Inverse Trasnsformation of multihot matrix with DataFrame

When dealing with attributes in classification problem etc, there are some data saved in onehot or multihot matrix despite attributes are too many. Such data is too large to handle, I want to get only the target attributes and hold the data with smaller size. It is inverse transformation of multihot matrix and I processed it with DataFrame.

First prepare the data. Attributes (a - d) are 1 in each row.

lis = [
df = pd.DataFrame(lis, columns=['name', 'a', 'b', 'c', 'd'])
    name  a  b  c  d
0  row_0  1  0  0  0
1  row_1  1  1  0  0
2  row_2  0  0  0  1

Get attributes in each row and insert them in one column. I am using a for statement, so I want to improve it.

lis = []
for i in range(len(df)):
    active_columns = df[df==1].ix[i].dropna().index.tolist()
df['attr'] = lis
df = df.drop(['a', 'b', 'c', 'd'], axis=1)
    name attr
0  row_0    a
1  row_1  a_b
2  row_2    d