Python pandas: selecting rows whose column value is null / None / nan [duplicate]

Question

How do I select those rows of a DataFrame whose value in a column is none?

I've coded these to np.nan and can't match against this type.

In [1]: import numpy as np  In [2]: import pandas as pd  In [3]: df = pd.DataFrame([[1, 2, 3], [3, 4, None]])  In [4]: df Out[4]:     0  1    2 0  1  2  3.0 1  3  4  NaN  In [5]: df = df.fillna(np.nan)  In [6]: df Out[6]:     0  1    2 0  1  2  3.0 1  3  4  NaN  In [7]: df.iloc[1][2] Out[7]: nan  In [8]: df.iloc[1][2] == np.nan Out[8]: False  In [9]: df[df[2] == None] Out[9]:  Empty DataFrame Columns: [0, 1, 2] Index: [] 

Answer 1

you can use .isna() method:

In [48]: df[df[2].isna()] Out[48]:    0  1   2 1  3  4 NaN