What is the efficiency of python's list slicing?
Question:
I am wondering what is the efficiency of slicing lists in python?
For Example:
myList = [1, 2, 3, 4, 5]
newList = myList[1:4]
I’m looking for big-O notation (e.g. O(n)).
Answers:
It’s O(n) for lists, and most sequence types, as slicing for most types performs a shallow copy, it doesn’t make views of the original data.
For some types, like memoryview, Python 3’s range, or third-party types like numpy arrays, slicing makes views, not copies, so the cost is O(1), since constructing the view has identical cost regardless of the size of the view. The downside is that this can keep huge allocations alive because a single view exists on a small part of the allocation; this flaw is why most built-in types use a copying strategy instead of a view strategy.
I am wondering what is the efficiency of slicing lists in python?
For Example:
myList = [1, 2, 3, 4, 5]
newList = myList[1:4]
I’m looking for big-O notation (e.g. O(n)).
It’s O(n) for lists, and most sequence types, as slicing for most types performs a shallow copy, it doesn’t make views of the original data.
For some types, like memoryview, Python 3’s range, or third-party types like numpy arrays, slicing makes views, not copies, so the cost is O(1), since constructing the view has identical cost regardless of the size of the view. The downside is that this can keep huge allocations alive because a single view exists on a small part of the allocation; this flaw is why most built-in types use a copying strategy instead of a view strategy.