19.6. OOP Relations

• ORM - Object-relational mapping

• Converts (map) between objects in code and database tables (relations)

• Declarative - First define model, which then maps to the database tables

• pickle - has relations

• json - has relations

• csv - non-relational format

Object–relational mapping (ORM, O/RM, and O/R mapping tool) in computer science is a programming technique for converting data between incompatible type systems using object-oriented programming languages. This creates, in effect, a 'virtual object database' that can be used from within the programming language. There are both free and commercial packages available that perform object–relational mapping, although some programmers opt to construct their own ORM tools [1].

In object-oriented programming, data-management tasks act on objects that are almost always non-scalar values. For example, consider an address book entry that represents a single person along with zero or more phone numbers and zero or more addresses. This could be modeled in an object-oriented implementation by a 'Person object' with an attribute/field to hold each data item that the entry comprises: the person's name, a list of phone numbers, and a list of addresses. The list of phone numbers would itself contain 'PhoneNumber objects' and so on. Each such address-book entry is treated as a single object by the programming language (it can be referenced by a single variable containing a pointer to the object, for instance). Various methods can be associated with the object, such as methods to return the preferred phone number, the home address, and so on [1].

By contrast, many popular database products such as SQL database management systems (DBMS) are not object-oriented and can only store and manipulate scalar values such as integers and strings organized within tables. The programmer must either convert the object values into groups of simpler values for storage in the database (and convert them back upon retrieval), or only use simple scalar values within the program. Object–relational mapping implements the first approach [2].

The heart of the problem involves translating the logical representation of the objects into an atomized form that is capable of being stored in the database while preserving the properties of the objects and their relationships so that they can be reloaded as objects when needed. If this storage and retrieval functionality is implemented, the objects are said to be persistent [1].

• normalization Database normalization is the process of structuring a database, usually a relational database, in accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity. Normalization entails organizing the columns (attributes) and tables (relations) of a database to ensure that their dependencies are properly enforced by database integrity constraints. It is accomplished by applying some formal rules either by a process of synthesis (creating a new database design) or decomposition (improving an existing database design). A relational database relation is often described as "normalized" if it meets third normal form. [5] [8]

• relation In relational database theory, a relation, as originally defined by E. F. Codd, [8] is a set of tuples (d1, d2, ..., dn), where each element dj is a member of Dj, a data domain. Codd's original definition notwithstanding, and contrary to the usual definition in mathematics, there is no ordering to the elements of the tuples of a relation. Instead, each element is termed an attribute value. An attribute is a name paired with a domain (nowadays more commonly referred to as a type or data type). An attribute value is an attribute name paired with an element of that attribute's domain, and a tuple is a set of attribute values in which no two distinct elements have the same name. Thus, in some accounts, a tuple is described as a function, mapping names to values. [11]

• consistency Consistency (or Correctness) in database systems refers to the requirement that any given database transaction must change affected data only in allowed ways. Any data written to the database must be valid according to all defined rules, including constraints, cascades, triggers, and any combination thereof. This does not guarantee correctness of the transaction in all ways the application programmer might have wanted (that is the responsibility of application-level code) but merely that any programming errors cannot result in the violation of any defined database constraints. [15] [12]

• integrity Data integrity is the maintenance of, and the assurance of, data accuracy and consistency over its entire life-cycle and is a critical aspect to the design, implementation, and usage of any system that stores, processes, or retrieves data. The term is broad in scope and may have widely different meanings depending on the specific context – even under the same general umbrella of computing. It is at times used as a proxy term for data quality, while data validation is a prerequisite for data integrity. Data integrity is the opposite of data corruption. The overall intent of any data integrity technique is the same: ensure data is recorded exactly as intended (such as a database correctly rejecting mutually exclusive possibilities). Moreover, upon later retrieval, ensure the data is the same as when it was originally recorded. In short, data integrity aims to prevent unintentional changes to information. Data integrity is not to be confused with data security, the discipline of protecting data from unauthorized parties. [14] [13]

19.6.1. Pros

Compared to traditional techniques of exchange between an object-oriented language and a relational database, ORM often reduces the amount of code that needs to be written. [3]

19.6.2. Cons

Disadvantages of ORM tools generally stem from the high level of abstraction obscuring what is actually happening in the implementation code. Also, heavy reliance on ORM software has been cited as a major factor in producing poorly designed databases. [4]

19.6.3. References

[1] (1,2,3)

https://en.wikipedia.org/wiki/Object–relational_mapping

[2]

What is Object/Relational Mapping?. Hibernate Overview. JBOSS Hibernate. Retrieved: 2011-04-19. URL: https://hibernate.org/orm/what-is-an-orm/

[3]

Barry, Douglas and Stanienda, Torsten. Solving the Java Object Storage Problem. Computer, vol. 31, no. 11, pp. 33-40, Nov. 1998, http://www2.computer.org/portal/web/csdl/doi/10.1109/2.730734 , Excerpt at http://www.service-architecture.com/object-relational-mapping/articles/transparent_persistence_vs_jdbc_call-level_interface.html. Lines of code using O/R are only a fraction of those needed for a call-level interface (1:4). For this exercise, 496 lines of code were needed using the ODMG Java Binding compared to 1,923 lines of code using JDBC.

[4]

Lorenz, M and Rudolph, J.P. and Hesse, G. and Uflacker, M. and Plattner, H. Object–Relational Mapping Revisited - A Quantitative Study on the Impact of Database Technology on O/R Mapping Strategies. Hawaii International Conference on System Sciences (HICSS), 4877-4886 (DOI:10.24251/hicss.2017.592). Year: 2017.

[5]

Database normalization. Wikipedia. Year: 2021. Retrieved: 2021-12-16. URL: https://en.wikipedia.org/wiki/Database_normalization

[6]

SQL. Wikipedia. Year: 2021. Retrieved: 2021-12-16. URL: https://en.wikipedia.org/wiki/SQL

[7]

Shafranovich, Y. The application/sql Media Type. Internet Engineering Task Force (IETF). Year: 2013. Retrieved: 2021-12-16. URL: https://datatracker.ietf.org/doc/html/rfc6922

[8] (1,2)

Codd, E. F. "Further Normalization of the Data Base Relational Model". (Presented at Courant Computer Science Symposia Series 6, "Data Base Systems", New York City, May 24–25, 1971.) IBM Research Report RJ909 (August 31, 1971). Republished in Randall J. Rustin (ed.), Data Base Systems: Courant Computer Science Symposia Series 6. Prentice-Hall, 1972.

[9]

Big data. Wikipedia. Year: 2021. Retrieved: 2021-12-16. URL: https://en.wikipedia.org/wiki/Big_data

[10]

Data retention. Wikipedia. Year: 2021. Retrieved: 2021-12-16. URL: https://en.wikipedia.org/wiki/Data_retention

[11]

Relation (database). Wikipedia. Year: 2021. Retrieved: 2021-12-16. URL: https://en.wikipedia.org/wiki/Relation_(database)

[12]

Consistency. Wikipedia. Year: 2021. Retrieved: 2021-12-16. URL: https://en.wikipedia.org/wiki/Consistency_(database_systems)

[13]

Data Integrity. Wikipedia. Year: 2021. Retrieved: 2021-12-16. URL: https://en.wikipedia.org/wiki/Data_integrity

[14]

Boritz, J. "IS Practitioners' Views on Core Concepts of Information Integrity". International Journal of Accounting Information Systems. Elsevier. Archived from the original on 5 October 2011. Retrieved 12 August 2011.

[15]

Date, C. J. "SQL and Relational Theory: How to Write Accurate SQL Code 2nd edition", O'reilly Media, Inc., 2012, pg. 180.

19.6.4. Assignments

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% About
# - Name: OOP Relations Association
# - Difficulty: easy
# - Lines: 7
# - Minutes: 5

# %% English
# 1. Define class `Point` with:
#    - attribute `x: int`
#    - attribute `y: int`
#    - method `__repr__()` returning f'Point(x={self.x}, y={self.y})'
# 2. Define class `Path` with attributes:
#    - `points: list[Point]` with default empty list
# 3. Run doctests - all must succeed

# %% Polish
# 1. Zdefiniuj klasę `Point` z:
#    - atrybut `x: int`
#    - atrybut `y: int`
#    - metodą `__repr__()` zwracającą f'Point(x={self.x}, y={self.y})'
# 2. Zdefiniuj klasę `Path` z atrybutami:
#    - `points: list[Point]` z domyślną pustą listą
# 3. Uruchom doctesty - wszystkie muszą się powieść

# %% Tests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> from inspect import isclass
>>> assert isclass(Point)
>>> assert isclass(Path)

>>> pt = Point(1, 2)
>>> pt.x
1
>>> pt.y
2
>>> pt
Point(x=1, y=2)

>>> path = Path([
...     Point(x=1, y=2),
...     Point(x=3, y=4),
...     Point(x=5, y=6)]
... )

>>> path.points
[Point(x=1, y=2), Point(x=3, y=4), Point(x=5, y=6)]
"""

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% About
# - Name: OOP Relations Composition
# - Difficulty: easy
# - Lines: 18
# - Minutes: 8

# %% English
# 1. Define class `Point`
# 2. Class `Point` has attributes `x: int = 0` and `y: int = 0`
# 3. Define class `Position`
# 4. In `Position` define method `get_position(self) -> Point`
# 5. In `Position` define method `set_position(self, x: int, y: int) -> None`
# 6. In `Position` define method `change_position(self, left: int = 0, right: int = 0, up: int = 0, down: int = 0) -> None`
# 7. Assume left-top screen corner as an initial coordinates position:
#    - going right add to `x`
#    - going left subtract from `x`
#    - going up subtract from `y`
#    - going down add to `y`
# 8. Run doctests - all must succeed

# %% Polish
# 1. Zdefiniuj klasę `Point`
# 2. Klasa `Point` ma atrybuty `x: int = 0` oraz `y: int = 0`
# 3. Zdefiniuj klasę `Position`
# 4. W `Position` zdefiniuj metodę `get_position(self) -> Point`
# 5. W `Position` zdefiniuj metodę `set_position(self, x: int, y: int) -> None`
# 6. W `Position` zdefiniuj metodę `change_position(self, left: int = 0, right: int = 0, up: int = 0, down: int = 0) -> None`
# 7. Przyjmij górny lewy róg ekranu za punkt początkowy:
#    - idąc w prawo dodajesz `x`
#    - idąc w lewo odejmujesz `x`
#    - idąc w górę odejmujesz `y`
#    - idąc w dół dodajesz `y`
# 8. Uruchom doctesty - wszystkie muszą się powieść

# %% Tests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> from inspect import isclass, ismethod
>>> assert isclass(Point)
>>> assert isclass(Position)
>>> assert hasattr(Position, 'get_position')
>>> assert hasattr(Position, 'set_position')
>>> assert hasattr(Position, 'change_position')
>>> assert ismethod(Position().get_position)
>>> assert ismethod(Position().set_position)
>>> assert ismethod(Position().change_position)

>>> class User(Position):
...     pass

>>> mark = User()

>>> mark.set_position(x=1, y=2)
>>> mark.get_position()
Point(x=1, y=2)

>>> mark.set_position(x=1, y=1)
>>> mark.change_position(right=1)
>>> mark.get_position()
Point(x=2, y=1)

>>> mark.set_position(x=1, y=1)
>>> mark.change_position(left=1)
>>> mark.get_position()
Point(x=0, y=1)

>>> mark.set_position(x=1, y=1)
>>> mark.change_position(down=1)
>>> mark.get_position()
Point(x=1, y=2)

>>> mark.set_position(x=1, y=1)
>>> mark.change_position(up=1)
>>> mark.get_position()
Point(x=1, y=0)
"""

class Point:
    def __init__(self, x=0, y=0):
        self.x = x
        self.y = y

    def __repr__(self):
        return f'Point(x={self.x}, y={self.y})'

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% About
# - Name: OOP Relations FlattenDicts
# - Difficulty: medium
# - Lines: 7
# - Minutes: 13

# %% English
# 1. Convert `DATA` to format with one column per each attrbute for example:
#    - `group1_gid`, `group2_gid`
#    - `group1_name`, `group2_name`
# 2. Note, that enumeration starts with one
# 3. Collect data to `result: map`
# 4. Run doctests - all must succeed

# %% Polish
# 1. Przekonweruj `DATA` do formatu z jedną kolumną dla każdego atrybutu, np:
#    - `group1_gid`, `group2_gid`
#    - `group1_name`, `group2_name`
# 2. Zwróć uwagę, że enumeracja zaczyna się od jeden
# 3. Zbierz dane do `result: map`
# 4. Uruchom doctesty - wszystkie muszą się powieść

# %% Tests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> result = list(result)
>>> assert type(result) is list
>>> assert len(result) > 0
>>> assert all(type(x) is dict for x in result)

>>> from pprint import pprint
>>> pprint(result, width=30, sort_dicts=False)
[{'firstname': 'Mark',
  'lastname': 'Watney',
  'group1_gid': 1,
  'group1_name': 'staff'},
 {'firstname': 'Melissa',
  'lastname': 'Lewis',
  'group1_gid': 1,
  'group1_name': 'staff',
  'group2_gid': 2,
  'group2_name': 'admins'},
 {'firstname': 'Rick',
  'lastname': 'Martinez'}]
"""

DATA = [
    {"firstname": "Mark", "lastname": "Watney", "groups": [
        {"gid": 1, "name": "staff"}]},

    {"firstname": "Melissa", "lastname": "Lewis", "groups": [
        {"gid": 1, "name": "staff"},
        {"gid": 2, "name": "admins"}]},

    {"firstname": "Rick", "lastname": "Martinez", "groups": []},
]

# Flatten data, each group field prefixed with group and number
# type: list[dict]
result = ...

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% About
# - Name: OOP Relations FlattenClasses
# - Difficulty: medium
# - Lines: 9
# - Minutes: 13

# %% English
# 1. Convert `DATA` to format with one column per each attrbute for example:
#    - `group1_gid`, `group2_gid`,
#    - `group1_name`, `group2_name`
# 2. Note, that enumeration starts with one
# 3. Run doctests - all must succeed

# %% Polish
# 1. Przekonweruj `DATA` do formatu z jedną kolumną dla każdego atrybutu, np:
#    - `group1_gid`, `group2_gid`,
#    - `group1_name`, `group2_name`
# 2. Zwróć uwagę, że enumeracja zaczyna się od jeden
# 3. Uruchom doctesty - wszystkie muszą się powieść

# %% Hints
# - `vars(obj)` -> `dict`

# %% Tests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> result = list(result)
>>> assert type(result) is list
>>> assert len(result) > 0
>>> assert all(type(x) is dict for x in result)

>>> from pprint import pprint
>>> pprint(result, width=30, sort_dicts=False)
[{'firstname': 'Mark',
  'lastname': 'Watney',
  'group1_gid': 1,
  'group1_name': 'staff'},
 {'firstname': 'Melissa',
  'lastname': 'Lewis',
  'group1_gid': 1,
  'group1_name': 'staff',
  'group2_gid': 2,
  'group2_name': 'admins'},
 {'firstname': 'Rick',
  'lastname': 'Martinez'}]
"""

class User:
    def __init__(self, firstname, lastname, groups=None):
        self.firstname = firstname
        self.lastname = lastname
        self.groups = groups if groups else []


class Group:
    def __init__(self, gid, name):
        self.gid = gid
        self.name = name

DATA = [
    User('Mark', 'Watney', groups=[
        Group(gid=1, name='staff')]),

    User('Melissa', 'Lewis', groups=[
        Group(gid=1, name='staff'),
        Group(gid=2, name='admins')]),

    User('Rick', 'Martinez'),
]


# Convert DATA
# type: list[dict]
result = ...

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% About
# - Name: OOP Relations Model
# - Difficulty: easy
# - Lines: 21
# - Minutes: 13

# %% English
# 1. In `DATA` we have two classes
# 2. Model data using classes and relations
# 3. Create instances dynamically based on `DATA`
# 4. Run doctests - all must succeed

# %% Polish
# 1. W `DATA` mamy dwie klasy
# 2. Zamodeluj problem wykorzystując klasy i relacje między nimi
# 3. Twórz instancje dynamicznie na podstawie `DATA`
# 4. Uruchom doctesty - wszystkie muszą się powieść

# %% Tests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> result = list(result)
>>> assert type(result) is list
>>> assert all(type(user) is User for user in result)

>>> assert all(type(addr) is Address
...            for user in result
...            for addr in user.addresses)

>>> from pprint import pprint
>>> pprint(result, sort_dicts=False)  # doctest: +NORMALIZE_WHITESPACE
[User('Mark', 'Watney', addresses=[Address('Houston', 'USA'), Address('Kennedy Space Center', 'USA')]),
 User('Melissa', 'Lewis', addresses=[Address('Pasadena', 'USA'), Address('Palmdale', 'USA')]),
 User('Rick', 'Martinez', addresses=[]),
 User('Alex', 'Vogel', addresses=[Address('Cologne', 'Germany')])]
"""

from dataclasses import dataclass


DATA = [
    {"firstname": "Mark", "lastname": "Watney", "addresses": [
        {"street": "2101 E NASA Pkwy",
         "city": "Houston",
         "postcode": 77058,
         "region": "Texas",
         "country": "USA"},
        {"street": "",
         "city": "Kennedy Space Center",
         "postcode": 32899,
         "region": "Florida",
         "country": "USA"}]},

    {"firstname": "Melissa", "lastname": "Lewis", "addresses": [
        {"street": "4800 Oak Grove Dr",
         "city": "Pasadena",
         "postcode": 91109,
         "region": "California",
         "country": "USA"},
        {"street": "2825 E Ave P",
         "city": "Palmdale",
         "postcode": 93550,
         "region": "California",
         "country": "USA"}]},

    {"firstname": "Rick", "lastname": "Martinez", "addresses": []},

    {"firstname": "Alex", "lastname": "Vogel", "addresses": [
        {"street": "Linder Hoehe",
         "city": "Cologne",
         "postcode": 51147,
         "region": "North Rhine-Westphalia",
         "country": "Germany"}]}
]


# Model `DATA`
# type: type
class Address:
    def __repr__(self):
        return f"Address('...', '...')"


# Model `DATA`
# type: type
class User:
    def __repr__(self):
        return f"User('...', '...', addresses=...)"


# Iterate over `DATA` and create instances
# type: list[User]
result = ...

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% About
# - Name: OOP Relations CSV
# - Difficulty: medium
# - Lines: 4
# - Minutes: 13

# %% English
# 1. Write data with relations to CSV format
# 2. Convert `DATA` to `result: list[dict[str,str]]`
# 3. Non-functional requirements:
#    - Use `,` to separate fields
#    - Use `;` to separate instances
# 4. Contact has zero or many addresses
# 5. Run doctests - all must succeed

# %% Polish
# 1. Zapisz dane relacyjne do formatu CSV
# 2. Przekonwertuj `DATA` do `result: list[dict[str,str]]`
# 3. Wymagania niefunkcjonalne:
#    - Użyj `,` do oddzielenia pól
#    - Użyj `;` do oddzielenia instancji
# 4. Kontakt ma zero lub wiele adresów
# 5. Uruchom doctesty - wszystkie muszą się powieść

# %% Tests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> result = list(result)
>>> assert type(result) is list
>>> assert len(result) > 0
>>> assert all(type(x) is dict for x in result)

>>> from pprint import pprint
>>> pprint(result, width=112, sort_dicts=False)  # doctest: +NORMALIZE_WHITESPACE
[{'firstname': 'Mark',
  'lastname': 'Watney',
  'addresses': '2101 E NASA Pkwy,Houston,77058,Texas,USA;,Kennedy Space Center,32899,Florida,USA'},
 {'firstname': 'Melissa',
  'lastname': 'Lewis',
  'addresses': '4800 Oak Grove Dr,Pasadena,91109,California,USA;2825 E Ave P,Palmdale,93550,California,USA'},
 {'firstname': 'Rick', 'lastname': 'Martinez', 'addresses': ''},
 {'firstname': 'Alex',
  'lastname': 'Vogel',
  'addresses': 'Linder Hoehe,Cologne,51147,North Rhine-Westphalia,Germany'}]
"""

DATA = [
    {"firstname": "Mark", "lastname": "Watney", "addresses": [
        {"street": "2101 E NASA Pkwy",
         "city": "Houston",
         "postcode": 77058,
         "region": "Texas",
         "country": "USA"},
        {"street": "",
         "city": "Kennedy Space Center",
         "postcode": 32899,
         "region": "Florida",
         "country": "USA"}]},

    {"firstname": "Melissa", "lastname": "Lewis", "addresses": [
        {"street": "4800 Oak Grove Dr",
         "city": "Pasadena",
         "postcode": 91109,
         "region": "California",
         "country": "USA"},
        {"street": "2825 E Ave P",
         "city": "Palmdale",
         "postcode": 93550,
         "region": "California",
         "country": "USA"}]},

    {"firstname": "Rick", "lastname": "Martinez", "addresses": []},

    {"firstname": "Alex", "lastname": "Vogel", "addresses": [
        {"street": "Linder Hoehe",
         "city": "Cologne",
         "postcode": 51147,
         "region": "North Rhine-Westphalia",
         "country": "Germany"}]}
]

# Flatten data, each address field prefixed with address and number
# type: list[dict]
result = ...