从包含特定值的 3D numpy 数组中删除所有行

Question

我有一个这样的 3D numpy 数组

position = 
    [[[ 12.86420681 825.87040876   1.           8.           0.        ]
      [753.26000819 280.1334669    2.           8.           1.        ]
      [ 51.6851021  330.65314794   3.           8.           0.        ]
      [661.07157006  78.15962738   4.           8.           1.        ]
      [878.59383346 550.5236096    5.           8.           1.        ]
      [774.49249941 942.74557677   6.           8.           1.        ]
      [301.20619756 206.50737851   7.           8.           1.        ]
      [240.50228642  91.21979947   8.           8.           0.        ]]
    
     [[  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]]
    
     [[  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]]]

在数组的第一页上，它有一些随机值，所有其他页面都是零，我只对第一页的第 4 列感兴趣，它有 1 或 0。我想删除所有所有页面的行，如果第一页上的该行的值为“0”。

我试图通过给出以下代码来解决这个问题：

positionNew = np.delete(position, np.where(position[0] == 0), axis=1)

但我得到了这个作为输出：

positionNew =
    [[[753.26000819 280.1334669    2.           8.           1.        ]
      [661.07157006  78.15962738   4.           8.           1.        ]
      [774.49249941 942.74557677   6.           8.           1.        ]
      [301.20619756 206.50737851   7.           8.           1.        ]]
    
     [[  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]]
    
     [[  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]]
    
     [[  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]
      [  0.           0.           0.           0.           0.        ]]]

但我错过了其中一个值为“1”的行，即这一行：

[878.59383346 550.5236096    5.           8.           1.        ]

我哪里做错了？

Answer 1

我无法准确地重建您的数据（请下次分享代码！），但这是一个类似的示例：

import numpy as np

d1 = [[1,2,3,0],
      [4,5,6,1],
      [7,8,9,0],
      [2,4,6,1]]

d2 = [[0,0,0,0],
      [0,0,0,0],
      [0,0,0,0],
      [0,0,0,0],]

d3 = [[0,0,0,0],
      [0,0,0,0],
      [0,0,0,0],
      [0,0,0,0],]

a = np.array([d1, d2, d3])

您可以使用布尔索引来选择您想要的数据：

# for d1 (0), find where all rows (:) have a 1 in the last column (-1)
mask = a[0, :, -1] == 1
print(mask)

# for all of d1, d2, d3 (:), index the rows with the mask
a = a[:, mask]
print(a)

输出：

[False  True False  True]

[[[4 5 6 1]
  [2 4 6 1]]

 [[0 0 0 0]
  [0 0 0 0]]

 [[0 0 0 0]
  [0 0 0 0]]]