5.4. OOP Attribute Slots

  • Faster attribute access

  • Space savings in memory (overhead of dict for every object)

  • Prevents from adding new attributes

  • The space savings is from:

  • Store value references in slots instead of __dict__

  • Denying __dict__ and __weakref__ creation

>>> class Astronaut:
...     __slots__ = ('firstname', 'lastname')
>>>
>>>
>>> astro = Astronaut()
>>> astro.firstname = 'Mark'
>>> astro.lastname = 'Watney'

When inheriting from a class without __slots__, the __dict__ and __weakref__ attribute of the instances will always be accessible.

Without a __dict__ variable, instances cannot be assigned new variables not listed in the __slots__ definition. Attempts to assign to an unlisted variable name raises AttributeError. If dynamic assignment of new variables is desired, then add '__dict__' to the sequence of strings in the __slots__ declaration.

Without a __weakref__ variable for each instance, classes defining __slots__ do not support weak references to its instances. If weak reference support is needed, then add '__weakref__' to the sequence of strings in the __slots__ declaration.

__slots__ are implemented at the class level by creating descriptors for each variable name. As a result, class attributes cannot be used to set default values for instance variables defined by __slots__; otherwise, the class attribute would overwrite the descriptor assignment.

The action of a __slots__ declaration is not limited to the class where it is defined. __slots__ declared in parents are available in child classes. However, child subclasses will get a __dict__ and __weakref__ unless they also define __slots__ (which should only contain names of any additional slots).

If a class defines a slot also defined in a base class, the instance variable defined by the base class slot is inaccessible (except by retrieving its descriptor directly from the base class). This renders the meaning of the program undefined. In the future, a check may be added to prevent this.

Nonempty __slots__ does not work for classes derived from 'variable-length' built-in types such as int, bytes and tuple.

Any non-string iterable may be assigned to __slots__.

If a dictionary is used to assign __slots__, the dictionary keys will be used as the slot names. The values of the dictionary can be used to provide per-attribute docstrings that will be recognised by inspect.getdoc() and displayed in the output of help().

__class__ assignment works only if both classes have the same __slots__.

Multiple inheritance with multiple slotted parent classes can be used, but only one parent is allowed to have attributes created by slots (the other bases must have empty slot layouts) - violations raise TypeError.

If an iterator is used for __slots__ then a descriptor is created for each of the iterator's values. However, the __slots__ attribute will be an empty iterator.

Source: 1

5.4.1. Weakref

A weak reference to an object is not enough to keep the object alive: when the only remaining references to a referent are weak references, garbage collection is free to destroy the referent and reuse its memory for something else. However, until the object is actually destroyed the weak reference may return the object even if there are no strong references to it. A primary use for weak references is to implement caches or mappings holding large objects, where it's desired that a large object not be kept alive solely because it appears in a cache or mapping 3.

__weakref__ is just an opaque object that references all the weak references to the current object. It's just an implementation detail that allows the garbage collector to inform weak references that its referent has been collected, and to not allow access to its underlying pointer anymore. The weak reference can't rely on checking the reference count of the object it refers to. This is because that memory may have been reclaimed and is now being used by another object. Best case scenario the VM will crash, worst case the weak reference will allow access to an object it wasn't originally referring to. This is why the garbage collector must inform the weak reference its referent is no longer valid. Weak references form a stack. The top of that stack (the most recent weak reference to an object) is available via __weakref__. Weakrefs are re-used whenever possible, so the stack is typically either empty or contains a single element. 4

Garbage collection is simply the process of freeing memory when it is not used/reached by any reference/pointer anymore. Python performs garbage collection via a technique called reference counting (and a cyclic garbage collector that is used to detect and break reference cycles). Using reference counting, GC collects the objects as soon as they become unreachable which happens when the number of references to the object is 0. 2

The way with which weak references perform the task of NOT protecting the object from being collected by GC, or better to say the way with which they cause an object to be collected by GC is that (in case of a GC that uses reference counting rather than tracing technique) they just don't get to be counted as a reference. Otherwise, if counted, they will be called strong references 5.

5.4.2. Empty Slots

>>> class Astronaut:
...     __slots__ = ()
>>>
>>>
>>> astro = Astronaut()
>>>
>>> astro.fullname = 'Mark Watney'
Traceback (most recent call last):
AttributeError: 'Astronaut' object has no attribute 'fullname'

5.4.3. One Slot

>>> class Astronaut:
...     __slots__ = ('fullname',)
>>>
>>>
>>> astro = Astronaut()
>>>
>>> astro.fullname = 'Mark Watney'
>>> astro.role = 'Botanist'
Traceback (most recent call last):
AttributeError: 'Astronaut' object has no attribute 'role'

5.4.4. Many Slots

>>> class Astronaut:
...     __slots__ = ('firstname', 'lastname')
>>>
>>>
>>> astro = Astronaut()
>>>
>>> astro.firstname = 'Mark'
>>> astro.lastname = 'Watney'
>>> astro.role = 'Botanist'
Traceback (most recent call last):
AttributeError: 'Astronaut' object has no attribute 'role'

5.4.5. Get Value

>>> class Astronaut:
...     __slots__ = ('firstname', 'lastname')
>>>
>>>
>>> astro = Astronaut()
>>> astro.firstname = 'Mark'
>>> astro.lastname = 'Watney'
>>>
>>> print(astro.firstname)
Mark
>>> print(astro.lastname)
Watney

5.4.6. Slots and Methods

>>> class Astronaut:
...     __slots__ = ('firstname', 'lastname')
...
...     def say_hello(self):
...         print(f'My name... {self.firstname} {self.lastname}')
>>>
>>>
>>> astro = Astronaut()
>>> astro.firstname = 'Mark'
>>> astro.lastname = 'Watney'
>>>
>>> astro.say_hello()
My name... Mark Watney

5.4.7. Slots and Init

>>> class Astronaut:
...     __slots__ = ('firstname', 'lastname')
...
...     def __init__(self, firstname, lastname):
...         self.firstname = firstname
...         self.lastname = lastname
>>>
>>>
>>> astro = Astronaut('Mark', 'Watney')
>>>
>>> print(astro.firstname)
Mark
>>> print(astro.lastname)
Watney
>>> class Astronaut:
...     __slots__ = ('firstname', 'lastname')
...
...     def __init__(self, firstname, lastname, role):
...         self.firstname = firstname
...         self.lastname = lastname
...         self.role = role
>>>
>>>
>>> astro = Astronaut('Mark', 'Watney', 'Botanist')
Traceback (most recent call last):
AttributeError: 'Astronaut' object has no attribute 'role'

5.4.8. Vars

  • Using __slots__ will prevent from creating __dict__

>>> class Astronaut:
...     __slots__ = ('firstname', 'lastname')
>>>
>>>
>>> astro = Astronaut()
>>> astro.firstname = 'Mark'
>>> astro.lastname = 'Watney'
>>>
>>> vars(astro)
Traceback (most recent call last):
TypeError: vars() argument must have __dict__ attribute
>>>
>>> print(astro.__dict__)
Traceback (most recent call last):
AttributeError: 'Astronaut' object has no attribute '__dict__'

5.4.9. Slots Internals

  • Slots are descriptors

>>> class Astronaut:
...     __slots__ = ('firstname', 'lastname')
>>>
>>>
>>> vars(Astronaut)  
mappingproxy({'__module__': '__main__',
              '__slots__': ('firstname', 'lastname'),
              'firstname': <member 'firstname' of 'Astronaut' objects>,
              'lastname': <member 'lastname' of 'Astronaut' objects>,
              '__doc__': None})
>>>
>>> Astronaut.firstname
<member 'firstname' of 'Astronaut' objects>
>>>
>>> type(Astronaut.firstname)
<class 'member_descriptor'>

5.4.10. Get Attributes and Values

  • To get values iterate over self.__slots__ and use getattr(self, x)

>>> class Astronaut:
...     __slots__ = ('firstname', 'lastname')
>>>
>>>
>>> astro = Astronaut()
>>> astro.firstname = 'Mark'
>>> astro.lastname = 'Watney'
>>>
>>> print(astro.__slots__)
('firstname', 'lastname')
>>>
>>> {x: getattr(astro, x) for x in astro.__slots__}
{'firstname': 'Mark', 'lastname': 'Watney'}

5.4.11. Slots and Dict

  • Using __slots__ will prevent from creating __dict__

  • Adding __dict__ to __slots__ will combine both worlds

>>> class Astronaut:
...     __slots__ = ('__dict__', 'firstname', 'lastname')
>>>
>>>
>>> astro = Astronaut()
>>> astro.firstname = 'Mark'   # will use __slots__
>>> astro.lastname = 'Watney'  # will use __slots__
>>> astro.role = 'Botanist'    # will use __dict__
>>> astro.mission = 'Ares3'    # will use __dict__
>>>
>>> print(astro.__slots__)
('__dict__', 'firstname', 'lastname')
>>>
>>> vars(astro)
{'role': 'Botanist', 'mission': 'Ares3'}
>>>
>>> {x:getattr(astro, x) for x in astro.__slots__ if x != '__dict__'} | vars(astro)  
{'firstname': 'Mark',
 'lastname': 'Watney',
 'role': 'Botanist',
 'mission': 'Ares3'}

5.4.12. Inheritance

  • Slots do not inherit, unless they are specified in subclass

  • Slots are added on inheritance

  • If class does not specify slots, the __dict__ will be added

>>> class Person:
...     __slots__ = ('firstname', 'lastname')
>>>
>>> class Astronaut(Person):
...     pass
>>>
>>>
>>> astro = Astronaut()
>>> astro.firstname = 'Mark'
>>> astro.lastname = 'Watney'
>>> astro.role = 'Botanist'
>>>
>>> print(astro.firstname)
Mark
>>> print(astro.lastname)
Watney
>>> print(astro.role)
Botanist
>>>
>>>
>>> vars(astro)
{'role': 'Botanist'}
>>>
>>> vars(Astronaut)  
mappingproxy({'__module__': '__main__',
              '__dict__': <attribute '__dict__' of 'Astronaut' objects>,
              '__weakref__': <attribute '__weakref__' of 'Astronaut' objects>,
              '__doc__': None})
>>> class Person:
...     __slots__ = ('firstname', 'lastname')
>>>
>>> class Astronaut(Person):
...     __slots__ = ()
>>>
>>>
>>> astro = Astronaut()
>>> astro.firstname = 'Mark'
>>> astro.lastname = 'Watney'
>>> astro.role = 'Botanist'
Traceback (most recent call last):
AttributeError: 'Astronaut' object has no attribute 'role'
>>>
>>>
>>> vars(astro)
Traceback (most recent call last):
TypeError: vars() argument must have __dict__ attribute
>>>
>>> vars(Astronaut)
mappingproxy({'__module__': '__main__', '__slots__': (), '__doc__': None})
>>> class Person:
...     __slots__ = ('firstname', 'lastname')
>>>
>>> class Astronaut(Person):
...     __slots__ = ('role',)
>>>
>>>
>>> astro = Astronaut()
>>> astro.firstname = 'Mark'
>>> astro.lastname = 'Watney'
>>> astro.role = 'Botanist'
>>> astro.agency = 'NASA'
Traceback (most recent call last):
AttributeError: 'Astronaut' object has no attribute 'agency'
>>>
>>>
>>> vars(astro)
Traceback (most recent call last):
TypeError: vars() argument must have __dict__ attribute
>>>
>>> vars(Person)  
mappingproxy({'__module__': '__main__',
              '__slots__': ('firstname', 'lastname'),
              'firstname': <member 'firstname' of 'Person' objects>,
              'lastname': <member 'lastname' of 'Person' objects>,
              '__doc__': None})
>>>
>>> vars(Astronaut)  
mappingproxy({'__module__': '__main__',
              '__slots__': ('role',),
              'role': <member 'role' of 'Astronaut' objects>,
              '__doc__': None})

5.4.13. Change Slots

>>> class Astronaut:
...     __slots__ = ('firstname', 'lastname')
>>>
>>>
>>> astro = Astronaut()
>>>
>>> astro.__slots__
('firstname', 'lastname')
>>>
>>> astro.__slots__ = ('myslot1', 'myslot2')
Traceback (most recent call last):
AttributeError: 'Astronaut' object attribute '__slots__' is read-only
>>> class Astronaut:
...     __slots__ = ('firstname', 'lastname')
>>>
>>>
>>> astro = Astronaut()
>>>
>>> Astronaut.__slots__ = ('myslot1', 'myslot2')
>>> Astronaut.__slots__
('myslot1', 'myslot2')
>>>
>>> vars(Astronaut)  
mappingproxy({'__module__': '__main__',
              '__slots__': ('myslot1', 'myslot2'),
              'firstname': <member 'firstname' of 'Astronaut' objects>,
              'lastname': <member 'lastname' of 'Astronaut' objects>,
              '__doc__': None})
>>> class Astronaut:
...     __slots__ = ('firstname', 'lastname')
>>>
>>>
>>> astro = Astronaut()
>>> astro.firstname = 'Mark'
>>> astro.lastname = 'Watney'
>>>
>>> Astronaut.__slots__ = ('myslot1', 'myslot2')
>>> Astronaut.__slots__
('myslot1', 'myslot2')
>>>
>>>
>>> Astronaut.firstname
<member 'firstname' of 'Astronaut' objects>
>>>
>>> Astronaut.lastname
<member 'lastname' of 'Astronaut' objects>
>>>
>>> Astronaut.myslot1
Traceback (most recent call last):
AttributeError: type object 'Astronaut' has no attribute 'myslot1'
>>>
>>> Astronaut.myslot2
Traceback (most recent call last):
AttributeError: type object 'Astronaut' has no attribute 'myslot2'
>>>
>>> astro.firstname
'Mark'
>>>
>>> astro.lastname
'Watney'
>>>
>>> astro.myslot1
Traceback (most recent call last):
AttributeError: 'Astronaut' object has no attribute 'myslot1'
>>>
>>> astro.myslot2
Traceback (most recent call last):
AttributeError: 'Astronaut' object has no attribute 'myslot2'

5.4.14. Slots in Dataclasses

  • Since Python 3.10

>>> from dataclasses import dataclass
>>>
>>>
>>> @dataclass(slots=True)
... class Iris:
...     sl: float
...     sw: float
...     pl: float
...     pw: float
...     species: str

5.4.15. Use Case - 0x01

>>> from dataclasses import dataclass
>>> from pprint import pprint
>>>
>>>
>>> DATA = [
...     ('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),
...     (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'),
...     (7.0, 3.2, 4.7, 1.4, 'versicolor'),
...     (7.6, 3.0, 6.6, 2.1, 'virginica'),
...     (4.9, 3.0, 1.4, 0.2, 'setosa'),
...     (4.9, 2.5, 4.5, 1.7, 'virginica'),
...     (7.1, 3.0, 5.9, 2.1, 'virginica'),
...     (4.6, 3.4, 1.4, 0.3, 'setosa'),
...     (5.4, 3.9, 1.7, 0.4, 'setosa'),
...     (5.7, 2.8, 4.5, 1.3, 'versicolor'),
...     (5.0, 3.6, 1.4, 0.3, 'setosa'),
...     (5.5, 2.3, 4.0, 1.3, 'versicolor'),
...     (6.5, 3.0, 5.8, 2.2, 'virginica'),
...     (6.5, 2.8, 4.6, 1.5, 'versicolor'),
...     (6.3, 3.3, 6.0, 2.5, 'virginica'),
...     (6.9, 3.1, 4.9, 1.5, 'versicolor'),
...     (4.6, 3.1, 1.5, 0.2, 'setosa'),
... ]
>>>
>>> @dataclass(slots=True)
... class Iris:
...     sl: float
...     sw: float
...     pl: float
...     pw: float
...     species: str
>>>
>>> data = [Iris(*row) for row in DATA[1:]]
>>>
>>>
>>> pprint(data)
[Iris(sl=5.8, sw=2.7, pl=5.1, pw=1.9, species='virginica'),
 Iris(sl=5.1, sw=3.5, pl=1.4, pw=0.2, species='setosa'),
 Iris(sl=5.7, sw=2.8, pl=4.1, pw=1.3, species='versicolor'),
 Iris(sl=6.3, sw=2.9, pl=5.6, pw=1.8, species='virginica'),
 Iris(sl=6.4, sw=3.2, pl=4.5, pw=1.5, species='versicolor'),
 Iris(sl=4.7, sw=3.2, pl=1.3, pw=0.2, species='setosa'),
 Iris(sl=7.0, sw=3.2, pl=4.7, pw=1.4, species='versicolor'),
 Iris(sl=7.6, sw=3.0, pl=6.6, pw=2.1, species='virginica'),
 Iris(sl=4.9, sw=3.0, pl=1.4, pw=0.2, species='setosa'),
 Iris(sl=4.9, sw=2.5, pl=4.5, pw=1.7, species='virginica'),
 Iris(sl=7.1, sw=3.0, pl=5.9, pw=2.1, species='virginica'),
 Iris(sl=4.6, sw=3.4, pl=1.4, pw=0.3, species='setosa'),
 Iris(sl=5.4, sw=3.9, pl=1.7, pw=0.4, species='setosa'),
 Iris(sl=5.7, sw=2.8, pl=4.5, pw=1.3, species='versicolor'),
 Iris(sl=5.0, sw=3.6, pl=1.4, pw=0.3, species='setosa'),
 Iris(sl=5.5, sw=2.3, pl=4.0, pw=1.3, species='versicolor'),
 Iris(sl=6.5, sw=3.0, pl=5.8, pw=2.2, species='virginica'),
 Iris(sl=6.5, sw=2.8, pl=4.6, pw=1.5, species='versicolor'),
 Iris(sl=6.3, sw=3.3, pl=6.0, pw=2.5, species='virginica'),
 Iris(sl=6.9, sw=3.1, pl=4.9, pw=1.5, species='versicolor'),
 Iris(sl=4.6, sw=3.1, pl=1.5, pw=0.2, species='setosa')]

5.4.16. Use Case - 0x02

>>> from sys import getsizeof
>>> from itertools import chain
>>> from collections import deque
>>> import logging
>>>
>>>
>>> logging.basicConfig(level='DEBUG')
>>> log = logging.getLogger('deepsizeof')
>>>
>>>
>>> def deepsizeof(o, handlers={}):
...     """
...     Returns the approximate memory footprint an object and all of its contents.
...
...     Automatically finds the contents of the following builtin containers and
...     their subclasses: tuple, list, deque, dict, set and frozenset
...     """
...     dict_handler = lambda d: chain.from_iterable(d.items())
...     all_handlers = {tuple: iter,
...                     list: iter,
...                     deque: iter,
...                     dict: dict_handler,
...                     set: iter,
...                     frozenset: iter}
...     all_handlers.update(handlers)     # user handlers take precedence
...     seen = set()                      # track which object id's have already been seen
...     default_size = getsizeof(0)       # estimate sizeof object without __sizeof__
...
...     def sizeof(o):
...         if id(o) in seen:       # do not double count the same object
...             return 0
...         seen.add(id(o))
...         s = getsizeof(o, default_size)
...
...         log.debug('Size: %s, Type: %s, Repr: %s', s, type(o), repr(o))
...
...         for typ, handler in all_handlers.items():
...             if isinstance(o, typ):
...                 s += sum(map(sizeof, handler(o)))
...                 break
...         else:
...             if not hasattr(o.__class__, '__slots__'):
...                 if hasattr(o, '__dict__'):
...                     # no __slots__ *usually* means a
...                     # __dict__, but some special builtin classes (such
...                     # as `type(None)`) have neither
...                     # else, `o` has no attributes at all, so sys.getsizeof()
...                     # actually returned the correct value
...                     s += sizeof(o.__dict__)
...             else:
...                 s += sum(
...                     sizeof(getattr(o, x))
...                            for x in o.__class__.__slots__
...                            if hasattr(o, x))
...         return s
...     return sizeof(o)
>>>
>>>
>>> 
... if __name__ == '__main__':
...     class Astronaut:
...        __slots__ = ('firstname', 'lastname')
...
...     class Cosmonaut:
...         pass
...
...     a = Astronaut()
...     a.firstname = 'Mark'
...     a.lastname = 'Watney'
...
...     c = Cosmonaut()
...     c.firstname = 'Mark'
...     c.lastname = 'Watney'
...
...     print('Astronaut', deepsizeof(a))
...     print('Cosmonaut', deepsizeof(c))
DEBUG:deepsizeof:Size: 48, Type: <class 'Astronaut'>, Repr: <Astronaut object at 0x10790b940>
DEBUG:deepsizeof:Size: 53, Type: <class 'str'>, Repr: 'Mark'
DEBUG:deepsizeof:Size: 55, Type: <class 'str'>, Repr: 'Watney'
DEBUG:deepsizeof:Size: 48, Type: <class 'Cosmonaut'>, Repr: <Cosmonaut object at 0x10790b9d0>
DEBUG:deepsizeof:Size: 104, Type: <class 'dict'>, Repr: {'firstname': 'Mark', 'lastname': 'Watney'}
DEBUG:deepsizeof:Size: 58, Type: <class 'str'>, Repr: 'firstname'
DEBUG:deepsizeof:Size: 53, Type: <class 'str'>, Repr: 'Mark'
DEBUG:deepsizeof:Size: 57, Type: <class 'str'>, Repr: 'lastname'
DEBUG:deepsizeof:Size: 55, Type: <class 'str'>, Repr: 'Watney'
Astronaut 156
Cosmonaut 375

5.4.17. Further Reading

5.4.18. References

1

Python core developers. Data model. Customizing attribute access. Notes on using __slots__. Python documentation. Year: 2022. Retrieved: 2022-03-16. URL: https://docs.python.org/3/reference/datamodel.html#slots

2

Python core developers. Data model. Objects, values and types. Python documentation. Year: 2022. Retrieved: 2022-04-01. URL: https://docs.python.org/3/reference/datamodel.html#objects-values-and-types

3

Python core developers. Weak references. Python documentation. Year: 2022. Retrieved: 2022-03-16. URL: https://docs.python.org/3/library/weakref.html

4

Dunes. What exactly is __weakref__ in Python? Year: 2016. Retrieved: 2022-03-16. URL https://stackoverflow.com/a/36789779

5

Mazdak. What exactly is __weakref__ in Python? Year: 2016. Retrieved: 2022-03-16. URL https://stackoverflow.com/a/36788031

5.4.19. Assignments

Code 5.14. Solution
"""
* Assignment: OOP Slots Define
* Complexity: easy
* Lines of code: 1 lines
* Time: 3 min

English:
    1. Define class `Iris` with attributes: `sepal_length, sepal_width,
       petal_length, petal_width, species`
    2. All attributes must be in `__slots__`
    3. Run doctests - all must succeed

Polish:
    1. Zdefiniuj klasę `Iris` z atrybutami: `sepal_length, sepal_width,
       petal_length, petal_width, species`
    2. Wszystkie atrybuty muszą być w `__slots__`
    3. Uruchom doctesty - wszystkie muszą się powieść

Tests:
    >>> import sys; sys.tracebacklimit = 0

    >>> iris = Iris()

    >>> assert not hasattr(iris, '__dict__')
    >>> assert not hasattr(iris, '__weakref__')
    >>> assert hasattr(iris, '__slots__')

    >>> assert 'sepal_length' in iris.__slots__
    >>> assert 'sepal_width' in iris.__slots__
    >>> assert 'petal_length' in iris.__slots__
    >>> assert 'petal_width' in iris.__slots__
    >>> assert 'species' in iris.__slots__
"""

class Iris:
    ...


Code 5.15. Solution
"""
* Assignment: OOP Slots Repr
* Complexity: easy
* Lines of code: 3 lines
* Time: 5 min

English:
    1. Define method `__repr__` which prints class name and all values
       positionally, ie. `Iris(5.8, 2.7, 5.1, 1.9, 'virginica')`
    2. Run doctests - all must succeed

Polish:
    1. Zdefiniuj metodę `__repr__` wypisującą nazwę klasy i wszystkie
       wartości atrybutów pozycyjnie, np. `Iris(5.8, 2.7, 5.1, 1.9,
       'virginica')`
    2. Uruchom doctesty - wszystkie muszą się powieść

Tests:
    >>> import sys; sys.tracebacklimit = 0

    >>> result = [Iris(*row) for row in DATA[1:]]
    >>> result  # doctest: +NORMALIZE_WHITESPACE
    [Iris(5.8, 2.7, 5.1, 1.9, 'virginica'),
     Iris(5.1, 3.5, 1.4, 0.2, 'setosa'),
     Iris(5.7, 2.8, 4.1, 1.3, 'versicolor'),
     Iris(6.3, 2.9, 5.6, 1.8, 'virginica'),
     Iris(6.4, 3.2, 4.5, 1.5, 'versicolor'),
     Iris(4.7, 3.2, 1.3, 0.2, 'setosa')]

    >>> iris = result[0]
    >>> iris
    Iris(5.8, 2.7, 5.1, 1.9, 'virginica')

    >>> iris.__slots__
    ('sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species')

    >>> [getattr(iris, x) for x in iris.__slots__]
    [5.8, 2.7, 5.1, 1.9, 'virginica']

    >>> {x: getattr(iris, x)
    ...  for x in iris.__slots__}  # doctest: +NORMALIZE_WHITESPACE
    {'sepal_length': 5.8,
     'sepal_width': 2.7,
     'petal_length': 5.1,
     'petal_width': 1.9,
     'species': 'virginica'}

    >>> iris.__dict__
    Traceback (most recent call last):
    AttributeError: 'Iris' object has no attribute '__dict__'

    >>> values = tuple(getattr(iris, x) for x in iris.__slots__)
    >>> print(f'Iris{values}')
    Iris(5.8, 2.7, 5.1, 1.9, 'virginica')

Hint:
    * In `__repr__()` use tuple comprehension to get `self.__slots__` values
"""

DATA = [
    ('Sepal length', 'Sepal width', 'Petal length', 'Petal width', 'Species'),
    (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')]


class Iris:
    __slots__ = ('sepal_length', 'sepal_width', 'petal_length',
                 'petal_width', 'species')

    def __init__(self, sepal_length, sepal_width, petal_length, petal_width, species):
        self.sepal_length = sepal_length
        self.sepal_width = sepal_width
        self.petal_length = petal_length
        self.petal_width = petal_width
        self.species = species