Specific answer
With list comprehensions:
In [2]: list1 = [1,2,3,4,5,6]
In [3]: [x+170 for x in list1]
Out[3]: [171, 172, 173, 174, 175, 176]
With map:
In [5]: map(lambda x: x+170, list1)
Out[5]: [171, 172, 173, 174, 175, 176]
Turns out that the list comprehension is twice as fast:
$ python -m timeit 'list1=[1,2,3,4,5,6]' '[x+170 for x in list1]'
1000000 loops, best of 3: 0.793 usec per loop
$ python -m timeit 'list1=[1,2,3,4,5,6]' 'map(lambda x: x+170, list1)'
1000000 loops, best of 3: 1.74 usec per loop
Some bench-marking
After @mgilson posted the comment about numpy, I wondered how it stacked up. I found that for lists shorter than 50 or so elements, list comprehensions are faster, but numpy is faster beyond that.
