Boolean values to column names in one list, dataframe pandas python

Question

I have a dataframe like this

     A    B    C    D    E
  0  0.0  1.0  0.0  0.0  1.0
  1  0.0  0.0  1.0  0.0  0.0
  2  0.0  1.0  1.0  1.0  0.0
  3  1.0  0.0  0.0  0.0  1.0
  4  0.0  0.0  0.0  1.0  0.0

The mission is to get a list like this

0  B,E
1  C
2  B,C,D
3  A,E
4  D

Any ideas, thanks in advance.

Answer 1

You can use apply with axis=1 for processing by rows and then compare each row with 1 for index values (because axis=1 each row is converted to Series with index from columns), which are joined by ,:

s1 = df.apply(lambda x: ','.join(x.index[x == 1]), axis=1)
print (s1)
0      B,E
1        C
2    B,C,D
3      A,E
4        D
dtype: object

Another solution, faster if larger DataFrame.

First change format of columns to list:

print (['{}, '.format(x) for x in df.columns])
['A, ', 'B, ', 'C, ', 'D, ', 'E, ']

Same like:

s = np.where(df == 1, ['{}, '.format(x) for x in df.columns], '')

because 1 values are casted to Trues. Compare values of DataFrame and for Trues use custom format of columns names:

s = np.where(df, ['{}, '.format(x) for x in df.columns], '')
print (s)
[['' 'B, ' '' '' 'E, ']
 ['' '' 'C, ' '' '']
 ['' 'B, ' 'C, ' 'D, ' '']
 ['A, ' '' '' '' 'E, ']
 ['' '' '' 'D, ' '']]

Last join all rows with removing empty values:

s1 = pd.Series([''.join(x).strip(', ') for x in s], index=df.index)
print (s1)
0       B, E
1          C
2    B, C, D
3       A, E
4          D
dtype: object

EDIT: Old answer another better solution:

s1 = df.eq(1).dot(df.columns + ',').str.rstrip(',')