Extracting values of a pandas DataFrame using maximal indexes from another DataFrame

I have two pandas dataframes:

df1 = pd.DataFrame({
 'a': [1, 2, 3],
 'b': [2, 4, 2],
 'c': [3, 1, 5],
 'd': [-1, 0, 1]
}, index=('A', 'B', 'C'))
df2 = pd.DataFrame({
 'a': [0, 5, 10],
 'b': [9, 5, 1],
 'c': [3, 4, 2],
 'd': [12, 3, 0]
}, index=('A', 'B', 'C'))

>>> print(df1)
   a  b  c  d
A  1  2  3 -1
B  2  4  1  0
C  3  2  5  1
>>> print(df2)
    a  b  c   d
A   0  9  3  12
B   5  5  4   3
C  10  1  2   0

I would like to get the value from each column of df1 that corresponds to (i.e. is at the same coordinates as) the maximal value of that same column in df2. So in the above example it should return the values [3, 2, 1, -1]. I was able to get the correct indices using idxmax:

>>> print(df2.idxmax())
a    C
b    A
c    B
d    A
dtype: object

As you can see, these are indeed the indices corresponding to the column-wise maximums in df2. However trying to index into df1 using these indices does not return the desired result:

>>> print(df1.loc[df2.idxmax()])
   a  b  c  d
C  3  2  5  1
A  1  2  3 -1
B  2  4  1  0
A  1  2  3 -1

This indexing seems to only use the values of the Series returned by idxmax. How do I correctly index df1 using both parts (labels and values) of the returned Series? Or is there maybe a simpler solution to achieve what I want?

>Solution :

Try this:

s = df2.idxmax()
[df1.loc[row, col] for col, row in s.items()]