df.str.get_dummies() vs pd.get_dummies() (Python)

I have a series like so:

0 mcdonalds, popeyes
1 wendys
2 popeyes
3 mcdonalds
4 mcdonalds

I use the following code:

df.str.get_dummies(sep = ', ')

to get the following data frame:

popeyes wendys mcdonalds
1       0      1
0       1      0
1       0      0
0       0      1
0       0      1

I want to remove a column though to account for the dummy variable trap. how do i do this like in the drop_first argument in pd.get_dummies()?

expected output might look something like this, but i don't want to hardcode to drop a random column:

popeyes wendys 
1       0      
0       1      
1       0      
0       0      
0       0      

You can explode your Series before using pd.get_dummies:

>>> (pd.get_dummies(df.str.split(', ').explode(), drop_first=True)
       .groupby(level=0).max())

   popeyes  wendys
0        1       0
1        0       1
2        1       0
3        0       0
4        0       0

Details:

>>> df.str.split(', ').explode()
0    mcdonalds
0      popeyes
1       wendys
2      popeyes
3    mcdonalds
4    mcdonalds
dtype: object

>>> pd.get_dummies(df.str.split(', ').explode(), drop_first=True)
   popeyes  wendys
0        0       0
0        1       0
1        0       1
2        1       0
3        0       0
4        0       0

Alternative:

>>> df.str.get_dummies(sep=', ').drop(columns=df.iloc[0].split(', ')[0])

   popeyes  wendys
0        1       0
1        0       1
2        1       0
3        0       0
4        0       0

You can slice to remove the first column:

s.str.get_dummies(', ').iloc[:, 1:]

Output:

   popeyes  wendys
0        1       0
1        0       1
2        1       0
3        0       0
4        0       0