Since you actually want a different array which is arr where arr < 255, and 255 otherwise, this can be done simply:
result = np.minimum(arr, 255)
More generally, for a lower and/or upper bound:
result = np.clip(arr, 0, 255)
If you just want to access the values over 255, or something more complicated, @mtitan8's answer is more general, but np.clip and np.minimum (or np.maximum) are nicer and much faster for your case:
In [292]: timeit np.minimum(a, 255)
100000 loops, best of 3: 19.6 µs per loop
In [293]: %%timeit
.....: c = np.copy(a)
.....: c[a>255] = 255
.....:
10000 loops, best of 3: 86.6 µs per loop
If you want to do it in-place (i.e., modify arr instead of creating result) you can use the out parameter of np.minimum:
np.minimum(arr, 255, out=arr)
or
np.clip(arr, 0, 255, arr)
(the out= name is optional since the arguments in the same order as the function's definition.)
For in-place modification, the boolean indexing speeds up a lot (without having to make and then modify the copy separately), but is still not as fast as minimum:
In [328]: %%timeit
.....: a = np.random.randint(0, 300, (100,100))
.....: np.minimum(a, 255, a)
.....:
100000 loops, best of 3: 303 µs per loop
In [329]: %%timeit
.....: a = np.random.randint(0, 300, (100,100))
.....: a[a>255] = 255
.....:
100000 loops, best of 3: 356 µs per loop
For comparison, if you wanted to restrict your values with a minimum as well as a maximum, without clip you would have to do this twice, with something like
np.minimum(a, 255, a)
np.maximum(a, 0, a)
or,
a[a>255] = 255
a[a<0] = 0