pandas GroupBy columns with NaN (missing) values

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I have a DataFrame with many missing values in columns which I wish to groupby:

import pandas as pd
import numpy as np
df = pd.DataFrame({'a': ['1', '2', '3'], 'b': ['4', np.NaN, '6']})

In [4]: df.groupby('b').groups
Out[4]: {'4': [0], '6': [2]}

see that Pandas has dropped the rows with NaN target values. (I want to include these rows!)

Since I need many such operations (many cols have missing values), and use more complicated functions than just medians (typically random forests), I want to avoid writing too complicated pieces of code.

Any suggestions? Should I write a function for this or is there a simple solution?

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