问题

假设我们有一个形状为(10, 20, 30)的数组。

我想要获取的是：

1) 形状为(10)的20*30数组，
2) 形状为(20)的10*30数组，
3) 形状为(30)的10*20数组。

手动锁定除一维外的所有维度很容易，但我在编写一个有效的循环来以这种方式迭代时遇到了困难。原因是在我的应用场景中，我将有几个维度的变化范围在2到5之间，我需要获取锁定除一维外的所有维度的所有子数组。

我曾经以为可以使用转置和滚动创建一个滚动的索引，例如

```python
data = np.zeros((10, 20, 30))
for ndim in range(len(data.shape)):
   index = np.arange(0, len(data.shape)), 1)
   index = np.roll(index, 1)
   _data = np.transpose(data, index)[0]

然后我意识到这是无用的，因为它与我需要的相反。

我甚至尝试向ChatGPT询问。所以现在是寻求帮助的时候了。我完全被卡住了。

<details>
<summary>英文:</summary>

Suppose there we have an array of shape(10, 20, 30).

What I would like to get are all the:

1) 20*30 arrays of shape(10),
2) 10*30 arrays of shape(20), 
3) 10*20 arrays of shape(30).

Locking all dimensions but one manually is easy, but I am struggling to write an efficient loop to iterate this way. The reason for this is that in my application case, I will have several dimensions which will vary between 2 to 5, and I will need to get all subarrays of locking all dimensions but one.


I thought I could use transpose and roll to create an index which rolls, such as 

data = np.zeros((10, 20, 30))
for ndim in range(len(data.shape)):
index = np.arange(0, len(data.shape)), 1)
index = np.roll(index, 1)
_data = np.transpose(data, index)[0]

  
... and then I realized this is useless, because its the opposite of what I need.

I got to the point I even tried asking chatgpt. So it's time to ask for help. I am utterly stuck

</details>


# 答案1
**得分**: 0

```python
我不完全确定您具体需要什么，但也许是这样的？
```python
for i in range(data.ndim):
    data2 = np.moveaxis(data, i, 0)
    for index in np.ndindex(data2.shape[1:]):
        print(data2[..., index])

for i in range(data.ndim):
    data2 = np.moveaxis(data, i, 0)
    for index in np.ndindex(data2.shape[1:]):
        print(data2[..., index])