从多维numpy数组中查找和删除 [英] find and delete from more-dimensional numpy array

问题描述

我有两个 numpy 数组:

I have two numpy-arrays:

p_a_colors=np.array([[0,0,0],
                     [0,2,0],
                     [119,103,82],
                     [122,122,122],
                     [122,122,122],
                     [3,2,4]])

p_rem = np.array([[119,103,82],
                     [122,122,122]])

我想从 p_rem 中的 p_a_colors 中删除所有列，所以我得到:

I want to delete all the columns from p_a_colors that are in p_rem, so I get:

p_r_colors=np.array([[0,0,0],
                    [0,2,0],
                    [3,2,4]])

我认为，有些事情应该像

I think, something should work like

p_r_colors= np.delete(p_a_colors, np.where(np.all(p_a_colors==p_rem, axis=0)),0)

但我只是不明白轴或 [:] 正确.

but I just don't get the axis or [:] right.

我知道，那个

p_r_colors=copy.deepcopy(p_a_colors)
for i in range(len(p_rem)):
    p_r_colors= np.delete(p_r_colors, np.where(np.all(p_r_colors==p_rem[i], axis=-1)),0)

会起作用，但我试图避免(python)循环，因为我也想要正确的性能.

would work, but I am trying to avoid (python)loops, because I also want the performance right.

推荐答案

我会这样做:

dtype = np.dtype((np.void, (p_a_colors.shape[1] * 
                            p_a_colors.dtype.itemsize)))
mask = np.in1d(p_a_colors.view(dtype), p_rem.view(dtype))
p_r_colors = p_a_colors[~mask]

>>> p_r_colors
array([[0, 0, 0],
       [0, 2, 0],
       [3, 2, 4]])

您需要执行 void dtype 操作，以便 numpy 将行作为一个整体进行比较.之后，使用内置的设置例程似乎是显而易见的方法.

You need to do the void dtype thing so that numpy compares rows as a whole. After that using the built-in set routines seems like the obvious way to go.

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