In Python, you can use NumPy to create deep copies of lists efficiently. Deep copying is especially useful when you need to duplicate a list that contains other lists (or nested structures) to ensure that changes to the copied list do not affect the original list.
import numpy as np
# Original nested list
original_list = [[1, 2, 3], [4, 5, 6]]
# Deep copy using NumPy
copied_list = np.copy(original_list)
# Modify the copied list
copied_list[0][0] = 99
print("Original List:", original_list) # Output: [[1, 2, 3], [4, 5, 6]]
print("Copied List:", copied_list) # Output: [[99, 2, 3], [4, 5, 6]]
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?