Given a list of lists `t`

,

`flat_list = [item for sublist in t for item in sublist] `

which means:

`flat_list = [] for sublist in t: for item in sublist: flat_list.append(item) `

is faster than the shortcuts posted so far. (`t`

is the list to flatten.)

Here is the corresponding function:

`def flatten(t): return [item for sublist in t for item in sublist] `

As evidence, you can use the `timeit`

module in the standard library:

`$ python -mtimeit -s't=[[1,2,3],[4,5,6], [7], [8,9]]*99' '[item for sublist in t for item in sublist]' 10000 loops, best of 3: 143 usec per loop $ python -mtimeit -s't=[[1,2,3],[4,5,6], [7], [8,9]]*99' 'sum(t, [])' 1000 loops, best of 3: 969 usec per loop $ python -mtimeit -s't=[[1,2,3],[4,5,6], [7], [8,9]]*99' 'reduce(lambda x,y: x+y,t)' 1000 loops, best of 3: 1.1 msec per loop `

Explanation: the shortcuts based on `+`

(including the implied use in `sum`

) are, of necessity, `O(T**2)`

when there are T sublists -- as the intermediate result list keeps getting longer, at each step a new intermediate result list object gets allocated, and all the items in the previous intermediate result must be copied over (as well as a few new ones added at the end). So, for simplicity and without actual loss of generality, say you have T sublists of k items each: the first k items are copied back and forth T-1 times, the second k items T-2 times, and so on; total number of copies is k times the sum of x for x from 1 to T excluded, i.e., `k * (T**2)/2`

.

The list comprehension just generates one list, once, and copies each item over (from its original place of residence to the result list) also exactly once.