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))

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。

用系统就行了。sys模块中定义的Getsizeof函数。

sys.getsizeof(object[, default]): Return the size of an object in bytes. The object can be any type of object. All built-in objects will return correct results, but this does not have to hold true for third-party extensions as it is implementation specific. Only the memory consumption directly attributed to the object is accounted for, not the memory consumption of objects it refers to. The default argument allows to define a value which will be returned if the object type does not provide means to retrieve the size and would cause a TypeError. getsizeof calls the object’s __sizeof__ method and adds an additional garbage collector overhead if the object is managed by the garbage collector. See recursive sizeof recipe for an example of using getsizeof() recursively to find the size of containers and all their contents.

python 3.0中的用法示例:

>>> import sys
>>> x = 2
>>> sys.getsizeof(x)
24
>>> sys.getsizeof(sys.getsizeof)
32
>>> sys.getsizeof('this')
38
>>> sys.getsizeof('this also')
48

如果你在python < 2.6并且没有sys. exe。Getsizeof可以使用这个扩展模块。但从来没用过。

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.