3.4. Star Arguments

• Unpack and Arbitrary Number of Parameters and Arguments

• * is used for positional arguments

• ** is used for keyword arguments

• function(*args) - unpacks from sequence (tuple, list, set, etc)

• function(**kwargs) - unpacks from mapping (dict, etc)

• function(*args, **kwargs) - unpacks from sequence and mappings

3.4.1. Recap

• Argument - value passed to the function

• Argument can be: positional or keyword

• Positional arguments - resolved by position, order is important, must be at the left side

• Keyword arguments - resolved by name, order is not important, must be on the right side

• After first keyword argument, all following arguments must also be keyword

>>> def login(username, password, remember=False):
...     ...

Positional arguments (order is important):

>>> login('alice', 'secret', True)

Keyword arguments (order is not important):

>>> login(username='alice', password='secret', remember=True)

Positional and keyword arguments:

>>> login('alice', 'secret', remember=True)

3.4.2. Positional Arguments

• function(*args) - unpacks from sequence (tuple, list, set, etc)

• * is used for positional arguments

• There is no convention, so you can use any name, for example *args

>>> def login(username, password, remember=False):
...     print(f'{username=}, {password=}, {remember=}')
>>>
>>> args = ('alice', 'secret', True)

Without star unpacking:

>>> login(args[0], args[1], args[2])
username='alice', password='secret', remember=True

With start unpacking:

>>> login(*args)
username='alice', password='secret', remember=True

3.4.3. Keyword Arguments

• function(**kwargs) - unpacks from mapping (dict, etc)

• ** is used for keyword arguments

• There is no convention, so you can use any name, for example **kwargs

Keyword arguments passed directly:

>>> def login(username, password, remember=False):
...     print(f'{username=}, {password=}, {remember=}')
>>>
>>> kwargs = {'username': 'alice', 'password': 'secret', 'remember': True}

Without star unpacking:

>>> login(username=kwargs['username'], password=kwargs['password'], remember=kwargs['remember'])
username='alice', password='secret', remember=True

With start unpacking:

>>> login(**kwargs)
username='alice', password='secret', remember=True

3.4.4. Positional and Keyword Arguments

• function(*args, **kwargs) - unpacks from sequence and mappings

• * is used for positional arguments

• ** is used for keyword arguments

• There is no convention, so you can use any name, for example *args

• There is no convention, so you can use any name, for example **kwargs

>>> def login(username, password, remember=False):
...     print(f'{username=}, {password=}, {remember=}')
>>>
>>> args = ('alice', 'secret')
>>> kwargs = {'remember': True}

Without star unpacking:

>>> login(args[0], args[1], remember=kwargs['remember'])
username='alice', password='secret', remember=True

With star unpacking:

>>> login(*args, **kwargs)
username='alice', password='secret', remember=True

3.4.5. Merge Kwargs

• function(**kwargs1, **kwargs2)

>>> def login(username, password, remember=False):
...     print(f'{username=}, {password=}, {remember=}')
>>>
>>> kwargs1 = {'username': 'alice', 'password': 'secret'}
>>> kwargs2 = {'remember': True}

With star unpacking:

>>> login(**kwargs1, **kwargs2)
username='alice', password='secret', remember=True

3.4.6. Merge Dicts

• dict(**data1, **data2) - old way

• {**data1, **data2} - old way

• data1 | data2 - since Python 3.9 preferred way

>>> data1 = {'a':1, 'b':2}
>>> data2 = {'c':3, 'd':4}

Before Python 3.9 merging dicts was done with dict or {}:

>>> dict(**data1, **data2)
{'a': 1, 'b': 2, 'c': 3, 'd': 4}

>>> {**data1, **data2}
{'a': 1, 'b': 2, 'c': 3, 'd': 4}

Since Python 3.9 there is a dedicated operator for merging dicts:

>>> data1 | data2
{'a': 1, 'b': 2, 'c': 3, 'd': 4}

3.4.7. Create Objects

• One object from sequence

• One object from mapping

• Many objects from sequence of sequences

• Many objects from sequence of mappings

>>> class User:
...     def __init__(self, firstname, lastname):
...         self.firstname = firstname
...         self.lastname = lastname
...
...     def __repr__(self):
...         return f"User(firstname='{self.firstname}', lastname='{self.lastname}')"

One object from sequence:

>>> data = ('Alice', 'Apricot')
>>>
>>> result = User(*data)
>>> result
User(firstname='Alice', lastname='Apricot')

One object from mapping:

>>> data = {'firstname': 'Alice', 'lastname': 'Apricot'}
>>>
>>> result = User(**data)
>>> result
User(firstname='Alice', lastname='Apricot')

Many objects from sequence of sequences:

>>> data = [
...     ('Alice', 'Apricot'),
...     ('Bob', 'Banana'),
...     ('Carol', 'Corn'),
...     ('Dave', 'Durian'),
...     ('Eve', 'Elderberry'),
...     ('Mallory', 'Melon'),
... ]
>>>
>>> result = [User(*row) for row in data]
>>> result
[User(firstname='Alice', lastname='Apricot'),
 User(firstname='Bob', lastname='Banana'),
 User(firstname='Carol', lastname='Corn'),
 User(firstname='Dave', lastname='Durian'),
 User(firstname='Eve', lastname='Elderberry'),
 User(firstname='Mallory', lastname='Melon')]

Many objects from sequence of mappings:

>>> data = [
...     {'firstname': 'Alice', 'lastname': 'Apricot'},
...     {'firstname': 'Bob', 'lastname': 'Banana'},
...     {'firstname': 'Carol', 'lastname': 'Corn'},
...     {'firstname': 'Dave', 'lastname': 'Durian'},
...     {'firstname': 'Eve', 'lastname': 'Elderberry'},
...     {'firstname': 'Mallory', 'lastname': 'Melon'},
... ]
>>>
>>> result = [User(**row) for row in data]
>>> result
[User(firstname='Alice', lastname='Apricot'),
 User(firstname='Bob', lastname='Banana'),
 User(firstname='Carol', lastname='Corn'),
 User(firstname='Dave', lastname='Durian'),
 User(firstname='Eve', lastname='Elderberry'),
 User(firstname='Mallory', lastname='Melon')]

3.4.8. Recap

• * is used for positional arguments

• ** is used for keyword arguments

• echo(*data) - unpacks from sequence (tuple, list, set, etc)

• echo(**data) - unpacks from mapping (dict, etc)

• echo(*data1, **data2) - unpacks from sequence and mappings

• Create one object from sequence

• Create one object from mapping

• Create many objects from sequence of sequences

• Create many objects from sequence of mappings

• Old way to merge dicts dict(**data1, **data2) or {**data1, **data2}

• Since Python 3.9 merge dicts with: data1 | data2

3.4.9. Use Case - 1

>>> def database_connect(host, port, username, password, database):
...     ...

After reading config from file we have a dict:

>>> CONFIG = {
...     'host': 'example.com',
...     'port': 1234,
...     'username': 'myusername',
...     'password': 'mypassword',
...     'database': 'mydatabase'}

Database connection configuration read from config file:

>>> connection = database_connect(
...     host=CONFIG['host'],
...     port=CONFIG['port'],
...     username=CONFIG['username'],
...     password=CONFIG['password'],
...     database=CONFIG['database'])

Or:

>>> connection = database_connect(**CONFIG)

3.4.10. Use Case - 2

Calling a function which has similar parameters. Passing configuration to the function, which sets parameters from the config:

>>> def draw_line(x, y, color, type, width, markers):
...     ...

>>> draw_line(x=1, y=2, color='red', type='dashed', width='2px', markers='disc')
>>> draw_line(x=3, y=4, color='red', type='dashed', width='2px', markers='disc')
>>> draw_line(x=5, y=6, color='red', type='dashed', width='2px', markers='disc')

>>> style = {
...     'color': 'red',
...     'type': 'dashed',
...     'width': '2px',
...     'markers': 'disc',
... }
>>>
>>> draw_line(x=1, y=2, **style)
>>> draw_line(x=3, y=4, **style)
>>> draw_line(x=5, y=6, **style)

3.4.11. Use Case - 3

>>> from dataclasses import dataclass
>>>
>>>
>>> @dataclass
... class Iris:
...     sepal_length: float
...     sepal_width: float
...     petal_length: float
...     petal_width: float
...     species: str

>>> DATA = [
...     (5.8, 2.7, 5.1, 1.9, 'virginica'),
...     (5.1, 3.5, 1.4, 0.2, 'setosa'),
...     (5.7, 2.8, 4.1, 1.3, 'versicolor'),
...     (6.3, 2.9, 5.6, 1.8, 'virginica'),
...     (6.4, 3.2, 4.5, 1.5, 'versicolor'),
...     (4.7, 3.2, 1.3, 0.2, 'setosa'),
... ]
>>>
>>> result = [Iris(*row) for row in DATA]
>>> print(result)
[Iris(sepal_length=5.8, sepal_width=2.7, petal_length=5.1, petal_width=1.9, species='virginica'),
 Iris(sepal_length=5.1, sepal_width=3.5, petal_length=1.4, petal_width=0.2, species='setosa'),
 Iris(sepal_length=5.7, sepal_width=2.8, petal_length=4.1, petal_width=1.3, species='versicolor'),
 Iris(sepal_length=6.3, sepal_width=2.9, petal_length=5.6, petal_width=1.8, species='virginica'),
 Iris(sepal_length=6.4, sepal_width=3.2, petal_length=4.5, petal_width=1.5, species='versicolor'),
 Iris(sepal_length=4.7, sepal_width=3.2, petal_length=1.3, petal_width=0.2, species='setosa')]

>>> DATA = [
...     {"sepal_length":5.8,"sepal_width":2.7,"petal_length":5.1,"petal_width":1.9,"species":"virginica"},
...     {"sepal_length":5.1,"sepal_width":3.5,"petal_length":1.4,"petal_width":0.2,"species":"setosa"},
...     {"sepal_length":5.7,"sepal_width":2.8,"petal_length":4.1,"petal_width":1.3,"species":"versicolor"},
...     {"sepal_length":6.3,"sepal_width":2.9,"petal_length":5.6,"petal_width":1.8,"species":"virginica"},
...     {"sepal_length":6.4,"sepal_width":3.2,"petal_length":4.5,"petal_width":1.5,"species":"versicolor"},
...     {"sepal_length":4.7,"sepal_width":3.2,"petal_length":1.3,"petal_width":0.2,"species":"setosa"},
... ]
>>>
>>> result = [Iris(**row) for row in DATA]
>>> print(result)
[Iris(sepal_length=5.8, sepal_width=2.7, petal_length=5.1, petal_width=1.9, species='virginica'),
 Iris(sepal_length=5.1, sepal_width=3.5, petal_length=1.4, petal_width=0.2, species='setosa'),
 Iris(sepal_length=5.7, sepal_width=2.8, petal_length=4.1, petal_width=1.3, species='versicolor'),
 Iris(sepal_length=6.3, sepal_width=2.9, petal_length=5.6, petal_width=1.8, species='virginica'),
 Iris(sepal_length=6.4, sepal_width=3.2, petal_length=4.5, petal_width=1.5, species='versicolor'),
 Iris(sepal_length=4.7, sepal_width=3.2, petal_length=1.3, petal_width=0.2, species='setosa')]

3.4.12. Use Case - 5

• Definition of pandas.read_csv() function [1]

• Proxy functions. One of the most common use of *args, **kwargs:

>>> def read_csv(filepath_or_buffer, /, *, sep=', ', delimiter=None,
...              header='infer', names=None, index_col=None, usecols=None,
...              squeeze=False, prefix=None, mangle_dupe_cols=True,
...              dtype=None, engine=None, converters=None, true_values=None,
...              false_values=None, skipinitialspace=False, skiprows=None,
...              nrows=None, na_values=None, keep_default_na=True,
...              na_filter=True, verbose=False, skip_blank_lines=True,
...              parse_dates=False, infer_datetime_format=False,
...              keep_date_col=False, date_parser=None, dayfirst=False,
...              iterator=False, chunksize=None, compression='infer',
...              thousands=None, decimal=b'.', lineterminator=None,
...              quotechar='"', quoting=0, escapechar=None, comment=None,
...              encoding=None, dialect=None, tupleize_cols=None,
...              error_bad_lines=True, warn_bad_lines=True, skipfooter=0,
...              doublequote=True, delim_whitespace=False, low_memory=True,
...              memory_map=False, float_precision=None): ...

Calling function with positional only arguments is insane. In Python we don't do that, because we have keyword arguments.

>>> read_csv('/tmp/myfile.csv', ';', None, 'infer', None, None, None, False,
...          True, None, None, None, None, None, False, None, None, None,
...          None, True, True, False, True, False, False, False, None, False,
...          False, None, 'infer', None, b',', None, '"', 0, None, None,
...          None, None, None, True, True, 0, True, False, True, False, None)
Traceback (most recent call last):
TypeError: read_csv() takes 1 positional argument but 49 were given

Keyword arguments with sensible defaults are your best friends. The number of function parameters suddenly is not a problem:

>>> read_csv('myfile1.csv', delimiter=';', decimal=b',')
>>> read_csv('myfile2.csv', delimiter=';', decimal=b',')
>>> read_csv('myfile3.csv', delimiter=';', decimal=b',')
>>> read_csv('myfile4.csv', delimiter=';', decimal=b',')
>>> read_csv('myfile5.csv', delimiter=';', decimal=b',')

Proxy functions allows for changing defaults to the original function. One simply define a function which has sensible defaults and call the original function setting default values automatically:

>>> def mycsv(file, delimiter=';', decimal=b',', **kwargs):
...     return read_csv(file, delimiter=delimiter, decimal=decimal, **kwargs)

Thanks to using **kwargs there is no need to specify all the values from the original function. The uncovered arguments will simply be put in kwargs dictionary and passed to the original function:

>>> mycsv('/tmp/myfile1.csv')
>>> mycsv('/tmp/myfile2.csv')
>>> mycsv('/tmp/myfile3.csv')
>>> mycsv('/tmp/myfile4.csv')
>>> mycsv('/tmp/myfile5.csv')

This allows for cleaner code. Each parameter will be passed to mycsv function. Then it will be checked if there is a different default value already defined. If not, then parameter will be stored in kwargs and passed to the original function:

>>> mycsv('/tmp/myfile.csv', encoding='utf-8')
>>> mycsv('/tmp/myfile.csv', encoding='utf-8', verbose=True)
>>> mycsv('/tmp/myfile.csv', verbose=True, usecols=['sepal_length', 'species'])

3.4.13. Use Case - 6

Decorators are functions, which get reference to the decorated function as it's argument, and has closure which gets original function arguments as positional and keyword arguments:

>>> def mydecorator(func):
...     def wrapper(*args, **kwargs):
...         return func(*args, **kwargs)
...     return wrapper

Decorators could be used on any function, therefore we could not predict what would be the name of the parameter passed to it:

>>> @mydecorator
... def add(a, b):
...     return a + b

>>> @mydecorator
... def echo(text):
...     return text

Moreover it depends on a user whether he/she chooses to run function positionally, using keyword arguments or even both at the same time:

>>> add(1, 2)
3

>>> add(a=1, b=2)
3

>>> add(1, b=2)
3

>>> echo('hello')
'hello'

3.4.14. References

[1]

The pandas development team. API Reference Pandas.read_csv(). Year: 2021. Retrieved: 2021-12-05. URL: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html

3.4.15. Assignments

# %% About
# - Name: Star Arguments Args
# - Difficulty: easy
# - Lines: 1
# - Minutes: 2

# %% 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

# %% English
# 1. Use star expression to create `User` from `DATA`
# 2. Define variable `result` with `User` object
# 3. Run doctests - all must succeed

# %% Polish
# 1. Użyj wyrażenia z gwiazdką do stworzenia `User` z `DATA`
# 2. Zdefiniuj zmienną `result` z obiektem `User`
# 3. Uruchom doctesty - wszystkie muszą się powieść

# %% Example
# >>> result
# User(firstname='Alice', lastname='Apricot', email='alice@example.com')

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

>>> assert result is not Ellipsis, \
'Assign result to variable: `result`'
>>> assert type(result) is User, \
'Variable `result` has invalid type, should be User'

>>> result  # doctest: +NORMALIZE_WHITESPACE
User(firstname='Alice', lastname='Apricot', email='alice@example.com')
"""

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

# %% Imports

# %% Types
result: 'User'

# %% Data
class User:
    def __init__(self, firstname, lastname, email):
        self.firstname = firstname
        self.lastname = lastname
        self.email = email

    def __repr__(self):
        clsname = self.__class__.__name__
        firstname = self.firstname
        lastname = self.lastname
        email = self.email
        return f'{clsname}({firstname=}, {lastname=}, {email=})'


DATA = ('Alice', 'Apricot', 'alice@example.com')

# %% Result

# %% About
# - Name: Star Arguments Args
# - Difficulty: easy
# - Lines: 1
# - Minutes: 2

# %% 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

# %% English
# 1. Use star expression to create `list[User]` from `DATA`
# 2. Define variable `result` with `list[User]` objects
# 3. Run doctests - all must succeed

# %% Polish
# 1. Użyj wyrażenia z gwiazdką do stworzenia `list[User]` z `DATA`
# 2. Zdefiniuj zmienną `result` z obiektami `list[User]`
# 3. Uruchom doctesty - wszystkie muszą się powieść

# %% Example
# >>> result
# [User(firstname='Alice', lastname='Apricot', email='alice@example.com'),
#  User(firstname='Bob', lastname='Banana', email='bob@example.com'),
#  User(firstname='Carol', lastname='Corn', email='carol@example.com'),
#  User(firstname='Dave', lastname='Durian', email='dave@example.org'),
#  User(firstname='Eve', lastname='Elderberry', email='eve@example.org'),
#  User(firstname='Mallory', lastname='Melon', email='mallory@example.net')]

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

>>> assert result is not Ellipsis, \
'Assign result to variable: `result`'
>>> assert type(result) is list, \
'Variable `result` has invalid type, should be list'
>>> assert all(type(row) is User for row in result), \
'All elements in result must be a User'

>>> from pprint import pprint
>>> pprint(result)  # doctest: +NORMALIZE_WHITESPACE
[User(firstname='Alice', lastname='Apricot', email='alice@example.com'),
 User(firstname='Bob', lastname='Banana', email='bob@example.com'),
 User(firstname='Carol', lastname='Corn', email='carol@example.com'),
 User(firstname='Dave', lastname='Durian', email='dave@example.org'),
 User(firstname='Eve', lastname='Elderberry', email='eve@example.org'),
 User(firstname='Mallory', lastname='Melon', email='mallory@example.net')]
"""

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

# %% Imports

# %% Types
result: list['User']

# %% Data
class User:
    def __init__(self, firstname, lastname, email):
        self.firstname = firstname
        self.lastname = lastname
        self.email = email

    def __repr__(self):
        clsname = self.__class__.__name__
        firstname = self.firstname
        lastname = self.lastname
        email = self.email
        return f'{clsname}({firstname=}, {lastname=}, {email=})'


DATA = [
    ('firstname', 'lastname', 'email'),
    ('Alice', 'Apricot', 'alice@example.com'),
    ('Bob', 'Banana', 'bob@example.com'),
    ('Carol', 'Corn', 'carol@example.com'),
    ('Dave', 'Durian', 'dave@example.org'),
    ('Eve', 'Elderberry', 'eve@example.org'),
    ('Mallory', 'Melon', 'mallory@example.net'),
]

# %% Result

# %% About
# - Name: Star Arguments Kwargs
# - Difficulty: easy
# - Lines: 1
# - Minutes: 2

# %% 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

# %% English
# 1. Use star expression to create `User` from `DATA`
# 2. Define variable `result` with `User` object
# 3. Run doctests - all must succeed

# %% Polish
# 1. Użyj wyrażenia z gwiazdką do stworzenia `User` z `DATA`
# 2. Zdefiniuj zmienną `result` z obiektem `User`
# 3. Uruchom doctesty - wszystkie muszą się powieść

# %% Example
# >>> result
# User(firstname='Alice', lastname='Apricot', email='alice@example.com')

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

>>> assert result is not Ellipsis, \
'Assign result to variable: `result`'
>>> assert type(result) is User, \
'Variable `result` has invalid type, should be User'

>>> result  # doctest: +NORMALIZE_WHITESPACE
User(firstname='Alice', lastname='Apricot', email='alice@example.com')
"""

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

# %% Imports

# %% Types
result: 'User'

# %% Data
class User:
    def __init__(self, firstname, lastname, email):
        self.firstname = firstname
        self.lastname = lastname
        self.email = email

    def __repr__(self):
        clsname = self.__class__.__name__
        firstname = self.firstname
        lastname = self.lastname
        email = self.email
        return f'{clsname}({firstname=}, {lastname=}, {email=})'


DATA = {'firstname':'Alice', 'lastname':'Apricot', 'email':'alice@example.com'}

# %% Result

# %% About
# - Name: Star Arguments Kwargs
# - Difficulty: easy
# - Lines: 1
# - Minutes: 2

# %% 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

# %% English
# 1. Use star expression to create `list[User]` from `DATA`
# 2. Define variable `result` with `list[User]` objects
# 3. Run doctests - all must succeed

# %% Polish
# 1. Użyj wyrażenia z gwiazdką do stworzenia `list[User]` z `DATA`
# 2. Zdefiniuj zmienną `result` z obiektami `list[User]`
# 3. Uruchom doctesty - wszystkie muszą się powieść

# %% Example
# >>> result
# [User(firstname='Alice', lastname='Apricot', email='alice@example.com'),
#  User(firstname='Bob', lastname='Banana', email='bob@example.com'),
#  User(firstname='Carol', lastname='Corn', email='carol@example.com'),
#  User(firstname='Dave', lastname='Durian', email='dave@example.org'),
#  User(firstname='Eve', lastname='Elderberry', email='eve@example.org'),
#  User(firstname='Mallory', lastname='Melon', email='mallory@example.net')]

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

>>> assert result is not Ellipsis, \
'Assign result to variable: `result`'
>>> assert type(result) is list, \
'Variable `result` has invalid type, should be list'
>>> assert all(type(row) is User for row in result), \
'All elements in result must be a User'

>>> from pprint import pprint
>>> pprint(result)  # doctest: +NORMALIZE_WHITESPACE
[User(firstname='Alice', lastname='Apricot', email='alice@example.com'),
 User(firstname='Bob', lastname='Banana', email='bob@example.com'),
 User(firstname='Carol', lastname='Corn', email='carol@example.com'),
 User(firstname='Dave', lastname='Durian', email='dave@example.org'),
 User(firstname='Eve', lastname='Elderberry', email='eve@example.org'),
 User(firstname='Mallory', lastname='Melon', email='mallory@example.net')]
"""

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

# %% Imports

# %% Types
result: list['User']

# %% Data
class User:
    def __init__(self, firstname, lastname, email):
        self.firstname = firstname
        self.lastname = lastname
        self.email = email

    def __repr__(self):
        clsname = self.__class__.__name__
        firstname = self.firstname
        lastname = self.lastname
        email = self.email
        return f'{clsname}({firstname=}, {lastname=}, {email=})'


DATA = [
    {'firstname': 'Alice', 'lastname': 'Apricot', 'email': 'alice@example.com'},
    {'firstname': 'Bob', 'lastname': 'Banana', 'email': 'bob@example.com'},
    {'firstname': 'Carol', 'lastname': 'Corn', 'email': 'carol@example.com'},
    {'firstname': 'Dave', 'lastname': 'Durian', 'email': 'dave@example.org'},
    {'firstname': 'Eve', 'lastname': 'Elderberry', 'email': 'eve@example.org'},
    {'firstname': 'Mallory', 'lastname': 'Melon', 'email': 'mallory@example.net'},
]

# %% Result