Python - Copying Lists

In Python, lists are used to store ordered collections of items that can be modified (e.g., by adding, removing, or changing elements). When working with lists, you often need to create a copy of them, and this is where beginners might encounter some unexpected pitfalls. Let’s explore how to properly copy lists in Python and avoid common mistakes. We’ll cover both shallow and deep copying techniques.

Why Is Copying Lists Important?

In Python, lists are mutable objects, meaning their contents can be altered after creation. When you assign a list to another variable using the = operator, you don’t create a new copy of the list—you simply create a new reference to the same object in memory. This means that changes made to one list will also affect the other, which can lead to unintended consequences.

📌 Example:

Here, new_list isn’t a copy of original_list. Both variables point to the same object in memory. The append() method modifies the original object, and this change is reflected through both variables.

To avoid such situations, you need to use proper copying methods.

Ways to Copy Lists

The copy() Method (Shallow Copying)

The copy() method creates a new copy of a list. This is called shallow copying, meaning it only copies the top level of the list. If the list contains nested mutable objects (e.g., other lists), those objects remain linked to the original.

📌 Example:

Now, new_list is a separate object, and changes to it don’t affect original_list. The copy() method only works at the top level of the list.

Limitations of copy()

If the list contains nested lists or other mutable objects, they aren’t fully copied—they remain references to the same objects.

📌 Example:

The nested list [2, 3] wasn’t fully copied—it remains a shared reference. Modifying it in new_list also affects original_list.

Slicing [:] (Shallow Copying)

Another way to create a shallow copy of a list is to use the slice operator [:] without specifying start or end indices. This is equivalent to the copy() method.

📌 Example:

The slice [:] creates a new list by copying all elements of original_list. Like copy(), this is shallow copying, and nested objects remain shared.

The list() Function (Shallow Copying)

The list() function can also be used to create a copy of a list. It takes any iterable object (like a list) and returns a new list.

📌 Example:

The list() function creates a new list, but like the previous methods, it performs only shallow copying.

The copy Module and Deep Copying (deepcopy)

If you need to copy a list along with all its nested objects so they become fully independent, you’ll use deep copying. Python provides the copy module and its deepcopy() function for this purpose.

📌 Example:

The deepcopy() function recursively copies all nested objects, creating a fully independent copy. Changes to new_list no longer affect original_list.

When to Use deepcopy()?

Deep copying is useful when you have complex data structures with multiple levels of nesting and want to ensure complete independence of the copy. However, it’s slower than shallow copying due to the recursive traversal of all objects.

Comparison of Methods

Method	Copy Type	Speed	Copies Nested Objects?
=	No copy	Very fast	No (reference)
copy()	Shallow	Fast	No
[:]	Shallow	Fast	No
list()	Shallow	Fast	No
deepcopy()	Deep	Slower	Yes

Different copying methods have varying performance characteristics. For small lists, the performance difference is negligible, but it can matter for large data structures.

Alternative Methods

1. Copying with List Comprehension

This approach is similar to list(list1) but allows you to modify elements during copying.

Read more in the section dedicated to list comprehensions.

2. Copying with map()

Copying with map() can be useful if data transformation is needed.

3. Copying with Unpacking

The * operator unpacks elements from the original list into a new list. This creates a shallow copy of the original list.

4. Copying via Serialization

In Python, deep copying can be achieved using the json module with a combination of json.dumps() and json.loads(), though it has limitations. For example, tuples are converted to lists, and sets will raise an error. While not a standard method for deep copying, it can be useful in certain scenarios.

The serialization and deserialization process completely severs the connection between the original object and the new one, creating an independent copy, including nested structures.

Read more in the section dedicated to the json module.

Creating Multiple Copies of a List

This example demonstrates using list comprehension and the copy() method to create multiple copies of an original list and then modify them.

• range(5) generates a sequence of numbers from 0 to 4 (5 iterations total).
• _ is a placeholder variable, unused in the expression, simply indicating the number of iterations.
• original.copy() is called 5 times, each creating a new independent copy of original.
• Each copy is then modified.
• Using enumerate() simplifies accessing both the index and the list element simultaneously.

As a result, copies becomes a list containing 5 independent copies.

If we replaced original.copy() with just original, all elements in copies would reference the same list, and changes would affect all copies simultaneously.

Copying Parts of a List

Sometimes you only need to copy a portion of a list:

Read more in the section dedicated to slicing.

Common Mistakes and How to Avoid Them

1. Assigning Instead of Copying

   • Mistake: Using = instead of a copying method.
   • Solution: Use copy(), [:], list(), or deepcopy() depending on the task.

2. Modifying Nested Objects with Shallow Copying

   • Mistake: Expecting copy() to copy nested lists.
   • Solution: Use deepcopy() for lists with nested mutable objects.

Practical Applications

1. Data Processing: When processing data and you want to keep the original set unchanged, use copying.
2. Algorithms: When implementing algorithms that require a temporary copy of a data structure.
3. Debugging: Copies help test code without altering the original data.

Conclusion

Copying lists in Python is an essential skill that helps avoid errors when working with mutable objects. Shallow copying (copy(), [:], list()) is suitable for simple lists, while deep copying (deepcopy()) is ideal for complex structures with nested objects. The choice of method depends on your needs: prioritize speed with shallow copying or full independence with deep copying.

Frequently Asked Questions About Copying Lists

Can You Use the = Operator to Copy a List?

Yes, you can use the = operator, but it creates a reference to the same list, not a copy. This means changes to the “copy” will also affect the original list.

What Are Shallow and Deep Copying?

Shallow copying creates a new list but doesn’t copy nested objects—they remain shared between the original and the copy. Deep copying creates a fully independent copy, including all nested objects.

How Do You Copy Nested Lists in Python?

To copy nested lists, use a deep copy:

When Should You Use Shallow Copying vs. Deep Copying?

Shallow copying is better for simple lists without nested objects—it’s more efficient than deep copying. Use deep copying when you need complete independence, especially with nested structures.

What Are the Drawbacks of Shallow Copying a List?

The main drawback of shallow copying is that it doesn’t clone nested objects.

What’s the Difference Between Copying and Cloning a List?

In the context of Python lists, “copying” and “cloning” are often used interchangeably, but there are subtle distinctions:

• Copying typically refers to creating a shallow copy.
• Cloning often implies creating a deep copy.

How Do You Copy Only Part of a List?

To copy a portion of a list, use slicing with specified start and end indices.