Python 中变量的内存大小

我正在编写 Python 代码来进行一些大量的计算，并对计算中使用的内存表示严重关切。

因此，我要计算每个变量的每个位。

例如，我有一个变量 X，它是一个很大的数字，我想要计算代表 X的位的数目。

下面的代码显然是无用的:

x=2**1000
len(x)

因此，我转而使用以下代码:

x=2**1000
len(repr(x))

变量 X(十进制)是:

5086071862673209484250490600018105614048117055360743750388370351051124936122493198378156958127594672917553146825187145285692314043598457757457469857469857480393456677774674842423098542107460542107460506237141876054182182153046474983581941267377777659165555439466654243868372766868693767676767676767

但是上面的代码返回 303

上面的长长序列的长度是302，所以我认为 303应该只与字符串长度有关。

所以，这是我最初的问题:

如何知道变量 X的内存大小？

还有一件事，在 C/C + + 语言中，如果我定义

int z=1;

这意味着为 Z分配了4个字节 = 32个位，这些位被排列为00。.001(310和1)。

这里，我的变量 X很大，我不知道它是否遵循相同的内存分配规则？

172618

小开

Regarding the internal structure of a Python long, check sys.int_info (or sys.long_info for Python 2.7).

>>> import sys
>>> sys.int_info
sys.int_info(bits_per_digit=30, sizeof_digit=4)

Python either stores 30 bits into 4 bytes (most 64-bit systems) or 15 bits into 2 bytes (most 32-bit systems). Comparing the actual memory usage with calculated values, I get

>>> import math, sys
>>> a=0
>>> sys.getsizeof(a)
24
>>> a=2**100
>>> sys.getsizeof(a)
40
>>> a=2**1000
>>> sys.getsizeof(a)
160
>>> 24+4*math.ceil(100/30)
40
>>> 24+4*math.ceil(1000/30)
160

There are 24 bytes of overhead for 0 since no bits are stored. The memory requirements for larger values matches the calculated values.

If your numbers are so large that you are concerned about the 6.25% unused bits, you should probably look at the gmpy2 library. The internal representation uses all available bits and computations are significantly faster for large values (say, greater than 100 digits).

最佳答案

Use sys.getsizeof to get the size of an object, in bytes.

>>> from sys import getsizeof
>>> a = 42
>>> getsizeof(a)
12
>>> a = 2**1000
>>> getsizeof(a)
146
>>>

Note that the size and layout of an object is purely implementation-specific. CPython, for example, may use totally different internal data structures than IronPython. So the size of an object may vary from implementation to implementation.