List Changes After Assignment — How to Clone or Copy It?

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Problem: If you assign a list object to a new variable using new_list = old_list, any modification to new_list changes old_list. What’s the reason for this and how can you clone or copy the list to prevent this problem?

Example: Let’s consider the following example.

old_list = ['Alice', 'Bob', 'Carl']
new_list = old_list
# ['Alice', 'Bob', 'Carl', 42]

Appending an element to the new_list also modifies the original list old_list. Thus, old_list has now four elements—even though you didn’t change it directly.


This problem of simultaneously modifying “two” lists arises because you don’t have two lists but only a single one.

In Python, everything is an object. You create a new list object ['Alice', 'Bob', 'Carl'] that resides in your machine’s memory. Both variable names new_list and old_list point to the same object in memory—if you modify one, you also modify the other!

List Changes After Assignment -- How to Clone or Copy It?

The following interactive tool visualizes the memory used by the Python interpreter when executing this particular code snippet:

Exercise: Visualize how the problem arises by clicking “Next”.

Do you understand the source of the problem? Great, let’s dive into the solutions starting with a short overview!

Solution Overview

You can see all three solutions discussed in this tutorial in our interactive Python shell:

Exercise: Change the original list. Do all three methods still produce the same output?

Next, you’ll learn about each method in greater detail!

Method 1: Slicing

The easiest way to create a shallow copy of a Python list is via slicing:

# Method 1: Slicing
old_list = ['Alice', 'Bob', 'Carl']
new_list = old_list[:]
# ['Alice', 'Bob', 'Carl']

The slicing operation old_list[:] creates a new list, so the variables new_list and old_list now point to different objects in memory. If you change one, the other doesn’t change.

This is the way with the least amount of characters and many Python coders would consider this the most Pythonic one. If you want to learn more about slicing, watch the following video and dive into the detailed blog tutorial.

Related Tutorial: Introduction to Slicing in Python

The Ultimate Guide to Slicing in Python

Method 2: Copy

An alternative is to use the list.copy() method that creates a shallow copy of the list.

# Method 2: Copy
old_list = ['Alice', 'Bob', 'Carl']
new_list = old_list.copy()
# ['Alice', 'Bob', 'Carl']

The list.copy() method copies all list elements into a new list. The new list is the return value of the method. It’s a shallow copy—you copy only the object references to the list elements and not the objects themselves.

The result is the same as the slicing method: you have two variables pointing to two different list objects in memory.

You can learn more about the list.copy() method in my detailed blog tutorial and the following video:

Related Tutorial: Python list.copy() [Ultimate Guide]

Python List copy()

Method 3: List Comprehension

A third way to solve the problem of two lists pointing to the same object in memory is the list comprehension way to create new lists.

# Method 3: List Comprehension
old_list = ['Alice', 'Bob', 'Carl']
new_list = [x for x in old_list]
# ['Alice', 'Bob', 'Carl']

List comprehension is a compact way of creating lists. The simple formula is [expression + context].

  • Expression: What to do with each list element?
  • Context: What elements to select? The context consists of an arbitrary number of for and if statements.

You can watch the tutorial video and read over the related blog article to learn more about it!

A Simple Introduction to List Comprehension in Python

Related Tutorial: An Introduction to List Comprehension

Where to Go From Here?

Enough theory. Let’s get some practice!

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