Nice copying from Python Interpreter
Copying objects in Python is a common operation when working with data structures, as it allows you to create a new object that is a duplicate of an existing one. The Python standard library provides the copy module, which offers a straightforward way to create copies of objects. In this tutorial, we will explore various methods of copying in Python using the copy module, focusing on "Nice Copying," which creates deep copies of complex objects while being mindful of the performance and memory usage.
Before you can start using the copy module, you need to import it in your Python script or interactive environment. You can do this using the following code:
Once you've imported the copy module, you can use its functions to copy objects.
A shallow copy creates a new object but references the same elements (or references) inside the object. This means that if the original object contains references to other objects, the shallow copy will point to the same referenced objects. To perform a shallow copy, you can use the copy.copy() function from the copy module:
In this example, changes made to shallow_copied_list also affect original_list since both lists share references to the same sub-list.
A deep copy creates a completely independent copy of an object, including all the objects it references. To create a deep copy, you can use the copy.deepcopy() function from the copy module:
As shown in the example, changes to deep_copied_list do not affect original_list because they are entirely independent.
A "Nice Copy" is a custom approach to copying objects, which combines shallow and deep copy operations to achieve a balance between performance and memory usage. It allows you to create a deep copy of the top-level object and shallow copies of the objects it references. This approach can be useful when you want to minimize the overhead of deep copying while maintaining isolation for top-level
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