遍历numpy矩阵行 [英] Iterate over a numpy Matrix rows

问题描述

首先，我试图在Google和网站中找到我的问题的答案(我认为这是非常基本的)，但没有任何反应.

First, I tried to find an answer to my question ( which I think is pretty basic) searching in google and in the site, but nothing came up.

我正在尝试从一个numpy矩阵中获取行，但是我不能.例如，如果我使用这个:

I'm trying to get the rows from a numpy matrix, but I can't. For example if I use this:

result = numpy.matrix([[11, 12, 13],
                       [21, 22, 23],
                       [31, 32, 33]])

for p in result:
    print(p[0])

打印此:

[[11 12 13]]
[[21 22 23]]
[[31 32 33]]

如果我只使用p

我要做什么才能访问每一行? numpy.nditer(result)打印一个数组，我需要每一行来执行一些操作.

What I have to do to access every row? numpy.nditer(result) prints an array, and I need every row to perform some operations.

推荐答案

问题是您正在使用np.matrix.使用np.array代替，而无需索引即可简单地进行迭代:

The problem is you are using np.matrix. Use np.array instead and simply iterate without indexing:

result = np.array([[11, 12, 13],
                   [21, 22, 23],
                   [31, 32, 33]])

for p in result:
    print(p)

[11 12 13]
[21 22 23]
[31 32 33]

说明

您看到的是numpy.matrix要求每个行具有2维的效果.对于NumPy而言，这是不必要的且是反模式.

What you are seeing is the effect of numpy.matrix requiring each row to have 2 dimensions. This is unnecessary and anti-pattern for NumPy.

numpy.matrix背后有一段历史.最初使用它是为了方便矩阵乘法运算符.但这不再是问题，因为可以使用@(Python 3.5+)而不是嵌套的dot调用.因此，默认情况下，使用numpy.array.

There is a history behind numpy.matrix. It was used initial for convenience of matrix multiplication operators. But this is no longer an issue since @ is possible (Python 3.5+) instead of nested dot calls. Therefore, by default, use numpy.array.

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