如何将python集转换为numpy数组？

I am using a set operation in python to perform a symmetric difference between two numpy arrays. The result, however, is a set and I need to convert it back to a numpy array to move forward. Is there a way to do this? Here's what I tried:

我在python中使用set操作来执行两个numpy数组之间的对称差异。然而，结果是一个集合，我需要将它转换回一个numpy数组继续前进。有没有办法做到这一点？这是我尝试过的：

a = numpy.array([1,2,3,4,5,6])
b = numpy.array([2,3,5])
c = set(a) ^ set(b)

The results is a set:

结果是一组：

In [27]: c
Out[27]: set([1, 4, 6])

If I convert to a numpy array, it places the entire set in the first array element.

如果我转换为numpy数组，它会将整个集合放在第一个数组元素中。

In [28]: numpy.array(c)
Out[28]: array(set([1, 4, 6]), dtype=object)

What I need, however, would be this:

但是，我需要的是：

array([1,4,6],dtype=int)

I could loop over the elements to convert one by one, but I will have 100,000 elements and hoped for a built-in function to save the loop. Thanks!

我可以循环遍历元素以逐个转换，但我将拥有100,000个元素，并希望有一个内置函数来保存循环。谢谢！

3 个解决方案

#1

Don't convert the numpy array to a set to perform exclusive-or. Use setxor1d directly.

不要将numpy数组转换为set来执行exclusive-or。直接使用setxor1d。

>>> import numpy
>>> a = numpy.array([1,2,3,4,5,6])
>>> b = numpy.array([2,3,5])
>>> numpy.setxor1d(a, b)
array([1, 4, 6])

#2

Do:

做：

>>> numpy.array(list(c))
array([1, 4, 6])

And dtype is int (int64 on my side.)

并且dtype是int（我身边的int64。）

#3

Try this.

尝试这个。

numpy.array(list(c))

Converting to list before initializing numpy array would set the individual elements to integer rather than the first element as the object.

在初始化numpy数组之前转换为list会将各个元素设置为整数而不是第一个元素作为对象。

#1