Just pass a list of column names to index df:

df[['nnn', 'mmm', 'yyy']]

   nnn  mmm  yyy
0    5    5   10
1    3    4    9
2    7    0    8

If you need to handle non-existent column names in your list, try filtering with df.columns.isin -

df.loc[:, df.columns.isin(['nnn', 'mmm', 'yyy', 'zzzzzz'])]

   yyy  nnn  mmm
0   10    5    5
1    9    3    4
2    8    7    0

You can filter the columns first to get the cols of interest and then call apply and use the boolean mask to mask the cols:

In [18]:
cat_cols = df.columns[df.columns.str.contains('Cat')]
df[cat_cols].apply(lambda row: ', '.join(cat_cols[row == 1]), axis=1)

Out[18]:
0          Cat3
1    Cat1, Cat3
dtype: object

Also possible to retrieve the columns by accessing the .index attribute:

df[cat_cols].apply(lambda row: ', '.join(row.index[row == 1]), axis=1)