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]])
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]])
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)
but I just don't get the axis or [:] right.
I know, that
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)
would work, but I am trying to avoid (python)loops, because I also want the performance right.
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