# How to convert a python set to a numpy array?

## Question:

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:

```
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:

```
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.

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

What I need, however, would be this:

```
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!

## Answers:

Do:

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

And dtype is int (int64 on my side.)

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

```
>>> 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])
```

Try this.

```
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.

Try:

```
numpy.fromiter(c, int, len(c))
```

This is twice as fast as the solution with list as a middle product.