通过布尔掩码数组选择numpy数组的元素 [英] Select elements of numpy array via boolean mask array

问题描述

我想答案就在眼前，但是我看不到它： - （

I guess the answer is close at hand, but I can't see it :-(

我有一个长度为n的布尔掩码数组：

I have a boolean mask array of length n:

a = np.array([True, True, True, False, False])

我有一个带有n列的二维数组：

I have a 2d array with n columns:

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

我想要一个只包含真值的新数组，例如：

I want a new array which contains only the "True"-values, eg:

c = ([[1,2,3], [1,2,3]])

c = a * b 不起作用，因为它对于假列而言也包含0我不想要的内容

c = a * b does not work because it contains also "0" for the false columns what I don't want

c = np.delete(b, a, 1) does not work

有什么建议吗？
谢谢！

Any suggestions? Thanks!

推荐答案

你可能想要这样的东西：

You probably want something like this:

>>> a = np.array([True, True, True, False, False])
>>> b = np.array([[1,2,3,4,5], [1,2,3,4,5]])
>>> b[:,a]
array([[1, 2, 3],
       [1, 2, 3]])

请注意，要使这种索引工作，它必须是 ndarray ，就像你一样使用，而不是列表，或者它将解释 False 和 True as 0 和 1 并为您提供以下列：

Note that for this kind of indexing to work, it needs to be an ndarray, like you were using, not a list, or it'll interpret the False and True as 0 and 1 and give you those columns:

>>> b[:,[True, True, True, False, False]]   
array([[2, 2, 2, 1, 1],
       [2, 2, 2, 1, 1]])

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