group.py

group = df.groupby(['user_id','product_id'])['user_score','user_purchase'].sum().reset_index()
group['user_purchase'] = group['user_purchase'].apply(lambda x: 1 if x>1 else x)
group['user_score'] = group['user_score'].apply(lambda x: 100 if x>100 else x)

std = MinMaxScaler(feature_range=(0.025, 1))
std.fit(group['user_score'].values.reshape(-1,1))
group['interaction_score'] = std.transform(group['user_score'].values.reshape(-1,1))

group = group.merge(df[['product_id','category_code','brand','price','price_category']].drop_duplicates('product_id'),on=['product_id'])