不是一个笨拙的人，我开了一枪：
>>> import numpy as np
>>> import itertools
>>>
>>> a = np.array([1,2,3,4,5,6,7,8,9])
>>> index=[2,3,6]
>>> a = np.array(list(press(a, [i not in index for i in range(len(a))])))
>>> a
array([1, 2, 5, 6, 8, 9])
根据我的测试，这优于numpy.delete()。我不知道为什么会这样，可能是由于初始数组的小尺寸？
python -m timeit -s "import numpy as np" -s "import itertools" -s "a = np.array([1,2,3,4,5,6,7,8,9])" -s "index=[2,3,6]" "a = np.array(list(press(a, [i not in index for i in range(len(a))])))"
100000 loops, best of 3: 12.9 usec per loop
python -m timeit -s "import numpy as np" -s "a = np.array([1,2,3,4,5,6,7,8,9])" -s "index=[2,3,6]" "np.delete(a, index)"
10000 loops, best of 3: 108 usec per loop
这是一个非常显着的差异（与我期望的方向相反），任何人都知道为什么会出现这种情况？
更奇怪的是，传递numpy.delete()列表比循环遍历列表并给它单个索引更糟糕。
python -m timeit -s "import numpy as np" -s "a = np.array([1,2,3,4,5,6,7,8,9])" -s "index=[2,3,6]" "for i in index:" " np.delete(a, i)"
10000 loops, best of 3: 33.8 usec per loop
编辑：它似乎与数组的大小有关。对于大型数组，numpy.delete()明显更快。
python -m timeit -s "import numpy as np" -s "import itertools" -s "a = np.array(list(range(10000)))" -s "index=[i for i in range(10000) if i % 2 == 0]" "a = np.array(list(press(a, [i not in index for i in range(len(a))])))"
10 loops, best of 3: 200 msec per loop
python -m timeit -s "import numpy as np" -s "a = np.array(list(range(10000)))" -s "index=[i for i in range(10000) if i % 2 == 0]" "np.delete(a, index)"
1000 loops, best of 3: 1.68 msec per loop
显然，这一切都非常无关紧要，因为你应该总是去清楚并避免重新发明轮子，但我发现它有点有趣，所以我想我会把它留在这里。
