How Does Python's Object Assignment Work, and How Can I Create Independent Copies?

Understanding Object Assignment in Python

In Python, assignment operators, such as =, do not create copies of objects. Instead, they refer to existing objects in memory. This behavior can lead to unintended consequences, as demonstrated in the following code:

dict_a = dict_b = dict_c = {}
dict_c['hello'] = 'goodbye'

print(dict_a)
print(dict_b)
print(dict_c)

Expected Output:

{}
{}
{'hello': 'goodbye'}

Actual Output:

{'hello': 'goodbye'}
{'hello': 'goodbye'}
{'hello': 'goodbye'}

In this example, we expected to create three independent dictionaries. However, the result shows that dict_a, dict_b, and dict_c all refer to the same dictionary object. Assigning a key to dict_c modifies the shared dictionary, which is reflected in all three variables.

Resolving Object Assignment Issues

To achieve the desired behavior of creating independent copies, Python provides several options:

1. dict.copy() method: This method creates a shallow copy of the dictionary, copying all its key-value pairs into a new object.

dict_a = {1: 2}
dict_b = dict_a.copy()
dict_b[3] = 4

print(dict_a)
print(dict_b)

Output:

{1: 2}
{1: 2, 3: 4}

2. copy.deepcopy() function: This function creates a deep copy of the dictionary, including all nested objects.

import copy

dict_a = {1: 2, 'nested': {3: 4}}
dict_b = copy.deepcopy(dict_a)
dict_b['nested'][5] = 6

print(dict_a)
print(dict_b)

Output:

{1: 2, 'nested': {3: 4}}
{1: 2, 'nested': {3: 4, 5: 6}}

By using these methods, it is possible to control object assignment in Python and prevent unintended modifications to shared objects.

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