Hi Sanant and Samo, Even easier and faster solution:
> df.columns[(df.values != 0).any(axis=0)] Or if some reason != 0 does not work for you: > df.columns[(~(df.values == 0)).any(axis=0)] ---- Pozdrawiam, | Best regards, Maciek Wójcikowski mac...@wojcikowski.pl 2016-10-05 13:35 GMT+02:00 Samo Turk <samo.t...@gmail.com>: > Something like this might work: > > def non_zero(row, columns): > return list(columns[~(row == 0)]) > > df.apply(lambda x: non_zero(x, df.columns), axis=1) > > Cheers, > Samo > > On Wed, Oct 5, 2016 at 11:58 AM, Startup Hire <blrstartuph...@gmail.com> > wrote: > >> Hi Pypers, >> >> Hope you are doing well. >> >> I am working on a project to find out the column names of non-zero values >> at a row level. >> >> How can this effectively done in python pandas/dataframe? >> >> >> For example, >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> >> *Column1* *Column *2 *Column *3 Column 4 Column 5 Column 6 *Column 7* New >> column to be created >> 1 1 1 0 0 0 1 Column1,Column 2,Column 3,Column7 >> >> >> >> >> >> >> >> >> >> >> I might have to do it on approximately million rows >> >> Regards, >> Sanant >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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