I have some list of data, for example:
some_data = [1, 2, 4, 1, 6, 23, 3, 56, 6, 2, 3, 5, 6, 32, 2, 12, 5, 3, 2]
and I want to get unique values with fixed length (I don't care which I will get) and I also want it to be a set.
I know that I can do set from some_data then make it list, crop it and then make it set again.
set(list(set(some_data))[:5]) # doesn't look so friendly
I understand that I don't have __getitem__ method in set which wouldn't make the whole slice thing possible, but if there is a chance to make it look better?
And I completely understand that set is unordered. So it doesn't matter which elements are in final set.
Possible options are to use:
using dict with None values:
set(dict(map(lambda x: (x, None), some_data)).keys()[:2]) # not that great
Sets are iterable. If you really don't care which items from your set are selected, you can use itertools.islice to get an iterator that will yield a specified number of items (whichever ones come first in the iteration order). Pass the iterator to the set constructor and you've got your subset without using any extra lists:
import itertools
some_data = [1, 2, 4, 1, 6, 23, 3, 56, 6, 2, 3, 5, 6, 32, 2, 12, 5, 3, 2]
big_set = set(some_data)
small_set = set(itertools.islice(big_set, 5))
While this is what you've asked for, I'm not sure you should really use it. Sets may iterate in a very deterministic order, so if your data often contains many similar values, you may end up selecting a very similar subset every time you do this. This is especially bad when the data consists of integers (as in the example), which hash to themselves. Consecutive integers will very frequently appear in order when iterating a set. With the code above, only 32 is out of order in big_set (using Python 3.5), so small_set is {32, 1, 2, 3, 4}. If you added 0 to the your data, you'd almost always end up with {0, 1, 2, 3, 4} even if the dataset grew huge, since those values will always fill up the first fives slots in the set's hash table.
To avoid such deterministic sampling, you can use random.sample as suggested by jprockbelly.