• Source 
    1. import itertools
    2.  
    3.  
    4. employees =[
    5.   {
    6.     'id': '101',
    7.     'name': 'joen',
    8.     'email': 'joen@abc.com',
    9.     'role': 'Owner',
    10.  
    11.   },
    12.   {
    13.     'id': '101',
    14.     'name': 'joen',
    15.     'email': 'joen@abc.com',
    16.     'role': 'Manger'
    17.  
    18.   },
    19.   {
    20.     'id': '101',
    21.     'name': 'joen',
    22.     'email': 'joen@abc.com',
    23.     'role': '',
    24.  
    25.   },
    26.   {
    27.     'id': '101',
    28.     'name': 'joen',
    29.     'email': 'joen@abc.com',
    30.     'role': '',
    31.  
    32.   },
    33.  {
    34.     'id': '103',
    35.     'name': 'test',
    36.     'email': 't@estabc.com',
    37.     'role': 'Manger' }
    38. ]
    39.  
    40. # Key function: e stands for employee
    41. key_func = lambda e: {'id': e['id'], 'name': e['name'], 'email': e['email']}
    42.  
    43. aggregated_roles = []
    44. for key, group in itertools.groupby(employees, key_func):
    45.     aggregated = list(group)
    46.     # print(key, "aggregates:", aggregated)
    47.     merged_employee = key
    48.     merged_employee['role'] = [e['role'] for e in aggregated]  # list-comprehension to extract the role of each element (map) and collect as list (reduce)
    49.     aggregated_roles.append(merged_employee)
    50. print(aggregated_roles)
    51.