Test if any column of a pandas DataFrame satisfies a condition

ne is the method form of !=. I use that so that pipelining any looks nicer. I use any(axis=1) to find if any are true in a row.

df['indicator'] = df[columns].ne(0).any(axis=1)

In this particular case you could also check whether the sum of corresponding columns !=0:

df['indicator'] = df[columns].prod(axis=1).ne(0)

PS @piRSquared's solution is much more generic...

Maybe using min

df['indicator']=(df[columns]!=0).min(axis=1).astype(bool)