How to Create a Deep Copy of an Object in Python?

General Discussions
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#1

How can I create a copy of an object in Python?

I would like to create a copy of an object in Python. The new object should inherit all properties and field values from the original object. However, I need these objects to be independent, meaning any changes made to the new object’s fields should not affect the original object.

How can I achieve this using the most effective method for python copy object?

#2

Okay, so here’s the thing: if you’re working with Python and want to create a copy of an object, the simplest way to do that is using a shallow copy. This means that a new object is created, but it doesn’t actually copy any nested objects—those still point to the same memory location as in the original object. This can be a problem if the object contains mutable items like lists or dictionaries, because changes to those items will be reflected in both the original and the new object. Here’s how you can do it:

import copy

class MyClass:
    def __init__(self, data):
        self.data = data

original_obj = MyClass([1, 2, 3])
shallow_copy = copy.copy(original_obj)  # This is a shallow copy

# Modify shallow_copy and check independence
shallow_copy.data.append(4)
print("Original:", original_obj.data)  # [1, 2, 3, 4] (nested object reference is shared)
print("Shallow Copy:", shallow_copy.data)  # [1, 2, 3, 4]

As you can see, shallow copying is fast and straightforward, but it doesn’t give you full independence when the object contains nested structures. You’ll need to use a deep copy for more complex cases.

#3

Ah, I see where you’re coming from. Shallow copies are useful for simple scenarios, but if you want complete independence between the original object and its copy—especially when your object contains nested elements—then deepcopy is the way to go. With python copy object, using copy.deepcopy() will ensure that all nested objects are copied recursively, creating a truly independent copy.

Here’s an example to demonstrate the difference:

import copy

class MyClass:
    def __init__(self, data):
        self.data = data

original_obj = MyClass([1, 2, 3])
deep_copy = copy.deepcopy(original_obj)  # A deep copy

# Modify deep_copy and check independence
deep_copy.data.append(4)
print("Original:", original_obj.data)  # [1, 2, 3] (no change)
print("Deep Copy:", deep_copy.data)  # [1, 2, 3, 4]

In this case, deepcopy does exactly what we need—python copy object is fully independent, so modifications to the new object won’t affect the original. It’s perfect for situations where you have complex objects with nested structures.

#4

Got it, you’ve seen the shallow vs deep copy approach. Now, here’s an interesting twist: instead of relying on built-in methods, you can define a custom copy method in your own class to handle how the copy is made. This approach can be really useful when you want more control over what gets copied and how. In fact, you can decide whether to do a shallow or deep copy based on your needs.

Check this out:

class MyClass:
    def __init__(self, data):
        self.data = data

    def custom_copy(self):
        # Custom copying logic: for shallow or deep copy depending on what you need
        return MyClass(self.data.copy())  # A shallow copy of the list

original_obj = MyClass([1, 2, 3])
custom_copy = original_obj.custom_copy()  # Create custom copy

# Modify custom_copy and check independence
custom_copy.data.append(4)
print("Original:", original_obj.data)  # [1, 2, 3]
print("Custom Copy:", custom_copy.data)  # [1, 2, 3, 4]

Here, I’m using self.data.copy() to create a shallow copy of the list, but you could easily adapt it to create a deep copy if needed. This gives you full control over the process, and for python copy object scenarios, it might just be what you need to customize your copying behavior. You can tailor this approach to your class’s structure for more nuanced control, making your code more efficient and clear.