Python 中的“三个点”在索引一个数字时是什么意思？
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What does "three dots" in Python mean when indexing what looks like a number?
提问by Nan Hua
What is the meaning of x[...]below?
x[...]下面是什么意思？
a = np.arange(6).reshape(2,3)
for x in np.nditer(a, op_flags=['readwrite']):
x[...] = 2 * x
回答by hpaulj
While the proposed duplicate What does the Python Ellipsis object do?answers the question in a general pythoncontext, its use in an nditerloop requires, I think, added information.
虽然建议重复Python Ellipsis 对象有什么作用？在一般python情况下回答这个问题nditer，我认为它在循环中的使用需要添加信息。
https://docs.scipy.org/doc/numpy/reference/arrays.nditer.html#modifying-array-values
https://docs.scipy.org/doc/numpy/reference/arrays.nditer.html#modifying-array-values
Regular assignment in Python simply changes a reference in the local or global variable dictionary instead of modifying an existing variable in place. This means that simply assigning to x will not place the value into the element of the array, but rather switch x from being an array element reference to being a reference to the value you assigned. To actually modify the element of the array, x should be indexed with the ellipsis.
Python 中的常规赋值只是更改局部或全局变量字典中的引用，而不是修改现有变量。这意味着简单地分配给 x 不会将值放入数组元素中，而是将 x 从数组元素引用切换为对您分配的值的引用。要实际修改数组的元素，应使用省略号对 x 进行索引。
That section includes your code example.
该部分包括您的代码示例。
So in my words, the x[...] = ...modifies xin-place; x = ...would have broken the link to the nditervariable, and not changed it. It's like x[:] = ...but works with arrays of any dimension (including 0d). In this context xisn't just a number, it's an array.
所以用我的话来说，就地x[...] = ...修改x； x = ...会破坏与nditer变量的链接，而不是更改它。它就像x[:] = ...但适用于任何维度（包括 0d）的数组。在这种情况下x，它不仅仅是一个数字，而是一个数组。
Perhaps the closest thing to this nditeriteration, without nditeris:
也许与此nditer迭代最接近的事情nditer是：
In [667]: for i, x in np.ndenumerate(a):
...: print(i, x)
...: a[i] = 2 * x
...:
(0, 0) 0
(0, 1) 1
...
(1, 2) 5
In [668]: a
Out[668]:
array([[ 0, 2, 4],
[ 6, 8, 10]])
Notice that I had to index and modify a[i]directly. I could not have used, x = 2*x. In this iteration xis a scalar, and thus not mutable
请注意，我必须a[i]直接索引和修改。我不能使用，x = 2*x。在这个迭代中x是一个标量，因此不可变
In [669]: for i,x in np.ndenumerate(a):
...: x[...] = 2 * x
...
TypeError: 'numpy.int32' object does not support item assignment
But in the nditercase xis a 0d array, and mutable.
但在这种nditer情况下x是一个 0d 数组，并且是可变的。
In [671]: for x in np.nditer(a, op_flags=['readwrite']):
...: print(x, type(x), x.shape)
...: x[...] = 2 * x
...:
0 <class 'numpy.ndarray'> ()
4 <class 'numpy.ndarray'> ()
...
And because it is 0d, x[:]cannot be used instead of x[...]
并且因为是0d，x[:]不能代替x[...]
----> 3 x[:] = 2 * x
IndexError: too many indices for array
A simpler array iteration might also give insight:
更简单的数组迭代也可能提供洞察力：
In [675]: for x in a:
...: print(x, x.shape)
...: x[:] = 2 * x
...:
[ 0 8 16] (3,)
[24 32 40] (3,)
this iterates on the rows (1st dim) of a. xis then a 1d array, and can be modified with either x[:]=...or x[...]=....
这迭代 . 的行（第一个暗淡）a。 x然后是一个一维数组，可以用x[:]=...或修改x[...]=...。
And if I add the external_loopflag from the next section, xis now a 1d array, and x[:] =would work. But x[...] =still works and is more general. x[...]is used all the other nditerexamples.
如果我添加了external_loop从下一个标志部分，x现在是一维数组，并x[:] =会工作。但x[...] =仍然有效并且更通用。 x[...]用于所有其他nditer示例。
In [677]: for x in np.nditer(a, op_flags=['readwrite'], flags=['external_loop']):
...: print(x, type(x), x.shape)
...: x[...] = 2 * x
[ 0 16 32 48 64 80] <class 'numpy.ndarray'> (6,)
Compare this simple row iteration (on a 2d array):
比较这个简单的行迭代（在二维数组上）：
In [675]: for x in a:
...: print(x, x.shape)
...: x[:] = 2 * x
...:
[ 0 8 16] (3,)
[24 32 40] (3,)
this iterates on the rows (1st dim) of a. xis then a 1d array, and can be modified with either x[:] = ...or x[...] = ....
这迭代 . 的行（第一个暗淡）a。 x然后是一个一维数组，可以用x[:] = ...或修改x[...] = ...。
Read and experiment with this nditerpage all the way through to the end. By itself, nditeris not that useful in python. It does not speed up iteration - not until you port your code to cython.np.ndindexis one of the few non-compiled numpyfunctions that uses nditer.
从头到尾阅读并尝试此nditer页面。就其本身而言，nditer在python. 它不会加速迭代 - 直到您将代码移植到cython. np.ndindex是少数numpy使用nditer.
