不复制numpy数组如何改变数据属性？

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如下我的MWE所示，在现有数组 a 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}