Python/Pandas: counting the number of missing/NaN in each row

Question

I\'ve got a dataset with a big number of rows. Some of the values are NaN, like this:

In [91]: df
Out[91]:
 1    3      1      1      1
 1    3      1      1         


        

Answer 1

You could first find if element is NaN or not by isnull() and then take row-wise sum(axis=1)

In [195]: df.isnull().sum(axis=1)
Out[195]:
0    0
1    0
2    0
3    3
4    0
5    0
dtype: int64

And, if you want the output as list, you can

In [196]: df.isnull().sum(axis=1).tolist()
Out[196]: [0, 0, 0, 3, 0, 0]

---

Or use count like

In [130]: df.shape[1] - df.count(axis=1)
Out[130]:
0    0
1    0
2    0
3    3
4    0
5    0
dtype: int64