如果你有一个布尔数组，你可以像这样直接选择:

>>>a = np.array([真、真、真、假、假])>>>b = np.array([1,2,3,4,5])>>>乙[一]数组([1, 2, 3])

为了配合您的初始示例，您可以执行以下操作:

>>>a = np.array([[1,2,3], [4,5,6], [7,8,9]])>>>b = np.array([[False,True,False],[True,False,False],[False,False,True]])>>>[b]数组([2, 4, 9])

您也可以添加一个 arange 并对其进行直接选择，但这取决于您生成布尔数组的方式以及您的代码看起来像 YMMV.

>>>a = np.array([[1,2,3], [4,5,6], [7,8,9]])>>>a[np.arange(len(a)), [1,0,2]]数组([2, 4, 9])

希望对您有所帮助，如果您还有其他问题，请告诉我.

I'm struggling to select the specific columns per row of a NumPy matrix.

Suppose I have the following matrix which I would call X:

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

I also have a list of column indexes per every row which I would call Y:

[1, 0, 2]

I need to get the values:

[2]
[4]
[9]

Instead of a list with indexes Y, I can also produce a matrix with the same shape as X where every column is a bool / int in the range 0-1 value, indicating whether this is the required column.

[0, 1, 0]
[1, 0, 0]
[0, 0, 1]

I know this can be done with iterating over the array and selecting the column values I need. However, this will be executed frequently on big arrays of data and that's why it has to run as fast as it can.

I was thus wondering if there is a better solution?

If you've got a boolean array you can do direct selection based on that like so:

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

To go along with your initial example you could do the following:

>>> a = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> b = np.array([[False,True,False],[True,False,False],[False,False,True]])
>>> a[b]
array([2, 4, 9])

You can also add in an arange and do direct selection on that, though depending on how you're generating your boolean array and what your code looks like YMMV.

>>> a = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> a[np.arange(len(a)), [1,0,2]]
array([2, 4, 9])

Hope that helps, let me know if you've got any more questions.