Python's classes are pretty flexible by default - you usually initialize them with some attributes, but you can attach new attributes to them after creation:

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

p = DefaultPoint(2, 3)
p.other = "Arbitrary values"

Allowing for these arbitary attributes means these classes use more memory than what's needed to just store the initial attributes - they use an internal dictionary that can accept new values. This extra memory use usually doesn't matter too much, but if you're creating lots of instances, it can add up.

If you just want to store known attributes, and use less memory to do it, you can declare the slots that the class uses:

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

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

Now the class only allows for storing x and y, and trying to add extra attributes will raise an error.

p = Point(2, 3)
p.other = "Not allowed!"
# AttributeError: 'Point' object has no attribute 'other'

If you use dataclasses, there's a shortcut to setting up the slots:

@dataclass(slots=True)
class Point:
    x: int
    y: int

Comparing the memory use for a large number of instances, borrowing the get_size() function from wissam on StackOverflow, it looks like in this simple case, the instances with slots use 1/3rd of the memory:

default_points = [DefaultPoint(2, 3) for i in range(10_000)]
slots = [Point(2, 3) for i in range(10_000)]
print(f"{get_size(default_points):,}")
# 1,525,332
print(f"{get_size(slots):,}")
# 565,176