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      1  2    3      1      1      1  1    1    NaN    NaN    NaN  1    3      1      1      1  1    1      1      1      1 

And I want to count the number of NaN values in each string, it would be like this:

In [91]: list = <somecode with df> In [92]: list     Out[91]:      [0,       0,       0,       3,       0,       0] 

What is the best and fastest way to do it?

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] 

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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