I have a Pandas dataframe like this:

>>> df = pd.DataFrame({'2012':['A','A','B','A'],'2013':['A','B','C','C'],'2014':['A','C','Z','C']})
>>> df
 

2012 2013 2014
0 A A A
1 A B C
2 B C Z
3 A C C

From it, I need to create another dataframe like this:
2012 2013 2014
A 3 1 1
B 1 1 0
C 0 2 2

where I am basically counting some (A,B,C but not Z) of the occurrences of the labels in every column, turning them into indexes, and showing their count per year.

I did come out with a solution that involves iteration:

>>> indexes = ['A','B','C']
>>> for idx in indexes:
        `df2.loc[idx] = (df == idx).sum()
>>> df2
 

2012 2013 2014
A 3 1 1
B 1 1 0
C 0 2 2

This is exactly what I need. But I wonder, is there a way to do it in one shot without iteration?

Thanks in advance to whoever may help!

I played around with values_counts(), pivot_table() and groupby() without success.
All Google searches I found point to this type of count but across one column only.
Eventually I found the solution but it involves iteration.