Programming

The core of that Life expression works by taking the array of cells and shifting it in the eight different directions, then summing those arrays to get the population counts.

I tried translating this into Python — but I've never written numpy code before, so this is probably less efficient than it could be. But it does work and you can see a glider move through a few generations.

The array-shifting logic is in the populations function, with np.roll being the equivalent of the APL rotate operation (written as ⌽ and ⊖ in the original).

import numpy as np

def alive(popu, cell):
    return int(popu == 3 or (popu == 4 and cell))
alive = np.frompyfunc(alive, 2, 1)

def populations(grid):
    return np.array(
            [ np.roll(r1, shift, 1)
                for shift in [-1, 0, 1]
                for r1 in (np.roll(grid, shift, 0) for shift in [-1, 0, 1]) ]
            ).sum(axis=0)

def nextgen(grid):
    return alive(populations(grid), grid)

grid = np.array([[0, 0, 0, 0, 0, 0],
                 [0, 0, 1, 0, 0, 0],
                 [0, 0, 0, 1, 0, 0],
                 [0, 1, 1, 1, 0, 0],
                 [0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0]])

for gens in range(5):
    print(grid)
    grid = nextgen(grid)
print(grid)