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Do lists of classes take up more space in memory? - Printable Version

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Do lists of classes take up more space in memory? - DreamingInsanity - Apr-10-2020

I'm going through refactoring and cleaning up my code.

At the moment, I have data written to a file to store it, then later on I take the file and create two lists out of the data.
One list is multiple lists of 3 rgb values:
rgb = [[55,35,133],[54,75,34],[,122,186,55],[42,134,5],[22,4,145]...]
and one is a list of absolute paths to the images.
Each are 30,000 items longs.

As I'm refactoring, to remove the use of any files, and only have one list, I thought about creating a simple class like this:
class CustomImage(): #my custom image class
    def __init__(self, name, path, size, rgb, ext="png"):
        self.name = name
        self.path = path
        self.size = size
        self.ext = ext
        self.avg = rgb
    @property
    def rgb(self):
        return self.avg
    @property
    def size(self):
        return self.size
    @property
    def path(self):
        return self.path
    @property
    def name(self):
        return self.name
    @property
    def ext(self):
        return self.ext
Once I have downloaded an image, I simply create a "CustomImage" out of that image, which I can easily use later in my code. I would store all the these "CustomImages" in a list (also of 30,000 items)

What would be most likely to take up more memory? I would test it but I'm not sure about how I can measure list sizes in memory.
Found out how to test it:
The two lists take up a total of 507,248 bytes in memory.

The class, which has become:
class CustomImage(): #my custom image class
    def __init__(self, name, path, size, rgb, ext="png"):
        self.name = name
        self.path = path
        self.size = size
        self.ext = ext
        self.avg = rgb
takes up only 253,624 bytes in an array of 30,000

RE: Do lists of classes take up more space in memory? - deanhystad - Apr-10-2020

Since each image always has the same attributes, you could save even more space using slots. Classes that use slots instead of __dict___ provide faster access and have a smaller memory footprint.
class CustomImage(): #my custom image class
    __slots__ = 'name', 'path', 'size', 'ext', 'avg'
    def __init__(self, name, path, size, rgb, ext="png"):
        self.name = name
        self.path = path
        self.size = size
        self.ext = ext
        self.avg = rgb
With slots you cannot add an attribute to the class. You can access or set attribute values, but I could not do custimg.rating = 4 because there is no slot for "rating".
If CustomImage is immutable you could also use a named tuple (import collections).