在NumPy中将行向量转换为列向量 [英] Convert row vector to column vector in NumPy

问题描述

import numpy as np

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

问题在于，当我选择matrix1的第一列并将其放入vector1时，它将转换为行向量，因此当我尝试与matrix2串联时，会出现尺寸错误.我可以做到的.

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，每次必须连接矩阵和向量时，这对于我来说都太难看了.有没有更简单的方法可以做到这一点?

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]

由于这个原因，让我介绍您我的另一个答案:

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

当您编写类似a[4]的内容时，这将访问数组的第五个元素，而不是让您查看原始数组的某些部分.因此，例如，如果a是一个数字数组，则a[4]将只是一个数字.如果a是二维数组，即有效地是数组数组，则a[4]将是一维数组.基本上，访问数组元素的操作返回的维数比原始数组小一.

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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