R expand.grid() function in Python

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Just use list comprehensions:

>>> [(x, y) for x in range(5) for y in range(5)]

[(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (2, 0), (2, 1), (2, 2), (2, 3), (2, 4), (3, 0), (3, 1), (3, 2), (3, 3), (3, 4), (4, 0), (4, 1), (4, 2), (4, 3), (4, 4)]

convert to numpy array if desired:

>>> import numpy as np
>>> x = np.array([(x, y) for x in range(5) for y in range(5)])
>>> x.shape
(25, 2)

I have tested for up to 10000 x 10000 and performance of python is comparable to that of expand.grid in R. Using a tuple (x, y) is about 40% faster than using a list [x, y] in the comprehension.

OR…

Around 3x faster with np.meshgrid and much less memory intensive.

%timeit np.array(np.meshgrid(range(10000), range(10000))).reshape(2, 100000000).T
1 loops, best of 3: 736 ms per loop

in R:

> system.time(expand.grid(1:10000, 1:10000))
   user  system elapsed 
  1.991   0.416   2.424

Keep in mind that R has 1-based arrays whereas Python is 0-based.

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