Merge dataframe with aggregation

Question

I want to aggregate a dataframe - to get the first row of every group and simultaneously to concatenate the values in column \'upc\':

df = pd.DataFrame({
            


        

Answer 1

I think you need as_index=False to first and add reset_index() to concat_upcs_df for DataFrames:

firsts_df = df.groupby(['id1', 'id2'], as_index=False).first()
concat_upcs_df = df[['id1', 'id2', 'upc']].groupby(['id1', 'id2']).apply(lambda x: '|'.join(x.upc)).reset_index(name='val')
firsts_df.merge(concat_upcs_df, how='inner',left_on=['id1', 'id2'], right_on=['id1', 'id2'])
print (df)
   id1  id2  upc   value1              val
0    1   11  100   1first          100|102
1    1   22  101  1second              101
2    2   11  103   2first              103
3    2   22  104  2second              104
4    3   33  105   3first  105|106|107|108
5    4   44  109   4first          109|110
6    5   55  111   5first              111
7    6   22  114   6third              114
8    6   66  112   6first          112|113
9    7   77  115   7first          115|116

You can also use drop_duplicates instead first and apply without lambda, also merge working with on, because left and right joined columns are same:

firsts_df = df.drop_duplicates(['id1', 'id2'])
concat_upcs_df = df.groupby(['id1', 'id2'])['upc'].apply('|'.join).reset_index(name='val')
df = firsts_df.merge(concat_upcs_df, on=['id1', 'id2'])