Pympler包的asizeof模块可以做到这一点。

使用方法如下:

from pympler import asizeof
asizeof.asizeof(my_object)

不像系统。Getsizeof，它适用于你自己创建的对象。它甚至可以与numpy一起工作。

>>> asizeof.asizeof(tuple('bcd'))
200
>>> asizeof.asizeof({'foo': 'bar', 'baz': 'bar'})
400
>>> asizeof.asizeof({})
280
>>> asizeof.asizeof({'foo':'bar'})
360
>>> asizeof.asizeof('foo')
40
>>> asizeof.asizeof(Bar())
352
>>> asizeof.asizeof(Bar().__dict__)
280
>>> A = rand(10)
>>> B = rand(10000)
>>> asizeof.asizeof(A)
176
>>> asizeof.asizeof(B)
80096

正如前面提到的,

可以通过设置option code=True来包含类、函数、方法、模块等对象的(字节)代码大小。

如果你需要实时数据的其他视图，请选择Pympler

模块muppy用于在线监控Python应用程序 和模块类跟踪器提供的生命周期的离线分析 选择Python对象。

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

下面是我根据之前对所有变量的列表大小的回答编写的一个快速脚本

for i in dir():
    print (i, sys.getsizeof(eval(i)) )

如果性能不是问题，最简单的解决方案是pickle和测量:

import pickle

data = ...
len(pickle.dumps(data))

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.