使用 numpy 数组连接列向量 [英] Concatenating column vectors using numpy arrays

问题描述

我想使用 numpy 数组连接列"向量，但因为默认情况下 numpy 将所有数组视为行向量，np.hstack 和 np.concatenate任何轴都无济于事(np.transpose 也没有如预期的那样).

a = np.array((0, 1))b = np.array((2, 1))c = np.array((-1, -1))np.hstack((a, b, c))# 数组([ 0, 1, 2, 1, -1, -1]) ## Noooooonp.reshape(np.hstack((a, b, c)), (2, 3))# array([[ 0, 1, 2], [ 1, -1, -1]]) ## 整形无济于事

一种可能性(但太麻烦)是

np.hstack((a[:, np.newaxis], b[:, np.newaxis], c[:, np.newaxis]))# 数组([[ 0, 2, -1], [ 1, 1, -1]]) ##

有更好的方法吗?

解决方案

我相信 numpy.column_stack 应该做你想做的.示例:

<预><代码>>>>a = np.array((0, 1))>>>b = np.array((2, 1))>>>c = np.array((-1, -1))>>>numpy.column_stack((a,b,c))数组([[ 0, 2, -1],[ 1, 1, -1]])

本质上等于

<预><代码>>>>numpy.vstack((a,b,c)).T

虽然.正如文档中所说.

I'd like to concatenate 'column' vectors using numpy arrays but because numpy sees all arrays as row vectors by default, np.hstack and np.concatenate along any axis don't help (and neither did np.transpose as expected).

a = np.array((0, 1))
b = np.array((2, 1))
c = np.array((-1, -1))

np.hstack((a, b, c))
# array([ 0,  1,  2,  1, -1, -1])  ## Noooooo
np.reshape(np.hstack((a, b, c)), (2, 3))
# array([[ 0,  1,  2], [ 1, -1, -1]]) ## Reshaping won't help

One possibility (but too cumbersome) is

np.hstack((a[:, np.newaxis], b[:, np.newaxis], c[:, np.newaxis]))
# array([[ 0,  2, -1], [ 1,  1, -1]]) ##

Are there better ways?

解决方案

I believe numpy.column_stack should do what you want. Example:

>>> a = np.array((0, 1))
>>> b = np.array((2, 1))
>>> c = np.array((-1, -1))
>>> numpy.column_stack((a,b,c))
array([[ 0,  2, -1],
       [ 1,  1, -1]])

It is essentially equal to

>>> numpy.vstack((a,b,c)).T

though. As it says in the documentation.

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