Split data into columns in pandas

I have a df as

name category   dummy
USA   fx,ft,fe   1
INDIA fx         13

I need to convert this as

name category_fx categoty_ft category_fe  dummy 
USA  True         True        True         1
INDIA True        False       False        13

tried with series.explode() function but not getting this output.

>Solution :

You can use str.get_dummies and astype(bool) to convert your strings to new columns of booleans, then add_prefix to change the column names, and finally join:

df2 = (df.drop(columns='category)
         .join(df['category']
              .str.get_dummies(sep=',')
              .astype(bool)
              .add_prefix('category_')
              )
      )

or, for modification of the original dataframe:

df = df.join(df.pop('category')
               .str.get_dummies(sep=',')
               .astype(bool)
               .add_prefix('category_'))

output:

    name  category_fe  category_ft  category_fx
0    USA         True         True         True
1  INDIA        False        False         True