You asked about efficiency. You can use timeit for that.
>python -m timeit -s "a = [[1,2,3,4],[2,4,5,1],[3,4,6,2],[2,3,4,5]]" "zip(*a)"
1000000 loops, best of 3: 0.569 usec per loop
>python -m timeit -s "a = [[1,2,3,4],[2,4,5,1],[3,4,6,2],[2,3,4,5]]" "map(None, *a)"
1000000 loops, best of 3: 0.644 usec per loop
>python -m timeit -s "a = [[1,2,3,4],[2,4,5,1],[3,4,6,2],[2,3,4,5]]" "[[row[i] for row in a] for i in xrange(len(a[0]))]"
1000000 loops, best of 3: 1.43 usec per loop
>python -m timeit -s "from numpy import array; a = array([[1,2,3,4],[2,4,5,1],[3,4,6,2],[2,3,4,5]])" "a.transpose()"
1000000 loops, best of 3: 0.249 usec per loop
For a large data set of [[1,2,3,4],[2,4,5,1],[3,4,6,2],[2,3,4,5]]*1000000
>python -m timeit -s "a = [[1,2,3,4],[2,4,5,1],[3,4,6,2],[2,3,4,5]]*1000000" "zip(*a)"
10 loops, best of 3: 400 msec per loop
>python -m timeit -s "a = [[1,2,3,4],[2,4,5,1],[3,4,6,2],[2,3,4,5]]*1000000" "map(None, *a)"
10 loops, best of 3: 458 msec per loop
>python -m timeit -s "a = [[1,2,3,4],[2,4,5,1],[3,4,6,2],[2,3,4,5]]*1000000" "[[row[i] for row in a] for i in xrange(len(a[0]))]"
10 loops, best of 3: 770 msec per loop
>python -m timeit -s "from numpy import array; a = array([[1,2,3,4],[2,4,5,1],[3,4,6,2],[2,3,4,5]]*1000000)" "a.transpose()"
1000000 loops, best of 3: 0.251 usec per loop
If your lists are of different lengths, zip
truncates to the shortest length. You can use 'map' or itertools.izip_longest
to instead fill the missing values with None
.