R expand.grid() function in Python

Question

Is there a Python function similar to the expand.grid() function in R ? Thanks in advance.

(EDIT) Below are the description of this R function and an example.

Create a Data Frame from All Combinations of Factors  Description:       Create a data frame from all combinations of the supplied vectors      or factors.    > x <- 1:3 > y <- 1:3 > expand.grid(x,y)   Var1 Var2 1    1    1 2    2    1 3    3    1 4    1    2 5    2    2 6    3    2 7    1    3 8    2    3 9    3    3 

(EDIT2) Below is an example with the rpy package. I would like to get the same output object but without using R :

>>> from rpy import * >>> a = [1,2,3] >>> b = [5,7,9] >>> r.assign("a",a) [1, 2, 3] >>> r.assign("b",b) [5, 7, 9] >>> r("expand.grid(a,b)") {'Var1': [1, 2, 3, 1, 2, 3, 1, 2, 3], 'Var2': [5, 5, 5, 7, 7, 7, 9, 9, 9]} 

EDIT 02/09/2012: I'm really lost with Python. Lev Levitsky's code given in his answer does not work for me:

>>> a = [1,2,3] >>> b = [5,7,9] >>> expandgrid(a, b) Traceback (most recent call last):   File "<stdin>", line 1, in <module>   File "<stdin>", line 2, in expandgrid NameError: global name 'itertools' is not defined 

However the itertools module seems to be installed (typing from itertools import * does not return any error message)

Answer 1

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.