Convert row vector to column vector in NumPy
arrays, matrix, numpy, python
Solution
The easier way is
vector1 = matrix1[:,0:1]
For the reason, let me refer you to another answer of mine:
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.
Problem
``` 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 ``` 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)) ``` 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?