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In [30]: df
Out[30]:
one two three
a NaN -0.282863 -1.509059
c NaN 1.212112 -0.173215
e 0.119209 -1.044236 -0.861849
f -2.104569 -0.494929 1.071804
h NaN -0.706771 -1.039575
In [31]: df.groupby('one').groups
Out[31]: {-2.1045692188948086: ['f'], 0.11920871129693428: ['e']}
http://pandas.pydata.org/pandas-docs/stable/missing_data.html#na-values-in-groupby
I can take this up.
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