numpy尺寸 [英] numpy dimensions

问题描述

我是Numpy的新手，并试图了解什么是维度的基本问题，

Im a newbie to Numpy and trying to understand the basic question of what is dimension,

我尝试了以下命令，并试图理解为什么最后两个数组的ndim相同?

I tried the following commands and trying to understand why the ndim for last 2 arrays are same?

>>> a= array([1,2,3])
>>> a.ndim
1
>>> a= array([[1,2,3],[4,5,6]])
>>> a
array([[1, 2, 3],
       [4, 5, 6]])
>>> a.ndim
2
>>> a=arange(15).reshape(3,5)
>>> a.ndim
2

>>> a
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])

我的理解.

Case 1:
    array([[1, 2, 3],
           [4, 5, 6]])

2 elements are present in main lists, so ndim is-2

Case 2:
    array([[ 0,  1,  2,  3,  4],
           [ 5,  6,  7,  8,  9],
           [10, 11, 12, 13, 14]])

3个元素出现在主列表中，ndim为-3

3 elements are present in the main lists, do ndim is-3

推荐答案

数组的shape是其尺寸的元组.一维数组的形状为(n，).二维数组的形状为(n，m)(类似于您的案例2和3)，而三维数组的形状为(n，m，k)，依此类推.

The shape of an array is a tuple of its dimensions. An array with one dimension has a shape of (n,). A two dimension array has a shape of (n,m) (like your case 2 and 3) and a three dimension array has a shape of (n,m,k) and so on.

因此，尽管您的第二个和第三个示例的形状不同，但没有.在这两种情况下，尺寸都是两个:

Therefore, whilst the shape of your second and third example are different, the no. dimensions is two in both cases:

>>> a= np.array([[1,2,3],[4,5,6]])
>>> a.shape
(2, 3)

>>> b=np.arange(15).reshape(3,5)
>>> b.shape
(3, 5)

如果您想在示例中添加另一个维度，则必须执行以下操作:

If you wanted to add another dimension to your examples you would have to do something like this:

a= np.array([[[1,2,3]],[[4,5,6]]])

或

np.arange(15).reshape(3,5,1)

您可以继续以这种方式添加尺寸:

You can keep adding dimensions in this way:

一个维度:

>>> a = np.zeros((2))
array([ 0.,  0.])
>>> a.shape
(2,)
>>> a.ndim
1

二维:

>>> b = np.zeros((2,2))
array([[ 0.,  0.],
       [ 0.,  0.]])
>>> b.shape
(2,2)
>>> b.ndim
2

三个维度:

>>> c = np.zeros((2,2,2))
array([[[ 0.,  0.],
        [ 0.,  0.]],

       [[ 0.,  0.],
        [ 0.,  0.]]])
>>> c.shape
(2,2,2)
>>> c.ndim
3

四个维度:

>>> d = np.zeros((2,2,2,2))
array([[[[ 0.,  0.],
         [ 0.,  0.]],

        [[ 0.,  0.],
         [ 0.,  0.]]],


       [[[ 0.,  0.],
         [ 0.,  0.]],

        [[ 0.,  0.],
         [ 0.,  0.]]]])
>>> d.shape
(2,2,2,2)
>>> d.ndim
4

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