使用以下函数获取python对象的实际大小:

import sys
import gc

def actualsize(input_obj):
    memory_size = 0
    ids = set()
    objects = [input_obj]
    while objects:
        new = []
        for obj in objects:
            if id(obj) not in ids:
                ids.add(id(obj))
                memory_size += sys.getsizeof(obj)
                new.append(obj)
        objects = gc.get_referents(*new)
    return memory_size

actualsize([1, 2, [3, 4, 5, 1]])

参考:https://towardsdatascience.com/the-strange-size-of-python-objects-in-memory-ce87bdfbb97f

对于numpy数组，getsizeof不起作用-对我来说，它总是出于某种原因返回40:

from pylab import *
from sys import getsizeof
A = rand(10)
B = rand(10000)

然后(在ipython中):

In [64]: getsizeof(A)
Out[64]: 40

In [65]: getsizeof(B)
Out[65]: 40

不过令人高兴的是,:

In [66]: A.nbytes
Out[66]: 80

In [67]: B.nbytes
Out[67]: 80000

这可能比看起来要复杂得多，这取决于你想要如何计数。例如，如果您有一个int类型的列表，您是否需要包含对int类型引用的列表的大小?(即-列表，而不是包含在其中的内容)，或者你想包括实际指向的数据，在这种情况下，你需要处理重复引用，以及如何防止重复计数当两个对象包含对同一对象的引用时。

您可能想要查看python内存分析器之一，例如pysizer，以查看它们是否满足您的需求。

你可以序列化对象，以获得与对象大小密切相关的度量值:

import pickle

## let o be the object whose size you want to measure
size_estimate = len(pickle.dumps(o))

如果您想测量无法pickle的对象(例如，由于lambda表达式)，dill或cloudpickle可以是一种解决方案。

这可能不是最相关的答案，但我只对对象存储和检索感兴趣。因此将对象转储为pickle并检查pickle的大小就足够了

If you don't need the exact size of the object but roughly to know how big it is, one quick (and dirty) way is to let the program run, sleep for an extended period of time, and check the memory usage (ex: Mac's activity monitor) by this particular python process. This would be effective when you are trying to find the size of one single large object in a python process. For example, I recently wanted to check the memory usage of a new data structure and compare it with that of Python's set data structure. First I wrote the elements (words from a large public domain book) to a set, then checked the size of the process, and then did the same thing with the other data structure. I found out the Python process with a set is taking twice as much memory as the new data structure. Again, you wouldn't be able to exactly say the memory used by the process is equal to the size of the object. As the size of the object gets large, this becomes close as the memory consumed by the rest of the process becomes negligible compared to the size of the object you are trying to monitor.