloc

Lets say I have following pandas DataFrame:

import pandas as pd
df = pd.DataFrame({"A":[1,pd.np.nan,2], "B":[5,6,0]})

Which would look like:

>>> df
     A  B
0  1.0  5
1  NaN  6
2  2.0  0

First option

I know one way to check if a particular value is NaN, which is as follows:

>>> df.isnull().ix[1,0]
True

Second option (not working)

I thought below option, using ix, would work as well, but it's not:

>>> df.ix[1,0]==pd.np.nan
False

I also tried iloc with same results:

>>> df.iloc[1,0]==pd.np.nan
False

However if I check for those values using ix or iloc I get:

>>> df.ix[1,0]
nan
>>> df.iloc[1,0]
nan

So, why is the second option not working? Is it possible to check for NaN values using ix or iloc?

Try this:

In [107]: pd.isnull(df.iloc[1,0])
Out[107]: True

UPDATE: in a newer Pandas versions use pd.isna():

In [7]: pd.isna(df.iloc[1,0])
Out[7]: True

The above answer is excellent. Here is the same with an example for better understanding.

>>> import pandas as pd
>>>
>>> import numpy as np
>>>
>>> pd.Series([np.nan, 34, 56])
0     NaN
1    34.0
2    56.0
dtype: float64
>>>
>>> s = pd.Series([np.nan, 34, 56])
>>> pd.isnull(s[0])
True
>>>

I also tried couple of times, the following trials did not work. Thanks to @MaxU.

>>> s[0]
nan
>>>
>>> s[0] == np.nan
False
>>>
>>> s[0] is np.nan
False
>>>
>>> s[0] == 'nan'
False
>>>
>>> s[0] == pd.np.nan
False
>>>