Python numpy数据指针地址变化无需修改

EDIT

经过一番摆弄后，到目前为止我已经隔离了以下状态：

1. A 1D array gives two直接输入变量时的地址不同，并且仅one使用时print()
2. A 2D array (or matrix) gives three直接输入变量时的不同地址，以及two使用时print()
3. A 3D array gives two直接输入变量时的地址不同，并且仅one使用时print()（显然与1D array)

Like so:

>>> a = numpy.array([1,2,3], dtype="int32")

>>> a.data
<memory at 0x7f02e85e4048>
>>> a.data
<memory at 0x7f02e85e4110>
>>> a.data
<memory at 0x7f02e85e4048>
>>> a.data
<memory at 0x7f02e85e4110>
>>> a.data
<memory at 0x7f02e85e4048>

>>> print(a.data)
<memory at 0x7f02e85e4110>
>>> print(a.data)
<memory at 0x7f02e85e4110>
>>> print(a.data)
<memory at 0x7f02e85e4110>
>>> print(a.data)
<memory at 0x7f02e85e4110>
>>> print(a.data)
<memory at 0x7f02e85e4110>


>>> d = numpy.array([[1,2,3]], dtype="int32")

>>> d.data
<memory at 0x7f02e863ae48>
>>> d.data
<memory at 0x7f02e863a9e8>
>>> d.data
<memory at 0x7f02e863aac8>
>>> d.data
<memory at 0x7f02e863ae48>
>>> d.data
<memory at 0x7f02e863a9e8>
>>> d.data
<memory at 0x7f02e863aac8>

>>> print(d.data)
<memory at 0x7f02e863ae48>
>>> print(d.data)
<memory at 0x7f02e863a9e8>
>>> print(d.data)
<memory at 0x7f02e863ae48>
>>> print(d.data)
<memory at 0x7f02e863a9e8>
>>> print(d.data)
<memory at 0x7f02e863ae48>


>>> b = numpy.matrix([[1,2,3],[4,5,6]], dtype="int32")

>>> b.data
<memory at 0x7f02e863a9e8>
>>> b.data
<memory at 0x7f02e863ae48>
>>> b.data
<memory at 0x7f02e863aac8>
>>> b.data
<memory at 0x7f02e863a9e8>
>>> b.data
<memory at 0x7f02e863ae48>

>>> print(b.data)
<memory at 0x7f02e863aac8>
>>> print(b.data)
<memory at 0x7f02e863a9e8>
>>> print(b.data)
<memory at 0x7f02e863aac8>
>>> print(b.data)
<memory at 0x7f02e863a9e8>
>>> print(b.data)
<memory at 0x7f02e863aac8>


>>> c = numpy.matrix([[1,2,3],[4,5,6],[7,8,9]], dtype="int32")

>>> c.data
<memory at 0x7f02e863aac8>
>>> c.data
<memory at 0x7f02e863a9e8>
>>> c.data
<memory at 0x7f02e863ae48>
>>> c.data
<memory at 0x7f02e863aac8>
>>> c.data
<memory at 0x7f02e863ae48>
>>> c.data
<memory at 0x7f02e863a9e8>
>>> c.data
<memory at 0x7f02e863aac8>

>>> print(c.data)
<memory at 0x7f02e863ae48>
>>> print(c.data)
<memory at 0x7f02e863a9e8>
>>> print(c.data)
<memory at 0x7f02e863ae48>
>>> print(c.data)
<memory at 0x7f02e863a9e8>
>>> print(c.data)
<memory at 0x7f02e863ae48>


>>> e = numpy.array([[[0,1],[2,3]],[[4,5],[6,7]]], dtype="int32")

>>> e.data
<memory at 0x7f8ca0fe1048>
>>> e.data
<memory at 0x7f8ca0fe1140>
>>> e.data
<memory at 0x7f8ca0fe1048>
>>> e.data
<memory at 0x7f8ca0fe1140>
>>> e.data
<memory at 0x7f8ca0fe1048>


>>> print(e.data)
<memory at 0x7f8ca0fe1048>
>>> print(e.data)
<memory at 0x7f8ca0fe1048>
>>> print(e.data)
<memory at 0x7f8ca0fe1048>

原帖

我的印象是，只需在 python 控制台中输入一个变量，并回显一个字符串，简单地描述它的值（和类型）。它的格式与 print() 不同，但我假设它们返回的值是相同的。

当我尝试输出 numpy 对象的数据指针对象的地址时，只需输入变量就会每隔一段时间给出不同的值，而 print() 给出相同的值。

这表明这两个操作的区别不仅在于输出的格式如何，还在于它们从何处获取信息。但这些额外的差异到底包括什么？

>>> a = numpy.array([0,1,2])

>>> a
array([0, 1, 2])
>>> print(a)
[0 1 2]

>>> print(a.data)
<memory at 0x7ff25120c110>
>>> print(a.data)
<memory at 0x7ff25120c110>
>>> print(a.data)
<memory at 0x7ff25120c110>

>>> a.data
<memory at 0x7ff25120c110>
>>> a.data
<memory at 0x7ff253099818>
>>> a.data
<memory at 0x7ff25120c110>
>>> a.data
<memory at 0x7ff253099818>
>>> a.data
<memory at 0x7ff25120c110>

The memoryview由返回a.data似乎在两个（或更多）视图之间交替。如果您存储给定的实例a.data，你会得到一致的输出：

>>> a.data
<memory at 0x7fb88ea1f828>
>>> a.data
<memory at 0x7fb88e98c4a8>
>>> t = a.data
>>> a.data
<memory at 0x7fb88e98ce48>
>>> a.data
<memory at 0x7fb88e98c3c8>
>>> a.data
<memory at 0x7fb88e98c4a8>
>>> a.data
<memory at 0x7fb88e98ce48>
>>> a.data
<memory at 0x7fb88e98c3c8>
>>> a.data
<memory at 0x7fb88e98c4a8>
>>> t
<memory at 0x7fb88ea1f828>
>>> t
<memory at 0x7fb88ea1f828>
>>> t
<memory at 0x7fb88ea1f828>

注意，上例中有 3 个地址轮换；我很确定这都是实现细节。我猜想涉及到一些缓存，这意味着每次访问时实际上并没有生成新视图a.data.

您还可以确保您正在查看单独的视图对象：

>>> id(a.data)
140430643088968
>>> id(a.data)
140430643086280
>>> id(a.data)
140430643088968
>>> id(a.data)
140430643086280

所以大部分的混乱可能来自这样一个事实：a.data表明我们正在谈论的是一个固定的对象，但事实并非如此。