How to turn Numpy array to set efficiently?

Question

I used:

df['ids'] = df['ids'].values.astype(set)

to turn lists into sets, but the output was a list not a set:

>>> x = np.array([[1, 2, 2.5],[12,35,12]])

>>> x.astype(set)
array([[1.0, 2.0, 2.5],
       [12.0, 35.0, 12.0]], dtype=object)

Is there an efficient way to turn list into set in Numpy?

EDIT 1:
My input is as big as below:
I have 3,000 records. Each has 30,000 ids: [[1,...,12,13,...,30000], [1,..,43,45,...,30000],...,[...]]

Answer 1

First flatten your ndarray to obtain a single dimensional array, then apply set() on it:

set(x.flatten())

Edit : since it seems you just want an array of set, not a set of the whole array, then you can do value = [set(v) for v in x] to obtain a list of sets.

Answer 2

A couple of earlier 'row-wise' unique questions:

vectorize numpy unique for subarrays

Numpy: Row Wise Unique elements

Count unique elements row wise in an ndarray

In a couple of these the count is more interesting than the actual unique values.

If the number of unique values per row differs, then the result cannot be a (2d) array. That's a pretty good indication that the problem cannot be fully vectorized. You need some sort of iteration over the rows.