Does Python List Slicing Create Copies of Objects?

Slicing Lists in Python: Maintaining Object Integrity

While it may seem logical to assume that slicing lists in Python creates copies of the contained objects, that is not actually the case. Instead, slicing simply produces new lists that reference the same underlying objects. This recognition plays a crucial role in understanding Python's list slicing mechanism.

Immutability in Action

Consider a list of integers:

[1000 + 1, 1000 + 1, 1000 + 1]

Despite having the same value, these objects are distinct entities with unique IDs, as evidenced by the following:

map(id, [1000 + 1, 1000 + 1, 1000 + 1])

Slicing this list maintains the integrity of these references:

b = [1000 + 1, 1000 + 1, 1000 + 1][1:3]
map(id, b)

The output of both map operations is identical, confirming that the slice does not generate new copies of the integers.

Mutable Objects Behave Similarly

Similar behavior is observed with mutable objects such as dictionaries or lists:

a = [{0: 'zero', 1: 'one'}, ['foo', 'bar']]
map(id, a[1:])

The slice still preserves the original references, demonstrating that slicing is a non-copy operation.

The Overhead of Copying

While slicing does not involve copying the objects themselves, it does copy the references. Each reference occupies 8 bytes on 64-bit machines, and each list has an additional 72 bytes of overhead:

for i in range(len(a)):
    x = a[:i]
    print('len: {}'.format(len(x)))
    print('size: {}'.format(sys.getsizeof(x)))

Nevertheless, this overhead is generally not a significant concern for most applications.

An Alternative: Views

Although Python lacks direct support for views, alternative options such as numpy arrays can be employed to achieve memory optimization. Slicing numpy arrays creates views that share memory with the original, reducing overhead but introducing the potential for unintended modifications.

In summary, slicing lists in Python preserves the references to the contained objects, avoiding costly copying operazioni. This mechanism simplifies code maintenance by ensuring that slices reflect changes made to the original list. While memory overhead is a consideration, it is typically not a major concern for most practical applications.

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