numpy中这些数组形状之间的区别 [英] Difference between these array shapes in numpy

问题描述

形状为-的2个数组有什么区别

What is the difference between 2 arrays whose shapes are-

(442,1) 和 (442,) ?

打印这两个会产生相同的输出，但是当我检查相等==时，我得到一个像这样的二维向量-

Printing both of these produces an identical output, but when I check for equality ==, I get a 2D vector like this-

array([[ True, False, False, ..., False, False, False],
       [False,  True, False, ..., False, False, False],
       [False, False,  True, ..., False, False, False],
       ..., 
       [False, False, False, ...,  True, False, False],
       [False, False, False, ..., False,  True, False],
       [False, False, False, ..., False, False,  True]], dtype=bool)

有人能解释一下区别吗?

Can someone explain the difference?

推荐答案

(442, 1) 形状的数组是二维的.它有 442 行和 1 列.

An array of shape (442, 1) is 2-dimensional. It has 442 rows and 1 column.

形状为 (442, ) 的数组是一维的，由 442 个元素组成.

An array of shape (442, ) is 1-dimensional and consists of 442 elements.

请注意，他们的代表也应该看起来不同.括号的数量和位置有区别:

Note that their reprs should look different too. There is a difference in the number and placement of parenthesis:

In [7]: np.array([1,2,3]).shape
Out[7]: (3,)

In [8]: np.array([[1],[2],[3]]).shape
Out[8]: (3, 1)

<小时>

请注意，您可以使用 np.squeeze 删除长度为 1 的轴:

---

Note that you could use np.squeeze to remove axes of length 1:

In [13]: np.squeeze(np.array([[1],[2],[3]])).shape
Out[13]: (3,)

<小时>

NumPy 广播规则 允许自动添加新轴需要时在左侧.所以(442,) 可以广播到(1, 442).长度为 1 的轴可以广播到任何长度.所以当您测试形状数组 (442, 1) 和形状数组 (442, ) 之间的相等性时，第二个数组被提升为形状 (1, 442) 然后两个数组展开它们长度为 1 的轴，使它们都变成形状为 (442, 442) 的广播数组.这就是为什么当您测试相等性时，结果是一个形状为 (442, 442) 的布尔数组.

---

NumPy broadcasting rules allow new axes to be automatically added on the left when needed. So (442,) can broadcast to (1, 442). And axes of length 1 can broadcast to any length. So when you test for equality between an array of shape (442, 1) and an array of shape (442, ), the second array gets promoted to shape (1, 442) and then the two arrays expand their axes of length 1 so that they both become broadcasted arrays of shape (442, 442). This is why when you tested for equality the result was a boolean array of shape (442, 442).

In [15]: np.array([1,2,3]) == np.array([[1],[2],[3]])
Out[15]: 
array([[ True, False, False],
       [False,  True, False],
       [False, False,  True]], dtype=bool)

In [16]: np.array([1,2,3]) == np.squeeze(np.array([[1],[2],[3]]))
Out[16]: array([ True,  True,  True], dtype=bool)

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