英文:
Put a list as element in 2D array in Python numpy
问题
我从numpy得到了一个2D矩阵（np.zeros），我希望这个矩阵的每个元素都是一个具有2个元素的1D列表，这是我的代码：```pythonimport numpy as npN = 3x = np.linspace(0, 10, N)y = np.linspace(0, 10, N)temp = np.zeros((N, N, 2))for i in range(len(x)):for j in range(len(y)):temp[i, j] = [x[i], y[j]]
但是我得到了错误：
TypeError: float() argument must be a string or a real number, not 'list'上述异常直接导致了以下异常：Traceback (most recent call last):File (...), line 15, in <module>temp[i, j] = [x[i], y[j]]ValueError: setting an array element with a sequence.
我理解了这个问题，但是不知道如何解决它。
<details><summary>英文:</summary>I've got a 2D matrix from numpy (np.zeros) and I would like that every element of this matrix is a 1D list with 2 elements, this is my code:
import numpy as np
N = 3
x = np.linspace(0,10,N)
y = np.linspace(0,10,N)
temp = np.zeros((N, N))
for i in range(len(x)):
for j in range(len(y)):
temp[i, j] = [x[i], y[j]]
but I've got error:
TypeError: float() argument must be a string or a real number, not 'list'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File (...), line 15, in <module>
temp[i, j] = [x[i], y[j]]
ValueError: setting an array element with a sequence.
And okay, I understand this problem, but I don't know how to solve it.</details># 答案1**得分**: 2你可以在构建数组时设置`dtype=object`并存储列表，但这样效率较低。也可以定义自定义的dtype：```pythonimport numpy as np# 定义包含两个float64字段的自定义类型pair_type = np.dtype("f8, f8")x = np.zeros((3, 3), dtype=pair_type)x[0, 0] = (1.5, 2.5)
你可以将x[i, j]检索为一个元组。
英文:
You can set the dtype=object when constructing the array and store lists, but that's quite inefficient. It's possible to define a custom dtype:
import numpy as np# custom type of 2 float64 fieldspair_type = np.dtype("f8, f8")x = np.zeros((3, 3), dtype=pair_type)x[0, 0] = (1.5, 2.5)
You can retrieve x[i, j] as a tuple.
答案2
得分: 0
以下是翻译好的部分：
使用numpy.meshgrid + numpy.stack，可以方便地获得您正在搜索的结果：
np.stack(np.meshgrid(x, y, indexing='ij'), axis=-1)
array([[[ 0., 0.],[ 0., 5.],[ 0., 10.]],[[ 5., 0.],[ 5., 5.],[ 5., 10.]],[[10., 0.],[10., 5.],[10., 10.]]])
英文:
The result you are searching for can be conveniently achieved with numpy.meshgrid + numpy.stack:
np.stack(np.meshgrid(x, y, indexing='ij'), axis=-1)
array([[[ 0., 0.],[ 0., 5.],[ 0., 10.]],[[ 5., 0.],[ 5., 5.],[ 5., 10.]],[[10., 0.],[10., 5.],[10., 10.]]])
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