blocks|key|996238|text|看看医生们，重要的是要注意到：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|996239|只计算直接归因于对象的内存消耗，而不是它所引用的对象的内存消耗。|blockquote|996240|那么，从sys.getsizeof(int(1))的返回值大于sys.getsizeof(float(1))的返回值这一事实可以推断出什么呢？|style|CODE|996241|简单地说，表示int比表示float需要更多的内存。这令人惊讶吗？如果我们可以期望用int“做更多的事情”而不是用float“做更多的事情”，那么也许不会。我们可以通过查看其属性的数量来衡量“功能的数量”：|996242|>>>+len(dir(int))
70
>>>+len(dir(float))
57|code-block|syntax|javascript|996243|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.python.org/3/library/sys.html#sys.getsizeof^0|2|3|0|0|0|4|L|V|N|0|7|3|D|5|16|3|1L|5|0|0^^$0|@$1|2|3|4|5|6|7|Z|8|@]|9|@$A|10|B|11|1|12]]|C|$]]|$1|D|3|E|5|F|7|13|8|@]|9|@]|C|$]]|$1|G|3|H|5|6|7|14|8|@$A|15|B|16|I|J]|$A|17|B|18|I|J]]|9|@]|C|$]]|$1|K|3|L|5|6|7|19|8|@$A|1A|B|1B|I|J]|$A|1C|B|1D|I|J]|$A|1E|B|1F|I|J]|$A|1G|B|1H|I|J]]|9|@]|C|$]]|$1|M|3|N|5|O|7|1I|8|@]|9|@]|C|$P|Q]]|$1|R|3|-4|5|6|7|1J|8|@]|9|@]|C|$]]]|S|$T|$5|U|V|W|C|$X|Y]]]]

Looking at <a href="https://docs.python.org/3/library/sys.html#sys.getsizeof" rel="nofollow noreferrer">the docs</a>, it's important to observe that:

<blockquote>
 Only the memory consumption directly attributed to the object is accounted for, not the memory consumption of objects it refers to.
</blockquote>

So what can you infer from the fact that the returned value of <code>sys.getsizeof(int(1))</code> is greater than that of the <code>sys.getsizeof(float(1))</code>? 

Simply that it takes more memory to represent an <code>int</code> than it does to represent a <code>float</code>. Is this surprising? Well, possibly not, if we can expect to "do more things" with an <code>int</code> than we can do with a <code>float</code>. We can gauge the "amount of functionality" to the first degree by looking at the number of their attributes:

<pre class="lang-py prettyprint-override"><code>&gt;&gt;&gt; len(dir(int))
70
&gt;&gt;&gt; len(dir(float))
57
</code></pre>

blocks|key|1629808|text|简而言之，这一切归结为Python如何表示任意长整数。float()类型表示(有限)为C+double。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1629809|在CPython实现中，每个对象(来源)都以引用计数和指向该对象的类型对象的指针开始。那是16个字节|1629810|浮点对象将其数据存储为C+double+(来源)，即8个字节。所以16+%2B8=浮点对象的24字节。|BOLD|1629811|对于整数，情况更加复杂。整数对象被表示为可变大小的对象(来源)，它为16个字节添加了另外8个字节。数字表示为数组。根据平台的不同，Python使用具有30位位数的32位无符号整数数组或具有15位位数的16位无符号整数数组。所以对于小整数，数组中只有一个32位整数，所以再添加4个字节=+16+%2B8%2B4=+28字节。|1629812|如果要表示较大的整数，则大小将增加：|1629813|sys.getsizeof(int(2**32))++#+prints+32+(24+%2B+2*4+bytes)
sys.getsizeof(int(2**64))++#+prints+36+(24+%2B+3*4+bytes)|code-block|syntax|javascript|1629814|编辑：|1629815|使用sys.getsizeof(int)，您将得到类的大小，而不是类实例的大小。float，bool，.|1629816|print(type(int))++#+prints+<class+'type'>|1629817|如果你查看一下来源，就会发现引擎盖下面有很多东西。在我的Python3.6.9(Linux/64位)版本中，它会打印400个字节。|1629818|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/python/cpython/blob/f7d72e48fb235684e17668a1e5107e6b0dab7b80/Include/object.h#L104|1|https://github.com/python/cpython/blob/e42b705188271da108de42b55d9344642170aa2b/Include/floatobject.h#L15|2|https://github.com/python/cpython/blob/e42b705188271da108de42b55d9344642170aa2b/Include/longintrepr.h#L85|3|https://github.com/python/cpython/blob/bf17d41826a8bb4bc1e34ba6345da98aac779e41/Include/cpython/object.h#L177^0|R|7|19|6|0|H|2|0|0|0|2|18|4|D|6|L|2|1|0|2|2|48|4|S|2|2|0|0|0|0|2|I|14|5|1A|4|0|0|7|2|3|0^^$0|@$1|2|3|4|5|6|7|1F|8|@$9|1G|A|1H|B|C]|$9|1I|A|1J|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|1K|8|@]|D|@$9|1L|A|1M|1|1N]]|E|$]]|$1|H|3|I|5|6|7|1O|8|@$9|1P|A|1Q|B|J]|$9|1R|A|1S|B|J]|$9|1T|A|1U|B|C]]|D|@$9|1V|A|1W|1|1X]]|E|$]]|$1|K|3|L|5|6|7|1Y|8|@$9|1Z|A|20|B|J]|$9|21|A|22|B|J]]|D|@$9|23|A|24|1|25]]|E|$]]|$1|M|3|N|5|6|7|26|8|@]|D|@]|E|$]]|$1|O|3|P|5|Q|7|27|8|@]|D|@]|E|$R|S]]|$1|T|3|U|5|6|7|28|8|@]|D|@]|E|$]]|$1|V|3|W|5|6|7|29|8|@$9|2A|A|2B|B|C]|$9|2C|A|2D|B|C]|$9|2E|A|2F|B|C]]|D|@]|E|$]]|$1|X|3|Y|5|Q|7|2G|8|@]|D|@]|E|$R|S]]|$1|Z|3|10|5|6|7|2H|8|@]|D|@$9|2I|A|2J|1|2K]]|E|$]]|$1|11|3|-4|5|6|7|2L|8|@]|D|@]|E|$]]]|12|$13|$5|14|15|16|E|$17|18]]|19|$5|14|15|16|E|$17|1A]]|1B|$5|14|15|16|E|$17|1C]]|1D|$5|14|15|16|E|$17|1E]]]]

In short, it all boils down to how Python represents arbitrary long integers. <code>float()</code> types are represented (limited) just as C <code>double</code>.
In CPython implementation, every object (<a href="https://github.com/python/cpython/blob/f7d72e48fb235684e17668a1e5107e6b0dab7b80/Include/object.h#L104" rel="nofollow noreferrer">source</a>) begins with a reference count and a pointer to the type object for that object. That's 16 bytes.
Float object stores its data as C <code>double</code> (<a href="https://github.com/python/cpython/blob/e42b705188271da108de42b55d9344642170aa2b/Include/floatobject.h#L15" rel="nofollow noreferrer">source</a>), that's 8 bytes. So 16 + 8 = 24 bytes for float objects.
With integers, situation is more complicated. The integer objects are represented as variable sized object (<a href="https://github.com/python/cpython/blob/e42b705188271da108de42b55d9344642170aa2b/Include/longintrepr.h#L85" rel="nofollow noreferrer">source</a>), which for 16 bytes adds another 8 bytes.
Digits are represented as array. Depending on the platform, Python uses either 32-bit unsigned integer arrays with 30-bit digits or 16-bit unsigned integer arrays with 15-bit digits. So for small integers there's only one 32bit integer in the array, so add another 4 bytes = 16 + 8 + 4 = 28 bytes.
If you want to represent larger integer number, the size will grow:
<pre><code>sys.getsizeof(int(2**32)) # prints 32 (24 + 2*4 bytes)
sys.getsizeof(int(2**64)) # prints 36 (24 + 3*4 bytes)
</code></pre>
<hr />
EDIT:
With <code>sys.getsizeof(int)</code> you're getting the size of the class, not of an instance of the class. That's same for <code>float</code>, <code>bool</code>, ...
<pre><code>print(type(int)) # prints &lt;class 'type'&gt;
</code></pre>
If you look into the <a href="https://github.com/python/cpython/blob/bf17d41826a8bb4bc1e34ba6345da98aac779e41/Include/cpython/object.h#L177" rel="nofollow noreferrer">source</a>, there's lot of stuff under the hood. In my version of Python 3.6.9 (Linux/64bit) this prints 400 bytes.

This is an example from Python 3.8.0 interpreter (however, it is similar in 3.7.5)

<pre class="lang-py prettyprint-override"><code>&gt;&gt;&gt; import sys
&gt;&gt;&gt; sys.getsizeof(int)
416
&gt;&gt;&gt; sys.getsizeof(float)
416
&gt;&gt;&gt; sys.getsizeof(list)
416
&gt;&gt;&gt; sys.getsizeof(tuple)
416
&gt;&gt;&gt; sys.getsizeof(dict)
416
&gt;&gt;&gt; sys.getsizeof(bool)
416
</code></pre>

<code>getsizeof()</code> returns how much bytes Python object consumes together with the garbage collector overhead (see <a href="https://docs.python.org/3/library/sys.html#sys.getsizeof" rel="noreferrer">here</a>). What is the reason that basic python classes consume the same amount of memory?

If we take a look at instances of these classes

<pre class="lang-py prettyprint-override"><code>&gt;&gt;&gt; import sys
&gt;&gt;&gt; sys.getsizeof(int())
24
&gt;&gt;&gt; sys.getsizeof(float())
24
</code></pre>

The default argument is <code>0</code> and these two instances have the same amount of memory usage for this argument. However, if I try to add an argument

<pre class="lang-py prettyprint-override"><code>&gt;&gt;&gt; sys.getsizeof(int(1))
28
&gt;&gt;&gt; sys.getsizeof(float(1))
24
</code></pre>

and this is where it gets strange. Why does the instance memory usage increase for int but not for float type?

Memory usage of Python base types (particulary int and float)

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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这是Python3.8.0解释器中的一个示例(但是，它在3.7.5中类似)>>> import sys>>> sys.getsizeof(int)416>>> sys.getsizeof(float)416>>> sys.getsizeof(list)416>>> sys.getsizeof(tuple)416>>> ...

问Python基类型(特别是int和浮点数)的内存使用情况
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