Iterate through rows and identify which columns is true, assign new column the name of the column.header

I have the following DataFrame:

There is only one "X" per row which identifies the cause of the time lost column. I am trying to iterate through each row (and column) to determine which column holds the "X". I would then like to add a "Type" column with the name of the column header that was True for each row. This is what I would like as a result:

Currently my code looks like this, I am trying to iterate through the DataFrame. However, I’m not sure if there is a function or non-iterative approach to assign the proper value to the "Type" column:

cols = ['Cause1', 'Cause 2', 'Cause 3']

for index, row in df.iterrows():
            for col in cols:
                if df.loc[index,col] =='X':
                    df.loc[index,'Type'] = col
                    continue
                else:
                    df.loc[index,'Type'] = 'Other'

                continue

    

The issue I get with this code is that it iterates but only identifies rows with the last item in the cols list and the remainder go to "Other".

Any help is appreciated!

>Solution :

You could use idxmax on the boolean array of your data:

df['Type'] = df.drop('Time Lost', axis=1).eq('x').idxmax(axis=1)

Note that this only report the first cause if several

output:

   Time Lost Cause 1 Cause 2 Cause 3     Type
0         40       x     Nan     Nan  Cause 1
1         15     Nan       x     Nan  Cause 2
2         65       x     Nan     Nan  Cause 1
3         10     Nan     Nan       x  Cause 3