In Python, tuples are immutable, which means that their contents cannot be modified after creation. Because of this immutability, you typically do not need to deep copy tuples in the same way you would with mutable data types like lists or dictionaries. However, if you have a tuple containing mutable objects, you may want to create a deep copy of the tuple to ensure that changes to the mutable objects do not affect the original tuple. This can be done using the `copy` module.
Here is a safe and idiomatic way to deep copy a tuple containing mutable objects:
import copy
# Example of a tuple containing a list (mutable object)
original_tuple = (1, 2, [3, 4])
# Deep copy the tuple
deep_copied_tuple = tuple(copy.deepcopy(item) if isinstance(item, list) else item for item in original_tuple)
# Modifying the list in the deep copied tuple
deep_copied_tuple[2].append(5)
print("Original Tuple:", original_tuple) # Outputs: (1, 2, [3, 4])
print("Deep Copied Tuple:", deep_copied_tuple) # Outputs: (1, 2, [3, 4, 5])
How do I avoid rehashing overhead with std::set in multithreaded code?
How do I find elements with custom comparators with std::set for embedded targets?
How do I erase elements while iterating with std::set for embedded targets?
How do I provide stable iteration order with std::unordered_map for large datasets?
How do I reserve capacity ahead of time with std::unordered_map for large datasets?
How do I erase elements while iterating with std::unordered_map in multithreaded code?
How do I provide stable iteration order with std::map for embedded targets?
How do I provide stable iteration order with std::map in multithreaded code?
How do I avoid rehashing overhead with std::map in performance-sensitive code?
How do I merge two containers efficiently with std::map for embedded targets?