itertools to numpy array

Question

I would like to use itertools' various functions to create numpy arrays. I can easily compute ahead of time the number of elements in the product, combinations, permutations, etc, so allotting space shouldn't be a problemo.

e.g.

coords = [[1,2,3],[4,5,6]]
iterable = itertools.product(*coords)
shape = (len(coords[0]), len(coords[1]))
arr = np.iterable_to_array(
    iterable, 
    shape=shape, 
    dtype=np.float64, 
    count=shape[0]*shape[1]
) #not a real thing
answer = np.array([
    [1,4],[1,5],[1,6],
    [2,4],[2,5],[2,6],
    [3,4],[3,5],[3,6]])
assert np.equal(arr, answer)

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| numpy   | python   | itertools   2017-01-05 19:01 1 Answers

Answers to itertools to numpy array ( 1 )

  1. 2017-01-05 20:01

    Here are several numpy ways of generating an array with these values

    In [469]: coords = [[1,2,3],[4,5,6]]
    In [470]: it = itertools.product(*coords)
    In [471]: arr = np.array(list(it))
    In [472]: arr
    Out[472]: 
    array([[1, 4],
           [1, 5],
           [1, 6],
           [2, 4],
           [2, 5],
           [2, 6],
           [3, 4],
           [3, 5],
           [3, 6]])
    

    fromiter will work with an appropriate structured dtype:

    In [473]: it = itertools.product(*coords)
    In [474]: arr = np.fromiter(it, dtype='i,i')
    In [475]: arr
    Out[475]: 
    array([(1, 4), (1, 5), (1, 6), (2, 4), (2, 5), (2, 6), (3, 4), (3, 5),
           (3, 6)], 
          dtype=[('f0', '<i4'), ('f1', '<i4')])
    

    But usually we use the tools that numpy provides for generating sequences and meshes. np.arange is used all over the place.

    meshgrid is widely used. With a bit of trial and error I found that I could transpose its output, and produce the same sequence:

    In [481]: np.transpose(np.meshgrid(coords[0], coords[1], indexing='ij'), (1,2,0)).reshape(-1,2)
    Out[481]: 
    array([[1, 4],
           [1, 5],
           [1, 6],
           [2, 4],
           [2, 5],
           [2, 6],
           [3, 4],
           [3, 5],
           [3, 6]])
    

    repeat and tile also useful for tasks like this:

    In [487]: np.column_stack((np.repeat(coords[0],3), np.tile(coords[1],3)))
    Out[487]: 
    array([[1, 4],
           [1, 5],
           [1, 6],
           [2, 4],
           [2, 5],
           [2, 6],
           [3, 4],
           [3, 5],
           [3, 6]])
    

    I've done some timings on fromiter in the past. My memory is that it offers only a modest time savings over np.array.

    A while back I explored itertools and fromiter, and found a way to combine them usingitertools.chain

    convert itertools array into numpy array

    In [499]: it = itertools.product(*coords)
    In [500]: arr = np.fromiter(itertools.chain(*it),int).reshape(-1,2)
    In [501]: arr
    Out[501]: 
    array([[1, 4],
           [1, 5],
           [1, 6],
           [2, 4],
           [2, 5],
           [2, 6],
           [3, 4],
           [3, 5],
           [3, 6]])
    

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