Iterate through dataframe and create new column based if values in columns are not NaN (Pandas)

df = pd.DataFrame({
    'subsegment': ['corp', np.nan, 'terr'],
    'region': ['japan', np.nan, np.nan],
    'subregion': [np.nan, 'se', 'ne'], 
    'segment': [np.nan,'ent','comm']
})

I am trying to iterate through the above dataframe and if the value is not NaN than adding the column header as the value or part of the value (depending on how many NaNs) in the new column "Mode".

Original DF

Desired Output DF

I have tried to create separate smaller dfs with all the combinations of the columns to which are not null and then concatenating those dfs together but this seems extremely inefficient.

df1 = df.loc[~(df['subsegment'].isna()) & (~df['region'].isna()) & (~df['region'].isna())]
df2 = df.loc[~(df['region'].isna()) & (~df['subregion'].isna()) & (~df['segment'].isna())]
df3 = df.loc[~(df['subsegment'].isna()) & (~df['subregion'].isna()) & (~df['segment'].isna())] 

pd.concat(df1,df2,df3.....)

>Solution :

You can use the below code. I’m using apply with a custom function that just finds the columns that are not null

from itertools import compress
def temp_func(x, cols):

    list_a = (~x.isna()).to_list()
    ret = list(compress(cols, list_a))
    return '-'.join(ret)
df['mode']= df.apply(lambda x: temp_func(x, list(df.columns)), axis=1)
print(df)

Ouput:

  subsegment region subregion segment                          mode
0       corp  japan       NaN     NaN             subsegment-region
1        NaN    NaN        se     ent             subregion-segment
2       terr    NaN        ne    comm  subsegment-subregion-segment