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。

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)) )

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

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.

在Python中如何确定对象的大小?

答案是:“用sys就行了。“Getsizeof”并不是一个完整的答案。

这个答案确实直接适用于内置对象，但它没有解释这些对象可能包含什么，特别是包含什么类型，例如自定义对象、元组、列表、字典和集。它们可以包含彼此的实例，也可以包含数字、字符串和其他对象。

一个更完整的答案

使用来自Anaconda发行版的64位Python 3.6，并使用sys。getsizeof，我已经确定了以下对象的最小大小，并注意set和dicts预先分配空间，因此空的对象直到设定的数量(可能因语言的实现而异)后才会再次增长:

Python 3:

Empty
Bytes  type        scaling notes
28     int         +4 bytes about every 30 powers of 2
37     bytes       +1 byte per additional byte
49     str         +1-4 per additional character (depending on max width)
48     tuple       +8 per additional item
64     list        +8 for each additional
224    set         5th increases to 736; 21nd, 2272; 85th, 8416; 341, 32992
240    dict        6th increases to 368; 22nd, 1184; 43rd, 2280; 86th, 4704; 171st, 9320
136    func def    does not include default args and other attrs
1056   class def   no slots 
56     class inst  has a __dict__ attr, same scaling as dict above
888    class def   with slots
16     __slots__   seems to store in mutable tuple-like structure
                   first slot grows to 48, and so on.

你如何理解这一点?假设你有一套包含10件物品的套装。如果每一项都是100字节，那么整个数据结构有多大?这个集合本身是736，因为它的大小是736字节。然后加上项的大小，总共是1736字节

函数和类定义的一些注意事项:

注意每个类定义都有一个用于类attrs的代理__dict__(48字节)结构。每个槽在类定义中都有一个描述符(类似于属性)。

插槽实例的第一个元素开始时为48字节，然后每个元素增加8字节。只有空的插槽对象才有16个字节，没有数据的实例没有什么意义。

此外，每个函数定义都有代码对象、文档字符串和其他可能的属性，甚至__dict__。

还要注意，我们使用sys.getsizeof()是因为我们关心边际空间的使用，其中包括来自文档的对象的垃圾收集开销:

Getsizeof()调用对象的__sizeof__方法并添加一个 对象管理的附加垃圾回收器开销 垃圾收集器。

还需要注意的是，调整列表的大小(例如重复地添加到列表中)会导致它们预先分配空间，类似于set和dicts。listobj.c源代码:

    /* This over-allocates proportional to the list size, making room
     * for additional growth.  The over-allocation is mild, but is
     * enough to give linear-time amortized behavior over a long
     * sequence of appends() in the presence of a poorly-performing
     * system realloc().
     * The growth pattern is:  0, 4, 8, 16, 25, 35, 46, 58, 72, 88, ...
     * Note: new_allocated won't overflow because the largest possible value
     *       is PY_SSIZE_T_MAX * (9 / 8) + 6 which always fits in a size_t.
     */
    new_allocated = (size_t)newsize + (newsize >> 3) + (newsize < 9 ? 3 : 6);

历史数据

Python 2.7分析，用孔雀鱼确认。Hpy和sys.getsizeof:

Bytes  type        empty + scaling notes
24     int         NA
28     long        NA
37     str         + 1 byte per additional character
52     unicode     + 4 bytes per additional character
56     tuple       + 8 bytes per additional item
72     list        + 32 for first, 8 for each additional
232    set         sixth item increases to 744; 22nd, 2280; 86th, 8424
280    dict        sixth item increases to 1048; 22nd, 3352; 86th, 12568 *
120    func def    does not include default args and other attrs
64     class inst  has a __dict__ attr, same scaling as dict above
16     __slots__   class with slots has no dict, seems to store in 
                    mutable tuple-like structure.
904    class def   has a proxy __dict__ structure for class attrs
104    old class   makes sense, less stuff, has real dict though.

注意字典(而不是集合)得到了more Python 3.6中的紧凑表示

我认为在64位机器上，每个额外的条目引用8个字节非常有意义。这8个字节指向所包含项在内存中的位置。如果我没记错的话，这4个字节是Python 2中unicode的固定宽度，但在Python 3中，str变成了宽度等于字符最大宽度的unicode。

更多关于老虎机的信息，请看这个答案。

更完整的功能

我们需要一个函数来搜索列表、元组、集、字典、obj中的元素。__dict__ ` s和obj。__slots__，以及其他我们可能还没有想到的东西。

我们想要依赖gc。get_referents来执行此搜索，因为它工作在C级(使它非常快)。缺点是get_referents可能返回冗余成员，因此需要确保不会重复计算。

类、模块和函数都是单例——它们只在内存中存在一次。我们对它们的大小不太感兴趣，因为我们对它们无能为力——它们是项目的一部分。如果它们被引用，我们就不计算了。

我们将使用类型黑名单，这样就不会将整个程序包括在大小计数中。

import sys
from types import ModuleType, FunctionType
from gc import get_referents

# Custom objects know their class.
# Function objects seem to know way too much, including modules.
# Exclude modules as well.
BLACKLIST = type, ModuleType, FunctionType


def getsize(obj):
    """sum size of object & members."""
    if isinstance(obj, BLACKLIST):
        raise TypeError('getsize() does not take argument of type: '+ str(type(obj)))
    seen_ids = set()
    size = 0
    objects = [obj]
    while objects:
        need_referents = []
        for obj in objects:
            if not isinstance(obj, BLACKLIST) and id(obj) not in seen_ids:
                seen_ids.add(id(obj))
                size += sys.getsizeof(obj)
                need_referents.append(obj)
        objects = get_referents(*need_referents)
    return size

与下面的白名单函数相比，大多数对象都知道如何为了垃圾收集的目的遍历自己(当我们想知道某些对象在内存中有多昂贵时，这大约是我们所寻找的。此功能由gc.get_referents使用。)然而，如果我们不小心，这项措施的范围将比我们预期的要广泛得多。

例如，函数非常了解创建它们的模块。

另一个不同之处在于，字典中作为键的字符串通常是互缩的，因此它们不会重复。检查id(key)还可以避免计算重复项，这将在下一节中执行。黑名单解决方案跳过了作为字符串的键的计数。

白名单类型，递归访问者

为了自己覆盖这些类型中的大多数，而不是依赖于gc模块，我编写了这个递归函数来尝试估计大多数Python对象的大小，包括大多数内置对象、collections模块中的类型和自定义类型(插槽或其他类型)。

这类函数对我们要计算内存使用的类型提供了更细粒度的控制，但有遗漏重要类型的危险:

import sys
from numbers import Number
from collections import deque
from collections.abc import Set, Mapping


ZERO_DEPTH_BASES = (str, bytes, Number, range, bytearray)


def getsize(obj_0):
    """Recursively iterate to sum size of object & members."""
    _seen_ids = set()
    def inner(obj):
        obj_id = id(obj)
        if obj_id in _seen_ids:
            return 0
        _seen_ids.add(obj_id)
        size = sys.getsizeof(obj)
        if isinstance(obj, ZERO_DEPTH_BASES):
            pass # bypass remaining control flow and return
        elif isinstance(obj, (tuple, list, Set, deque)):
            size += sum(inner(i) for i in obj)
        elif isinstance(obj, Mapping) or hasattr(obj, 'items'):
            size += sum(inner(k) + inner(v) for k, v in getattr(obj, 'items')())
        # Check for custom object instances - may subclass above too
        if hasattr(obj, '__dict__'):
            size += inner(vars(obj))
        if hasattr(obj, '__slots__'): # can have __slots__ with __dict__
            size += sum(inner(getattr(obj, s)) for s in obj.__slots__ if hasattr(obj, s))
        return size
    return inner(obj_0)

我很随意地测试了它(我应该进行单元测试):

>>> getsize(['a', tuple('bcd'), Foo()])
344
>>> getsize(Foo())
16
>>> getsize(tuple('bcd'))
194
>>> getsize(['a', tuple('bcd'), Foo(), {'foo': 'bar', 'baz': 'bar'}])
752
>>> getsize({'foo': 'bar', 'baz': 'bar'})
400
>>> getsize({})
280
>>> getsize({'foo':'bar'})
360
>>> getsize('foo')
40
>>> class Bar():
...     def baz():
...         pass
>>> getsize(Bar())
352
>>> getsize(Bar().__dict__)
280
>>> sys.getsizeof(Bar())
72
>>> getsize(Bar.__dict__)
872
>>> sys.getsizeof(Bar.__dict__)
280

这个实现分解为类定义和函数定义，因为我们不需要获取它们的所有属性，但是由于它们对于进程来说应该只在内存中存在一次，所以它们的大小实际上并不太重要。

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