Understanding When to Make Copies of Data Before Modifications in Python

In Python, the scenario where modifying a dependent variable impacts the original variable’s value occurs with mutable data types, such as lists, dictionaries, sets, and user-defined objects. Here’s an explanation of how this behavior manifests:

Mutable vs. Immutable Data Types

• Immutable Data Types: These include integers, floats, strings, and tuples. Changes to a variable assigned from another do not affect the original variable because any modification results in a new object being created.
• Mutable Data Types: These include lists, dictionaries, sets, and most user-defined classes. If you assign one variable to another and modify the second variable, the original variable is affected because both variables refer to the same object in memory.

Example with Lists

Here’s how this behavior works with a list, which is a mutable data type:

# Original list
a = [1, 2, 3]
# Assign 'b' from 'a'
b = a
# Modify 'b'
b.append(4)

# Check values
print("a:", a)
print("b:", b)

Output:

a: [1, 2, 3, 4]
b: [1, 2, 3, 4]

In this example, because lists are mutable, both a and b point to the same list in memory. When b is modified (b.append(4)), a is also affected because it refers to the same list.

Example with User-Defined Classes

Consider a simple user-defined class:

class Container:
    def __init__(self, value):
        self.value = value

# Create an instance
a = Container(90)
# Assign 'b' from 'a'
b = a
# Modify 'b'
b.value = 82

# Check values
print("a.value:", a.value)
print("b.value:", b.value)

Output::

a.value: 82
b.value: 82

In this case, since b is assigned the same instance as a, any modification to the attributes of b will reflect in a because both a and b reference the same object.

How to Avoid Unintended Mutations

To avoid unintentionally modifying the original data when dealing with mutable types, you can create a copy of the data. For lists, you can use the list() function or slicing to create a shallow copy. For deeper structures, you may need the copy module’s deepcopy function.

import copy

# Original list
a = [[1, 2], [3, 4]]
# Create a deep copy of 'a' for 'b'
b = copy.deepcopy(a)
# Modify 'b'
b[0].append(3)

# Check values
print("a:", a)
print("b:", b)

output:

a: [[1, 2], [3, 4]]
b: [[1, 2, 3], [3, 4]]

With deepcopy, the modifications to b do not affect a, ensuring that each variable can be modified independently of the other.

Note:

1. Using the list() Function

original = [1, 2, 3, [4, 5, 6]]
copy_using_list = list(original)

# Modify the copy
copy_using_list.append(7)
copy_using_list[3].append(7)

# Check the changes
print("Original:", original)
print("Copy:", copy_using_list)

Original: [1, 2, 3, [4, 5, 6, 7]]
Copy: [1, 2, 3, [4, 5, 6, 7], 7]

In this example, appending to the top level of the copy does not affect the original, but modifying the nested list ([4, 5, 6]) impacts both the original and the copy because the shallow copy does not duplicate the nested lists.

2.Using Slicing

original = [1, 2, 3, [4, 5, 6]]
copy_using_slice = original[:]

# Modify the copy
copy_using_slice.append(8)
copy_using_slice[3].append(8)

# Check the changes
print("Original:", original)
print("Copy:", copy_using_slice)

Original: [1, 2, 3, [4, 5, 6, 8]]
Copy: [1, 2, 3, [4, 5, 6, 8], 8]

Similarly, the top-level modifications do not affect the original, but changes to the nested list do.

When to Use Shallow Copies

Shallow copies are suitable when:

• You are certain that the objects within the list are immutable (like integers, floats, or tuples).
• You want to avoid accidental modification of the original list but don’t have nested structures or are not modifying nested mutable objects.

For nested lists or lists containing other mutable types where you need complete independence, you should use a deep copy, as explained in the previous response.