slots

slots

Restrict instance attributes to a fixed set — saves memory by eliminating per-instance __dict__.

Files

File	Description
example.py	Slotted vs regular class comparison

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Descriptive Example

Scenario

Create millions of point objects — __slots__ reduces memory per instance.

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

class PointWithSlots:
    __slots__ = ("x", "y")

    def __init__(self, x, y):
        self.x = x
        self.y = y

p = PointWithSlots(1, 2)
print(hasattr(p, "__dict__"))   # False — no dict overhead

p.label = "origin"              # AttributeError — can't add new attrs

Subclassing slotted classes

class Point3D(PointWithSlots):
    __slots__ = ("z",)          # must declare new slots

    def __init__(self, x, y, z):
        super().__init__(x, y)
        self.z = z

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Interview Q&A

Q1: What does __slots__ do?
A: Declares fixed instance attributes. Python stores them in a compact array instead of a __dict__, reducing memory and slightly speeding attribute access.

Q2: When should you use __slots__?
A: Many homogeneous instances (ORM rows, game entities, data points) where memory is a concern. Not for general-purpose classes.

Q3: Can slotted classes use @property?
A: Yes. Properties work normally. You can also put property names in __slots__.

Q4: Trade-offs of __slots__?
A: Pros: less memory, faster access, prevents typos. Cons: no dynamic attributes, tricky multiple inheritance, weakref needs explicit slot.

Q5: __slots__ vs @dataclass?
A: Different purposes. Dataclass reduces boilerplate. Slots reduces memory. Combine with @dataclass(slots=True) in Python 3.10+.

Q6: Does __slots__ affect __dict__ on the class itself?
A: No — the class object still has __dict__. Only instances lose their per-instance __dict__.

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Run

python3 example.py