performance - Efficient iteration over slice in Python

Question

How efficient are iterations over slice operations in Python? And if a copy is inevitable with slices, is there an alternative?

I know that a slice operation over a list is O(k), where k is the size of the slice.

x[5 : 5+k]  # O(k) copy operation

However, when iterating over a part of a list, I find that the cleanest (and most Pythonic?) way to do this (without having to resort to indices) is to do:

for elem in x[5 : 5+k]:
  print elem

However my intuition is that this still results in an expensive copy of the sublist, rather than simply iterating over the existing list.

Answer 1

Use:

for elem in x[5 : 5+k]:

It's Pythonic! Don't change this until you've profiled your code and determined that this is a bottleneck -- though I doubt you will ever find this to be the main source of a bottleneck.

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In terms of speed it will probably be your best choice:

In [30]: x = range(100)

In [31]: k = 90

In [32]: %timeit x[5:5+k]
1000000 loops, best of 3: 357 ns per loop

In [35]: %timeit list(IT.islice(x, 5, 5+k))
100000 loops, best of 3: 2.42 us per loop

In [36]: %timeit [x[i] for i in xrange(5, 5+k)]
100000 loops, best of 3: 5.71 us per loop

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In terms of memory, it is not as bad you might think. x[5: 5+k] is a shallow copy of part of x. So even if the objects in x are large, x[5: 5+k] is creating a new list with k elements which reference the same objects as in x. So you only need extra memory to create a list with k references to pre-existing objects. That probably is not going to be the source of any memory problems.