Reversing 'one-hot' encoding in Pandas

Question

I want to go from this data frame which is basically one hot encoded.

 In [2]: pd.DataFrame({"monkey":[0,1,0],"rabbit":[1,0,0],"fox":[0,0,1]})      Out[2]:        fox  monkey  rabbit     0    0       0       1     1    0       1       0     2    1       0       0     3    0       0       0     4    0       0       0 

To this one which is 'reverse' one-hot encoded.

    In [3]: pd.DataFrame({"animal":["monkey","rabbit","fox"]})     Out[3]:        animal     0  monkey     1  rabbit     2     fox 

I imagine there's some sort of clever use of apply or zip to do thins but I'm not sure how... Can anyone help?

I've not had much success using indexing etc to try to solve this problem.

Answer 1

UPDATE: i think ayhan is right and it should be:

df.idxmax(axis=1) 

Demo:

In [40]: s = pd.Series(['dog', 'cat', 'dog', 'bird', 'fox', 'dog'])  In [41]: s Out[41]: 0     dog 1     cat 2     dog 3    bird 4     fox 5     dog dtype: object  In [42]: pd.get_dummies(s) Out[42]:    bird  cat  dog  fox 0   0.0  0.0  1.0  0.0 1   0.0  1.0  0.0  0.0 2   0.0  0.0  1.0  0.0 3   1.0  0.0  0.0  0.0 4   0.0  0.0  0.0  1.0 5   0.0  0.0  1.0  0.0  In [43]: pd.get_dummies(s).idxmax(1) Out[43]: 0     dog 1     cat 2     dog 3    bird 4     fox 5     dog dtype: object 

OLD answer: (most probably, incorrect answer)

try this:

In [504]: df.idxmax().reset_index().rename(columns={'index':'animal', 0:'idx'}) Out[504]:    animal  idx 0     fox    2 1  monkey    1 2  rabbit    0 

data:

In [505]: df Out[505]:    fox  monkey  rabbit 0    0       0       1 1    0       1       0 2    1       0       0 3    0       0       0 4    0       0       0