Find integer index of rows with NaN in pandas dataframe

Question

I have a pandas DataFrame like this:

                    a         b

2011-01-01 00:00:00 1.883381  -0.416629

2011-01-01 01:00:00 0.149948  -1.782170

2011-01-01 02:00:00 -0.407604 0.314168

2011-01-01 03:00:00 1.452354  NaN

2011-01-01 04:00:00 -1.224869 -0.947457

2011-01-01 05:00:00 0.498326  0.070416

2011-01-01 06:00:00 0.401665  NaN

2011-01-01 07:00:00 -0.019766 0.533641

2011-01-01 08:00:00 -1.101303 -1.408561

2011-01-01 09:00:00 1.671795  -0.764629

Is there an efficient way to find the "integer" index of rows with NaNs? In this case the desired output should be [3, 6].

Answer 1

Here is a simple solution:

inds = pd.isnull(df).any(1).nonzero()[0]

In [9]: df

Out[9]: 

          0         1

0  0.450319  0.062595

1 -0.673058  0.156073

2 -0.871179 -0.118575

3  0.594188       NaN

4 -1.017903 -0.484744

5  0.860375  0.239265

6 -0.640070       NaN

7 -0.535802  1.632932

8  0.876523 -0.153634

9 -0.686914  0.131185

In [10]: pd.isnull(df).any(1).nonzero()[0]

Out[10]: array([3, 6])

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