Deep copy of a dict in python

Understanding deep copy vs. shallow copy

Distinguishing between copy types

A shallow copy is created using the native dict.copy() method, and this only duplicates the uppermost key-value pairs, while shared references persist in deeper levels. In other words, shallow copies share children with the originals. It's like "Hey, I got my own place... but can I still do my laundry at your house?"

However, copy.deepcopy() goes several steps further to ensure that even nested objects like lists or sets have new, independent identities in the copied dict.

deepcopy() in the inner circle

The copy.deepcopy() function in Python accomplishes deep copying by recursively traversing the original dict to create copies of nested objects. Therefore, each modification to deep_copied_dict neither affects original_dict nor vice-versa. It's as if both copies decided that "What happens in original_dict stays in original_dict".

For custom objects needing special consideration

Python allows you to specify a __deepcopy__() method for your custom objects, ensuring copy.deepcopy() correctly copies your object. So it's like saying: "Hey deepcopy(), let me guide you through the jungle of my object!"

Pitfalls and precautions with deepcopy()

copy.deepcopy() proves oddly futile with objects that barely support deep copying, like file handles or database connections. Also, the method is processing-intensive, making it inefficient for exceptionally large or composite objects.††No one likes a slow deep copy, amirite?

Alternative strategies beyond deepcopy()

Virtual deep copy using JSON serialization

For dicts containing only JSON parts, json module can perform a form of deep copying:

import json
# "JSON, the JavaScript outsider doing wonders in Python!" - Another Reddit pythonista
copied_dict = json.loads(json.dumps(original_dict))
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This approach also serves as a way to serialize the dict for storage or network delivery purposes. Limitation: this doesn't work for non-JSON friendly datatypes like datetime objects or tuples.

Manual deep copy for non-serializable objects

The presence of non-serializable objects in your dictionary necessitates the creation of a custom function for deep copying.

def deep_copy_custom(obj):
    # Your magic deep copy logic for our peculiar object
    pass

# Python's own reality show: So You Think You Can DeepCopy
deep_copied_dict = {k: deep_copy_custom(v) if isinstance(v, NonSerializableType) else v for k, v in original_dict.items()}
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Addressing complex use cases

Navigating multithreaded scenarios

In multithreading scenarios, deep copying provides an individual copy to each thread, thereby preventing race conditions that can lead to inconsistent states. Consider deep copying your thread-safe lifesaver!

Maintaining structure with data schemas

When dict data follows specific schemas or models, it's critical that deep copies maintain the holistic structure. Being able to reproduce relationships and constraints inherently is paramount!

Coercing non-standard datatypes

Finally, when non-standard or non-jsonifiable datatypes are in play, before a JSON-based deep copy, you may need to convert these resistance elements into equivalent JSON-friendly formats.