numpy的行向量转换为列向量 [英] numpy convert row vector to column vector

问题描述

matrix1 = np.array([[1,2,3],[4,5,6]])
vector1 = matrix1[:,0] # this should have shape (2,1) but actually has (2,)
matrix2 = np.array([[2,3],[5,6]])
np.hstack((vector1, matrix2))

ValueError: all the input arrays must have same number of dimensions

问题是，当我选择矩阵的第一列，把它放在向量1，它被转换成一个行向量，所以当我尝试用矩阵2来连接，我得到一个尺寸误差。我能做到这一点。

The problem is that when I select the first column of matrix1 and put it in vector1, it gets converted to a row vector, so when I try to concatenate with matrix2, I get a dimension error. I could do this.

np.hstack((vector1.reshape(matrix2.shape[0],1), matrix2))

但是，这看起来太丑陋的人，我做我每次来连接矩阵和矢量时间。有没有一种简单的方法来做到这一点？

But this looks too ugly for me to do every time I have to concatenate a matrix and a vector. Is there a simpler way to do this?

推荐答案

更​​简单的方法是

vector1 = matrix1[:,0:1]

有关的原因，让我向您推荐矿的另一个答案：

For the reason, let me refer you to another answer of mine:

当你写的东西，比如 A [4] ，这是访问数组的第五个元素，而不是给你的原始数组的某些部分的视图。因此，举例来说，如果一个是数字数组，那么 A [4] 将只是一个数字。如果 A 是一个二维数组，即有效数组的数组，那么 A [4] 将是一个一维阵列。基本上，访问数组元素的操作返回的东西与小于原来阵列的维数。

When you write something like a[4], that's accessing the fifth element of the array, not giving you a view of some section of the original array. So for instance, if a is an array of numbers, then a[4] will be just a number. If a is a two-dimensional array, i.e. effectively an array of arrays, then a[4] would be a one-dimensional array. Basically, the operation of accessing an array element returns something with a dimensionality of one less than the original array.

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