Find integer index of rows with NaN in pandas dataframe

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].

Here is a simpler 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])

For DataFrame df:

import numpy as np
index = df['b'].index[df['b'].apply(np.isnan)]

will give you back the MultiIndex that you can use to index back into df, e.g.:

df['a'].ix[index[0]]
>>> 1.452354

For the integer index:

df_index = df.index.values.tolist()
[df_index.index(i) for i in index]
>>> [3, 6]