To quote from PEP 424:
Rationale =========
Being able to pre-allocate lists based on the expected size, as estimated by ``__length_hint__``, can be a significant optimization. CPython has been observed to run some code faster than PyPy, purely because of this optimization being present.
Why is it a significant optimisation? How much slower is it? Where is the data? *If* resizing list is so slow, then why not make it faster? To quote wikipedia (http://en.wikipedia.org/wiki/Dynamic_array) """ As n elements are inserted, the capacities form a geometric progression. Expanding the array by any constant proportion ensures that inserting n elements takes O(n) time overall, meaning that each insertion takes amortized constant time. The value of this proportion a leads to a time-space tradeoff: the average time per insertion operation is about a/(a-1), while the number of wasted cells is bounded above by (a-1)n. The choice of a depends on the library or application: some textbooks use a = 2, but Java's ArrayList implementation uses a = 3/2 and the C implementation of Python's list data structure uses a = 9/8. """ If resizing of lists is too slow, then we should reconsider the 9/8 factor and/or look to tweak the resizing code. Cheers, Mark.