convert a set of tuples into a numpy array of lists in python

Question

So, I've been using the set method "symmetric_difference" between 2 ndarray matrices in the following way:

x_set = list(set(tuple(i) for i in x_spam_matrix.tolist()).symmetric_difference(
                 set(tuple(j) for j in partitioned_x[i].tolist())))

x = np.array([list(i) for i in x_set])

this method works fine for me, but it feel a little clumsy...is there anyway to conduct this in a slightly more elegant way?

Answer 1

A simple list of tuples:

In [146]: alist = [(1,2),(3,4),(2,1),(3,4)]

put it in a set:

In [147]: aset = set(alist)
In [148]: aset
Out[148]: {(1, 2), (2, 1), (3, 4)}

np.array just wraps that set in an object dtype:

In [149]: np.array(aset)
Out[149]: array({(1, 2), (3, 4), (2, 1)}, dtype=object)

but make it into a list, and get a 2d array:

In [150]: np.array(list(aset))
Out[150]: 
array([[1, 2],
       [3, 4],
       [2, 1]])

Since it is a list of tuples, it can also be made into a structured array:

In [151]: np.array(list(aset),'i,f')
Out[151]: array([(1, 2.), (3, 4.), (2, 1.)], dtype=[('f0', '<i4'), ('f1', '<f4')])

If the tuples varied in length, the list of tuples would be turned into a 1d array of tuples (object dtype):

In [152]: np.array([(1,2),(3,4),(5,6,7)])
Out[152]: array([(1, 2), (3, 4), (5, 6, 7)], dtype=object)
In [153]: _.shape
Out[153]: (3,)

Answer 2

The Zen of Python:

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Explicit is better than implicit.
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Complex is better than complicated.
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[...]

There is nothing wrong with your code. Though if I had to code review it, I would suggest the following

spam_matrix_set = set(tuple(item) for item in x_spam_matrix.tolist())
partitioned_set = set(tuple(item) for item in partitioned_x[index].tolist())
disjunctive_union = spam_matrix_set.symmetric_difference(partitioned_set)

x = np.array([list(item) for item in disjunctive_union])