Adventures in Machine Learning

Mastering Deep and Shallow Copy in Python using the Copy Module

Introduction to Python Copy Module

When programming in Python, there may come a time when you need to make a copy of an object. However, simply using the assignment operator (=) to copy an object may not yield the desired result, especially when dealing with complex data structures.

This is where the Python Copy module comes in handy. The Python Copy module provides a set of functions for making copies of objects.

These copies may be either a shallow copy or a deep copy, depending on the nature of the original object. In this article, we will explore the difference between shallow and deep copies and how to use these concepts in your Python programs.

Explanation of

Deep Copy and

Shallow Copy

Before delving into the Copy module, we must first understand what deep copy and shallow copy mean. A shallow copy creates a new object with the same reference as the original.

This means that any changes made to the copy will also affect the original object. Shallow copies are useful when you want to create a new object with the same values as the original, but don’t need a completely independent copy.

Shallow copies are created using the slicing syntax or the copy() method. On the other hand, a deep copy creates a completely independent copy of the original object, including all of its nested objects.

This means that any changes made to the copy will not affect the original object. Deep copies are useful when you need to make changes to an object without affecting the original.

Deep copies are created using the deepcopy() function.

Explanation of the Need for the Python Copy Module

It may seem like the assignment operator (=) would be sufficient for making copies of objects. However, this is not always the case.

When you use the assignment operator to copy an object, you are simply creating a new reference to the original object. This means that changes made to the new object will also affect the original object.

This can lead to unexpected behavior in your programs. For example, let’s say we have a list of lists:

“`

>>> original = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

>>> new = original

>>> new[0][0] = 0

>>> print(original)

[[0, 2, 3], [4, 5, 6], [7, 8, 9]]

“`

In this example, we first create a list of lists called original.

We then assign this object to a variable called new using the assignment operator. We then modify the first element of the first list in the new object.

When we print the original object, we see that it has also been modified. This is because the assignment operator creates a new reference to the original object, rather than a completely independent copy.

Now let’s take a look at how we can use the Python Copy module to create a deep copy of the original list of lists:

“`

>>> import copy

>>> original = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

>>> new = copy.deepcopy(original)

>>> new[0][0] = 0

>>> print(original)

[[1, 2, 3], [4, 5, 6], [7, 8, 9]]

“`

In this example, we first import the Copy module. We then create a list of lists called original.

We then use the deepcopy() method to create a completely independent copy of the original list of lists called new. We then modify the first element of the first list in the new object.

When we print the original object, we see that it has not been modified. This is because deepcopy() creates a completely independent copy of the original object.

Shallow Copy

Now that we understand the difference between deep copy and shallow copy, let’s take a closer look at shallow copy.

Explanation of How to Perform a

Shallow Copy Operation

To create a shallow copy, we can use the slicing syntax or the copy() method.

The slicing syntax works by creating a new list with the same contents as the original list. The copy() method works in a similar way, but is specifically designed for making shallow copies of objects.

“`

>>> original = [1, 2, 3]

>>> new_slice = original[:]

>>> new_copy = original.copy()

>>> new_slice[0] = 0

>>> new_copy[0] = 0

>>> print(original)

[1, 2, 3]

“`

In this example, we first create a list called original. We then use the slicing syntax to create a new list with the same contents as the original list called new_slice.

We then use the copy() method to create a new shallow copy of the original list called new_copy. We then modify the first element of both the new_slice and new_copy objects.

When we print the original object, we see that it has not been modified. This is because the slicing syntax and the copy() method create new objects with the same contents as the original object, but each object is a new reference.

Illustrative Examples of

Shallow Copy Operation on a Simple List and a List of Lists

Now let’s take a look at some illustrative examples of shallow copy operations. “`

>>> import copy

>>> original = [1, 2, 3]

>>> list_of_lists = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

>>> new_slice = original[:]

>>> new_copy = original.copy()

>>> new_list_of_lists_slice = list_of_lists[:]

>>> new_list_of_lists_copy = copy.copy(list_of_lists)

>>> new_list_of_lists_deep = copy.deepcopy(list_of_lists)

>>> new_slice[0] = 0

>>> new_copy[0] = 0

>>> new_list_of_lists_slice[0][0] = 0

>>> new_list_of_lists_copy[0][0] = 0

>>> new_list_of_lists_deep[0][0] = 0

>>> print(original)

[1, 2, 3]

>>> print(new_slice)

[0, 2, 3]

>>> print(new_copy)

[0, 2, 3]

>>> print(list_of_lists)

[[0, 2, 3], [4, 5, 6], [7, 8, 9]]

>>> print(new_list_of_lists_slice)

[[0, 2, 3], [4, 5, 6], [7, 8, 9]]

>>> print(new_list_of_lists_copy)

[[0, 2, 3], [4, 5, 6], [7, 8, 9]]

>>> print(new_list_of_lists_deep)

[[0, 2, 3], [4, 5, 6], [7, 8, 9]]

“`

In this example, we first import the Copy module.

We then create a simple list called original and a list of lists called list_of_lists. We then create a new shallow copy of the original list using the slicing syntax and the copy() method.

We also create a new shallow copy of the list of lists using the slicing syntax, copy(), and deepcopy() methods. We then modify the first element of both the new_slice and new_copy objects.

We also modify the first element of the first list in the new_list_of_lists_slice, new_list_of_lists_copy, and new_list_of_lists_deep objects. When we print the original object, we see that it has not been modified.

When we print the new_slice and new_copy objects, we see that they are both shallow copies and that modifying one modifies the other. When we print the list_of_lists object, we see that it has been modified due to the shallow copy.

When we print the new_list_of_lists_slice and new_list_of_lists_copy objects, we see that they are both shallow copies and that modifying one modifies the other. When we print the new_list_of_lists_deep object, we see that it is a deep copy and that modifying it does not affect the original list of lists.

Conclusion

In conclusion, understanding the difference between deep copy and shallow copy is important when programming in Python. The Copy module provides a useful set of functions for making copies of objects that can help you avoid unexpected behavior in your programs.

Shallow copy can be useful when you need to create a new object with the same contents as the original, but don’t need a completely independent copy. Shallow copies are created using the slicing syntax or the copy() method.

Deep copy is useful when you need to make changes to an object without affecting the original. Deep copies are created using the deepcopy() function.

Deep Copy

In the previous sections, we discussed the concepts of deep copy and shallow copy and their differences. In this section, we will focus on how to perform a deep copy operation using the Copy module in Python.

Explanation of How to Perform a

Deep Copy Operation

To create a deep copy of an object, we can use the deepcopy() method provided by the Copy module. “`

>>> import copy

>>> original = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

>>> new = copy.deepcopy(original)

>>> new[0][0] = 0

>>> print(original)

[[1, 2, 3], [4, 5, 6], [7, 8, 9]]

“`

In the example above, we first import the Copy module.

We then create a list of lists called original. We then use the deepcopy() method to create a new object called new that is a deep copy of the original object.

We then modify the first element of the first list in the new object. Finally, we print the original object to check whether it has been modified.

We see that it has not been modified, indicating that the deepcopy operation has successfully created an independent copy of the original object. Illustrative Examples of

Deep Copy Operation on a List of Lists

Now let’s consider some more examples of deep copy operations on nested objects such as a list of lists.

“`

>>> import copy

>>> original = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

>>> new = copy.deepcopy(original)

>>> new[0][0] = 0

>>> print(original)

[[1, 2, 3], [4, 5, 6], [7, 8, 9]]

>>> print(new)

[[0, 2, 3], [4, 5, 6], [7, 8, 9]]

“`

In the example above, we once again create a list of lists called original. We then use copy.deepcopy() to create a new object, new, that is a deep copy of the original object.

After that, we modify the first element of the first list in the new object. By doing so, we verify that the original object remains unchanged.

“`

>>> original = {‘fruit’: [‘apple’, ‘banana’, ‘cherry’], ‘veggies’: [‘carrot’, ‘cabbage’, ‘celery’]}

>>> new = copy.deepcopy(original)

>>> new[‘fruit’][0] = ‘pear’

>>> new[‘veggies’].append(‘lettuce’)

>>> print(original)

{‘fruit’: [‘apple’, ‘banana’, ‘cherry’], ‘veggies’: [‘carrot’, ‘cabbage’, ‘celery’]}

>>> print(new)

{‘fruit’: [‘pear’, ‘banana’, ‘cherry’], ‘veggies’: [‘carrot’, ‘cabbage’, ‘celery’, ‘lettuce’]}

“`

In this example, we create a dictionary where each value is a list. We use deepcopy() to create a new object called new that is a deep copy of the original object.

We then modify the first element of the ‘fruit’ list and append a new value to the ‘veggies’ list of the new object. We see that the original object remains unchanged while the new object has been successfully modified.

These examples demonstrate the power of deep copy operations. By creating a truly independent copy of an object, we can make changes without affecting the original.

Summary

In summary, the Copy module in Python provides a powerful set of functions for making copies of objects. While the assignment operator creates new references to the original object, creating copies using the Copy module produces independent copies of the original object.

Shallow copies can be created using slicing syntax or the copy() method and involve copying only the references to the original object, whereas deep copies can be created using deepcopy() and involve copying all the nested objects. In this section, we explored how to perform deep copy operations, examined illustrative examples of deep copy operations on nested objects such as list of lists and dictionaries, and showed the usefulness of the Copy module in creating independent copies of objects.

In summary, the Copy module in Python is a powerful tool for making deep and shallow copies of objects. A shallow copy creates a new object with the same reference as the original, while a deep copy creates a completely independent copy of the original object, including all of its nested objects.

The Copy module provides functions such as copy(), deepcopy() and slicing syntax for creating shallow and deep copies of objects. Understanding how to make use of these functions and concepts is important for avoiding unexpected behavior in programs and making changes without affecting the original object.

The takeaway is that the Copy module is a valuable tool for creating independent copies of objects, as it helps developers write better code and build more robust programs.

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