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

Question

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?

Answer 1

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 

Answer 2

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 >>>