Question

Python 2.7.1 (r271:86832, Nov 27 2010, 18:30:46) [MSC v.1500 32 bit (Intel)] on win32
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# RAM usage: 2100
>>> class Test:
...     def __init__(self, i):
...             self.one = i
...             self.hundred = 100*i
...
# RAM usage: 2108
>>> list1 = [ Test(i) for i in xrange(10000) ]
# RAM usage: 4364
>>> del(list1)
# RAM usage: 2780
>>> list2 = [ {"one": i, "hundred": 100*i} for i in xrange(10000) ]
# RAM usage: 3960
>>> del(list2)
# RAM usage: 2908

Why does a list of objects take twice as much memory as a list of equivalent dictionaries? I thought an object would be much more efficient since there is no need to store copies of attribute names for each object.

Answer 1

If you define a class in Python (as opposed to writing it as C extension) then by default it will use a dictionary to store all of its attributes. This is why it’s impossible for it to be smaller than a dictionary, and why you can assign arbitrary attributes to most Python objects.

If you know know in advance which attributes your object will require, you can specify them with the __slots__ attribute^[docs] on your class. This allows Python to be more efficient and not require an entire dictionary for each object. In your case, you could do this by adding

__slots__ = ["one", "hundred"]

on the line below class Test:. However, I’d be a little surprised if this were enough to make the objects smaller than the dictionaries; Python’s dictionaries are highly optimized for use with a small number of values. (edit: I am a little surprised, apparently it does make them smaller than dictionaries.)