Python 如何使用numpy按降序排序？
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/36265930/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
How to sort in descending order with numpy?
提问by Ribz
I have a numpy array like this:
我有一个像这样的 numpy 数组：
A = array([[1, 3, 2, 7],
[2, 4, 1, 3],
[6, 1, 2, 3]])
I would like to sort the rows of this matrix in descending order and get the arguments of the sorted matrix like this:
我想按降序对该矩阵的行进行排序，并像这样获取已排序矩阵的参数：
As = array([[3, 1, 2, 0],
[1, 3, 0, 2],
[0, 3, 2, 1]])
I did the following:
我做了以下事情：
import numpy
A = numpy.array([[1, 3, 2, 7], [2, 4, 1, 3], [6, 1, 2, 3]])
As = numpy.argsort(A, axis=1)
But this gives me the sorting in ascending order. Also, after I spent some time looking for a solution in the internet, I expect that there must be an argument to argsortfunction from numpy that would reverse the order of sorting. But, apparently there is no such argument! Why!?
但这给了我升序排序。此外，在我花了一些时间在互联网上寻找解决方案之后，我希望一定有一个参数可以使argsortnumpy 的功能颠倒排序。但是，显然没有这样的论点！为什么！？
There is an argument called order. I tried, by guessing, numpy.argsort(..., order=reverse)but it does not work.
有一种说法叫做order。我通过猜测尝试过，numpy.argsort(..., order=reverse)但它不起作用。
I looked for a solution in previous questions here and I found that I can do:
我在这里寻找以前问题的解决方案，我发现我可以做到：
import numpy
A = numpy.array([[1, 3, 2, 7], [2, 4, 1, 3], [6, 1, 2, 3]])
As = numpy.argsort(A, axis=1)
As = As[::-1]
For some reason, As = As[::-1]does not give me the desired output.
出于某种原因，As = As[::-1]没有给我想要的输出。
Well, I guess it must be simple but I am missing something.
好吧，我想它一定很简单，但我遗漏了一些东西。
How can I sort a numpy array in descending order?
如何按降序对 numpy 数组进行排序？
回答by Swier
Just multiply your matrix by -1 to reverse order:
只需将您的矩阵乘以 -1 即可反转顺序：
[In]: A = np.array([[1, 3, 2, 7],
[2, 4, 1, 3],
[6, 1, 2, 3]])
[In]: print( np.argsort(-A) )
[Out]: [[3 1 2 0]
[1 3 0 2]
[0 3 2 1]]
