Memory footprint comparison of python classes

mem.py

import sys

from abc import ABC, abstractmethod

class DictBase(ABC):
    @abstractmethod
    def do(self):
        pass

class SlotBase(ABC):
    __slots__=()
    @abstractmethod
    def do(self):
        pass

def make_dict_class(n: int, bases:list[type]) -> type:
    def __init__(self):
        for i in range(n):
            setattr(self, f"m{i}", i)

    def do(self):
        pass

    return type(f"DictClass_{n}", tuple(bases), {"__init__": __init__, "do": do})


def make_slots_class(n: int, bases:list[type]) -> type:
    slots = tuple(f"m{i}" for i in range(n))

    def __init__(self):
        for i in range(n):
            setattr(self, f"m{i}", i)

    def do(self):
        pass

    return type(f"SlotsClass_{n}", tuple(bases), {"__slots__": slots, "__init__": __init__, "do": do})

def getsize(obj):
    return sys.getsizeof(obj) + ( sys.getsizeof(obj.__dict__) if hasattr(obj, "__dict__") else 0 ) + ( sys.getsizeof(obj.__weakref__) if hasattr(obj, "__weakref__") else 0 )

data = []
for n in range(50):
    ddict_obj = make_dict_class(n, [DictBase])()
    dslots_obj = make_slots_class(n, [DictBase])()
    sdict_obj = make_dict_class(n, [SlotBase])()
    sslots_obj = make_slots_class(n, [SlotBase])()
    dict_obj = make_dict_class(n, [])()
    slots_obj = make_slots_class(n, [])()

    ddict_size = getsize(ddict_obj)
    dslots_size = getsize(dslots_obj)
    sdict_size = getsize(sdict_obj)
    sslots_size = getsize(sslots_obj)
    dict_size = getsize(dict_obj)
    slots_size = getsize(slots_obj)

    data.append([n, ddict_size , dslots_size , sdict_size , sslots_size, slots_size, dict_size])

import pandas
df = pandas.DataFrame(data, columns=["n", "dB d", "dB s", "sB d", "sB s", "s", "d"])

print(df.to_markdown(index=False))