text: """
  * Assignment: OOP Relations Nested
  * Complexity: medium
  * Lines of code: 7 lines
  * Time: 13 min
  
  English:
      1. Convert `DATA` to format with one column per each attribute for example:
         a. `address1_street`, `address2_street`,
         b. `address1_city`, `address2_city`
         c. `address1_city`, `address2_city`
      2. Note, that enumeration starts with one
      3. Run doctests - all must succeed
  
  Polish:
      1. Przekonwertuj `DATA` do formatu z jedną kolumną dla każdego atrybutu, np:
         a. `address1_street`, `address2_street`,
         b. `address1_city`, `address2_city`
         c. `address1_city`, `address2_city`
      2. Zwróć uwagę, że enumeracja zaczyna się od jeden
      3. Uruchom doctesty - wszystkie muszą się powieść
  
  Tests:
      >>> import sys; sys.tracebacklimit = 0
  
      >>> assert type(result) is list
      >>> assert len(result) > 0
      >>> assert all(type(x) is dict for x in result)
  
      >>> result  # doctest: +NORMALIZE_WHITESPACE
      [{'firstname': 'Jan',
        'lastname': 'Twardowski',
        'address1_street': 'Kamienica Pod św. Janem Kapistranem',
        'address1_city': 'Kraków',
        'address1_post_code': '31-008',
        'address1_region': 'Małopolskie',
        'address1_country': 'Poland'},
       {'firstname': 'José',
        'lastname': 'Jiménez',
        'address1_street': '2101 E NASA Pkwy',
        'address1_city': 'Houston',
        'address1_post_code': 77058,
        'address1_region': 'Texas',
        'address1_country': 'USA',
        'address2_street': '',
        'address2_city': 'Kennedy Space Center',
        'address2_post_code': 32899,
        'address2_region': 'Florida',
        'address2_country': 'USA'},
       {'firstname': 'Mark',
        'lastname': 'Watney',
        'address1_street': '4800 Oak Grove Dr',
        'address1_city': 'Pasadena',
        'address1_post_code': 91109,
        'address1_region': 'California',
        'address1_country': 'USA', 'address2_street': '2825 E Ave P',
        'address2_city': 'Palmdale',
        'address2_post_code': 93550,
        'address2_region': 'California',
        'address2_country': 'USA'},
       {'firstname': 'Иван',
        'lastname': 'Иванович',
        'address1_street': '',
        'address1_city': 'Космодро́м Байкону́р',
        'address1_post_code': '',
        'address1_region': 'Кызылординская область',
        'address1_country': 'Қазақстан',
        'address2_street': '',
        'address2_city': 'Звёздный городо́к',
        'address2_post_code': 141160,
        'address2_region': 'Московская область',
        'address2_country': 'Россия'},
       {'firstname': 'Melissa',
        'lastname': 'Lewis'},
       {'firstname': 'Alex',
        'lastname': 'Vogel',
        'address1_street': 'Linder Hoehe',
        'address1_city': 'Köln',
        'address1_post_code': 51147,
        'address1_region': 'North Rhine-Westphalia',
        'address1_country': 'Germany'}]
  """
  
  
  import json
  
  DATA = """[
      {"firstname": "Jan", "lastname": "Twardowski", "addresses": [
          {"street": "Kamienica Pod św. Janem Kapistranem", "city": "Kraków", "post_code": "31-008", "region": "Małopolskie", "country": "Poland"}]},
  
      {"firstname": "José", "lastname": "Jiménez", "addresses": [
          {"street": "2101 E NASA Pkwy", "city": "Houston", "post_code": 77058, "region": "Texas", "country": "USA"},
          {"street": "", "city": "Kennedy Space Center", "post_code": 32899, "region": "Florida", "country": "USA"}]},
  
      {"firstname": "Mark", "lastname": "Watney", "addresses": [
          {"street": "4800 Oak Grove Dr", "city": "Pasadena", "post_code": 91109, "region": "California", "country": "USA"},
          {"street": "2825 E Ave P", "city": "Palmdale", "post_code": 93550, "region": "California", "country": "USA"}]},
  
      {"firstname": "Иван", "lastname": "Иванович", "addresses": [
          {"street": "", "city": "Космодро́м Байкону́р", "post_code": "", "region": "Кызылординская область", "country": "Қазақстан"},
          {"street": "", "city": "Звёздный городо́к", "post_code": 141160, "region": "Московская область", "country": "Россия"}]},
  
      {"firstname": "Melissa", "lastname": "Lewis", "addresses": []},
  
      {"firstname": "Alex", "lastname": "Vogel", "addresses": [
          {"street": "Linder Hoehe", "city": "Köln", "post_code": 51147, "region": "North Rhine-Westphalia", "country": "Germany"}]}
  ]"""
  
  # list[dict]: flatten data, each address field prefixed with address and number
  result = ...
  
  
  