Pandas map groupby with multiple columns in same dataframe

You can add as_index=False for new DataFrame:

df1 = (df[df['key'].eq(102)]
             .groupby(['user_id', 'area_id', 'group_id', 'year'], as_index=False)['value']
             .sum())
print (df1)
   user_id  area_id  group_id  year  value
0    10835    48299         2  2013  13100
1    10837    48752         2  2016   5000
2    10839    49226         1  2016   7900
3    22489    49226         1  2017   5000

Then if possible duplicated user_id first get unique rows by DataFrame.drop_duplicates, create Series by DataFrame.set_index and map:

df['new'] = df['user_id'].map(df1.drop_duplicates('user_id').set_index('user_id')['value'])
#if never duplicates
#df['new'] = df['user_id'].map(df1.set_index('user_id')['value'])
print (df)
    user_id  area_id  group_id  key  year  value      new
0     10835    48299         1    5  2011      0  13100.0
1     10835    48299         1    2  2010      0  13100.0
2     10835    48299         2  102  2013  13100  13100.0
3     10835    48299         2    5  2016      0  13100.0
4     10836    48299         1   78  2017  67100      NaN
5     10836    48299         1    1  2012  54000      NaN
6     10836    48299         1   12  2018      0      NaN
7     10836    48752         1    7  2014      0      NaN
8     10836    48752         2  103  2015   5000      NaN
9     10837    48752         2  102  2016   5000   5000.0
10    10837    48752         1    3  2017      0   5000.0
11    10837    48752         1  103  2017      0   5000.0
12    10837    49226         1    2  2011   4000   5000.0
13    10837    49226         1   83  2011   4000   5000.0
14    10838    49226         2   16  2011      0      NaN
15    10838    49226         1   75  2012      0      NaN
16    10838    49226         1    2  2012   4000      NaN
17    10838    49226         1   12  2013   1000      NaN
18    10839    49226         1    3  2015   6500   7900.0
19    10839    49226         1  102  2016   7900   7900.0
20    10839    49226         1   16  2017      0   7900.0
21    10839    49226         2    6  2017   5500   7900.0
22    22489    49226         2   89  2017   5000   5000.0
23    22489    49226         1  102  2017   5000   5000.0