如果不想包含链接(嵌套)对象的大小，请使用sys.getsizeof()。

然而，如果你想计算嵌套在列表、字典、集、元组中的子对象——通常这就是你要找的——使用如下所示的递归深层sizeof()函数:

import sys
def sizeof(obj):
    size = sys.getsizeof(obj)
    if isinstance(obj, dict): return size + sum(map(sizeof, obj.keys())) + sum(map(sizeof, obj.values()))
    if isinstance(obj, (list, tuple, set, frozenset)): return size + sum(map(sizeof, obj))
    return size

你也可以在漂亮的工具箱中找到这个函数，以及许多其他有用的一行程序:

https://github.com/mwojnars/nifty/blob/master/util.py

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。

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。

您可以使用下面提到的getSizeof()来确定对象的大小

import sys
str1 = "one"
int_element=5
print("Memory size of '"+str1+"' = "+str(sys.getsizeof(str1))+ " bytes")
print("Memory size of '"+ str(int_element)+"' = "+str(sys.getsizeof(int_element))+ " bytes")

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.