How to clamp an integer to some range?

I have the following code:

new_index = index + offset
if new_index < 0:
    new_index = 0
if new_index >= len(mylist):
    new_index = len(mylist) - 1
return mylist[new_index]

Basically, I calculate a new index and use that to find some element from a list. In order to make sure the index is inside the bounds of the list, I needed to write those 2 if statements spread into 4 lines. That's quite verbose, a bit ugly... Dare I say, it's quite un-pythonic.

Is there any other simpler and more compact solution? (and more pythonic)

Yes, i know I can use if else in one line, but it is not readable:

new_index = 0 if new_index < 0 else len(mylist) - 1 if new_index >= len(mylist) else new_index

I also know I can chain max() and min() together. It's more compact, but I feel it's kinda obscure, more difficult to find bugs if I type it wrong. In other words, I don't find it very straightforward.

new_index = max(0, min(new_index, len(mylist)-1))

Solution 1:

This is pretty clear, actually. Many folks learn it quickly. You can use a comment to help them.

new_index = max(0, min(new_index, len(mylist)-1))

Solution 2:

sorted((minval, value, maxval))[1]

for example:

>>> minval=3
>>> maxval=7
>>> for value in range(10):
...   print sorted((minval, value, maxval))[1]
... 
3
3
3
3
4
5
6
7
7
7

Solution 3:

many interesting answers here, all about the same, except... which one's faster?

import numpy
np_clip = numpy.clip
mm_clip = lambda x, l, u: max(l, min(u, x))
s_clip = lambda x, l, u: sorted((x, l, u))[1]
py_clip = lambda x, l, u: l if x < l else u if x > u else x

>>> import random
>>> rrange = random.randrange
>>> %timeit mm_clip(rrange(100), 10, 90)
1000000 loops, best of 3: 1.02 µs per loop

>>> %timeit s_clip(rrange(100), 10, 90)
1000000 loops, best of 3: 1.21 µs per loop

>>> %timeit np_clip(rrange(100), 10, 90)
100000 loops, best of 3: 6.12 µs per loop

>>> %timeit py_clip(rrange(100), 10, 90)
1000000 loops, best of 3: 783 ns per loop

paxdiablo has it!, use plain ol' python. The numpy version is, perhaps not surprisingly, the slowest of the lot. Probably because it's looking for arrays, where the other versions just order their arguments.

Solution 4:

See numpy.clip:

index = numpy.clip(index, 0, len(my_list) - 1)