Fundamentals 6 min read

Why Python’s [:] List Copy Is Misleading and Better Alternatives

Python’s slice syntax new = old[:] appears to copy a list, but it actually creates a reference to the same object, leading to subtle bugs; this article explains Python’s object model, demonstrates the pitfalls, and presents clearer alternatives such as list(), copy.copy(), and deepcopy.

MaGe Linux Operations
MaGe Linux Operations
MaGe Linux Operations
Why Python’s [:] List Copy Is Misleading and Better Alternatives

Python’s slice syntax new = old[:] copies a list, but many beginners misunderstand it and should avoid this pattern.

In Python, variables are merely labels that reference objects; they are not memory locations as in C. Assigning a = [1, 2, 3] creates a list object and binds the name a to it. A subsequent assignment b = a does not create a new list; it creates another label b that points to the same list object.

Modifying the list through one label affects the other, as shown by appending to a and printing both a and b. The built‑in function id() returns the unique identifier (memory address) of an object, confirming that a and b share the same id.

To obtain an actual copy, a new list must be created and populated with the contents of the original. The slice operator [:] returns a shallow copy of the sequence, creating a new list object that contains references to the same elements.

a[1:3]   # returns [2, 3]
id(a)    # e.g., 3086056
id(a[1:3])  # different id, e.g., 3063400

Omitting the start index ( a[:3]) copies from the beginning, while omitting the end index ( a[1:]) copies to the end. However, slice copying is not the only method.

b = list(a)   # creates a new list using the list constructor

The list() constructor is more explicit and Pythonic; it can build a list from any iterable, including tuples and generators, which slice syntax cannot handle.

my_tuple = (1, 2, 3)
my_list = list(my_tuple)   # [1, 2, 3]

Generators are also convertible:

generator = (x * 3 for x in range(4))
list(generator)   # [0, 3, 6, 9]

For deeper copying, especially when the list contains nested mutable objects, copy.deepcopy() is required; shallow copies ( a[:], list(a), a*1, copy.copy(a)) duplicate the outer list but keep references to inner objects.

import copy
a = [[10], 20]
b = a[:]          # shallow copy
c = list(a)      # shallow copy
d = a * 1        # shallow copy
e = copy.copy(a) # shallow copy
f = copy.deepcopy(a) # deep copy

Printing the ids and contents shows that f is the only fully independent copy when the original list contains sub‑lists.

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MaGe Linux Operations
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MaGe Linux Operations

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