通过使用另一个数组作为沿轴的切片索引来切片数组 [英] Slicing array by using another array as the slice indices along axis

问题描述

说我有一个看起来像下面的数组:

Say I have an array that looks like the following:

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

我还有另一个数组slicer = [1,3,2].我想将这些值应用为沿轴1的0轴上的切片索引.

And I have another array slicer = [1,3,2]. I want to apply these values as the slice index over axis 0 measure along axis 1.

这是行不通的(实际上没有办法指定其沿部分是ndarray中的轴1)，但是假设我尝试了arr[:slicer, :]

This doesn't work (and in fact contains no way of specifying that the along part is axis 1 in an ndarray) but suppose I tried arr[:slicer, :]

我希望获得

out = [[1,   2,   3],
       [nan, 5,   6],
       [nan, 8, nan]]

是应用切片arr[:1, :]，arr[:3, :]，arr[:2, :]，然后分别从第一列，第二列和第三列中进行选择，然后重新组合到上面的数组中，从而删除缺失值的组合.

which is the combination of applying the slice arr[:1, :], arr[:3, :], arr[:2, :] and then selecting from those the 1st, 2nd and 3rd columns respectively and reassembling into the array above, dropping missing values.

我想避免循环，并试图找到一种快速的矢量化解决方案

I want to avoid loops and trying to find a fast vectorised solution

推荐答案

对于此操作，您需要首先生成一个布尔索引掩码，该掩码标记要设置为nan的所有字段.通过广播，可以轻松地进行外部比较"，从而产生所需的结果

For this operation you need to first generate a boolean index mask that marks all fields you want to set to nan. Broadcasting makes it easy to perform an "outer comparison" that yields the desired result

slicer = numpy.asarray([1, 3, 2])
mask = numpy.arange(3)[:, None] >= slicer
mask
# array([[False, False, False],
#        [ True, False, False],
#        [ True, False,  True]])

然后您可以简单地使用此掩码为data

You can then simply use this mask to index data

data = numpy.arange(1, 10, dtype=float).reshape(3, 3)
data[mask] = numpy.nan
data
# array([[ 1.,  2.,  3.],
#        [nan,  5.,  6.],
#        [nan,  8., nan]])

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