熊猫：每组的最大值索引

Question

My Pandas DataFrame, df , looks like this:

parameter1   parameter2   value

1            1            0.1
             2            0.2

2            1            0.6
             2            0.3

value is the result of a groupby(['parameter1','parameter2']).mean() on another DataFrame . Now, I can find the maximum value of value for each value of parameter1 using

df.max(level='parameter1')

However, I need to find the corresponding value of parameter2 for this maximum value. It seems df.idxmax() does not support level= , so how can I do this instead?

Answer 1

A nice way would be

df.unstack().idxmax(axis=1)

Unstacking the dataframe gives a dataframe with parameter_1 as the column index.

Answer 2

I eventually found a trick:

Groupby on level 0 (parameter1) and apply idxmax() and get the values:

v = df.groupby(level=0).idxmax().values
v
array([[(1, 2)],
       [(2, 1)]], dtype=object)

This is what df.idxmax(level=0) would give if implemented.

So v contains the index giving the max value for that level. So you can get the real values with:

df.loc[v.ravel()]

                       value
parameter1 parameter2       
1          2             0.2
2          1             0.6

and finally get the value of parameter2 corresponding to max values:

df.loc[v.ravel()].index.values[1]
(2, 1)

HTH