Numpy: reshape list of tuples

Question

I have the following list of tuples:

>>> import itertools
>>> import numpy as np
>>> grid = list(itertools.product((1,2,3),repeat=2))
>>> grid
[(1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3), (3, 1), (3, 2), (3, 3)]

I'd like to reshape this list in a sensible way (e.g. using numpy if possible) to be 3x3 as follows:

[[(1, 1), (1, 2), (1, 3)],
 [(2, 1), (2, 2), (2, 3)],
 [(3, 1), (3, 2), (3, 3)]]

When I do np.reshape(grid, (3, 3)) I get the following error: ValueError: cannot reshape array of size 18 into shape (3,3) (size 18??)

I've tried variations of np.reshape(grid, (3, 3, 2)) but these don't return the 3x3 grid given above.

While it is possible to make a (3,3) array of tuples, is that really what you need? What's wrong with the (3,3,2) array of numbers? Do you understand the difference? — hpaulj
– hpaulj, Commented Mar 25, 2022 at 0:33

LeonardoVaz · Accepted Answer · 2022-03-25 00:08:18Z

1

This will do the job:

new_grid = np.empty(len(grid), dtype='object')
new_grid[:] = grid
new_grid = new_grid.reshape(3, 3)

This outputs:

array([[(1, 1), (1, 2), (1, 3)],
       [(2, 1), (2, 2), (2, 3)],
       [(3, 1), (3, 2), (3, 3)]], dtype=object)

The object type will remain tuple:

type(new_grid[0, 0])
tuple

edited Mar 25, 2022 at 0:08

answered Mar 24, 2022 at 23:58

LeonardoVaz

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Comments

score 1 · Accepted Answer · 2022-03-25 14:32:14Z

1

18 comes from the fact that you have a list of 9 tuples, each containing 2 items; thus, 9 * 2 = 18. numpy automatically converts the tuples to part of the array.

You can either use LeonardoVaz's answer or do it speedily with nested list comprehension:

reshaped_grid = [[grid[i+j] for j in range(3)] for i in range(0, len(grid), 3)]

Output:

>>> reshaped_grid
[
    [(1, 1), (1, 2), (1, 3)],
    [(2, 1), (2, 2), (2, 3)],
    [(3, 1), (3, 2), (3, 3)]
]

edited Mar 25, 2022 at 14:32

answered Mar 25, 2022 at 0:03

user17242583

2 Comments

K-- Over a year ago

The output grid here is not what the question asks for.

user17242583 Over a year ago

@K-- Oops! Corrected :)

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