Posted By: Anonymous
I wonder whether there is a shortcut to make a simple list out of list of lists in Python.
I can do that in a
for loop, but maybe there is some cool “one-liner”? I tried it with
reduce(), but I get an error.
l = [[1, 2, 3], [4, 5, 6], , [8, 9]] reduce(lambda x, y: x.extend(y), l)
Traceback (most recent call last): File "<stdin>", line 1, in <module> File "<stdin>", line 1, in <lambda> AttributeError: 'NoneType' object has no attribute 'extend'
Given a list of lists
flat_list = [item for sublist in t for item in sublist]
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:
flatten = lambda t: [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], , [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], , [8,9]]*99' 'sum(t, )' 1000 loops, best of 3: 969 usec per loop $ python -mtimeit -s't=[[1,2,3],[4,5,6], , [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.