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Python: A Deep Dive into Copying Lists
When working with lists in Python, it's essential to understand how copying operates. As the question highlights, seemingly independent copies can affect the original list, leading to unexpected behavior.
The Pitfalls of Assignment
The crux of the issue lies in Python's assignment semantics. Unlike languages like Java, Python assigns references to variables rather than actual values. In the provided example:
org_list = ['y', 'c', 'gdp', 'cap'] copy_list = org_list
copy_list does not contain its own copy of the list; instead, it points to the same underlying list as org_list. Any modifications to one list are reflected in the other.
Shallow vs. Deep Copies
To create a truly independent copy, Python offers two approaches:
copy_list = list(org_list) copy_list[1] = 'a' print(org_list) # Output: ['y', 'a', 'gdp', 'cap']
import copy copy_list = copy.deepcopy(org_list) copy_list[1] = 'a' print(org_list) # Output: ['y', 'c', 'gdp', 'cap']
Additional Considerations
For pandas DataFrames, you can use the copy() or copy(deep=True) methods to create independent copies. However, note that deep copies for complex objects can be computationally expensive if not necessary.
In conclusion, understanding the difference between references and copies in Python is crucial for working with lists and complex objects effectively. By leveraging shallow or deep copies as appropriate, you can ensure the integrity of your data and avoid unintended consequences when modifying copies.
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