不复制numpy数组如何改变数据属性？ [英] How come not-copying a numpy array changes the data attribute?

问题描述

如下我的MWE所示，在现有数组 a np.array（a，copy = False） >返回的行为与预期的完全一样，除了 之外， .data 属性似乎有所不同。

As my MWE below shows, calling np.array(a, copy=False) on an existing array a returns something that behaves exactly as expected, except that the .data attributes seem to differ. How can this be?

>>> a                               # My original array
array([2])
>>> b = np.array(a, copy=False)     # Not-a-copy of the original array
>>> b is a                          # The Python objects seem to be identical
True
>>> b.data is a.data                # But their .data attributes aren't??
False
>>> a.data
<memory at 0x7f82ebd757c8>
>>> b.data
<memory at 0x7f82ebd75888>
>>> b 
array([2])
>>> a
array([2])
>>> a[:] = 3                        # Changing a indeed also changes b
>>> a
array([3])
>>> b
array([3])
>>> a.data
<memory at 0x7f82ebd757c8>
>>> b.data
<memory at 0x7f82ebd75888>

编辑

在玩耍时，我什至发现 .data 属性在查看时会发生变化！

While playing around, I even found that the .data attribute changes while looking at it!

>>> a.data is a.data        # a.data isn't equal to itself?!
False
>>> a.data
<memory at 0x7f82ebd75948>
>>> a.data
<memory at 0x7f82ebd75888>  # A different value than a minute ago
>>> a.data
<memory at 0x7f82ebd75948>
>>> a.data
<memory at 0x7f82ebd75888>
>>> a.data
<memory at 0x7f82ebd75948>
>>> a.data
<memory at 0x7f82ebd75888>
>>> a.data
<memory at 0x7f82ebd75948>
>>> a.data
<memory at 0x7f82ebd75888>
>>> a.data
<memory at 0x7f82ebd75948>

推荐答案

In [33]: a = np.array([2])                                                                             
In [34]: b = np.array(a, copy=False)  

一种易于理解的共享数据缓冲区检查方法是 __ array_interface __ 字典。

A nice human-readable way of checking for shared data buffer is the __array_interface__ dictionary.

In [36]: a.__array_interface__                                                                         
Out[36]: 
{'data': (69508768, False),
 'strides': None,
 'descr': [('', '<i8')],
 'typestr': '<i8',
 'shape': (1,),
 'version': 3}
In [37]: b.__array_interface__                                                                         
Out[37]: 
{'data': (69508768, False),
 'strides': None,
 'descr': [('', '<i8')],
 'typestr': '<i8',
 'shape': (1,),
 'version': 3}

a.data 可以用来创建一个新数组，否则不是很有用。甚至对于大多数用途而言，这种用法也太低级了：

a.data can be used to make a new array, but otherwise isn't very useful. And even this use is too low-level for most purposes:

In [44]: c = np.ndarray(shape=(1,1), dtype=int, buffer=a.data)                                         
In [45]: c                                                                                             
Out[45]: array([[2]])
In [46]: c.__array_interface__                                                                         
Out[46]: 
{'data': (69508768, False),
 'strides': None,
 'descr': [('', '<i8')],
 'typestr': '<i8',
 'shape': (1, 1),
 'version': 3}

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