Pandas: Flag groups and then change the data structure

Question

Here is my raw data:

raw_data =  pd.DataFrame({'Year': [1991, 1991, 1991, 2000, 2000],
                          'ID': ['A', 'A', 'A', 'B', 'B',],
                          'Group': ['a', 'b', 'c', 'a', 'b'],
                          'score': [6252, 6252,6252, 2342, 2342]})

I need to generate three group columns indicating if the each ID belongs to that group. Pivot function can only change the data structure and achieves part of my goals.

out_data = pd.DataFrame({'Year': [1991, 2000],
             'Group a':['Yes','Yes'],
             'Group b':['Yes','Yes'],
             'Group c':['Yes','No'],
             'ID': ['A', 'B'],
             'score': [6252, 2342]})

Answer 1

This is a variant on a pivot_table :

(df
 .pivot_table(index=['Year', 'ID'], columns='Group', values='score', aggfunc=any)
 .replace({True: 'Yes'}).fillna('No')
 .add_prefix('Group_')
 .reset_index().rename_axis(columns=None)
)

or crosstab :

(pd
 .crosstab([df['Year'], df['ID']], df['Group'], values=df['score'], aggfunc=any)
 .replace({True: 'Yes'}).fillna('No')
 .add_prefix('Group_')
 .reset_index().rename_axis(columns=None)
)

output:

   Year ID Group_a Group_b Group_c
0  1991  A     Yes     Yes     Yes
1  2000  B     Yes     Yes      No

Answer 2

def function1(dd:pd.DataFrame):
    return dd.assign(col1=1).pivot_table(index=['Year','ID','score'],columns='Group',values='col1')\
        .add_prefix('Group ')

raw_data.groupby(['Year','ID']).apply(function1)\
    .applymap(lambda x:"Yes" if pd.notna(x) else 'No')\
    .droplevel([0,1]).reset_index()

out：

Group  Year ID  score Group a Group b Group c
0      1991  A   6252     Yes     Yes     Yes
1      2000  B   2342     Yes     Yes      No